HCP Young Adult


The following articles on human connectomics include members of the WU-Minn Human Connectome Project Consortium as authors and have been supported entirely or in part by Human Connectome Project grant 1U54MH091657 from the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research.

  • Genetic Complexity of Cortical Structure: Differences in Genetic and Environmental Factors Influencing Cortical Surface Area and Thickness.

    Lachlan T Strike, Narelle K Hansell, Baptiste Couvy-Duchesne, Paul M Thompson, Greig I de Zubicaray, Katie L McMahon, Margaret J Wright
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    Quantifying the genetic architecture of the cerebral cortex is necessary for understanding disease and changes to the brain across the lifespan. Prior work shows that both surface area (SA) and cortical thickness (CT) are heritable. However, we do not yet understand the extent to which region-specific genetic factors (i.e., independent of global effects) play a dominant role in the regional patterning or inter-regional associations across the cortex. Using a population sample of young adult twins (N = 923), we show that the heritability of SA and CT varies widely across regions, generally independent of measurement error. When global effects are controlled for, we detected a complex pattern of genetically mediated clusters of inter-regional associations, which varied between hemispheres. There were generally weak associations between the SA of different regions, except within the occipital lobe, whereas CT was positively correlated within lobar divisions and negatively correlated across lobes, mostly due to genetic covariation. These findings were replicated in an independent sample of twins and siblings (N = 698) from the Human Connectome Project. The different genetic contributions to SA and CT across regions reveal the value of quantifying sources of covariation to appreciate the genetic complexity of cortical structures.

  • Subcortical evidence for a contribution of arousal to fMRI studies of brain activity.

    Xiao Liu, Jacco A de Zwart, Marieke L Schölvinck, Catie Chang, Frank Q Ye, David A Leopold, Jeff H Duyn
    Nature communications, Jan 28, 2018 PMID: 29374172
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    Cortical activity during periods of rest is punctuated by widespread, synchronous events in both electrophysiological and hemodynamic signals, but their behavioral relevance remains unclear. Here we report that these events correspond to momentary drops in cortical arousal and are associated with activity changes in the basal forebrain and thalamus. Combining fMRI and electrophysiology in macaques, we first establish that fMRI transients co-occur with spectral shifts in local field potentials (LFPs) toward low frequencies. Applying this knowledge to fMRI data from the human connectome project, we find that the fMRI transients are strongest in sensory cortices. Surprisingly, the positive cortical transients occur together with negative transients in focal subcortical areas known to be involved with arousal regulation, most notably the basal forebrain. This subcortical involvement, combined with the prototypical pattern of LFP spectral shifts, suggests that commonly observed widespread variations in fMRI cortical activity are associated with momentary drops in arousal.

  • Mapping population-based structural connectomes.

    Zhengwu Zhang, Maxime Descoteaux, Jingwen Zhang, Gabriel Girard, Maxime Chamberland, David Dunson, Anuj Srivastava, Hongtu Zhu
    NeuroImage, Jan 23, 2018 PMID: 29355769
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    Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects' brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural connectomes in healthy subjects.

  • General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set.

    J D Kruschwitz, L Waller, L S Daedelow, H Walter, I M Veer
    NeuroImage, Jan 18, 2018 PMID: 29339311
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    One hallmark example of a link between global topological network properties of complex functional brain connectivity and cognitive performance is the finding that general intelligence may depend on the efficiency of the brain's intrinsic functional network architecture. However, although this association has been featured prominently over the course of the last decade, the empirical basis for this broad association of general intelligence and global functional network efficiency is quite limited. In the current study, we set out to replicate the previously reported association between general intelligence and global functional network efficiency using the large sample size and high quality data of the Human Connectome Project, and extended the original study by testing for separate association of crystallized and fluid intelligence with global efficiency, characteristic path length, and global clustering coefficient. We were unable to provide evidence for the proposed association between general intelligence and functional brain network efficiency, as was demonstrated by van den Heuvel et al. (2009), or for any other association with the global network measures employed. More specifically, across multiple network definition schemes, ranging from voxel-level networks to networks of only 100 nodes, no robust associations and only very weak non-significant effects with a maximal R2 of 0.01 could be observed. Notably, the strongest (non-significant) effects were observed in voxel-level networks. We discuss the possibility that the low power of previous studies and publication bias may have led to false positive results fostering the widely accepted notion of general intelligence being associated to functional global network efficiency.

  • Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity.

    Chao Zhang, Chase C Dougherty, Stefi A Baum, Tonya White, Andrew M Michael
    Human brain mapping, Jan 12, 2018 PMID: 29322586
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    Prevalence of certain forms of psychopathology, such as autism and depression, differs between genders and understanding gender differences of the neurotypical brain may provide insights into risk and protective factors. In recent research, resting state functional magnetic resonance imaging (rfMRI) is widely used to map the inherent functional networks of the brain. Although previous studies have reported gender differences in rfMRI, the robustness of gender differences is not well characterized. In this study, we use a large data set to test whether rfMRI functional connectivity (FC) can be used to predict gender and identify FC features that are most predictive of gender. We utilized rfMRI data from 820 healthy controls from the Human Connectome Project. By applying a predefined functional template and partial least squares regression modeling, we achieved a gender prediction accuracy of 87% when multi-run rfMRI was used. Permutation tests confirmed that gender prediction was reliable ( p<.001). Effects of motion, age, handedness, blood pressure, weight, and brain volume on gender prediction are discussed. Further, we found that FC features within the default mode (DMN), fronto-parietal and sensorimotor networks contributed most to gender prediction. In the DMN, right fusiform gyrus and right ventromedial prefrontal cortex were important contributors. The above regions have been previously implicated in aspects of social functioning and this suggests potential gender differences in social cognition mediated by the DMN. Our findings demonstrate that gender can be reliably predicted using rfMRI data and highlight the importance of controlling for gender in brain imaging studies.

  • The Superior Fronto-Occipital Fasciculus in the Human Brain Revealed by Diffusion Spectrum Imaging Tractography: An Anatomical Reality or a Methodological Artifact?

    Yue Bao, Yong Wang, Wei Wang, Yibao Wang
    Frontiers in neuroanatomy, Jan 12, 2018 PMID: 29321729
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    The existence of the superior fronto-occipital fasciculus (SFOF) in the human brain remains controversial. The aim of the present study was to clarify the existence, course, and terminations of the SFOF. High angular diffusion spectrum imaging (DSI) analysis was performed on six healthy adults and on a template of 842 subjects from the Human Connectome Project. To verify tractography results, we performed fiber microdissections of four post-mortem human brains. Based on DSI tractography, we reconstructed the SFOF in the subjects and the template from the Human Connectome Project that originated from the rostral and medial parts of the superior and middle frontal gyri. By tractography, we found that the fibers formed a compact fascicle at the level of the anterior horn of the lateral ventricle coursing above the head of caudate nucleus, medial to the corona radiate and under the corpus callosum (CC), and terminated at the parietal region via the lower part of the caudate nucleus. We consider that this fiber bundle observed by tractography is the SFOF, although it terminates mainly at the parietal region, rather than occipital lobe. By contrast, we were unable to identify a fiber bundle corresponding to the SFOF in our fiber dissection study. Although we did not provide definite evidence of the SFOF in the human brain, these findings may be useful for future studies in this field.

  • Preliminary evidence for genetic overlap between body mass index and striatal reward response.

    T M Lancaster, I Ihssen, L M Brindley, D E Linden
    Translational psychiatry, Jan 11, 2018 PMID: 29317597
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    The reward-processing network is implicated in the aetiology of obesity. Several lines of evidence suggest obesity-linked genetic risk loci (such as DRD2 and FTO) may influence individual variation in body mass index (BMI) through neuropsychological processes reflected in alterations in activation of the striatum during reward processing. However, no study has tested the broader hypotheses that (a) the relationship between BMI and reward-related brain activation (measured through the blood oxygenation-dependent (BOLD) signal) may be observed in a large population study and (b) the overall genetic architecture of these phenotypes overlap, an assumption critical for the progression of imaging genetic studies in obesity research. Using data from the Human Connectome Project (N = 1055 healthy, young individuals: average BMI = 26.4), we first establish a phenotypic relationship between BMI and ventral striatal (VS) BOLD during the processing of rewarding (monetary) stimuli (β = 0.44, P = 0.013), accounting for potential confounds. BMI and VS BOLD were both significantly influenced by additive genetic factors (H2r = 0.57; 0.12, respectively). Further decomposition of this variance suggested that the relationship was driven by shared genetic (ρ g = 0.47, P = 0.011), but not environmental (ρ E = -0.07, P = 0.29) factors. To validate the assumption of genetic pleiotropy between BMI and VS BOLD, we further show that polygenic risk for higher BMI is also associated with increased VS BOLD response to appetitive stimuli (calorically high food images), in an independent sample (N = 81; P FWE-ROI < 0.005). Together, these observations suggest that the genetic factors link risk to obesity to alterations within key nodes of the brain's reward circuity. These observations provide a basis for future work exploring the mechanistic role of genetic loci that confer risk for obesity using the imaging genetics approach.

  • Functional Connectivity of Cognitive Brain Networks in Schizophrenia during a Working Memory Task.

    Douglass Godwin, Andrew Ji, Sridhar Kandala, Daniel Mamah
    Frontiers in psychiatry, Jan 10, 2018 PMID: 29312020
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    Task-based connectivity studies facilitate the understanding of how the brain functions during cognition, which is commonly impaired in schizophrenia (SZ). Our aim was to investigate functional connectivity during a working memory task in SZ. We hypothesized that the task-negative (default mode) network and the cognitive control (frontoparietal) network would show dysconnectivity. Twenty-five SZ patient and 31 healthy control scans were collected using the customized 3T Siemens Skyra MRI scanner, previously used to collect data for the Human Connectome Project. Blood oxygen level dependent signal during the 0-back and 2-back conditions were extracted within a network-based parcelation scheme. Average functional connectivity was assessed within five brain networks: frontoparietal (FPN), default mode (DMN), cingulo-opercular (CON), dorsal attention (DAN), and ventral attention network; as well as between the DMN or FPN and other networks. For within-FPN connectivity, there was a significant interaction between n-back condition and group (p = 0.015), with decreased connectivity at 0-back in SZ subjects compared to controls. FPN-to-DMN connectivity also showed a significant condition × group effect (p = 0.003), with decreased connectivity at 0-back in SZ. Across groups, connectivity within the CON and DAN were increased during the 2-back condition, while DMN connectivity with either CON or DAN were decreased during the 2-back condition. Our findings support the role of the FPN, CON, and DAN in working memory and indicate that the pattern of FPN functional connectivity differs between SZ patients and control subjects during the course of a working memory task.

  • Multivariate Heteroscedasticity Models for Functional Brain Connectivity.

    Christof Seiler, Susan Holmes
    Frontiers in neuroscience, Jan 10, 2018 PMID: 29311777
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    Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI). We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP) comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.

  • Thresholding functional connectomes by means of mixture modeling.

    Natalia Z Bielczyk, Fabian Walocha, Patrick W Ebel, Koen V Haak, Alberto Llera, Jan K Buitelaar, Jeffrey C Glennon, Christian F Beckmann
    NeuroImage, Jan 09, 2018 PMID: 29309896
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    Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.

  • A method for pre-operative single-subject thalamic segmentation based on probabilistic tractography for essential tremor deep brain stimulation.

    Erik H Middlebrooks, Vanessa M Holanda, Ibrahim S Tuna, Hrishikesh D Deshpande, Markus Bredel, Leonardo Almeida, Harrison C Walker, Barton L Guthrie, Kelly D Foote, Michael S Okun
    Neuroradiology, Jan 08, 2018 PMID: 29307012
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    Deep brain stimulation is a common treatment for medication-refractory essential tremor. Current coordinate-based targeting methods result in variable outcomes due to variation in thalamic structure and the optimal patient-specific functional location. The purpose of this study was to compare the coordinate-based pre-operative targets to patient-specific thalamic segmentation utilizing a probabilistic tractography methodology.

  • Assessing diffusion kurtosis tensor estimation methods using a digital brain phantom derived from human connectome project data.

    Daniel V Olson, Volkan E Arpinar, L Tugan Muftuler
    Magnetic resonance imaging, Jan 07, 2018 PMID: 29305126
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    Diffusion kurtosis imaging (DKI) has gained popularity in recent years as an advanced diffusion-weighted MRI technique. This work aims to quantitatively compare the performance and accuracy of four DKI processing algorithms. For this purpose, a digital DKI brain phantom is developed.

  • Knowing left from right: asymmetric functional connectivity during resting state.

    Mathijs Raemaekers, Wouter Schellekens, Natalia Petridou, Nick F Ramsey
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    The functional organization of left and right hemispheres is different, and hemispheric asymmetries are thought to underlie variations in brain function across individuals. In this study, we assess how differences between hemispheres are reflected in Asymmetric Functional Connectivity (AFC), which provides a full description of how the brain's connectivity structure during resting state differs from that of the same brain mirrored over the longitudinal fissure. In addition, we assess how AFC varies across subjects. Data were provided by the Human Connectome Project, including 423 resting state and combined language task fMRI data sets, and the pattern of AFC was established for all subjects. While we could quantify the symmetry of brain connectivity at 95%, significant asymmetries were observed, consisting foremost of: (1) higher correlations between language areas in the left hemisphere than between their right hemisphere homologues. (2) Higher correlations between language homologue areas in the right hemisphere and left default mode network, than between language areas in the left hemisphere and the default mode network in the right hemisphere. The extent to which subjects exhibited this pattern correlated with language lateralization and handedness. Further exploration in intersubject variation in AFC revealed several additional patterns, one involving entire hemispheres, and another correlations with limbic areas. These results show that language is an important, but not only determinant of AFC. The additional patterns of AFC require further research to be linked to specific asymmetric neuronal states or events.

  • Using diffusion MRI to discriminate areas of cortical grey matter.

    Tharindu Ganepola, Zoltan Nagy, Aurobrata Ghosh, Theodore Papadopoulo, Daniel C Alexander, Martin I Sereno
    NeuroImage, Dec 24, 2017 PMID: 29274501
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    Cortical area parcellation is a challenging problem that is often approached by combining structural imaging (e.g., quantitative T1, diffusion-based connectivity) with functional imaging (e.g., task activations, topological mapping, resting state correlations). Diffusion MRI (dMRI) has been widely adopted to analyse white matter microstructure, but scarcely used to distinguish grey matter regions because of the reduced anisotropy there. Nevertheless, differences in the texture of the cortical 'fabric' have long been mapped by histologists to distinguish cortical areas. Reliable area-specific contrast in the dMRI signal has previously been demonstrated in selected occipital and sensorimotor areas. We expand upon these findings by testing several diffusion-based feature sets in a series of classification tasks. Using Human Connectome Project (HCP) 3T datasets and a supervised learning approach, we demonstrate that diffusion MRI is sensitive to architectonic differences between a large number of different cortical areas defined in the HCP parcellation. By employing a surface-based cortical imaging pipeline, which defines diffusion features relative to local cortical surface orientation, we show that we can differentiate areas from their neighbours with higher accuracy than when using only fractional anisotropy or mean diffusivity. The results suggest that grey matter diffusion may provide a new, independent source of information for dividing up the cortex.

  • Considering factors affecting the connectome-based identification process: Comment on Waller et al.

    Corey Horien, Stephanie Noble, Emily S Finn, Xilin Shen, Dustin Scheinost, R Todd Constable
    NeuroImage, Dec 19, 2017 PMID: 29253655
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    A recent study by Waller and colleagues evaluated the reliability, specificity, and generalizability of using functional connectivity data to identify individuals from a group. The authors note they were able to replicate identification rates in a larger version of the original Human Connectome Project (HCP) dataset. However, they also report lower identification accuracies when using historical neuroimaging acquisitions with low spatial and temporal resolution. The authors suggest that their results indicate connectomes derived from historical imaging data may be similar across individuals, to the extent that this connectome-based approach may be inappropriate for precision psychiatry and the goal of drawing inferences based on subject-level data. Here we note that the authors did not take into account factors affecting data quality and hence identification rates, independent of whether a low spatiotemporal resolution acquisition or a high spatiotemporal resolution acquisition is used. Specifically, we show here that the amount of data collected per subject and in-scanner motion are the predominant factors influencing identification rates, not the spatiotemporal resolution of the acquisition. To do this, we investigated identification rates in the HCP dataset as a function of the amount of data and motion. Using a dataset from the Consortium for Reliability and Reproducibility (CoRR), we investigated the impact of multiband versus non-multiband imaging parameters; that is, high spatiotemporal resolution versus low spatiotemporal resolution acquisitions. We show scan length and motion affect identification, whereas the imaging protocol does not affect these rates. Our results suggest that motion and amount of data per subject are the primary factors impacting individual connectivity profiles, but that within these constraints, individual differences in the connectome are readily observable.

  • A probabilistic atlas of human brainstem pathways based on connectome imaging data.

    Yuchun Tang, Wei Sun, Arthur W Toga, John M Ringman, Yonggang Shi
    NeuroImage, Dec 19, 2017 PMID: 29253653
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    The brainstem is a critical structure that regulates vital autonomic functions, houses the cranial nerves and their nuclei, relays motor and sensory information between the brain and spinal cord, and modulates cognition, mood, and emotions. As a primary relay center, the fiber pathways of the brainstem include efferent and afferent connections among the cerebral cortex, spinal cord, and cerebellum. While diffusion MRI has been successfully applied to map various brain pathways, its application for the in vivo imaging of the brainstem pathways has been limited due to inadequate resolution and large susceptibility-induced distortion artifacts. With the release of high-resolution data from the Human Connectome Project (HCP), there is increasing interest in mapping human brainstem pathways. Previous works relying on HCP data to study brainstem pathways, however, did not consider the prevalence (>80%) of large distortions in the brainstem even after the application of correction procedures from the HCP-Pipeline. They were also limited in the lack of adequate consideration of subject variability in either fiber pathways or region of interests (ROIs) used for bundle reconstruction. To overcome these limitations, we develop in this work a probabilistic atlas of 23 major brainstem bundles using high-quality HCP data passing rigorous quality control. For the large-scale data from the 500-Subject release of HCP, we conducted extensive quality controls to exclude subjects with severe distortions in the brainstem area. After that, we developed a systematic protocol to manually delineate 1300 ROIs on 20 HCP subjects (10 males; 10 females) for the reconstruction of fiber bundles using tractography techniques. Finally, we leveraged our novel connectome modeling techniques including high order fiber orientation distribution (FOD) reconstruction from multi-shell diffusion imaging and topography-preserving tract filtering algorithms to successfully reconstruct the 23 fiber bundles for each subject, which were then used to calculate the probabilistic atlases in the MNI152 space for public release. In our experimental results, we demonstrate that our method yielded anatomically faithful reconstruction of the brainstem pathways and achieved improved performance in comparison with an existing atlas of cerebellar peduncles based on HCP data. These atlases have been publicly released on NITRIC (https://www.nitrc.org/projects/brainstem_atlas/) and can be readily used by brain imaging researchers interested in studying brainstem pathways.

  • LEICA: Laplacian eigenmaps for group ICA decomposition of fMRI data.

    Chihuang Liu, Joseph JaJa, Luiz Pessoa
    NeuroImage, Dec 17, 2017 PMID: 29246846
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    Independent component analysis (ICA) is a data-driven method that has been increasingly used for analyzing functional Magnetic Resonance Imaging (fMRI) data. However, generalizing ICA to multi-subject studies is non-trivial due to the high-dimensionality of the data, the complexity of the underlying neuronal processes, the presence of various noise sources, and inter-subject variability. Current group ICA based approaches typically use several forms of the Principal Component Analysis (PCA) method to extend ICA for generating group inferences. However, linear dimensionality reduction techniques have serious limitations including the fact that the underlying BOLD signal is a complex function of several nonlinear processes. In this paper, we propose an effective non-linear ICA-based model for extracting group-level spatial maps from multi-subject fMRI datasets. We use a non-linear dimensionality reduction algorithm based on Laplacian eigenmaps to identify a manifold subspace common to the group, such that this mapping preserves the correlation among voxels' time series as much as possible. These eigenmaps are modeled as linear mixtures of a set of group-level spatial features, which are then extracted using ICA. The resulting algorithm is called LEICA (Laplacian Eigenmaps for group ICA decomposition). We introduce a number of methods to evaluate LEICA using 100-subject resting state and 100-subject working memory task fMRI datasets from the Human Connectome Project (HCP). The test results show that the extracted spatial maps from LEICA are meaningful functional networks similar to those produced by some of the best known methods. Importantly, relative to state-of-the-art methods, our algorithm compares favorably in terms of the functional cohesiveness of the spatial maps generated, as well as in terms of the reproducibility of the results.

  • Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline.

    Bhim M Adhikari, Neda Jahanshad, Dinesh Shukla, David C Glahn, John Blangero, Richard C Reynolds, Robert W Cox, Els Fieremans, Jelle Veraart, Dmitry S Novikov, Thomas E Nichols, L Elliot Hong, Paul M Thompson, Peter Kochunov
    Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Dec 09, 2017 PMID: 29218892
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    Big data initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analysis consortium (ENIGMA), combine data collected by independent studies worldwide to achieve more generalizable estimates of effect sizes and more reliable and reproducible outcomes. Such efforts require harmonized image analyses protocols to extract phenotypes consistently. This harmonization is particularly challenging for resting state fMRI due to the wide variability of acquisition protocols and scanner platforms; this leads to site-to-site variance in quality, resolution and temporal signal-to-noise ratio (tSNR). An effective harmonization should provide optimal measures for data of different qualities. We developed a multi-site rsfMRI analysis pipeline to allow research groups around the world to process rsfMRI scans in a harmonized way, to extract consistent and quantitative measurements of connectivity and to perform coordinated statistical tests. We used the single-modality ENIGMA rsfMRI preprocessing pipeline based on modelfree Marchenko-Pastur PCA based denoising to verify and replicate resting state network heritability estimates. We analyzed two independent cohorts, GOBS (Genetics of Brain Structure) and HCP (the Human Connectome Project), which collected data using conventional and connectomics oriented fMRI protocols, respectively. We used seed-based connectivity and dual-regression approaches to show that the rsfMRI signal is consistently heritable across twenty major functional network measures. Heritability values of 20-40% were observed across both cohorts.

  • An integrated brain-behavior model for working memory.

    D A Moser, G E Doucet, A Ing, D Dima, G Schumann, R M Bilder, S Frangou
    Molecular psychiatry, Dec 06, 2017 PMID: 29203849
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    Working memory (WM) is a central construct in cognitive neuroscience because it comprises mechanisms of active information maintenance and cognitive control that underpin most complex cognitive behavior. Individual variation in WM has been associated with multiple behavioral and health features including demographic characteristics, cognitive and physical traits and lifestyle choices. In this context, we used sparse canonical correlation analyses (sCCAs) to determine the covariation between brain imaging metrics of WM-network activation and connectivity and nonimaging measures relating to sensorimotor processing, affective and nonaffective cognition, mental health and personality, physical health and lifestyle choices derived from 823 healthy participants derived from the Human Connectome Project. We conducted sCCAs at two levels: a global level, testing the overall association between the entire imaging and behavioral-health data sets; and a modular level, testing associations between subsets of the two data sets. The behavioral-health and neuroimaging data sets showed significant interdependency. Variables with positive correlation to the neuroimaging variate represented higher physical endurance and fluid intelligence as well as better function in multiple higher-order cognitive domains. Negatively correlated variables represented indicators of suboptimal cardiovascular and metabolic control and lifestyle choices such as alcohol and nicotine use. These results underscore the importance of accounting for behavioral-health factors in neuroimaging studies of WM and provide a neuroscience-informed framework for personalized and public health interventions to promote and maintain the integrity of the WM network.Molecular Psychiatry advance online publication, 5 December 2017; doi:10.1038/mp.2017.247.

  • Linking functional connectivity and dynamic properties of resting-state networks.

    Won Hee Lee, Sophia Frangou
    Scientific reports, Dec 02, 2017 PMID: 29192157
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    Spontaneous brain activity is organized into resting-state networks (RSNs) involved in internally-guided, higher-order mental functions (default mode, central executive and salience networks) and externally-driven, specialized sensory and motor processing (auditory, visual and sensorimotor networks). RSNs are characterized by their functional connectivity in terms of within-network cohesion and between-network integration, and by their dynamic properties in terms of synchrony and metastability. We examined the relationship between functional connectivity and dynamic network features using fMRI data and an anatomically constrained Kuramoto model. Extrapolating from simulated data, synchrony and metastability across the RSNs emerged at coupling strengths of 5 ≤ k ≤ 12. In the empirical RSNs, higher metastability and synchrony were respectively associated with greater cohesion and lower integration. Consistent with their dual role in supporting both sustained and diverse mental operations, higher-order RSNs had lower metastability and synchrony. Sensory and motor RSNs showed greater cohesion and metastability, likely to respectively reflect their functional specialization and their greater capacity for altering network states in response to multiple and diverse external demands. Our findings suggest that functional and dynamic RSN properties are closely linked and expand our understanding of the neural architectures that support optimal brain function.

  • Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury.

    Sara De Simoni, Peter O Jenkins, Niall J Bourke, Jessica J Fleminger, Peter J Hellyer, Amy E Jolly, Maneesh C Patel, James H Cole, Robert Leech, David J Sharp
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    Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated. We used structural and functional MRI measures of connectivity to investigate this. Corticostriatal functional connectivity in healthy individuals was initially defined using a data-driven approach. A constrained independent component analysis approach was applied in 100 healthy adult dataset from the Human Connectome Project. Diffusion tractography was also performed to generate white matter tracts. The output of this analysis was used to compare corticostriatal functional connectivity and structural integrity between groups of 42 patients with traumatic brain injury and 21 age-matched controls. Subdivisions of the caudate and putamen had distinct patterns of functional connectivity. Traumatic brain injury patients showed disruption to functional connectivity between the caudate and a distributed set of cortical regions, including the anterior cingulate cortex. Cognitive impairments in the patients were mainly seen in processing speed and executive function, as well as increased levels of apathy and fatigue. Abnormalities of caudate functional connectivity correlated with these cognitive impairments, with reductions in right caudate connectivity associated with increased executive dysfunction, information processing speed and memory impairment. Structural connectivity, measured using diffusion tensor imaging between the caudate and anterior cingulate cortex was impaired and this also correlated with measures of executive dysfunction. We show for the first time that altered subcortical connectivity is associated with large-scale network disruption in traumatic brain injury and that this disruption is related to the cognitive impairments seen in these patients.

  • Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal.

    Behnaz Yousefi, Jaemin Shin, Eric H Schumacher, Shella D Keilholz
    NeuroImage, Nov 28, 2017 PMID: 29175200
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    Quasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain's intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual's global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

  • Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects: a resting-state functional MRI study.

    Jisu Hong, Bo-Yong Park, Hwan-Ho Cho, Hyunjin Park
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    Attention deficit and hyperactivity disorder (ADHD) is a disorder characterized by behavioral symptoms including hyperactivity/impulsivity among children, adolescents, and adults. These ADHD related symptoms are influenced by the complex interaction of brain networks which were under explored. We explored age-related brain network differences between ADHD patients and typically developing (TD) subjects using resting state fMRI (rs-fMRI) for three age groups of children, adolescents, and adults. We collected rs-fMRI data from 184 individuals (27 ADHD children and 31 TD children; 32 ADHD adolescents and 32 TD adolescents; and 31 ADHD adults and 31 TD adults). The Brainnetome Atlas was used to define nodes in the network analysis. We compared three age groups of ADHD and TD subjects to identify the distinct regions that could explain age-related brain network differences based on degree centrality, a well-known measure of nodal centrality. The left middle temporal gyrus showed significant interaction effects between disease status (i.e., ADHD or TD) and age (i.e., child, adolescent, or adult) (P < 0.001). Additional regions were identified at a relaxed threshold (P < 0.05). Many of the identified regions (the left inferior frontal gyrus, the left middle temporal gyrus, and the left insular gyrus) were related to cognitive function. The results of our study suggest that aberrant development in cognitive brain regions might be associated with age-related brain network changes in ADHD patients. These findings contribute to better understand how brain function influences the symptoms of ADHD.

  • The Robustness and the Doubly-Preferential Attachment Simulation of the Consensus Connectome Dynamics of the Human Brain.

    Balázs Szalkai, Bálint Varga, Vince Grolmusz
    Scientific reports, Nov 25, 2017 PMID: 29170405
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    Consensus Connectome Dynamics (CCD) is a remarkable phenomenon of the human connectomes (braingraphs) that was discovered by continuously decreasing the minimum confidence-parameter at the graphical interface of the Budapest Reference Connectome Server, which depicts the cerebral connections of n = 418 subjects with a frequency-parameter k: For any k = 1, 2, …, n one can view the graph of the edges that are present in at least k connectomes. If parameter k is decreased one-by-one from k = n through k = 1 then more and more edges appear in the graph, since the inclusion condition is relaxed. The surprising observation is that the appearance of the edges is far from random: it resembles a growing, complex structure. We hypothesize that this growing structure copies the axonal development of the human brain. Here we show the robustness of the CCD phenomenon: it is almost independent of the particular choice of the set of underlying connectomes. This result shows that the CCD phenomenon is most likely a biological property of the human brain and not just a property of the data sets examined. We also present a simulation that well-describes the growth of the CCD structure: in our random graph model a doubly-preferential attachment distribution is found to mimic the CCD.

  • Dynamic effective connectivity in resting state fMRI.

    Hae-Jeong Park, Karl J Friston, Chongwon Pae, Bumhee Park, Adeel Razi
    NeuroImage, Nov 22, 2017 PMID: 29158202
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    Context-sensitive and activity-dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition-specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we introduce a novel method to identify dynamic effective connectivity using spectral dynamic causal modelling (spDCM). We used parametric empirical Bayes (PEB) to model fluctuations in directed coupling over consecutive windows of resting state fMRI time series. Hierarchical PEB can model random effects on connectivity parameters at the second (between-window) level given connectivity estimates from the first (within-window) level. In this work, we used a discrete cosine transform basis set or eigenvariates (i.e., expression of principal components) to model fluctuations in effective connectivity over windows. We evaluated the ensuing dynamic effective connectivity in terms of the consistency of baseline connectivity within default mode network (DMN), using the resting state fMRI from Human Connectome Project (HCP). To model group-level baseline and dynamic effective connectivity for DMN, we extended the PEB approach by conducting a multilevel PEB analysis of between-session and between-subject group effects. Model comparison clearly spoke to dynamic fluctuations in effective connectivity - and the dynamic functional connectivity these changes explain. Furthermore, baseline effective connectivity was consistent across independent sessions - and notably more consistent than estimates based upon conventional models. This work illustrates the advantage of hierarchical modelling with spDCM, in characterizing the dynamics of effective connectivity.

  • The relation between statistical power and inference in fMRI.

    Henk R Cremers, Tor D Wager, Tal Yarkoni
    PloS one, Nov 21, 2017 PMID: 29155843
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    Statistically underpowered studies can result in experimental failure even when all other experimental considerations have been addressed impeccably. In fMRI the combination of a large number of dependent variables, a relatively small number of observations (subjects), and a need to correct for multiple comparisons can decrease statistical power dramatically. This problem has been clearly addressed yet remains controversial-especially in regards to the expected effect sizes in fMRI, and especially for between-subjects effects such as group comparisons and brain-behavior correlations. We aimed to clarify the power problem by considering and contrasting two simulated scenarios of such possible brain-behavior correlations: weak diffuse effects and strong localized effects. Sampling from these scenarios shows that, particularly in the weak diffuse scenario, common sample sizes (n = 20-30) display extremely low statistical power, poorly represent the actual effects in the full sample, and show large variation on subsequent replications. Empirical data from the Human Connectome Project resembles the weak diffuse scenario much more than the localized strong scenario, which underscores the extent of the power problem for many studies. Possible solutions to the power problem include increasing the sample size, using less stringent thresholds, or focusing on a region-of-interest. However, these approaches are not always feasible and some have major drawbacks. The most prominent solutions that may help address the power problem include model-based (multivariate) prediction methods and meta-analyses with related synthesis-oriented approaches.

  • Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.

    Jin Liu, Xuhong Liao, Mingrui Xia, Yong He
    Human brain mapping, Nov 17, 2017 PMID: 29143409
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    The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.

  • Fluid intelligence relates to the resting state amplitude of low-frequency fluctuation and functional connectivity: a multivariate pattern analysis.

    Changjun Li, Guocheng Yang, Meiling Li, Bo Li
    Neuroreport, Nov 15, 2017 PMID: 29135806
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    The goal of this study was to investigate the relationship between fluid intelligence (gF) and the pattern of the functional characteristics in the resting state in adults using multivariate pattern analysis. Resting-state functional images from 100 participants in the Human Connectome Project data set were analyzed. The amplitude of low-frequency fluctuation (ALFF) was first calculated, and a support vector regression approach was used to identify the association with gF. To discover whether the connectivity of the gF-associated areas was also related to gF, we further checked the seed-based functional connectivity using the seeds from the ALFF. The ALFF showed that gF was correlated with the left anterior cingulate cortex, which is involved in high cognitive control processes. The functional connectivity showed that the connection between the right prefrontal cortex (Brodmann area 8) and the left anterior cingulate cortex could predict gF. The multivariate pattern analysis result indicated that the brain functional activity and functional integrity that we identified have the potential to become an objective biomarker for evaluating individual differences in gF.

  • A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI.

    Pramod Kumar Pisharady, Stamatios N Sotiropoulos, Guillermo Sapiro, Christophe Lenglet
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    We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements.

  • Bayesian Rician Regression for Neuroimaging.

    Bertil Wegmann, Anders Eklund, Mattias Villani
    Frontiers in neuroscience, Nov 07, 2017 PMID: 29104529
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    It is well-known that data from diffusion weighted imaging (DWI) follow the Rician distribution. The Rician distribution is also relevant for functional magnetic resonance imaging (fMRI) data obtained at high temporal or spatial resolution. We propose a general regression model for non-central χ (NC-χ) distributed data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the Rician distribution to be linked to explanatory variables, with the relevant variables chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed to capture full model uncertainty by simulating from the joint posterior distribution of all model parameters and the binary variable selection indicators. Simulated regression data is used to demonstrate that the Rician model is able to detect the signal much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the Rician model.

  • Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs.

    Jing Yuan, Xiang Li, Jinhe Zhang, Liao Luo, Qinglin Dong, Jinglei Lv, Yu Zhao, Xi Jiang, Shu Zhang, Wei Zhang, Tianming Liu
    NeuroImage, Nov 06, 2017 PMID: 29102809
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    Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions. Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions. Experimental results on the Human Connectome Project (HCP) task fMRI data showed that our methods can reveal meaningful, diverse behavior patterns of connectome-scale network interactions. In particular, those networks' behavior patterns are distinct across HCP tasks such as motor, working memory, language and social tasks, and their dynamics well correspond to the temporal changes of specific task designs. In general, our framework offers a new approach to characterizing human brain function by quantitative description for the temporal evolution of spatial overlaps/interactions of connectome-scale brain networks in a standard reference space.

  • AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity.

    Viviana Siless, Ken Chang, Bruce Fischl, Anastasia Yendiki
    NeuroImage, Nov 05, 2017 PMID: 29100937
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    Diffusion MRI tractography produces massive sets of streamlines that contain a wealth of information on brain connections. The size of these datasets creates a need for automated clustering methods to group the streamlines into meaningful bundles. Conventional clustering techniques group streamlines based on their spatial coordinates. Neuroanatomists, however, define white-matter bundles based on the anatomical structures that they go through or next to, rather than their spatial coordinates. Thus we propose a similarity measure for clustering streamlines based on their position relative to cortical and subcortical brain regions. We incorporate this measure into a hierarchical clustering algorithm and compare it to a measure that relies on Euclidean distance, using data from the Human Connectome Project. We show that the anatomical similarity measure leads to a 20% improvement in the overlap of clusters with manually labeled tracts. Importantly, this is achieved without introducing any prior information from a tract atlas into the clustering algorithm, therefore without imposing the existence of any named tracts.

  • Graph theory reveals amygdala modules consistent with its anatomical subdivisions.

    Elisabeth C Caparelli, Thomas J Ross, Hong Gu, Xia Liang, Elliot A Stein, Yihong Yang
    Scientific reports, Nov 02, 2017 PMID: 29089582
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    Similarities on the cellular and neurochemical composition of the amygdaloid subnuclei suggests their clustering into subunits that exhibit unique functional organization. The topological principle of community structure has been used to identify functional subnetworks in neuroimaging data that reflect the brain effective organization. Here we used modularity to investigate the organization of the amygdala using resting state functional magnetic resonance imaging (rsfMRI) data. Our goal was to determine whether such topological organization would reliably reflect the known neurobiology of individual amygdaloid nuclei, allowing for human imaging studies to accurately reflect the underlying neurobiology. Modularity analysis identified amygdaloid elements consistent with the main anatomical subdivisions of the amygdala that embody distinct functional and structural properties. Additionally, functional connectivity pathways of these subunits and their correlation with task-induced amygdala activation revealed distinct functional profiles consistent with the neurobiology of the amygdala nuclei. These modularity findings corroborate the structure-function relationship between amygdala anatomical substructures, supporting the use of network analysis techniques to generate biologically meaningful partitions of brain structures.

  • Kernel-Regularized ICA for Computing Functional Topography from Resting-state fMRI.

    Junyan Wang, Yonggang Shi
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    Topographic regularity is a fundamental property in brain connectivity. In this work, we present a novel method for studying topographic regularity of functional connectivity based on resting-state fMRI (rfMRI), which is widely available and easy to acquire in large-scale studies. The main idea in our method is the incorporation of topographically regular structural connectivity for independent component analysis (ICA). This is enabled by the recent development of novel tractography and tract filtering algorithms that can generate highly organized fiber bundles connecting different brain regions. By leveraging these cutting-edge tractography algorithms, here we develop a kernel-regularized ICA method for the extraction of functional topography with rfMRI signals. In our experiments, we use rfMRI scans of 35 unrelated, right-handed subjects from the Human Connectome Project (HCP) to study the functional topography of the motor cortex. We first demonstrate that our method can generate functional connectivity maps with more regular topography than conventional group ICA. We also show that the components extracted by our algorithm are able to capture co-activation patterns that respect the organized topography of the motor cortex across the hemisphere. Finally, we show that our method achieves improved reproducibility as compared to conventional group ICA.

  • The braingraph.org database of high resolution structural connectomes and the brain graph tools.

    Csaba Kerepesi, Balázs Szalkai, Bálint Varga, Vince Grolmusz
    Cognitive neurodynamics, Oct 26, 2017 PMID: 29067135
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    Based on the data of the NIH-funded Human Connectome Project, we have computed structural connectomes of 426 human subjects in five different resolutions of 83, 129, 234, 463 and 1015 nodes and several edge weights. The graphs are given in anatomically annotated GraphML format that facilitates better further processing and visualization. For 96 subjects, the anatomically classified sub-graphs can also be accessed, formed from the vertices corresponding to distinct lobes or even smaller regions of interests of the brain. For example, one can easily download and study the connectomes, restricted to the frontal lobes or just to the left precuneus of 96 subjects using the data. Partially directed connectomes of 423 subjects are also available for download. We also present a GitHub-deposited set of tools, called the Brain Graph Tools, for several processing tasks of the connectomes on the site http://braingraph.org.

  • Simultaneous estimation of the in-mean and in-variance causal connectomes of the human brain.

    A Duggento, L Passamonti, M Guerrisi, N Toschi
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    In recent years, the study of the human connectome (i.e. of statistical relationships between non spatially contiguous neurophysiological events in the human brain) has been enormously fuelled by technological advances in high-field functional magnetic resonance imaging (fMRI) as well as by coordinated world wide data-collection efforts like the Human Connectome Project (HCP). In this context, Granger Causality (GC) approaches have recently been employed to incorporate information about the directionality of the influence exerted by a brain region on another. However, while fluctuations in the Blood Oxygenation Level Dependent (BOLD) signal at rest also contain important information about the physiological processes that underlie neurovascular coupling and associations between disjoint brain regions, so far all connectivity estimation frameworks have focused on central tendencies, hence completely disregarding so-called in-variance causality (i.e. the directed influence of the volatility of one signal on the volatility of another). In this paper, we develop a framework for simultaneous estimation of both in-mean and in-variance causality in complex networks. We validate our approach using synthetic data from complex ensembles of coupled nonlinear oscillators, and successively employ HCP data to provide the very first estimate of the in-variance connectome of the human brain.

  • Resting-state brain correlates of instantaneous autonomic outflow.

    G Valenza, A Duggento, L Passamonti, S Diciotti, C Tessa, R Barbieri, N Toschi
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    A prominent pathway of brain-heart interaction is represented by autonomic nervous system (ANS) heartbeat modulation. While within-brain resting state networks have been the object of intense functional Magnetic Resonance Imaging (fMRI) research, technological and methodological limitations have hampered research on the central correlates of cardiovascular control dynamics. Here we combine the high temporal and spatial resolution as well as data volume afforded by the Human Connectome Project with a probabilistic model of heartbeat dynamics to characterize central correlates of sympathetic and parasympathetic ANS activity at rest. We demonstrate an involvement of a number of brain regions such as the Insular cortex, Frontal Gyrus, Lateral Occipital Cortex, Paracingulate and Cingulate Gyrus and Precuneous Cortex, as well as subcortical structures (Thalamus, Putamen, Pallidum, Brain-Stem, Hippocampus, Amygdala, and Right Caudate) in the modulation of ANS-mediated cardiovascular control, possibly indicating a broader definition of the central autonomic network (CAN). Our findings provide a basis for an informed neurobiological interpretation of the numerous studies which employ HRV-related measures as standalone biomarkers in health and disease.

  • Resting-state brain correlates of cardiovascular complexity.

    G Valenza, A Duggento, L Passamonti, S Diciotti, C Tessa, N Toschi, R Barbieri
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    While estimates of complex heartbeat dynamics have provided effective prognostic and diagnostic markers for a wide range of pathologies, brain correlates of complex cardiac measures in general and of complex sympatho-vagal dynamics in particular are still unknown. In this study we combine resting state functional Magnetic Resonance Imaging (fMRI) and physiological signal acquisition from 34 healthy subjects selected from the Human Connectome Project (HCP) repository with inhomogeneous point-process approximate and sample heartbeat entropy measures (ipApEn and ipSampEn) to investigate brain areas involved in complex cardiovascular control. Our results show that activity in the Temporal Gyrus, Frontal Orbital Cortex, Temporal Fusiform and Opercular cortices, Planum Temporale, and Paracingulate cortex are negatively correlated with ipApEn dynamics. Activity in the same cortical areas as well as in the Temporal Fusiform cortex are negatively correlated with ipSampEn dynamics. No significant positive correlations were found. These pioneering results suggest that cardiovascular complexity at rest is linked to a few specific cortical brain structures, including crucial areas connected with parasympathetic outflow. This corroborates the hypothesis of a multidimensional central network which controls nonlinear cardiac dynamics under a predominantly vagally-driven tone.

  • Dynamic inter-network connectivity in the human brain.

    R Riccelli, L Passamonti, A Duggento, M Guerrisi, I Indovina, N Toschi
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    Recently, the field of functional brain connectivity has shifted its attention on studying how functional connectivity (FC) between remote regions changes over time. It is becoming increasingly evident that the human "connectome" is a dynamical entity whose variations are effected over very short timescales and reflect crucial mechanisms which underline the physiological functioning of the brain. In this study, we employ ad-hoc statistical and surrogate data generation methods to quantify whether and which brain networks displayed dynamic behaviors in a very large sample of healthy subjects provided by the Human Connectome Project (HCP). Our findings provided evidences that there are specific pairs of networks and specific networks within the healthy brain that are more likely to display dynamic behaviors. This new set of findings supports the notion that studying the time-variant connectivity in the brain could reveal useful and important properties about brain functioning in health and disease.

  • Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience.

    R Riccelli, L Passamonti, A Duggento, M Guerrisi, I Indovina, A Terracciano, N Toschi
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    It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI. Here, we employ a GARCH-based method (dynamic conditional correlation - DCC) to estimate dynamical brain connectivity in the Human Connectome Project (HCP) dataset and study how the dynamic functional connectivity behaviors related to personality as described by the five-factor model. Openness, a trait related to curiosity and creativity, is the only trait associated with significant differences in the amount of time-variability (but not in absolute median connectivity) of several inter-network functional connections in the human brain. The DCC method offers a novel window to extract dynamical information which can aid in elucidating the neurophysiological underpinning of phenomena to which conventional static brain connectivity estimates are insensitive.

  • Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience.

    R Riccelli, L Passamonti, A Duggento, M Guerrisi, I Indovina, A Terracciano, N Toschi
    Show Summary

    It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI. Here, we employ a GARCH-based method (dynamic conditional correlation - DCC) to estimate dynamical brain connectivity in the Human Connectome Project (HCP) dataset and study how the dynamic functional connectivity behaviors related to personality as described by the five-factor model. Openness, a trait related to curiosity and creativity, is the only trait associated with significant differences in the amount of time-variability (but not in absolute median connectivity) of several inter-network functional connections in the human brain. The DCC method offers a novel window to extract dynamical information which can aid in elucidating the neurophysiological underpinning of phenomena to which conventional static brain connectivity estimates are insensitive.

  • FOD Restoration for Enhanced Mapping of White Matter Lesion Connectivity.

    Wei Sun, Lilyana Amezcua, Yonggang Shi
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    To achieve improved understanding of white matter (WM) lesions and their effect on brain functions, it is important to obtain a comprehensive map of their connectivity. However, changes of the cellular environment in WM lesions attenuate diffusion MRI (dMRI) signals and make the robust estimation of fiber orientation distributions (FODs) difficult. In this work, we integrate techniques from image inpainting and compartment modeling to develop a novel method for enhancing FOD estimation in WM lesions from multi-shell dMRI, which is becoming increasingly popular with the success of the Human Connectome Project (HCP). By using FODs estimated from normal WM as the boundary condition, our method iteratively cycles through two key steps: diffusion-based inpainting and FOD reconstruction with compartment modeling for the successful restoration of FODs in WM lesions. In our experiments, we carry out extensive simulations to quantitatively demonstrate that our method outperforms a state-of-the-art method in angular accuracy and compartment parameter estimation. We also apply our method to multi-shell imaging data from 23 multiple sclerosis (MS) patients and one LifeSpan subject of HCP with WM lesion. We show that our method achieves superior performance in mapping the connectivity of WM lesions with FOD-based tractography.

  • Holistic Mapping of Striatum Surfaces in the Laplace-Beltrami Embedding Space.

    Jin Kyu Gahm, Yonggang Shi
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    In brain shape analysis, the striatum is typically divided into three parts: the caudate, putamen, and accumbens nuclei for its analysis. Recent connectivity and animal studies, however, indicate striatum-cortical inter-connections do not always follow such subdivisions. For the holistic mapping of striatum surfaces, conventional spherical registration techniques are not suitable due to the large metric distortions in spherical parameterization of striatal surfaces. To overcome this difficulty, we develop a novel striatal surface mapping method using the recently proposed Riemannian metric optimization techniques in the Laplace-Beltrami (LB) embedding space. For the robust resolution of sign ambiguities in the LB spectrum, we also devise novel anatomical contextual features to guide the surface mapping in the embedding space. In our experimental results, we compare with spherical registration tools from FreeSurfer and FSL to demonstrate that our novel method provides a superior solution to the striatal mapping problem. We also apply our method to map the striatal surfaces from 211 subjects of the Human Connectome Project (HCP), and use the surface maps to construct a cortical connectivity atlas. Our atlas results show that the striato-cortical connectivity is not distinctive according to traditional structural subdivision of the striatum, and further confirms the holistic approach for mapping striatal surfaces.

  • Robust Fusion of Diffusion MRI Data for Template Construction.

    Zhanlong Yang, Geng Chen, Dinggang Shen, Pew-Thian Yap
    Scientific reports, Oct 13, 2017 PMID: 29021588
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    Construction of brain templates is generally carried out using a two-step procedure involving registering a population of images to a common space and then fusing the aligned images to form a template. In practice, image registration is not perfect and simple averaging of the images will blur structures and cause artifacts. In diffusion MRI, this is further complicated by intra-voxel inter-subject differences in fiber orientation, fiber configuration, anisotropy, and diffusivity. In this paper, we propose a method to improve the construction of diffusion MRI templates in light of inter-subject differences. Our method involves a novel q-space (i.e., wavevector space) patch matching mechanism that is incorporated in a mean shift algorithm to seek the most probable signal at each point in q-space. Our method relies on the fact that the mean shift algorithm is a mode seeking algorithm that converges to the mode of a distribution and is hence robust to outliers. Our method is therefore in effect seeking the most probable signal profile at each voxel given a distribution of signal profiles. Experimental results show that our method yields diffusion MRI templates with cleaner fiber orientations and less artifacts caused by inter-subject differences in fiber orientation.

  • Individual Variability of the Human Cerebral Cortex Identified Using Intraoperative Mapping.

    Johan Pallud, Marc Zanello, Grégory Kuchcinski, Alexandre Roux, Jun Muto, Charles Mellerio, Edouard Dezamis, Catherine Oppenheim
    World neurosurgery, Oct 11, 2017 PMID: 28989049
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    Intraoperative functional cortical mapping using direct electrical stimulation may show a wider individual variability than suggested by noninvasive imaging data of healthy subjects.

  • Dynamics of functional connectivity at high spatial resolution reveal long-range interactions and fine-scale organization.

    Maria Giulia Preti, Dimitri Van De Ville
    Scientific reports, Oct 08, 2017 PMID: 28986564
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    Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging sheds light onto moment-to-moment reconfigurations of large-scale functional brain networks. Due to computational limits, connectivity is typically computed using pre-defined atlases, a non-trivial choice that might influence results. Here, we leverage new computational methods to retrieve dFC at the voxel level in terms of dominant patterns of fluctuations, and demonstrate that this new representation is informative to derive meaningful brain parcellations, capturing both long-range interactions and fine-scale local organization. Specifically, voxelwise dFC dominant patterns were captured through eigenvector centrality followed by clustering across time/subjects to yield most representative dominant patterns (RDPs). Voxel-wise labeling according to positive/negative contributions to RDPs, led to 37 unique labels identifying strikingly symmetric dFC long-range patterns. These included 449 contiguous regions, defining a fine-scale parcellation consistent with known cortical/subcortical subdivisions. Our contribution provides an alternative to obtain a whole-brain parcellation that is for the first time driven by voxel-level dFC and bridges the gap between voxel-based approaches and graph theoretical analysis.

  • Frequency dependent hub role of the dorsal and ventral right anterior insula.

    Yifeng Wang, Lixia Zhu, Qijun Zou, Qian Cui, Wei Liao, Xujun Duan, Bharat Biswal, Huafu Chen
    NeuroImage, Oct 08, 2017 PMID: 28986206
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    The right anterior insula (rAI) plays a crucial role in generating adaptive behavior by orchestrating multiple brain networks. Based on functional separation findings of the insula and spectral fingerprints theory of cognitive functions, we hypothesize that the hub role of the rAI is region and frequency dependent. Using the Human Connectome Project dataset and backtracking approach, we segregate the rAI into dorsal and ventral parts at frequency bands from slow 6 to slow 3, indicating the frequency dependent functional separation of the rAI. Functional connectivity analysis shows that, within lower than 0.198 Hz frequency range, the dorsal and ventral parts of rAI form a complementary system to synchronize with externally and internally-oriented networks. Moreover, the relationship between the dorsal and ventral rAIs predicts the relationship between anti-correlated networks associated with the dorsal rAI at slow 6 and slow 5, suggesting a frequency dependent regulation of the rAI to brain networks. These findings could improve our understanding of the rAI by supporting the region and frequency dependent function of rAI and its essential role in coordinating brain systems relevant to internal and external environments.

  • Hierarchical multi-resolution mesh networks for brain decoding.

    Itir Onal Ertugrul, Mete Ozay, Fatos T Yarman Vural
    Brain imaging and behavior, Oct 06, 2017 PMID: 28980144
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    Human brain is supposed to process information in multiple frequency bands. Therefore, we can extract diverse information from functional Magnetic Resonance Imaging (fMRI) data by processing it at multiple resolutions. We propose a framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple resolutions of fMRI signal to represent the underlying cognitive process. Our framework, first, decomposes the fMRI signal into various frequency subbands using wavelet transform. Then, a brain network is formed at each subband by ensembling a set of local meshes. Arc weights of each local mesh are estimated by ridge regression. Finally, adjacency matrices of mesh networks obtained at different subbands are used to train classifiers in an ensemble learning architecture, called fuzzy stacked generalization (FSG). Our decoding performances on Human Connectome Project task-fMRI dataset reflect that HMMNs can successfully discriminate tasks with 99% accuracy, across 808 subjects. Diversity of information embedded in mesh networks of multiple subbands enables the ensemble of classifiers to collaborate with each other for brain decoding. The suggested HMMNs decode the cognitive tasks better than a single classifier applied to any subband. Also mesh networks have a better representation power compared to pairwise correlations or average voxel time series. Moreover, fusion of diverse information using FSG outperforms fusion with majority voting. We conclude that, fMRI data, recorded during a cognitive task, provide diverse information in multi-resolution mesh networks. Our framework fuses this complementary information and boosts the brain decoding performances obtained at individual subbands.

  • Biological Relevance of Network Architecture.

    Ioannis Gkigkitzis, Ioannis Haranas, Ilias Kotsireas
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    Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.

  • Influences on the Test-Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility.

    Stephanie Noble, Marisa N Spann, Fuyuze Tokoglu, Xilin Shen, R Todd Constable, Dustin Scheinost
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    Best practices are currently being developed for the acquisition and processing of resting-state magnetic resonance imaging data used to estimate brain functional organization-or "functional connectivity." Standards have been proposed based on test-retest reliability, but open questions remain. These include how amount of data per subject influences whole-brain reliability, the influence of increasing runs versus sessions, the spatial distribution of reliability, the reliability of multivariate methods, and, crucially, how reliability maps onto prediction of behavior. We collected a dataset of 12 extensively sampled individuals (144 min data each across 2 identically configured scanners) to assess test-retest reliability of whole-brain connectivity within the generalizability theory framework. We used Human Connectome Project data to replicate these analyses and relate reliability to behavioral prediction. Overall, the historical 5-min scan produced poor reliability averaged across connections. Increasing the number of sessions was more beneficial than increasing runs. Reliability was lowest for subcortical connections and highest for within-network cortical connections. Multivariate reliability was greater than univariate. Finally, reliability could not be used to improve prediction; these findings are among the first to underscore this distinction for functional connectivity. A comprehensive understanding of test-retest reliability, including its limitations, supports the development of best practices in the field.

  • Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI.

    Mark S Graham, Ivana Drobnjak, Mark Jenkinson, Hui Zhang
    PloS one, Oct 03, 2017 PMID: 28968429
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    In this paper we evaluate the three main methods for correcting the susceptibility-induced artefact in diffusion-weighted magnetic-resonance (DW-MR) data, and assess how correction is affected by the susceptibility field's interaction with motion. The susceptibility artefact adversely impacts analysis performed on the data and is typically corrected in post-processing. Correction strategies involve either registration to a structural image, the application of an acquired field-map or the use of additional images acquired with different phase-encoding. Unfortunately, the choice of which method to use is made difficult by the absence of any systematic comparisons of them. In this work we quantitatively evaluate these methods, by extending and employing a recently proposed framework that allows for the simulation of realistic DW-MR datasets with artefacts. Our analysis separately evaluates the ability for methods to correct for geometric distortions and to recover lost information in regions of signal compression. In terms of geometric distortions, we find that registration-based methods offer the poorest correction. Field-mapping techniques are better, but are influenced by noise and partial volume effects, whilst multiple phase-encode methods performed best. We use our simulations to validate a popular surrogate metric of correction quality, the comparison of corrected data acquired with AP and LR phase-encoding, and apply this surrogate to real datasets. Furthermore, we demonstrate that failing to account for the interaction of the susceptibility field with head movement leads to increased errors when analysing DW-MR data. None of the commonly used post-processing methods account for this interaction, and we suggest this may be a valuable area for future methods development.

  • Linking left hemispheric tissue preservation to fMRI language task activation in chronic stroke patients.

    Joseph C Griffis, Rodolphe Nenert, Jane B Allendorfer, Jerzy P Szaflarski
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    The preservation of near-typical function in distributed brain networks is associated with less severe deficits in chronic stroke patients. However, it remains unclear how task-evoked responses in networks that support complex cognitive functions such as semantic processing relate to the post-stroke brain anatomy. Here, we used recently developed methods for the analysis of multimodal MRI data to investigate the relationship between regional tissue concentration and functional MRI activation evoked during auditory semantic decisions in a sample of 43 chronic left hemispheric stroke patients and 43 age, handedness, and sex-matched controls. Our analyses revealed that closer-to-normal levels of tissue concentration in left temporo-parietal cortex and the underlying white matter correlated with the level of task-evoked activation in distributed regions associated with the semantic network. This association was not attributable to the effects of left hemispheric lesion or brain volumes, and similar results were obtained when using explicit lesion data. Left temporo-parietal tissue concentration and the associated task-evoked activations predicted patient performance on the in-scanner task, and also predicted patient performance on out-of-scanner naming and verbal fluency tasks. Exploratory analyses using the average HCP-842 tractography dataset revealed the presence of fronto-temporal, fronto-parietal, and temporo-parietal semantic network connections in the locations where tissue concentration was found to correlate with task-evoked activation in the semantic network. In summary, our results link the preservation of left posterior temporo-parietal structures with the preservation of task-evoked semantic network function in chronic left hemispheric stroke patients. Speculatively, this relationship may reflect the status of posterior temporo-parietal areas as cortical and white matter convergence zones that support coordinated processing in the distributed semantic network. Damage to these regions may contribute to atypical task-evoked responses during semantic processing in chronic stroke patients.

  • Linking left hemispheric tissue preservation to fMRI language task activation in chronic stroke patients.

    Joseph C Griffis, Rodolphe Nenert, Jane B Allendorfer, Jerzy P Szaflarski
    Show Summary

    The preservation of near-typical function in distributed brain networks is associated with less severe deficits in chronic stroke patients. However, it remains unclear how task-evoked responses in networks that support complex cognitive functions such as semantic processing relate to the post-stroke brain anatomy. Here, we used recently developed methods for the analysis of multimodal MRI data to investigate the relationship between regional tissue concentration and functional MRI activation evoked during auditory semantic decisions in a sample of 43 chronic left hemispheric stroke patients and 43 age, handedness, and sex-matched controls. Our analyses revealed that closer-to-normal levels of tissue concentration in left temporo-parietal cortex and the underlying white matter correlated with the level of task-evoked activation in distributed regions associated with the semantic network. This association was not attributable to the effects of left hemispheric lesion or brain volumes, and similar results were obtained when using explicit lesion data. Left temporo-parietal tissue concentration and the associated task-evoked activations predicted patient performance on the in-scanner task, and also predicted patient performance on out-of-scanner naming and verbal fluency tasks. Exploratory analyses using the average HCP-842 tractography dataset revealed the presence of fronto-temporal, fronto-parietal, and temporo-parietal semantic network connections in the locations where tissue concentration was found to correlate with task-evoked activation in the semantic network. In summary, our results link the preservation of left posterior temporo-parietal structures with the preservation of task-evoked semantic network function in chronic left hemispheric stroke patients. Speculatively, this relationship may reflect the status of posterior temporo-parietal areas as cortical and white matter convergence zones that support coordinated processing in the distributed semantic network. Damage to these regions may contribute to atypical task-evoked responses during semantic processing in chronic stroke patients.

  • Bayesian Tractography Using Geometric Shape Priors.

    Xiaoming Dong, Zhengwu Zhang, Anuj Srivastava
    Frontiers in neuroscience, Sep 23, 2017 PMID: 28936158
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    The problem of estimating neuronal fiber tracts connecting different brain regions is important for various types of brain studies, including understanding brain functionality and diagnosing cognitive impairments. The popular techniques for tractography are mostly sequential-tracts are grown sequentially following principal directions of local water diffusion profiles. Despite several advancements on this basic idea, the solutions easily get stuck in local solutions, and can't incorporate global shape information. We present a global approach where fiber tracts between regions of interest are initialized and updated via deformations based on gradients of a posterior energy. This energy has contributions from diffusion data, global shape models, and roughness penalty. The resulting tracts are relatively immune to issues such as tensor noise and fiber crossings, and achieve more interpretable tractography results. We demonstrate this framework using both simulated and real dMRI and HARDI data.

  • The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density.

    Fernando Calamante, Ben Jeurissen, Robert E Smith, Jacques-Donald Tournier, Alan Connelly
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    To investigate whether diffusion MRI can be used to study cortical segregation based on a contrast related to neurite density, thus providing a complementary tool to myelin-based MRI techniques used for myeloarchitecture.

  • Interpreting temporal fluctuations in resting-state functional connectivity MRI.

    Raphaël Liégeois, Timothy O Laumann, Abraham Z Snyder, Juan Zhou, B T Thomas Yeo
    NeuroImage, Sep 17, 2017 PMID: 28916180
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    Resting-state functional connectivity is a powerful tool for studying human functional brain networks. Temporal fluctuations in functional connectivity, i.e., dynamic functional connectivity (dFC), are thought to reflect dynamic changes in brain organization and non-stationary switching of discrete brain states. However, recent studies have suggested that dFC might be attributed to sampling variability of static FC. Despite this controversy, a detailed exposition of stationarity and statistical testing of dFC is lacking in the literature. This article seeks an in-depth exploration of these statistical issues at a level appealing to both neuroscientists and statisticians. We first review the statistical notion of stationarity, emphasizing its reliance on ensemble statistics. In contrast, all FC measures depend on sample statistics. An important consequence is that the space of stationary signals is much broader than expected, e.g., encompassing hidden markov models (HMM) widely used to extract discrete brain states. In other words, stationarity does not imply the absence of brain states. We then expound the assumptions underlying the statistical testing of dFC. It turns out that the two popular frameworks - phase randomization (PR) and autoregressive randomization (ARR) - generate stationary, linear, Gaussian null data. Therefore, statistical rejection can be due to non-stationarity, nonlinearity and/or non-Gaussianity. For example, the null hypothesis can be rejected for the stationary HMM due to nonlinearity and non-Gaussianity. Finally, we show that a common form of ARR (bivariate ARR) is susceptible to false positives compared with PR and an adapted version of ARR (multivariate ARR). Application of PR and multivariate ARR to Human Connectome Project data suggests that the stationary, linear, Gaussian null hypothesis cannot be rejected for most participants. However, failure to reject the null hypothesis does not imply that static FC can fully explain dFC. We find that first order AR models explain temporal FC fluctuations significantly better than static FC models. Since first order AR models encode both static FC and one-lag FC, this suggests the presence of dynamical information beyond static FC. Furthermore, even in subjects where the null hypothesis was rejected, AR models explain temporal FC fluctuations significantly better than a popular HMM, suggesting the lack of discrete states (as measured by resting-state fMRI). Overall, our results suggest that AR models are not only useful as a means for generating null data, but may be a powerful tool for exploring the dynamical properties of resting-state fMRI. Finally, we discuss how apparent contradictions in the growing dFC literature might be reconciled.

  • A probabilistic atlas of fiber crossings for variability reduction of anisotropy measures.

    Lukas J Volz, M Cieslak, S T Grafton
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    Diffusion imaging enables assessment of human brain white matter (WM) in vivo. WM microstructural integrity is routinely quantified via fractional anisotropy (FA). However, FA is also influenced by the number of differentially oriented fiber populations per voxel. To date, the precise statistical relationship between FA and fiber populations has not been characterized, complicating microstructural integrity assessment. Here, we used 630 state-of-the-art diffusion datasets from the Human Connectome Project, which allowed us to infer the number of fiber populations per voxel in a model-free fashion. Beyond the known impact on mean FA, variance of anisotropy distributions was drastically impacted, not only for FA, but also the more recent anisotropy indices generalized FA and multidimensional anisotropy. To ameliorate this bias, we introduce a probabilistic WM atlas delineating the number of distinctly oriented fiber populations per voxel. Our atlas shows that the majority of WM voxels features two differentially directed fiber populations (44.7%) rather than unidirectional fibers (32.9%) and identified WM regions with high numbers of crossing fibers, referred to as crossing pockets. Compartmentalizing anisotropy drastically reduced variance in group comparisons ranging from the whole brain to a few voxels in a single slice. In summary, we demonstrate a systematic effect of intra-voxel diffusion inhomogeneity on anisotropy. Moreover, we introduce a potential solution: The provided probabilistic WM atlas can easily be used with any given diffusion dataset to enhance the statistical robustness of anisotropy measures and increase their neurobiological utility.

  • Multidimensional encoding of brain connectomes.

    Cesar F Caiafa, Franco Pestilli
    Scientific reports, Sep 15, 2017 PMID: 28904382
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    The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome nodes, edges, white matter fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing by evaluating 1,490 connectomes, thirteen tractography methods, and three data sets. The framework dramatically reduces storage requirements for connectome evaluation methods, with up to 40x compression factors. Evaluation of multiple, diverse datasets demonstrates the importance of spatial resolution in dMRI. We measured large increases in connectome resolution as function of data spatial resolution (up to 52%). Moreover, we demonstrate that the framework allows performing anatomical manipulations on white matter tracts for statistical inference and to study the white matter geometrical organization. Finally, we provide open-source software implementing the method and data to reproduce the results.

  • A flexible graphical model for multi-modal parcellation of the cortex.

    Sarah Parisot, Ben Glocker, Sofia Ira Ktena, Salim Arslan, Markus D Schirmer, Daniel Rueckert
    NeuroImage, Sep 11, 2017 PMID: 28889005
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    Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal parcellation task. At each iteration, we compute a set of parcellations from different modalities and fuse them based on their local reliabilities. The fused parcellation is used to initialise the next iteration, forcing the parcellations to converge towards a set of mutually informed modality specific parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population.

  • An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks.

    Mehraveh Salehi, Amin Karbasi, Xilin Shen, Dustin Scheinost, R Todd Constable
    NeuroImage, Sep 09, 2017 PMID: 28882628
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    Recent work with functional connectivity data has led to significant progress in understanding the functional organization of the brain. While the majority of the literature has focused on group-level parcellation approaches, there is ample evidence that the brain varies in both structure and function across individuals. In this work, we introduce a parcellation technique that incorporates delineation of functional networks both at the individual- and group-level. The proposed technique deploys the notion of "submodularity" to jointly parcellate the cerebral cortex while establishing an inclusive correspondence between the individualized functional networks. Using this parcellation technique, we successfully established a cross-validated predictive model that predicts individuals' sex, solely based on the parcellation schemes (i.e. the node-to-network assignment vectors). The sex prediction finding illustrates that individualized parcellation of functional networks can reveal subgroups in a population and suggests that the use of a global network parcellation may overlook fundamental differences in network organization. This is a particularly important point to consider in studies comparing patients versus controls or even patient subgroups. Network organization may differ between individuals and global configurations should not be assumed. This approach to the individualized study of functional organization in the brain has many implications for both neuroscience and clinical applications.

  • Multimodal neural correlates of cognitive control in the Human Connectome Project.

    Dov B Lerman-Sinkoff, Jing Sui, Srinivas Rachakonda, Sridhar Kandala, Vince D Calhoun, Deanna M Barch
    NeuroImage, Sep 05, 2017 PMID: 28867339
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    Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function.

  • Topography of the human acoustic radiation as revealed by ex vivo fibers micro-dissection and in vivo diffusion-based tractography.

    Chiara Maffei, Jorge Jovicich, Alessandro De Benedictis, Francesco Corsini, Mattia Barbareschi, Franco Chioffi, Silvio Sarubbo
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    The acoustic radiation is a compact bundle of fibers conveying auditory information from the medial geniculate nucleus of the thalamus to the auditory cortex. Topographical knowledge of this bundle in primates is scarce and in vivo diffusion-based tractography reconstructions in humans remains challenging, especially with the most widely used MRI acquisition protocols. Therefore, the AR represents a notable anatomical omission in the neurobiological investigation of acoustic and linguistic functional mechanisms in humans. In this study, we combine blunt micro-dissections and advanced diffusion tractography methods to provide novel insights into the topographical anatomy of this bundle in humans. Evidences from ex vivo blunt micro-dissection in three human (two right) hemispheres are compared to the 3D profile of this bundle as reconstructed by tractography techniques in four healthy adult data sets provided by the Human Connectome Project. Both techniques show the unique trajectory of the AR, a transversal course from the midline to the lateral convexity of the posterior temporal lobe. Blunt dissections demonstrated three portions of this bundle that we defined as the genu, stem, and fan, revealing the intimate relationships that each of these components has with neighboring association and projection pathways. Probabilistic tractography and ultra-high b values provided results comparable to blunt micro-dissections and highlighted the main limitations in tracking the AR. This is, to our knowledge, the first ex vivo/in vivo integrated study providing novel and reliable information about the precise anatomy of the AR, which will be important for future investigations in the neuroscientific, clinical, and surgical field.

  • Anatomy and white matter connections of the orbitofrontal gyrus.

    Joshua D Burks, Andrew K Conner, Phillip A Bonney, Chad A Glenn, Cordell M Baker, Lillian B Boettcher, Robert G Briggs, Daniel L O'Donoghue, Dee H Wu, Michael E Sughrue
    Journal of neurosurgery, Sep 02, 2017 PMID: 28862541
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    OBJECTIVE The orbitofrontal cortex (OFC) is understood to have a role in outcome evaluation and risk assessment and is commonly involved with infiltrative tumors. A detailed understanding of the exact location and nature of associated white matter tracts could significantly improve postoperative morbidity related to declining capacity. Through diffusion tensor imaging-based fiber tracking validated by gross anatomical dissection as ground truth, the authors have characterized these connections based on relationships to other well-known structures. METHODS Diffusion imaging from the Human Connectome Project for 10 healthy adult controls was used for tractography analysis. The OFC was evaluated as a whole based on connectivity with other regions. All OFC tracts were mapped in both hemispheres, and a lateralization index was calculated with resultant tract volumes. Ten postmortem dissections were then performed using a modified Klingler technique to demonstrate the location of major tracts. RESULTS The authors identified 3 major connections of the OFC: a bundle to the thalamus and anterior cingulate gyrus, passing inferior to the caudate and medial to the vertical fibers of the thalamic projections; a bundle to the brainstem, traveling lateral to the caudate and medial to the internal capsule; and radiations to the parietal and occipital lobes traveling with the inferior fronto-occipital fasciculus. CONCLUSIONS The OFC is an important center for processing visual, spatial, and emotional information. Subtle differences in executive functioning following surgery for frontal lobe tumors may be better understood in the context of the fiber-bundle anatomy highlighted by this study.

  • In Vivo Magnetic Recording of Neuronal Activity.

    Laure Caruso, Thomas Wunderle, Christopher Murphy Lewis, Joao Valadeiro, Vincent Trauchessec, Josué Trejo Rosillo, José Pedro Amaral, Jianguang Ni, Patrick Jendritza, Claude Fermon, Susana Cardoso, Paulo Peixeiro Freitas, Pascal Fries, Myriam Pannetier-Lecoeur
    Neuron, Aug 29, 2017 PMID: 28844526
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    Neuronal activity generates ionic flows and thereby both magnetic fields and electric potential differences, i.e., voltages. Voltage measurements are widely used but suffer from isolating and smearing properties of tissue between source and sensor, are blind to ionic flow direction, and reflect the difference between two electrodes, complicating interpretation. Magnetic field measurements could overcome these limitations but have been essentially limited to magnetoencephalography (MEG), using centimeter-sized, helium-cooled extracranial sensors. Here, we report on in vivo magnetic recordings of neuronal activity from visual cortex of cats with magnetrodes, specially developed needle-shaped probes carrying micron-sized, non-cooled magnetic sensors based on spin electronics. Event-related magnetic fields inside the neuropil were on the order of several nanoteslas, informing MEG source models and efforts for magnetic field measurements through MRI. Though the signal-to-noise ratio is still inferior to electrophysiology, this proof of concept demonstrates the potential to exploit the fundamental advantages of magnetophysiology.

  • Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

    Yu Zhao, Qinglin Dong, Hanbo Chen, Armin Iraji, Yujie Li, Milad Makkie, Zhifeng Kou, Tianming Liu
    Medical image analysis, Aug 27, 2017 PMID: 28843214
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    State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data.

  • The epitrigeminal approach to the brainstem.

    Georgios Andrea Zenonos, David Fernandes-Cabral, Maximiliano Nunez, Stefan Lieber, Juan Carlos Fernandez-Miranda, Robert Max Friedlander
    Journal of neurosurgery, Aug 26, 2017 PMID: 28841124
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    OBJECTIVE Surgical approaches to the ventrolateral pons pose a significant challenge. In this report, the authors describe a safe entry zone to the brainstem located just above the trigeminal entry zone which they refer to as the "epitrigeminal entry zone." METHODS The approach is presented in the context of an illustrative case of a cavernous malformation and is compared with the other commonly described approaches to the ventrolateral pons. The anatomical nuances were analyzed in detail with the aid of surgical images and video, anatomical dissections, and high-definition fiber tractography (HDFT). In addition, using the HDFT maps obtained in 77 normal subjects (154 sides), the authors performed a detailed anatomical study of the surgically relevant distances between the trigeminal entry zone and the corticospinal tracts. RESULTS The patient treated with this approach had a complete resection of his cavernous malformation, and improvement of his symptoms. With regard to the HDFT anatomical study, the average direct distance of the corticospinal tracts from the trigeminal entry zone was 12.6 mm (range 8.7-17 mm). The average vertical distance was 3.6 mm (range -2.3 to 8.7 mm). The mean distances did not differ significantly from side to side, or across any of the groups studied (right-handed, left-handed, and ambidextrous). CONCLUSIONS The epitrigeminal entry zone to the brainstem appears to be safe and effective for treating intrinsic ventrolateral pontine pathological entities. A possible advantage of this approach is increased versatility in the rostrocaudal axis, providing access both above and below the trigeminal nerve. Familiarity with the subtemporal transtentorial approach, and the reliable surgical landmark of the trigeminal entry zone, should make this a straightforward approach.

  • Lipid Metabolism, Abdominal Adiposity, and Cerebral Health in the Amish.

    Meghann Ryan, Peter Kochunov, Laura M Rowland, Braxton D Mitchell, S Andrea Wijtenburg, Els Fieremans, Jelle Veraart, Dmitry S Novikov, Xiaoming Du, Bhim Adhikari, Feven Fisseha, Heather Bruce, Joshua Chiappelli, Hemalatha Sampath, Seth Ament, Jeffrey O'Connell, Alan R Shuldiner, L Elliot Hong
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    To assess the association between peripheral lipid/fat profiles and cerebral gray matter (GM) and white matter (WM) in healthy Old Order Amish (OOA).

  • A theoretical signal processing framework for linear diffusion MRI: Implications for parameter estimation and experiment design.

    Divya Varadarajan, Justin P Haldar
    NeuroImage, Aug 24, 2017 PMID: 28830765
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    The data measured in diffusion MRI can be modeled as the Fourier transform of the Ensemble Average Propagator (EAP), a probability distribution that summarizes the molecular diffusion behavior of the spins within each voxel. This Fourier relationship is potentially advantageous because of the extensive theory that has been developed to characterize the sampling requirements, accuracy, and stability of linear Fourier reconstruction methods. However, existing diffusion MRI data sampling and signal estimation methods have largely been developed and tuned without the benefit of such theory, instead relying on approximations, intuition, and extensive empirical evaluation. This paper aims to address this discrepancy by introducing a novel theoretical signal processing framework for diffusion MRI. The new framework can be used to characterize arbitrary linear diffusion estimation methods with arbitrary q-space sampling, and can be used to theoretically evaluate and compare the accuracy, resolution, and noise-resilience of different data acquisition and parameter estimation techniques. The framework is based on the EAP, and makes very limited modeling assumptions. As a result, the approach can even provide new insight into the behavior of model-based linear diffusion estimation methods in contexts where the modeling assumptions are inaccurate. The practical usefulness of the proposed framework is illustrated using both simulated and real diffusion MRI data in applications such as choosing between different parameter estimation methods and choosing between different q-space sampling schemes.

  • Sensory-biased attention networks in human lateral frontal cortex revealed by intrinsic functional connectivity.

    Sean M Tobyne, David E Osher, Samantha W Michalka, David C Somers
    NeuroImage, Aug 24, 2017 PMID: 28830764
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    Human frontal cortex is commonly described as being insensitive to sensory modality, however several recent studies cast doubt on this view. Our laboratory previously reported two visual-biased attention regions interleaved with two auditory-biased attention regions, bilaterally, within lateral frontal cortex. These regions selectively formed functional networks with posterior visual-biased and auditory-biased attention regions. Here, we conducted a series of functional connectivity analyses to validate and expand this analysis to 469 subjects from the Human Connectome Project (HCP). Functional connectivity analyses replicated the original findings and revealed a novel hemispheric connectivity bias. We also subdivided lateral frontal cortex into 21 thin-slice ROIs and observed bilateral patterns of spatially alternating visual-biased and auditory-biased attention network connectivity. Finally, we performed a correlation difference analysis that revealed five additional bilateral lateral frontal regions differentially connected to either the visual-biased or auditory-biased attention networks. These findings leverage the HCP dataset to demonstrate that sensory-biased attention networks may have widespread influence in lateral frontal cortical organization.

  • A morphometric assessment of type I Chiari malformation above the McRae line: A retrospective case-control study in 302 adult female subjects.

    James R Houston, Maggie S Eppelheimer, Soroush Heidari Pahlavian, Dipankar Biswas, Aintzane Urbizu, Bryn A Martin, Jayapalli Rajiv Bapuraj, Mark Luciano, Philip A Allen, Francis Loth
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    Type I Chiari malformation (CMI) is a radiologically-defined structural dysmorphism of the hindbrain and posterior cranial fossa (PCF). Traditional radiographic identification of CMI relies on the measurement of the cerebellar tonsils in relation to the foramen magnum with or without associated abnormalities of the neuraxis. The primary goal of this retrospective study was to comprehensively assess morphometric parameters above the McRea line in a group of female CMI patients and normal controls.

  • Anatomical and functional organization of the human substantia nigra and its connections.

    Yu Zhang, Kevin Michel-Herve Larcher, Bratislav Misic, Alain Dagher
    eLife, Aug 23, 2017 PMID: 28826495
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    We investigated the anatomical and functional organization of the human substantia nigra (SN) using diffusion and functional MRI data from the Human Connectome Project. We identified a tripartite connectivity-based parcellation of SN with a limbic, cognitive, motor arrangement. The medial SN connects with limbic striatal and cortical regions and encodes value (greater response to monetary wins than losses during fMRI), while the ventral SN connects with associative regions of cortex and striatum and encodes salience (equal response to wins and losses). The lateral SN connects with somatomotor regions of striatum and cortex and also encodes salience. Behavioral measures from delay discounting and flanker tasks supported a role for the value-coding medial SN network in decisional impulsivity, while the salience-coding ventral SN network was associated with motor impulsivity. In sum, there is anatomical and functional heterogeneity of human SN, which underpins value versus salience coding, and impulsive choice versus impulsive action.

  • Instantaneous brain dynamics mapped to a continuous state space.

    Jacob C W Billings, Alessio Medda, Sadia Shakil, Xiaohong Shen, Amrit Kashyap, Shiyang Chen, Anzar Abbas, Xiaodi Zhang, Maysam Nezafati, Wen-Ju Pan, Gordon J Berman, Shella D Keilholz
    NeuroImage, Aug 22, 2017 PMID: 28823826
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    Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain's dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting-state and task-active data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. Upon observing the local neighborhood of brain-states adopted subsequent to each stimulus, we may conclude that resting brain activity includes brain states that are, at times, similar to those adopted during tasks, but that are at other times distinct from task-active brain states. As task-active brain states often populate a local neighborhood, back-projection of segments of the dynamical state space onto the brain's surface reveals the patterns of brain activity that support many experimentally-defined states.

  • Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology.

    Deanna M Barch
    Show Summary

    A key tenet of modern psychiatry is that psychiatric disorders arise from abnormalities in brain circuits that support human behavior. Our ability to examine hypotheses around circuit-level abnormalities in psychiatric disorders has been made possible by advances in human neuroimaging technologies. These advances have provided the basis for recent efforts to develop a more complex understanding of the function of brain circuits in health and of their relationship to behavior-providing, in turn, a foundation for our understanding of how disruptions in such circuits contribute to the development of psychiatric disorders. This review focuses on the use of resting-state functional connectivity MRI to assess brain circuits, on the advances generated by the Human Connectome Project, and on how these advances potentially contribute to understanding neural circuit dysfunction in psychopathology. The review gives particular attention to the methods developed by the Human Connectome Project that may be especially relevant to studies of psychopathology; it outlines some of the key findings about what constitutes a brain region; and it highlights new information about the nature and stability of brain circuits. Some of the Human Connectome Project's new findings particularly relevant to psychopathology-about neural circuits and their relationships to behavior-are also presented. The review ends by discussing the extension of Human Connectome Project methods across the lifespan and into manifest illness. Potential treatment implications are also considered.

  • A dynamic regression approach for frequency-domain partial coherence and causality analysis of functional brain networks.

    Lipeng Ning, Yogesh Rathi
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    Coherence and causality measures are often used to analyze the influence of one region on another during analysis of functional brain networks. The analysis methods usually involve a regression problem where the signal of interest is decomposed into a mixture of regressor and a residual signal. In this paper, we revisit this basic problem and present solutions that provide the minimal-entropy residuals for different types of regression filters, such as causal, instantaneously causal and noncausal filters. Using optimal prediction theory, we derive several novel frequency-domain expressions for partial coherence, causality and conditional causality analysis. In particular, our solution provides a more accurate estimation of the frequency-domain causality compared to the classical Geweke causality measure. Using synthetic examples and in vivo resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP), we show that the proposed solution is more accurate at revealing frequency-domain linear dependence among high dimensional signals.

  • Neuroimaging biomarkers to associate obesity and negative emotions.

    Bo-Yong Park, Jisu Hong, Hyunjin Park
    Scientific reports, Aug 11, 2017 PMID: 28794427
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    Obesity is a serious medical condition highly associated with health problems such as diabetes, hypertension, and stroke. Obesity is highly associated with negative emotional states, but the relationship between obesity and emotional states in terms of neuroimaging has not been fully explored. We obtained 196 emotion task functional magnetic resonance imaging (t-fMRI) from the Human Connectome Project database using a sampling scheme similar to a bootstrapping approach. Brain regions were specified by automated anatomical labeling atlas and the brain activity (z-statistics) of each brain region was correlated with body mass index (BMI) values. Regions with significant correlation were identified and the brain activity of the identified regions was correlated with emotion-related clinical scores. Hippocampus, amygdala, and inferior temporal gyrus consistently showed significant correlation between brain activity and BMI and only the brain activity in amygdala consistently showed significant negative correlation with fear-affect score. The brain activity in amygdala derived from t-fMRI might be good neuroimaging biomarker for explaining the relationship between obesity and a negative emotional state.

  • Motion-corrected k-space reconstruction for interleaved EPI diffusion imaging.

    Zijing Dong, Fuyixue Wang, Xiaodong Ma, Erpeng Dai, Zhe Zhang, Hua Guo
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    To develop a new approach to correct for physiological and macroscopic motion in multishot, interleaved echo-planar diffusion imaging.

  • Elucidating functional differences between cortical gyri and sulci via sparse representation HCP grayordinate fMRI data.

    Huan Liu, Xi Jiang, Tuo Zhang, Yudan Ren, Xintao Hu, Lei Guo, Junwei Han, Tianming Liu
    Brain research, Aug 02, 2017 PMID: 28760438
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    The highly convoluted cerebral cortex is characterized by two different topographic structures: convex gyri and concave sulci. Increasing studies have demonstrated that cortical gyri and sulci exhibit different structural connectivity patterns. Inspired by the intrinsic structural differences between gyri and sulci, in this paper, we present a data-driven framework based on sparse representation of fMRI data for functional network inferences, then examine the interactions within and across gyral and sulcal functional networks and finally elucidate possible functional differences using graph theory based properties. We apply the proposed framework to the high-resolution Human Connectome Project (HCP) grayordinate fMRI data. Extensive experimental results on both resting state fMRI data and task-based fMRI data consistently suggested that gyri are more functionally integrated, while sulci are more functionally segregated in the organizational architecture of cerebral cortex, offering novel understanding of the byzantine cerebral cortex.

  • Pseudo-Bootstrap Network Analysis-an Application in Functional Connectivity Fingerprinting.

    Hu Cheng, Ao Li, Andrea A Koenigsberger, Chunfeng Huang, Yang Wang, Jinhua Sheng, Sharlene D Newman
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    Brain parcellation divides the brain's spatial domain into small regions, which are represented by nodes within the network analysis framework. While template-based parcellations are widely used, the parcels on the template do not necessarily match individual's functional nodes. A new method is developed to overcome the inconsistent network analysis results by by-passing the difficulties of parcellating the brain into functionally meaningful areas. First, roughly equal-sized parcellations are obtained. Second, these random parcellations are applied to individual subjects multiple times and a pseudo-bootstrap (PBS) of the network is obtained for statistical inferences. It was found that the variation of mean global network metrics from PBS sampling is smaller compared with inter-subject variation or within-subject variation between two diffusion MRI scans. Using the mean global network metrics from PBS sampling, the intra-class correlation is always higher than the average obtained from using a single random parcellation. As one application, the PBS method was tested on the Human Connectome Project resting state dataset to identify individuals across scan sessions based on the mean functional connectivity (FC)-a trivial network property that has little information about the connectivity between nodes. An accuracy rate of ∼90% was achieved by simply finding the maximum correlation of mean FC of PBS samples between two scan sessions.

  • Perceived stress is associated with increased rostral middle frontal gyrus cortical thickness: a family-based and discordant-sibling investigation.

    L J Michalski, C H Demers, D A A Baranger, D M Barch, M P Harms, G C Burgess, R Bogdan
    Genes, brain, and behavior, Jul 28, 2017 PMID: 28749606
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    Elevated stress perception and depression commonly co-occur, suggesting that they share a common neurobiology. Cortical thickness of the rostral middle frontal gyrus (RMFG), a region critical for executive function, has been associated with depression- and stress-related phenotypes. Here, we examined whether RMFG cortical thickness is associated with these phenotypes in a large family-based community sample. RMFG cortical thickness was estimated using FreeSurfer among participants (n = 879) who completed the ongoing Human Connectome Project. Depression-related phenotypes (i.e. sadness, positive affect) and perceived stress were assessed via self-report. After accounting for sex, age, ethnicity, average whole-brain cortical thickness, twin status and familial structure, RMFG thickness was positively associated with perceived stress and sadness and negatively associated with positive affect at small effect sizes (accounting for 0.2-2.4% of variance; p-fdr: 0.0051-0.1900). Perceived stress was uniquely associated with RMFG thickness after accounting for depression-related phenotypes. Further, among siblings discordant for perceived stress, those reporting higher perceived stress had increased RMFG thickness (P = 4 × 10-7 ). Lastly, RMFG thickness, perceived stress, depressive symptoms, and positive affect were all significantly heritable, with evidence of shared genetic and environmental contributions between self-report measures. Stress perception and depression share common genetic, environmental, and neural correlates. Variability in RMFG cortical thickness may play a role in stress-related depression, although effects may be small in magnitude. Prospective studies are required to examine whether variability in RMFG thickness may function as a risk factor for stress exposure and/or perception, and/or arises as a consequence of these phenotypes.

  • Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks.

    Rastko Ciric, Jason S Nomi, Lucina Q Uddin, Ajay B Satpute
    Scientific reports, Jul 28, 2017 PMID: 28747717
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    Investigations of the human brain's connectomic architecture have produced two alternative models: one describes the brain's spatial structure in terms of static localized networks, and the other describes the brain's temporal structure in terms of dynamic whole-brain states. Here, we used tools from connectivity dynamics to develop a synthesis that bridges these models. Using resting fMRI data, we investigated the assumptions undergirding current models of the human connectome. Consistent with state-based models, our results suggest that static localized networks are superordinate approximations of underlying dynamic states. Furthermore, each of these localized, dynamic connectivity states is associated with global changes in the whole-brain functional connectome. By nesting localized dynamic connectivity states within their whole-brain contexts, we demonstrate the relative temporal independence of brain networks. Our assay for functional autonomy of coordinated neural systems is broadly applicable, and our findings provide evidence of structure in temporal state dynamics that complements the well-described static spatial organization of the brain.

  • The heritability of multi-modal connectivity in human brain activity.

    Giles L Colclough, Stephen M Smith, Thomas E Nichols, Anderson M Winkler, Stamatios N Sotiropoulos, Matthew F Glasser, David C Van Essen, Mark W Woolrich
    eLife, Jul 27, 2017 PMID: 28745584
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    Patterns of intrinsic human brain activity exhibit a profile of functional connectivity that is associated with behaviour and cognitive performance, and deteriorates with disease. This paper investigates the relative importance of genetic factors and the common environment between twins in determining this functional connectivity profile. Using functional magnetic resonance imaging (fMRI) on 820 subjects from the Human Connectome Project, and magnetoencephalographic (MEG) recordings from a subset, the heritability of connectivity among 39 cortical regions was estimated. On average over all connections, genes account for about 15% of the observed variance in fMRI connectivity (and about 10% in alpha-band and 20% in beta-band oscillatory power synchronisation), which substantially exceeds the contribution from the environment shared between twins. Therefore, insofar as twins share a common upbringing, it appears that genes, rather than the developmental environment, have the dominant role in determining the coupling of neuronal activity.

  • Cortex Parcellation Associated Whole White Matter Parcellation in Individual Subjects.

    Patrick Schiffler, Jan-Gerd Tenberge, Heinz Wiendl, Sven G Meuth
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    The investigation of specific white matter areas is a growing field in neurological research and is typically achieved through the use of atlases. However, the definition of anatomically based regions remains challenging for the white matter and thus hinders region-specific analysis in individual subjects. In this article, we focus on creating a whole white matter parcellation method for individual subjects where these areas can be associated to cortex regions. This is done by combining cortex parcellation and fiber tracking data. By tracking fibers out of each cortex region and labeling the fibers according to their origin, we populate a candidate image. We then derive the white matter parcellation by classifying each white matter voxel according to the distribution of labels in the corresponding voxel from the candidate image. The parcellation of the white matter with the presented method is highly reliable and is not as dependent on registration as with white matter atlases. This method allows for the parcellation of the whole white matter into individual cortex region associated areas and, therefore, associates white matter alterations to cortex regions. In addition, we compare the results from the presented method to existing atlases. The areas generated by the presented method are not as sharply defined as the areas in most existing atlases; however, they are computed directly in the DWI space of the subject and, therefore, do not suffer from distortion caused by registration. The presented approach might be a promising tool for clinical and basic research to investigate modalities or system specific micro structural alterations of white matter areas in a quantitative manner.

  • Contextual and Developmental Differences in the Neural Architecture of Cognitive Control.

    Raluca Petrican, Cheryl L Grady
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    Because both development and context impact functional brain architecture, the neural connectivity signature of a cognitive or affective predisposition may similarly vary across different ages and circumstances. To test this hypothesis, we investigated the effects of age and cognitive versus social-affective context on the stable and time-varying neural architecture of inhibition, the putative core cognitive control component, in a subsample (N = 359, 22-36 years, 174 men) of the Human Connectome Project. Among younger individuals, a neural signature of superior inhibition emerged in both stable and dynamic connectivity analyses. Dynamically, a context-free signature emerged as stronger segregation of internal cognition (default mode) and environmentally driven control (salience, cingulo-opercular) systems. A dynamic social-affective context-specific signature was observed most clearly in the visual system. Stable connectivity analyses revealed both context-free (greater default mode segregation) and context-specific (greater frontoparietal segregation for higher cognitive load; greater attentional and environmentally driven control system segregation for greater reward value) signatures of inhibition. Superior inhibition in more mature adulthood was typified by reduced segregation in the default network with increasing reward value and increased ventral attention but reduced cingulo-opercular and subcortical system segregation with increasing cognitive load. Failure to evidence this neural profile after the age of 30 predicted poorer life functioning. Our results suggest that distinguishable neural mechanisms underlie individual differences in cognitive control during different young adult stages and across tasks, thereby underscoring the importance of better understanding the interplay among dispositional, developmental, and contextual factors in shaping adaptive versus maladaptive patterns of thought and behavior.SIGNIFICANCE STATEMENT The brain's functional architecture changes across different contexts and life stages. To test whether the neural signature of a trait similarly varies, we investigated cognitive versus social-affective context effects on the stable and time-varying neural architecture of inhibition during a period of neurobehavioral fine-tuning (age 22-36 years). Younger individuals with superior inhibition showed distinguishable context-free and context-specific neural profiles, evidenced in both static and dynamic connectivity analyses. More mature individuals with superior inhibition evidenced only context-specific profiles, revealed in the static connectivity patterns linked to increased reward or cognitive load. Delayed expression of this profile predicted poorer life functioning. Our results underscore the importance of understanding the interplay among dispositional, developmental, and contextual factors in shaping behavior.

  • Functional density and edge maps: Characterizing functional architecture in individuals and improving cross-subject registration.

    Tong Tong, Iman Aganj, Tian Ge, Jonathan R Polimeni, Bruce Fischl
    NeuroImage, Jul 19, 2017 PMID: 28716714
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    Population-level inferences and individual-level analyses are two important aspects in functional magnetic resonance imaging (fMRI) studies. Extracting reliable and informative features from fMRI data that capture biologically meaningful inter-subject variation is critical for aligning and comparing functional networks across subjects, and connecting the properties of functional brain organization with variations in behavior, cognition and genetics. In this study, we derive two new measures, which we term functional density map and edge map, and demonstrate their usefulness in characterizing the function of individual brains. Specifically, using data from the Human Connectome Project (HCP), we show that (1) both functional maps capture intrinsic properties of the functional connectivity pattern in individuals while exhibiting large variation across subjects; (2) functional maps derived from either resting-state or task-evoked fMRI can be used to accurately identify subjects from a population; and (3) cross-subject alignment using these functional maps considerably reduces functional variation and improves functional correspondence across subjects over state-of-the-art multimodal registration algorithms. Our results suggest that the proposed functional density and edge maps are promising features in characterizing the functional architecture in individuals and provide an alternative way to explore the functional variation across subjects.

  • Investigations into within- and between-subject resting-state amplitude variations.

    Janine Bijsterbosch, Samuel Harrison, Eugene Duff, Fidel Alfaro-Almagro, Mark Woolrich, Stephen Smith
    NeuroImage, Jul 18, 2017 PMID: 28712995
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    The amplitudes of spontaneous fluctuations in brain activity may be a significant source of within-subject and between-subject variability, and this variability is likely to be carried through into functional connectivity (FC) estimates (whether directly or indirectly). Therefore, improving our understanding of amplitude fluctuations over the course of a resting state scan and variation in amplitude across individuals is of great relevance to the interpretation of FC findings. We investigate resting state amplitudes in two large-scale studies (HCP and UK Biobank), with the aim of determining between-subject and within-subject variability. Between-subject clustering distinguished between two groups of brain networks whose amplitude variation across subjects were highly correlated with each other, revealing a clear distinction between primary sensory and motor regions ('primary sensory/motor cluster') and cognitive networks. Within subjects, all networks in the primary sensory/motor cluster showed a consistent increase in amplitudes from the start to the end of the scan. In addition to the strong increases in primary sensory/motor amplitude, a large number of changes in FC were found when comparing the two scans acquired on the same day (HCP data). Additive signal change analysis confirmed that all of the observed FC changes could be fully explained by changes in amplitude. Between-subject correlations in UK Biobank data showed a negative correlation between primary sensory/motor amplitude and average sleep duration, suggesting a role of arousal. Our findings additionally reveal complex relationships between amplitude and head motion. These results suggest that network amplitude is a source of significant variability both across subjects, and within subjects on a within-session timescale. Future rfMRI studies may benefit from obtaining arousal-related (self report) measures, and may wish to consider the influence of amplitude changes on measures of (dynamic) functional connectivity.

  • Evaluating the replicability, specificity, and generalizability of connectome fingerprints.

    Lea Waller, Henrik Walter, Johann D Kruschwitz, Lucia Reuter, Sabine Müller, Susanne Erk, Ilya M Veer
    NeuroImage, Jul 16, 2017 PMID: 28710040
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    Establishing reliable, robust, and unique brain signatures from neuroimaging data is a prerequisite for precision psychiatry, and therefore a highly sought-after goal in contemporary neuroscience. Recently, the procedure of connectome fingerprinting, using brain functional connectivity profiles as such signatures, was shown to be able to accurately identify individuals from a group of 126 subjects from the Human Connectome Project (HCP). However, the specificity and generalizability of this procedure were not tested. In this replication study, we show both for the original and an extended HCP data set (n = 900 subjects), as well as for an additional data set of more commonly acquired imaging quality (n = 84) that (i) although the high accuracy can be replicated for the larger HCP 900 data set, accuracy is (ii) lower for standard neuroimaging data, and, that (iii) connectome fingerprinting may not be specific enough to distinguish between individuals. In addition, both accuracy and specificity are projected to drop considerably as the size of a data set increases. Although the moderate-to-high accuracies do suggest there is a portion of unique variance, our results suggest that connectomes may actually be quite similar across individuals. This outcome may be relevant to how precision psychiatry could benefit from inferences based on functional connectomes.

  • Imaging at ultrahigh magnetic fields: History, challenges, and solutions.

    Kamil Uğurbil
    NeuroImage, Jul 13, 2017 PMID: 28698108
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    Following early efforts in applying nuclear magnetic resonance (NMR) spectroscopy to study biological processes in intact systems, and particularly since the introduction of 4 T human scanners circa 1990, rapid progress was made in imaging and spectroscopy studies of humans at 4 T and animal models at 9.4 T, leading to the introduction of 7 T and higher magnetic fields for human investigation at about the turn of the century. Work conducted on these platforms has provided numerous technological solutions to challenges posed at these ultrahigh fields, and demonstrated the existence of significant advantages in signal-to-noise ratio and biological information content. Primary difference from lower fields is the deviation from the near field regime at the radiofrequencies (RF) corresponding to hydrogen resonance conditions. At such ultrahigh fields, the RF is characterized by attenuated traveling waves in the human body, which leads to image non-uniformities for a given sample-coil configuration because of destructive and constructive interferences. These non-uniformities were initially considered detrimental to progress of imaging at high field strengths. However, they are advantageous for parallel imaging in signal reception and transmission, two critical technologies that account, to a large extend, for the success of ultrahigh fields. With these technologies and improvements in instrumentation and imaging methods, today ultrahigh fields have provided unprecedented gains in imaging of brain function and anatomy, and started to make inroads into investigation of the human torso and extremities. As extensive as they are, these gains still constitute a prelude to what is to come given the increasingly larger effort committed to ultrahigh field research and development of ever better instrumentation and techniques.

  • Comparing test-retest reliability of dynamic functional connectivity methods.

    Ann S Choe, Mary Beth Nebel, Anita D Barber, Jessica R Cohen, Yuting Xu, James J Pekar, Brian Caffo, Martin A Lindquist
    NeuroImage, Jul 09, 2017 PMID: 28687517
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    Due to the dynamic, condition-dependent nature of brain activity, interest in estimating rapid functional connectivity (FC) changes that occur during resting-state functional magnetic resonance imaging (rs-fMRI) has recently soared. However, studying dynamic FC is methodologically challenging, due to the low signal-to-noise ratio of the blood oxygen level dependent (BOLD) signal in fMRI and the massive number of data points generated during the analysis. Thus, it is important to establish methods and summary measures that maximize reliability and the utility of dynamic FC to provide insight into brain function. In this study, we investigated the reliability of dynamic FC summary measures derived using three commonly used estimation methods - sliding window (SW), tapered sliding window (TSW), and dynamic conditional correlations (DCC) methods. We applied each of these techniques to two publicly available rs-fMRI test-retest data sets - the Multi-Modal MRI Reproducibility Resource (Kirby Data) and the Human Connectome Project (HCP Data). The reliability of two categories of dynamic FC summary measures were assessed, specifically basic summary statistics of the dynamic correlations and summary measures derived from recurring whole-brain patterns of FC ("brain states"). The results provide evidence that dynamic correlations are reliably detected in both test-retest data sets, and the DCC method outperforms SW methods in terms of the reliability of summary statistics. However, across all estimation methods, reliability of the brain state-derived measures was low. Notably, the results also show that the DCC-derived dynamic correlation variances are significantly more reliable than those derived using the non-parametric estimation methods. This is important, as the fluctuations of dynamic FC (i.e., its variance) has a strong potential to provide summary measures that can be used to find meaningful individual differences in dynamic FC. We therefore conclude that utilizing the variance of the dynamic connectivity is an important component in any dynamic FC-derived summary measure.

  • Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis.

    Kuldeep Kumar, Christian Desrosiers, Kaleem Siddiqi, Olivier Colliot, Matthew Toews
    NeuroImage, Jul 08, 2017 PMID: 28684331
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    White matter characterization studies use the information provided by diffusion magnetic resonance imaging (dMRI) to draw cross-population inferences. However, the structure, function, and white matter geometry vary across individuals. Here, we propose a subject fingerprint, called Fiberprint, to quantify the individual uniqueness in white matter geometry using fiber trajectories. We learn a sparse coding representation for fiber trajectories by mapping them to a common space defined by a dictionary. A subject fingerprint is then generated by applying a pooling function for each bundle, thus providing a vector of bundle-wise features describing a particular subject's white matter geometry. These features encode unique properties of fiber trajectories, such as their density along prominent bundles. An analysis of data from 861 Human Connectome Project subjects reveals that a fingerprint based on approximately 3000 fiber trajectories can uniquely identify exemplars from the same individual. We also use fingerprints for twin/sibling identification, our observations consistent with the twin data studies of white matter integrity. Our results demonstrate that the proposed Fiberprint can effectively capture the variability in white matter fiber geometry across individuals, using a compact feature vector (dimension of 50), making this framework particularly attractive for handling large datasets.

  • Functional connectivity density mapping: comparing multiband and conventional EPI protocols.

    Alexander D Cohen, Dardo Tomasi, Ehsan Shokri-Kojori, Andrew S Nencka, Yang Wang
    Brain imaging and behavior, Jul 06, 2017 PMID: 28676985
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    Functional connectivity density mapping (FCDM) is a newly developed data-driven technique that quantifies the number of local and global functional connections for each voxel in the brain. In this study, we evaluated reproducibility, sensitivity, and specificity of both local functional connectivity density (lFCD) and global functional connectivity density (gFCD). We compared these metrics using the human connectome project (HCP) compatible high-resolution (2 mm isotropic, TR = 0.8 s) multiband (MB), and more typical, lower resolution (3.5 mm isotropic, TR = 2.0 s) single-band (SB) resting state functional MRI (rs-fMRI) acquisitions. Furthermore, in order to be more clinically feasible, only rs-fMRI scans that lasted seven minutes were tested. Subjects were scanned twice within a two-week span. We found sensitivity and specificity increased and reproducibility either increased or did not change for the MB compared to the SB acquisitions. The MB scans also showed improved gray matter/white matter contrast compared to the SB scans. The lFCD and gFCD patterns were similar across MB and SB scans and confined predominantly to gray matter. We also observed a strong spatial correlation of FCD between MB and SB scans indicating the two acquisitions provide similar information. These findings indicate high-resolution MB acquisitions improve the quality of FCD data, and seven minute rs-fMRI scan can provide robust FCD measurements.

  • The significance of negative correlations in brain connectivity.

    Liang Zhan, Lisanne M Jenkins, Ouri E Wolfson, Johnson Jonaris GadElkarim, Kevin Nocito, Paul M Thompson, Olusola A Ajilore, Moo K Chung, Alex D Leow
    Show Summary

    Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative. We validated this novel probability-based modularity approach on two independent publicly-available resting-state connectome data sets (the Human Connectome Project [HCP] and the 1,000 functional connectomes) and demonstrated that negative correlations alone are sufficient in understanding resting-state modularity. In fact, this approach (a) permits a dual formulation, leading to equivalent solutions regardless of whether one considers positive or negative edges; (b) is theoretically linked to the Ising model defined on the connectome, thus yielding modularity result that maximizes data likelihood. Additionally, we were able to detect novel and consistent sex differences in modularity in both data sets. As data sets like HCP become widely available for analysis by the neuroscience community at large, alternative and perhaps more advantageous computational tools to understand the neurobiological information of negative edges in fMRI connectomes are increasingly important.

  • Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning.

    Pramod Kumar Pisharady, Stamatios N Sotiropoulos, Julio M Duarte-Carvajalino, Guillermo Sapiro, Christophe Lenglet
    NeuroImage, Jul 04, 2017 PMID: 28669918
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    We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates.

  • Discovering dynamic brain networks from big data in rest and task.

    Diego Vidaurre, Romesh Abeysuriya, Robert Becker, Andrew J Quinn, Fidel Alfaro-Almagro, Stephen M Smith, Mark W Woolrich
    NeuroImage, Jul 04, 2017 PMID: 28669905
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    Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.

  • Autoreject: Automated artifact rejection for MEG and EEG data.

    Mainak Jas, Denis A Engemann, Yousra Bekhti, Federico Raimondo, Alexandre Gramfort
    NeuroImage, Jun 25, 2017 PMID: 28645840
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    We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold - a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience.

  • Autoreject: Automated artifact rejection for MEG and EEG data.

    Mainak Jas, Denis A Engemann, Yousra Bekhti, Federico Raimondo, Alexandre Gramfort
    NeuroImage, Jun 25, 2017 PMID: 28645840
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    We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold - a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience.

  • Modeling Task fMRI Data via Deep Convolutional Autoencoder.

    Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, Tianming Liu
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    Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps. As growing evidence shows that human brain function is hierarchically organized, new approaches that can infer and model the hierarchical structure of brain networks are widely called for. Recently, deep convolutional neural network (CNN) has drawn much attention, in that deep CNN has proven to be a powerful method for learning high-level and mid-level abstractions from low-level raw data. Inspired by the power of deep CNN, in this study, we developed a new neural network structure based on CNN, called Deep Convolutional Auto-Encoder (DCAE), in order to take the advantages of both data-driven approach and CNN's hierarchical feature abstraction ability for the purpose of learning mid-level and high-level features from complex, large-scale tfMRI time series in an unsupervised manner. The DCAE has been applied and tested on the publicly available human connectome project (HCP) tfMRI datasets, and promising results are achieved.

  • Automatic Recognition of fMRI-derived Functional Networks using 3D Convolutional Neural Networks.

    Yu Zhao, Qinglin Dong, Shu Zhang, Wei Zhang, Hanbo Chen, Xi Jiang, Lei Guo, Xintao Hu, Junwei Han, Tianming Liu
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    Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications. However, this task is still a challenging and open problem due to the tremendous variability of various types of functional brain networks and the presence of various sources of noises. In recognition of the fact that convolutional neural networks (CNN) has superior capability of representing spatial patterns with huge variability and dealing with large noises, in this paper, we design, apply and evaluate a deep 3D CNN framework for automatic, effective and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fMRI signals. Our extensive experimental results based on the Human Connectome Project (HCP) fMRI data showed that the proposed deep 3D CNN can effectively and robustly perform functional networks classification and recognition tasks, while maintaining a high tolerance for mistakenly labelled training instances. Our work provides a new deep learning approach for modeling functional connectomes based on fMRI data.

  • Cross-population myelination covariance of human cerebral cortex.

    Zhiwei Ma, Nanyin Zhang
    Human brain mapping, Jun 21, 2017 PMID: 28631354
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    Cross-population covariance of brain morphometric quantities provides a measure of interareal connectivity, as it is believed to be determined by the coordinated neurodevelopment of connected brain regions. Although useful, structural covariance analysis predominantly employed bulky morphological measures with mixed compartments, whereas studies of the structural covariance of any specific subdivisions such as myelin are rare. Characterizing myelination covariance is of interest, as it will reveal connectivity patterns determined by coordinated development of myeloarchitecture between brain regions. Using myelin content MRI maps from the Human Connectome Project, here we showed that the cortical myelination covariance was highly reproducible, and exhibited a brain organization similar to that previously revealed by other connectivity measures. Additionally, the myelination covariance network shared common topological features of human brain networks such as small-worldness. Furthermore, we found that the correlation between myelination covariance and resting-state functional connectivity (RSFC) was uniform within each resting-state network (RSN), but could considerably vary across RSNs. Interestingly, this myelination covariance-RSFC correlation was appreciably stronger in sensory and motor networks than cognitive and polymodal association networks, possibly due to their different circuitry structures. This study has established a new brain connectivity measure specifically related to axons, and this measure can be valuable to investigating coordinated myeloarchitecture development. Hum Brain Mapp 38:4730-4743, 2017. © 2017 Wiley Periodicals, Inc.

  • Functional brain networks reconstruction using group sparsity-regularized learning.

    Qinghua Zhao, Will X Y Li, Xi Jiang, Jinglei Lv, Jianfeng Lu, Tianming Liu
    Brain imaging and behavior, Jun 11, 2017 PMID: 28600738
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    Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date, most of data-driven network reconstruction approaches rarely take consideration of anatomical structures, which are the substrate of brain function. Furthermore, it has been rarely explored whether structured sparse representation with anatomical guidance could facilitate functional networks reconstruction. To address this problem, in this paper, we propose to reconstruct brain networks utilizing the structure guided group sparse regression (S2GSR) in which 116 anatomical regions from the AAL template, as prior knowledge, are employed to guide the network reconstruction when performing sparse representation of whole-brain fMRI data. Specifically, we extract fMRI signals from standard space aligned with the AAL template. Then by learning a global over-complete dictionary, with the learned dictionary as a set of features (regressors), the group structured regression employs anatomical structures as group information to regress whole brain signals. Finally, the decomposition coefficients matrix is mapped back to the brain volume to represent functional brain networks and patterns. We use the publicly available Human Connectome Project (HCP) Q1 dataset as the test bed, and the experimental results indicate that the proposed anatomically guided structure sparse representation is effective in reconstructing concurrent functional brain networks.

  • Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI.

    Shijie Zhao, Junwei Han, Xintao Hu, Xi Jiang, Jinglei Lv, Tuo Zhang, Shu Zhang, Lei Guo, Tianming Liu
    Brain imaging and behavior, Jun 11, 2017 PMID: 28600737
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    Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

  • Top-Down Beta Enhances Bottom-Up Gamma.

    Craig G Richter, William H Thompson, Conrado A Bosman, Pascal Fries
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    Several recent studies have demonstrated that the bottom-up signaling of a visual stimulus is subserved by interareal gamma-band synchronization, whereas top-down influences are mediated by alpha-beta band synchronization. These processes may implement top-down control of stimulus processing if top-down and bottom-up mediating rhythms are coupled via cross-frequency interaction. To test this possibility, we investigated Granger-causal influences among awake macaque primary visual area V1, higher visual area V4, and parietal control area 7a during attentional task performance. Top-down 7a-to-V1 beta-band influences enhanced visually driven V1-to-V4 gamma-band influences. This enhancement was spatially specific and largest when beta-band activity preceded gamma-band activity by ∼0.1 s, suggesting a causal effect of top-down processes on bottom-up processes. We propose that this cross-frequency interaction mechanistically subserves the attentional control of stimulus selection.SIGNIFICANCE STATEMENT Contemporary research indicates that the alpha-beta frequency band underlies top-down control, whereas the gamma-band mediates bottom-up stimulus processing. This arrangement inspires an attractive hypothesis, which posits that top-down beta-band influences directly modulate bottom-up gamma band influences via cross-frequency interaction. We evaluate this hypothesis determining that beta-band top-down influences from parietal area 7a to visual area V1 are correlated with bottom-up gamma frequency influences from V1 to area V4, in a spatially specific manner, and that this correlation is maximal when top-down activity precedes bottom-up activity. These results show that for top-down processes such as spatial attention, elevated top-down beta-band influences directly enhance feedforward stimulus-induced gamma-band processing, leading to enhancement of the selected stimulus.

  • Heritability of hippocampal subfield volumes using a twin and non-twin siblings design.

    Sejal Patel, Min Tae M Park, Gabriel A Devenyi, Raihaan Patel, Mario Masellis, Jo Knight, M Mallar Chakravarty
    Human brain mapping, Jun 01, 2017 PMID: 28561418
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    The hippocampus is composed of distinct subfields linked to diverse functions and disorders. The subfields can be mapped using high-resolution magnetic resonance images, and their volumes can potentially be used as quantitative phenotypes for genetic investigation of hippocampal function. We estimated the heritability of hippocampus subfield volumes of 465 subjects from the Human Connectome Project (twins and non-twin siblings) using two methods. The first used a univariate model to estimate heritability with and without adjustment for total brain volume (TBV) and ipsilateral hippocampal volume to determine if heritability was uniquely attributable to subfield volume rather than confounds that attributed to global volumes. We observed the right: subiculum, cornu ammonis 2/3, and cornu ammonis 4/dentate gyrus subfields had the highest significant heritability estimates after adjusting for ipsilateral hippocampal volume. In the second analysis, we used a bivariate model to investigate the shared heritability and genetic correlation of the subfield volumes with TBV and ipsilateral hippocampal volume. Genetic correlation demonstrates shared genetic architecture between phenotypes and shared heritability is what proportion of the genetic architecture of one trait is shared by the other. Highest genetic correlations were between subfield volumes and ipsilateral hippocampal volume than with TBV. The pattern was opposite for shared heritability suggesting that subfields share greater proportion of the genetic architecture with TBV than with ipsilateral hippocampal volume. The relationship between the genetic architecture of TBV, hippocampal volume, and of individual subfields should be accounted for when using hippocampal subfield volumes as quantitative phenotypes for imaging genetics studies. Hum Brain Mapp 38:4337-4352, 2017. © 2017 Wiley Periodicals, Inc.

  • Time-efficient and flexible design of optimized multishell HARDI diffusion.

    Jana Hutter, J Donald Tournier, Anthony N Price, Lucilio Cordero-Grande, Emer J Hughes, Shaihan Malik, Johannes Steinweg, Matteo Bastiani, Stamatios N Sotiropoulos, Saad Jbabdi, Jesper Andersson, A David Edwards, Joseph V Hajnal
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    Advanced diffusion magnetic resonance imaging benefits from collecting as much data as is feasible but is highly sensitive to subject motion and the risk of data loss increases with longer acquisition times. Our purpose was to create a maximally time-efficient and flexible diffusion acquisition capability with built-in robustness to partially acquired or interrupted scans. Our framework has been developed for the developing Human Connectome Project, but different application domains are equally possible.

  • Multimodal connectivity-based parcellation reveals a shell-core dichotomy of the human nucleus accumbens.

    Xiaoluan Xia, Lingzhong Fan, Chen Cheng, Simon B Eickhoff, Junjie Chen, Haifang Li, Tianzi Jiang
    Human brain mapping, May 27, 2017 PMID: 28548226
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    The subdifferentiation of the nucleus accumbens (NAc) has been extensively studied using neuroanatomy and histochemistry, yielding a well-accepted dichotomic shell/core architecture that reflects dissociable roles, such as in reward and aversion, respectively. However, in vivo parcellation of these structures in humans has been rare, potentially impairing future research into the structural and functional characteristics and alterations of putative NAc subregions. Here, we used three complementary parcellation schemes based on tractography, task-independent functional connectivity, and task-dependent co-activation to investigate the regional differentiation within the NAc. We found that a 2-cluster solution with shell-like and core-like subdivisions provided the best description of the data and was consistent with the earlier anatomical shell/core architecture. The consensus clusters from this optimal solution, which was based on the three schemes, were used as the final parcels for the subsequent connection analyses. The resulting connectivity patterns presented inter-hemispheric symmetry, convergence and divergence across the modalities, and, most importantly, clearly distinct patterns between the two subregions. This convergent connectivity patterns also confirmed the connections in animal models, supporting views that the two subregions could have antagonistic roles in some circumstances. Finally, the identified parcels should be helpful in further neuroimaging studies of the NAc. Hum Brain Mapp 38:3878-3898, 2017. © 2017 Wiley Periodicals, Inc.

  • Quantifying the brain's sheet structure with normalized convolution.

    Chantal M W Tax, Carl-Fredrik Westin, Tom Dela Haije, Andrea Fuster, Max A Viergever, Evan Calabrese, Luc Florack, Alexander Leemans
    Medical image analysis, May 17, 2017 PMID: 28511065
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    The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as "pages of a book" has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of "path neighborhoods" reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure.

  • Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to Crystallized IQ and Gender.

    Vasileios C Pezoulas, Michalis Zervakis, Sifis Michelogiannis, Manousos A Klados
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    During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.

  • Association Between Reward Reactivity and Drug Use Severity is Substance Dependent: Preliminary Evidence From the Human Connectome Project.

    Alyssa L Peechatka, Amy C Janes
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    Blunted nucleus accumbens (NAc) reactivity to reward is common across drug users. One theory is that individuals abuse substances due to this reward deficit. However, whether there is a relationship between the amount an individual uses and the severity of NAc dysfunction is unclear. It also is possible that such a relationship is substance specific, as nicotine transiently increases reward system sensitivity while alcohol, another commonly used substance, does not. As smokers may use nicotine to bolster NAc reward function, we hypothesize that NAc reactivity to reward will be related to volume of cigarette use, but not volume of alcohol use.

  • Heritability analysis with repeat measurements and its application to resting-state functional connectivity.

    Tian Ge, Avram J Holmes, Randy L Buckner, Jordan W Smoller, Mert R Sabuncu
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    Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing heritability analysis methods do not discriminate between stable effects (e.g., due to the subject's unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements-a prototypic data modality that exhibits variable levels of test-retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional heritability analyses.

  • Protein synthesis is associated with high-speed dynamics and broad-band stability of functional hubs in the brain.

    Peter J Hellyer, Erica F Barry, Alberto Pellizzon, Mattia Veronese, Gaia Rizzo, Matteo Tonietto, Manuel Schütze, Michael Brammer, Marco Aurélio Romano-Silva, Alessandra Bertoldo, Federico E Turkheimer
    NeuroImage, May 04, 2017 PMID: 28465163
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    L-[1-11C]leucine PET can be used to measure in vivo protein synthesis in the brain. However, the relationship between regional protein synthesis and on-going neural dynamics is unclear. We use a graph theoretical approach to examine the relationship between cerebral protein synthesis (rCPS) and both static and dynamical measures of functional connectivity (measured using resting state functional MRI, R-fMRI). Our graph theoretical analysis demonstrates a significant positive relationship between protein turnover and static measures of functional connectivity. We compared these results to simple measures of metabolism in the cortex using [18F]FDG PET). Whilst some relationships between [18F]FDG binding and graph theoretical measures was present, there remained a significant relationship between protein turnover and graph theoretical measures, which were more robustly explained by L-[1-11C]Leucine than [18F]FDG PET. This relationship was stronger in dynamics at a faster temporal resolution relative to dynamics measured over a longer epoch. Using a Dynamic connectivity approach, we also demonstrate that broad-band dynamic measures of Functional Connectivity (FC), are inversely correlated with protein turnover, suggesting greater stability of FC in highly interconnected hub regions is supported by protein synthesis. Overall, we demonstrate that cerebral protein synthesis has a strong relationship independent of tissue metabolism to neural dynamics at the macroscopic scale.

  • Gray-matter structural variability in the human cerebellum: Lobule-specific differences across sex and hemisphere.

    Christopher J Steele, M Mallar Chakravarty
    NeuroImage, May 04, 2017 PMID: 28461060
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    Though commonly thought of as a "motor structure", we now know that the cerebellum's reciprocal connections to the cerebral cortex underlie contributions to both motor and non-motor behavior. Further, recent research has shown that cerebellar dysfunction may contribute to a wide range of neuropsychiatric disorders. However, there has been little characterization of normative variability at the level of cerebellar structure that can facilitate and further our understanding of disease biomarkers. In this manuscript we examine normative variation of the cerebellum using data from the Human Connectome Project (HCP). The Multiple Automatically Generated Templates (MAGeT) segmentation tool was used to identify the cerebella and 33 anatomically-defined lobules from 327 individuals from the HCP. To characterize normative variation, we estimated population mean volume and variability, assessed differences in hemisphere and sex, and related lobular volume to motor and non-motor behavior. We found that the effects of hemisphere and sex were not homogeneous across all lobules of the cerebellum. Greater volume in the right hemisphere was primarily driven by lobules Crus I, II, and H VIIB, with H VIIIA exhibiting the greatest left>right asymmetry. Relative to total cerebellar gray-matter volume, females had larger Crus II (known to be connected with non-motor regions of the cerebral cortex) while males had larger motor-connected lobules including H V, and VIIIA/B. When relating lobular volume to memory, motor performance, and emotional behavior, we found some evidence for relationships that have previously been identified in the literature. Our observations of normative cerebellar structure and variability in young adults provide evidence for lobule-specific differences in volume and the relationship with sex and behavior - indicating that the cerebellum cannot be considered a single structure with uniform function, but as a set of regions with functions that are likely as diverse as their connectivity with the cerebral cortex.

  • Objective analysis of the topological organization of the human cortical visual connectome suggests three visual pathways.

    Koen V Haak, Christian F Beckmann
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    The cortical visual system is composed of many areas serving various visual functions. In non-human primates, these are broadly organised into two distinct processing pathways: a ventral pathway for object recognition, and a dorsal pathway for action. In humans, recent theoretical proposals suggest the possible existence of additional pathways, but direct empirical evidence has yet to be presented. Here, we estimated the connectivity patterns between 22 human visual areas using resting-state functional MRI data of 470 individuals, leveraging the unprecedented data quantity and quality of the Human Connectome Project and a novel probabilistic atlas. An objective, data-driven analysis into the topological organisation of connectivity and subsequent quantitative confirmation revealed a highly significant triple dissociation between the retinotopic areas on the dorsal, ventral and lateral surfaces of the human occipital lobe. This suggests that the functional organisation of the human visual system involves not two but three cortical pathways.

  • Genetic Influence on the Sulcal Pits: On the Origin of the First Cortical Folds.

    Yann Le Guen, Guillaume Auzias, François Leroy, Marion Noulhiane, Ghislaine Dehaene-Lambertz, Edouard Duchesnay, Jean-François Mangin, Olivier Coulon, Vincent Frouin
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    The influence of genes on cortical structures has been assessed through various phenotypes. The sulcal pits, which are the putative first cortical folds, have for long been assumed to be under tight genetic control, but this was never quantified. We estimated the pit depth heritability in various brain regions using the high quality and large sample size of the Human Connectome Project pedigree cohort. Analysis of additive genetic variance indicated that their heritability ranges between 0.2 and 0.5 and displays a regional genetic control with an overall symmetric pattern between hemispheres. However, a noticeable asymmetry of heritability estimates is observed in the superior temporal sulcus and could thus be related to language lateralization. The heritability range estimated in this study reinforces the idea that cortical shape is determined primarily by nongenetic factors, which is consistent with the important increase of cortical folding from birth to adult life and thus predominantly constrained by environmental factors. Nevertheless, the genetic cues, implicated with various local levels of heritability in the formation of sulcal pits, play a fundamental role in the normal gyral pattern development. Quantifying their influence and identifying the underlying genetic variants would provide insight into neurodevelopmental disorders.

  • Defining the most probable location of the parahippocampal place area using cortex-based alignment and cross-validation.

    Kevin S Weiner, Michael A Barnett, Nathan Witthoft, Golijeh Golarai, Anthony Stigliani, Kendrick N Kay, Jesse Gomez, Vaidehi S Natu, Katrin Amunts, Karl Zilles, Kalanit Grill-Spector
    NeuroImage, Apr 25, 2017 PMID: 28435097
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    The parahippocampal place area (PPA) is a widely studied high-level visual region in the human brain involved in place and scene processing. The goal of the present study was to identify the most probable location of place-selective voxels in medial ventral temporal cortex. To achieve this goal, we first used cortex-based alignment (CBA) to create a probabilistic place-selective region of interest (ROI) from one group of 12 participants. We then tested how well this ROI could predict place selectivity in each hemisphere within a new group of 12 participants. Our results reveal that a probabilistic ROI (pROI) generated from one group of 12 participants accurately predicts the location and functional selectivity in individual brains from a new group of 12 participants, despite between subject variability in the exact location of place-selective voxels relative to the folding of parahippocampal cortex. Additionally, the prediction accuracy of our pROI is significantly higher than that achieved by volume-based Talairach alignment. Comparing the location of the pROI of the PPA relative to published data from over 500 participants, including data from the Human Connectome Project, shows a striking convergence of the predicted location of the PPA and the cortical location of voxels exhibiting the highest place selectivity across studies using various methods and stimuli. Specifically, the most predictive anatomical location of voxels exhibiting the highest place selectivity in medial ventral temporal cortex is the junction of the collateral and anterior lingual sulci. Methodologically, we make this pROI freely available (vpnl.stanford.edu/PlaceSelectivity), which provides a means to accurately identify a functional region from anatomical MRI data when fMRI data are not available (for example, in patient populations). Theoretically, we consider different anatomical and functional factors that may contribute to the consistent anatomical location of place selectivity relative to the folding of high-level visual cortex.

  • White matter connections of the inferior parietal lobule: A study of surgical anatomy.

    Joshua D Burks, Lillian B Boettcher, Andrew K Conner, Chad A Glenn, Phillip A Bonney, Cordell M Baker, Robert G Briggs, Nathan A Pittman, Daniel L O'Donoghue, Dee H Wu, Michael E Sughrue
    Brain and behavior, Apr 18, 2017 PMID: 28413699
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    Interest in the function of the inferior parietal lobule (IPL) has resulted in increased understanding of its involvement in visuospatial and cognitive functioning, and its role in semantic networks. A basic understanding of the nuanced white-matter anatomy in this region may be useful in improving outcomes when operating in this region of the brain. We sought to derive the surgical relationship between the IPL and underlying major white-matter bundles by characterizing macroscopic connectivity.

  • Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex.

    Salim Arslan, Sofia Ira Ktena, Antonios Makropoulos, Emma C Robinson, Daniel Rueckert, Sarah Parisot
    NeuroImage, Apr 17, 2017 PMID: 28412442
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    The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.

  • In vivo visualization of connections among revised Papez circuit hubs using full q-space diffusion spectrum imaging tractography.

    Peng-Hu Wei, Zhi-Qi Mao, Fei Cong, Fang-Cheng Yeh, Bo Wang, Zhi-Pei Ling, Shu-Li Liang, Lin Chen, Xin-Guang Yu
    Neuroscience, Apr 16, 2017 PMID: 28411159
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    Structural connections among the hubs of the revised Papez circuit remain to be elucidated in the human brain. As the original Papez circuit failed to explain functional imaging findings, a more detailed investigation is needed to delineate connections among the circuit's key hubs. Here we acquired diffusion spectrum imaging (DSI) from eight normal subjects and used data from the Human Connectome Project (HCP) to elucidate connections among hubs in the retrosplenial gyrus, hippocampus, mammillary bodies, and anterior thalamic nuclei. Our results show that the ventral hippocampal commissure (VHC) was visualized in all eight individual DSI datasets, as well as in the DSI and HCP group datasets, but a strictly defined VHC was only visualized in one individual dataset. Thalamic fibers were observed to connect with both the posterior cingulate cortex (PCC) and retrosplenial cortex (RSC). The RSC was mainly responsible for direct hippocampal connections, while the PCC was not. This indicates that the RSC and PCC represent separate functional hubs in humans, as also shown by previous primate axonal tracing studies and functional magnetic resonance imaging observations.

  • Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods.

    Ricardo P Monti, Romy Lorenz, Peter Hellyer, Robert Leech, Christoforos Anagnostopoulos, Giovanni Montana
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    An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project.

  • Can brain state be manipulated to emphasize individual differences in functional connectivity?

    Emily S Finn, Dustin Scheinost, Daniel M Finn, Xilin Shen, Xenophon Papademetris, R Todd Constable
    NeuroImage, Apr 05, 2017 PMID: 28373122
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    While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within- and between-subject variability across conditions using data from the Human Connectome Project, and outline questions for future study.

  • Damage to white matter bottlenecks contributes to language impairments after left hemispheric stroke.

    Joseph C Griffis, Rodolphe Nenert, Jane B Allendorfer, Jerzy P Szaflarski
    NeuroImage. Clinical, Mar 25, 2017 PMID: 28337410
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    Damage to the white matter underlying the left posterior temporal lobe leads to deficits in multiple language functions. The posterior temporal white matter may correspond to a bottleneck where both dorsal and ventral language pathways are vulnerable to simultaneous damage. Damage to a second putative white matter bottleneck in the left deep prefrontal white matter involving projections associated with ventral language pathways and thalamo-cortical projections has recently been proposed as a source of semantic deficits after stroke. Here, we first used white matter atlases to identify the previously described white matter bottlenecks in the posterior temporal and deep prefrontal white matter. We then assessed the effects of damage to each region on measures of verbal fluency, picture naming, and auditory semantic decision-making in 43 chronic left hemispheric stroke patients. Damage to the posterior temporal bottleneck predicted deficits on all tasks, while damage to the anterior bottleneck only significantly predicted deficits in verbal fluency. Importantly, the effects of damage to the bottleneck regions were not attributable to lesion volume, lesion loads on the tracts traversing the bottlenecks, or damage to nearby cortical language areas. Multivariate lesion-symptom mapping revealed additional lesion predictors of deficits. Post-hoc fiber tracking of the peak white matter lesion predictors using a publicly available tractography atlas revealed evidence consistent with the results of the bottleneck analyses. Together, our results provide support for the proposal that spatially specific white matter damage affecting bottleneck regions, particularly in the posterior temporal lobe, contributes to chronic language deficits after left hemispheric stroke. This may reflect the simultaneous disruption of signaling in dorsal and ventral language processing streams.

  • Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume.

    Zaixu Cui, Mengmeng Su, Liangjie Li, Hua Shu, Gaolang Gong
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    Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.

  • Agreement between functional connectivity and cortical thickness-driven correlation maps of the medial frontal cortex.

    Hyunjin Park, Yeong-Hun Park, Jungho Cha, Sang Won Seo, Duk L Na, Jong-Min Lee
    PloS one, Mar 23, 2017 PMID: 28328993
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    Parcellation of the human cortex has important implications in neuroscience. Parcellation is often a crucial requirement before meaningful regional analysis can occur. The human cortex can be parcellated into distinct regions based on structural features, such as gyri and sulci. Brain network patterns in a given region with respect to its neighbors, known as connectional fingerprints, can be used to parcellate the cortex. Distinct imaging modalities might provide complementary information for brain parcellation. Here, we established functional connectivity with time series data from functional MRI (fMRI) combined with a correlation map of cortical thickness obtained from T1-weighted MRI. We aimed to extend the previous study, which parcellated the medial frontal cortex (MFC) using functional connectivity, and to test the value of additional information regarding cortical thickness. Two types of network information were used to parcellate the MFC into two sub-regions with spectral and Ward's clustering approaches. The MFC region was defined using manual delineation based on in-house data (n = 12). Parcellation was applied to independent large-scale data obtained from the Human Connectome Project (HCP, n = 248). Agreement between parcellation using fMRI- and thickness-driven connectivity yielded dice coefficient overlaps of 0.74 (Ward's clustering) and 0.54 (spectral clustering). We also explored whole brain connectivity using the MFC sub-regions as seed regions based on these two types of information. The results of whole brain connectivity analyses were also consistent for both types of information. We observed that an inter-regional correlation map derived from cortical thickness strongly reflected the underlying functional connectivity of MFC region.

  • Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement.

    Jesper L R Andersson, Mark S Graham, Ivana Drobnjak, Hui Zhang, Nicola Filippini, Matteo Bastiani
    NeuroImage, Mar 13, 2017 PMID: 28284799
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    Most motion correction methods work by aligning a set of volumes together, or to a volume that represents a reference location. These are based on an implicit assumption that the subject remains motionless during the several seconds it takes to acquire all slices in a volume, and that any movement occurs in the brief moment between acquiring the last slice of one volume and the first slice of the next. This is clearly an approximation that can be more or less good depending on how long it takes to acquire one volume and in how rapidly the subject moves. In this paper we present a method that increases the temporal resolution of the motion correction by modelling movement as a piecewise continous function over time. This intra-volume movement correction is implemented within a previously presented framework that simultaneously estimates distortions, movement and movement-induced signal dropout. We validate the method on highly realistic simulated data containing all of these effects. It is demonstrated that we can estimate the true movement with high accuracy, and that scalar parameters derived from the data, such as fractional anisotropy, are estimated with greater fidelity when data has been corrected for intra-volume movement. Importantly, we also show that the difference in fidelity between data affected by different amounts of movement is much reduced when taking intra-volume movement into account. Additional validation was performed on data from a healthy volunteer scanned when lying still and when performing deliberate movements. We show an increased correspondence between the "still" and the "movement" data when the latter is corrected for intra-volume movement. Finally we demonstrate a big reduction in the telltale signs of intra-volume movement in data acquired on elderly subjects.

  • Reconstructing multivariate causal structure between functional brain networks through a Laguerre-Volterra based Granger causality approach.

    Andrea Duggento, Gaetano Valenza, Luca Passamonti, Maria Guerrisi, Riccardo Barbieri, Nicola Toschi
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    Classical multivariate approaches based on Granger causality (GC) which estimate functional connectivity in the brain are almost exclusively based on autoregressive models. Nevertheless, information available from past samples is limited due to both signal autocorrelation and necessarily low model orders. Consequently, multiple time-scales interactions are usually unaccounted for. To overcome these limitations, in this study we propose the use of discrete-time orthogonal Laguerre basis functions within a Wiener-Volterra decomposition of the BOLD signals to perform effective GC assessments of brain functional connectivity. We validate our method in synthetic noisy oscillator networks, and analyze experimental fMRI data from 30 healthy subjects publicly available within the Human Connectome Project (HCP). Synthetic results demonstrate that our Laguerre-Volterra based GC estimates outperform classical approaches in terms of accuracy in detecting true causal links while rejecting false causal links in complex nonlinear networks. Human data analysis shows for the first time that the default mode network modulates both the salience network as well as fronto-temporal circuits in a causal fashion.

  • Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

    Jalil Taghia, Srikanth Ryali, Tianwen Chen, Kaustubh Supekar, Weidong Cai, Vinod Menon
    NeuroImage, Mar 08, 2017 PMID: 28267626
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    There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity.

  • Image quality transfer and applications in diffusion MRI.

    Daniel C Alexander, Darko Zikic, Aurobrata Ghosh, Ryutaro Tanno, Viktor Wottschel, Jiaying Zhang, Enrico Kaden, Tim B Dyrby, Stamatios N Sotiropoulos, Hui Zhang, Antonio Criminisi
    NeuroImage, Mar 07, 2017 PMID: 28263925
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    This paper introduces a new computational imaging technique called image quality transfer (IQT). IQT uses machine learning to transfer the rich information available from one-off experimental medical imaging devices to the abundant but lower-quality data from routine acquisitions. The procedure uses matched pairs to learn mappings from low-quality to corresponding high-quality images. Once learned, these mappings then augment unseen low quality images, for example by enhancing image resolution or information content. Here, we demonstrate IQT using a simple patch-regression implementation and the uniquely rich diffusion MRI data set from the human connectome project (HCP). Results highlight potential benefits of IQT in both brain connectivity mapping and microstructure imaging. In brain connectivity mapping, IQT reveals, from standard data sets, thin connection pathways that tractography normally requires specialised data to reconstruct. In microstructure imaging, IQT shows potential in estimating, from standard "single-shell" data (one non-zero b-value), maps of microstructural parameters that normally require specialised multi-shell data. Further experiments show strong generalisability, highlighting IQT's benefits even when the training set does not directly represent the application domain. The concept extends naturally to many other imaging modalities and reconstruction problems.

  • Latent Variable Graphical Model Selection using Harmonic Analysis: Applications to the Human Connectome Project (HCP).

    Won Hwa Kim, Hyunwoo J Kim, Nagesh Adluru, Vikas Singh
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    A major goal of imaging studies such as the (ongoing) Human Connectome Project (HCP) is to characterize the structural network map of the human brain and identify its associations with covariates such as genotype, risk factors, and so on that correspond to an individual. But the set of image derived measures and the set of covariates are both large, so we must first estimate a 'parsimonious' set of relations between the measurements. For instance, a Gaussian graphical model will show conditional independences between the random variables, which can then be used to setup specific downstream analyses. But most such data involve a large list of 'latent' variables that remain unobserved, yet affect the 'observed' variables sustantially. Accounting for such latent variables is not directly addressed by standard precision matrix estimation, and is tackled via highly specialized optimization methods. This paper offers a unique harmonic analysis view of this problem. By casting the estimation of the precision matrix in terms of a composition of low-frequency latent variables and high-frequency sparse terms, we show how the problem can be formulated using a new wavelet-type expansion in non-Euclidean spaces. Our formulation poses the estimation problem in the frequency space and shows how it can be solved by a simple sub-gradient scheme. We provide a set of scientific results on ~500 scans from the recently released HCP data where our algorithm recovers highly interpretable and sparse conditional dependencies between brain connectivity pathways and well-known covariates.

  • JIVE integration of imaging and behavioral data.

    Qunqun Yu, Benjamin B Risk, Kai Zhang, J S Marron
    NeuroImage, Mar 02, 2017 PMID: 28246033
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    A major goal in neuroscience is to understand the neural pathways underlying human behavior. We introduce the recently developed Joint and Individual Variation Explained (JIVE) method to the neuroscience community to simultaneously analyze imaging and behavioral data from the Human Connectome Project. Motivated by recent computational and theoretical improvements in the JIVE approach, we simultaneously explore the joint and individual variation between and within imaging and behavioral data. In particular, we demonstrate that JIVE is an effective and efficient approach for integrating task fMRI and behavioral variables using three examples: one example where task variation is strong, one where task variation is weak and a reference case where the behavior is not directly related to the image. These examples are provided to visualize the different levels of signal found in the joint variation including working memory regions in the image data and accuracy and response time from the in-task behavioral variables. Joint analysis provides insights not available from conventional single block decomposition methods such as Singular Value Decomposition. Additionally, the joint variation estimated by JIVE appears to more clearly identify the working memory regions than Partial Least Squares (PLS), while Canonical Correlation Analysis (CCA) gives grossly overfit results. The individual variation in JIVE captures the behavior unrelated signals such as a background activation that is spatially homogeneous and activation in the default mode network. The information revealed by this individual variation is not examined in traditional methods such as CCA and PLS. We suggest that JIVE can be used as an alternative to PLS and CCA to improve estimation of the signal common to two or more datasets and reveal novel insights into the signal unique to each dataset.

  • Individual differences and time-varying features of modular brain architecture.

    Xuhong Liao, Miao Cao, Mingrui Xia, Yong He
    NeuroImage, Mar 01, 2017 PMID: 28242315
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    Recent studies have suggested that human brain functional networks are topologically organized into functionally specialized but inter-connected modules to facilitate efficient information processing and highly flexible cognitive function. However, these studies have mainly focused on group-level network modularity analyses using "static" functional connectivity approaches. How these extraordinary modular brain structures vary across individuals and spontaneously reconfigure over time remain largely unknown. Here, we employed multiband resting-state functional MRI data (N=105) from the Human Connectome Project and a graph-based modularity analysis to systematically investigate individual variability and dynamic properties in modular brain networks. We showed that the modular structures of brain networks dramatically vary across individuals, with higher modular variability primarily in the association cortex (e.g., fronto-parietal and attention systems) and lower variability in the primary systems. Moreover, brain regions spontaneously changed their module affiliations on a temporal scale of seconds, which cannot be simply attributable to head motion and sampling error. Interestingly, the spatial pattern of intra-subject dynamic modular variability largely overlapped with that of inter-subject modular variability, both of which were highly reproducible across repeated scanning sessions. Finally, the regions with remarkable individual/temporal modular variability were closely associated with network connectors and the number of cognitive components, suggesting a potential contribution to information integration and flexible cognitive function. Collectively, our findings highlight individual modular variability and the notable dynamic characteristics in large-scale brain networks, which enhance our understanding of the neural substrates underlying individual differences in a variety of cognition and behaviors.

  • Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability.

    Chiara Giordano, Stefano Zappalà, Svein Kleiven
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    Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to predict injury. However, when assessing the reliability of axonal strain to predict injury in a population, it is important to consider the predictor sensitivity to the biological inter-subject variability of the human brain. The present study investigated the axonal strain response of 485 white matter subject-specific anisotropic finite element models of the head subjected to the same loading conditions. It was observed that the biological variability affected the orientation of the preferential directions (coefficient of variation of 39.41% for the elevation angle-coefficient of variation of 29.31% for the azimuth angle) and the determination of the mechanical fiber alignment parameter in the model (gray matter volume 55.55-70.75%). The magnitude of the maximum axonal strain showed coefficients of variation of 11.91%. On the contrary, the localization of the maximum axonal strain was consistent: the peak of strain was typically located in a 2 cm3 volume of the brain. For a sport concussive event, the predictor was capable of discerning between non-injurious and concussed populations in several areas of the brain. It was concluded that, despite its sensitivity to biological variability, axonal strain is an appropriate mechanical parameter to predict traumatic brain injury.

  • Functional connectivity in amygdalar-sensory/(pre)motor networks at rest: new evidence from the Human Connectome Project.

    Nicola Toschi, Andrea Duggento, Luca Passamonti
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    The word 'e-motion' derives from the Latin word 'ex-moveo' which literally means 'moving away from something/somebody'. Emotions are thus fundamental to prime action and goal-directed behavior with obvious implications for individual's survival. However, the brain mechanisms underlying the interactions between emotional and motor cortical systems remain poorly understood. A recent diffusion tensor imaging study in humans has reported the existence of direct anatomical connections between the amygdala and sensory/(pre)motor cortices, corroborating an initial observation in animal research. Nevertheless, the functional significance of these amygdala-sensory/(pre)motor pathways remain uncertain. More specifically, it is currently unclear whether a distinct amygdala-sensory/(pre)motor circuit can be identified with resting-state functional magnetic resonance imaging (rs-fMRI). This is a key issue, as rs-fMRI offers an opportunity to simultaneously examine distinct neural circuits that underpin different cognitive, emotional and motor functions, while minimizing task-related performance confounds. We therefore tested the hypothesis that the amygdala and sensory/(pre)motor cortices could be identified as part of the same resting-state functional connectivity network. To this end, we examined independent component analysis results in a very large rs-fMRI data-set drawn from the Human Connectome Project (n = 820 participants, mean age: 28.5 years). To our knowledge, we report for the first time the existence of a distinct amygdala-sensory/(pre)motor functional network at rest. rs-fMRI studies are now warranted to examine potential abnormalities in this circuit in psychiatric and neurological diseases that may be associated with alterations in the amygdala-sensory/(pre)motor pathways (e.g. conversion disorders, impulse control disorders, amyotrophic lateral sclerosis and multiple sclerosis).

  • Neural Mechanism Underling Comprehension of Narrative Speech and Its Heritability: Study in a Large Population.

    Abbas Babajani-Feremi
    Brain topography, Feb 20, 2017 PMID: 28214981
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    Comprehension of narratives constitutes a fundamental part of our everyday life experience. Although the neural mechanism of auditory narrative comprehension has been investigated in some studies, the neural correlates underlying this mechanism and its heritability remain poorly understood. We investigated comprehension of naturalistic speech in a large, healthy adult population (n = 429; 176/253 M/F; 22-36 years of age) consisting of 192 twin pairs (49 monozygotic and 47 dizygotic pairs) and 237 of their siblings. We used high quality functional MRI datasets from the Human Connectome Project (HCP) in which a story-based paradigm was utilized for the auditory narrative comprehension. Our results revealed that narrative comprehension was associated with activations of the classical language regions including superior temporal gyrus (STG), middle temporal gyrus (MTG), and inferior frontal gyrus (IFG) in both hemispheres, though STG and MTG were activated symmetrically and activation in IFG were left-lateralized. Our results further showed that the narrative comprehension was associated with activations in areas beyond the classical language regions, e.g. medial superior frontal gyrus (SFGmed), middle frontal gyrus (MFG), and supplementary motor area (SMA). Of subcortical structures, only the hippocampus was involved. The results of heritability analysis revealed that the oral reading recognition and picture vocabulary comprehension were significantly heritable (h 2  > 0.56, p < 10- 13). In addition, the extent of activation of five areas in the left hemisphere, i.e. STG, IFG pars opercularis, SFGmed, SMA, and precuneus, and one area in the right hemisphere, i.e. MFG, were significantly heritable (h 2  > 0.33, p < 0.0004). The current study, to the best of our knowledge, is the first to investigate auditory narrative comprehension and its heritability in a large healthy population. Referring to the excellent quality of the HCP data, our results can clarify the functional contributions of linguistic and extra-linguistic cortices during narrative comprehension.

  • Connection between bilateral temporal regions: Tractography using human connectome data and diffusion spectrum imaging.

    Peng-Hu Wei, Zhi-Qi Mao, Fei Cong, Bo Wang, Zhi-Pei Ling, Shu-Li Liang, Xin-Guang Yu
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    Temporal lobe epilepsy often propagates inter-hemispherically. Although the pathway of the propagation was verified by electrophysiology, the trajectory remains poorly defined. DTI can depict fiber trajectory but it has limited angular resolution and cannot adequately assess cortical regions. We visualized potential pathways of bitemporal epilepsy propagation using diffusion spectrum imaging (DSI) with data consisting of 8 groups of 514 directions and diffusion templates of 842 subjects from the human connectome project (HCP). We verified the results with reference to the axonal-tracing literature. Both the large population overall and individual connection properties were investigated. In both the HCP 842 atlas and DSI individual data, the bilateral temporal pole was found to connect via the anterior commissure. The splenium of the corpus callosum was divided into 3 subregions (CS1, CS2, CS3) according to the form of connections. CS1 was predominately located at the rostral third and the dorsal part of middle third of the splenium; it communicated with the bilateral parietal lobe. SC2 was predominately located at the ventral middle third of the splenium. Fibers passed through the lateral wall of the lateral ventricle and connected to regions lateral of the occipitotemporal sulci. CS3 was located at the caudal third of the splenium. Together with the hippocampal commissure, its fibers constituted the medial wall of the lateral ventricle and distributed medially to the occipitotemporal sulci. The trajectory of bilateral temporal connections was visualized in this study; the results might help in the understanding and treatment of inter-hemispherical propagation of temporal-lobe epilepsy.

  • Whole-Brain High-Resolution Structural Connectome: Inter-Subject Validation and Application to the Anatomical Segmentation of the Striatum.

    Pierre Besson, Nicolas Carrière, S Kathleen Bandt, Marc Tommasi, Xavier Leclerc, Philippe Derambure, Renaud Lopes, Louise Tyvaert
    Brain topography, Feb 09, 2017 PMID: 28176164
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    The present study describes extraction of high-resolution structural connectome (HRSC) in 99 healthy subjects, acquired and made available by the Human Connectome Project. Single subject connectomes were then registered to the common surface space to allow assessment of inter-individual reproducibility of this novel technique using a leave-one-out approach. The anatomic relevance of the surface-based connectome was examined via a clustering algorithm, which identified anatomic subdivisions within the striatum. The connectivity of these striatal subdivisions were then mapped on the cortical and other subcortical surfaces. Findings demonstrate that HRSC analysis is robust across individuals and accurately models the actual underlying brain networks related to the striatum. This suggests that this method has the potential to model and characterize the healthy whole-brain structural network at high anatomic resolution.

  • Parameterizable consensus connectomes from the Human Connectome Project: the Budapest Reference Connectome Server v3.0.

    Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince Grolmusz
    Cognitive neurodynamics, Feb 09, 2017 PMID: 28174617
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    Connections of the living human brain, on a macroscopic scale, can be mapped by a diffusion MR imaging based workflow. Since the same anatomic regions can be corresponded between distinct brains, one can compare the presence or the absence of the edges, connecting the very same two anatomic regions, among multiple cortices. Previously, we have constructed the consensus braingraphs on 1015 vertices first in five, then in 96 subjects in the Budapest Reference Connectome Server v1.0 and v2.0, respectively. Here we report the construction of the version 3.0 of the server, generating the common edges of the connectomes of variously parameterizable subsets of the 1015-vertex connectomes of 477 subjects of the Human Connectome Project's 500-subject release. The consensus connectomes are downloadable in CSV and GraphML formats, and they are also visualized on the server's page. The consensus connectomes of the server can be considered as the "average, healthy" human connectome since all of their connections are present in at least k subjects, where the default value of [Formula: see text], but it can also be modified freely at the web server. The webserver is available at http://connectome.pitgroup.org.

  • Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach.

    Guillermo Gallardo, William Wells, Rachid Deriche, Demian Wassermann
    NeuroImage, Feb 06, 2017 PMID: 28161314
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    Current theories hold that brain function is highly related to long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcellation based on extrinsic connectivity remains challenging. Current parcellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with structural and functional parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data.

  • In vivo Exploration of the Connectivity between the Subthalamic Nucleus and the Globus Pallidus in the Human Brain Using Multi-Fiber Tractography.

    Sonia Pujol, Ryan Cabeen, Sophie B Sébille, Jérôme Yelnik, Chantal François, Sara Fernandez Vidal, Carine Karachi, Yulong Zhao, G Rees Cosgrove, Pierre Jannin, Ron Kikinis, Eric Bardinet
    Frontiers in neuroanatomy, Feb 04, 2017 PMID: 28154527
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    The basal ganglia is part of a complex system of neuronal circuits that play a key role in the integration and execution of motor, cognitive and emotional function in the human brain. Parkinson's disease is a progressive neurological disorder of the motor circuit characterized by tremor, rigidity, and slowness of movement. Deep brain stimulation (DBS) of the subthalamic nucleus and the globus pallidus pars interna provides an efficient treatment to reduce symptoms and levodopa-induced side effects in Parkinson's disease patients. While the underlying mechanism of action of DBS is still unknown, the potential modulation of white matter tracts connecting the surgical targets has become an active area of research. With the introduction of advanced diffusion MRI acquisition sequences and sophisticated post-processing techniques, the architecture of the human brain white matter can be explored in vivo. The goal of this study is to investigate the white matter connectivity between the subthalamic nucleus and the globus pallidus. Two multi-fiber tractography methods were used to reconstruct pallido-subthalamic, subthalamo-pallidal and pyramidal fibers in five healthy subjects datasets of the Human Connectome Project. The anatomical accuracy of the tracts was assessed by four judges with expertise in neuroanatomy, functional neurosurgery, and diffusion MRI. The variability among subjects was evaluated based on the fractional anisotropy and mean diffusivity of the tracts. Both multi-fiber approaches enabled the detection of complex fiber architecture in the basal ganglia. The qualitative evaluation by experts showed that the identified tracts were in agreement with the expected anatomy. Tract-derived measurements demonstrated relatively low variability among subjects. False-negative tracts demonstrated the current limitations of both methods for clinical decision-making. Multi-fiber tractography methods combined with state-of-the-art diffusion MRI data have the potential to help identify white matter tracts connecting DBS targets in functional neurosurgery intervention.

  • Surface-based morphometry reveals the neuroanatomical basis of the five-factor model of personality.

    Roberta Riccelli, Nicola Toschi, Salvatore Nigro, Antonio Terracciano, Luca Passamonti
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    The five-factor model (FFM) is a widely used taxonomy of human personality; yet its neuro anatomical basis remains unclear. This is partly because past associations between gray-matter volume and FFM were driven by different surface-based morphometry (SBM) indices (i.e. cortical thickness, surface area, cortical folding or any combination of them). To overcome this limitation, we used Free-Surfer to study how variability in SBM measures was related to the FFM in n = 507 participants from the Human Connectome Project.Neuroticism was associated with thicker cortex and smaller area and folding in prefrontal-temporal regions. Extraversion was linked to thicker pre-cuneus and smaller superior temporal cortex area. Openness was linked to thinner cortex and greater area and folding in prefrontal-parietal regions. Agreeableness was correlated to thinner prefrontal cortex and smaller fusiform gyrus area. Conscientiousness was associated with thicker cortex and smaller area and folding in prefrontal regions. These findings demonstrate that anatomical variability in prefrontal cortices is linked to individual differences in the socio-cognitive dispositions described by the FFM. Cortical thickness and surface area/folding were inversely related each others as a function of different FFM traits (neuroticism, extraversion and consciousness vs openness), which may reflect brain maturational effects that predispose or protect against psychiatric disorders.

  • Elevated Body Mass Index is Associated with Increased Integration and Reduced Cohesion of Sensory-Driven and Internally Guided Resting-State Functional Brain Networks.

    Gaelle E Doucet, Natalie Rasgon, Bruce S McEwen, Nadia Micali, Sophia Frangou
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    Elevated body mass index (BMI) is associated with increased multi-morbidity and mortality. The investigation of the relationship between BMI and brain organization has the potential to provide new insights relevant to clinical and policy strategies for weight control. Here, we quantified the association between increasing BMI and the functional organization of resting-state brain networks in a sample of 496 healthy individuals that were studied as part of the Human Connectome Project. We demonstrated that higher BMI was associated with changes in the functional connectivity of the default-mode network (DMN), central executive network (CEN), sensorimotor network (SMN), visual network (VN), and their constituent modules. In siblings discordant for obesity, we showed that person-specific factors contributing to obesity are linked to reduced cohesiveness of the sensory networks (SMN and VN). We conclude that higher BMI is associated with widespread alterations in brain networks that balance sensory-driven (SMN, VN) and internally guided (DMN, CEN) states which may augment sensory-driven behavior leading to overeating and subsequent weight gain. Our results provide a neurobiological context for understanding the association between BMI and brain functional organization while accounting for familial and person-specific influences.

  • Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI.

    Sebastiano Stramaglia, Leonardo Angelini, Guorong Wu, Jesus M Cortes, Luca Faes, Daniele Marinazzo
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    We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network.

  • Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex.

    Xi Jiang, Xiang Li, Jinglei Lv, Shijie Zhao, Shu Zhang, Wei Zhang, Tuo Zhang, Junwei Han, Lei Guo, Tianming Liu
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    Various studies in the brain mapping field have demonstrated that there exist multiple concurrent functional networks that are spatially overlapped and interacting with each other during specific task performance to jointly realize the total brain function. Assessing such spatial overlap patterns of functional networks (SOPFNs) based on fMRI has thus received increasing interest for brain function studies. However, there are still two crucial issues to be addressed. First, the SOPFNs are assessed over the entire fMRI scan assuming the temporal stationarity, while possibly time-dependent dynamics of the SOPFNs is not sufficiently explored. Second, the SOPFNs are assessed within individual subjects, while group-wise consistency of the SOPFNs is largely unknown.

  • Inferring Individual-level Variations in the Functional Parcellation of the Cerebral Cortex.

    Lei Nie, Paul M Matthews, Yike Guo
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    Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual - or state - dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects.

  • Hemispheric lateralization of resting-state functional connectivity of the ventral striatum: an exploratory study.

    Sheng Zhang, Sien Hu, Herta H Chao, Chiang-Shan R Li
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    Resting-state functional connectivity (rsFC) is widely used to examine cerebral functional organization. The ventral striatum (VS) is critical to motivated behavior, with extant studies suggesting functional hemispheric asymmetry. The current work investigated differences in rsFC between the left (L) and right (R) VS and explored gender differences in the extent of functional lateralization. In 106 adults, we computed a laterality index (fcLI) to query whether a target region shows greater or less connectivity to the L vs R VS. A total of 45 target regions with hemispheric masks were examined from the Automated Anatomic Labeling atlas. One-sample t test was performed to explore significant laterality in the whole sample and in men and women separately. Two-sample t test was performed to examine gender differences in fcLI. At a corrected threshold (p < 0.05/45 = 0.0011), the dorsomedial prefrontal cortex (dmPFC) and posterior cingulate cortex (pCC) showed L lateralization and the intraparietal sulcus (IPS) and supramarginal gyrus (SMG) showed R lateralization in VS connectivity. Except for the pCC, these findings were replicated in a different data set (n = 97) from the Human Connectome Project. Furthermore, the fcLI of VS-pCC was negatively correlated with a novelty seeking trait in women but not in men. Together, the findings may suggest a more important role of the L VS in linking saliency response to self control and other internally directed processes. Right lateralization of VS connectivity to the SMG and IPS may support attention and action directed to external behavioral contingencies.

  • Probabilistic Tractography for Topographically Organized Connectomes.

    Dogu Baran Aydogan, Yonggang Shi
    Show Summary

    While tractography is widely used in brain imaging research, its quantitative validation is highly difficult. Many fiber systems, however, have well-known topographic organization which can even be quantitatively mapped such as the retinotopy of visual pathway. Motivated by this previously untapped anatomical knowledge, we develop a novel tractography method that preserves both topographic and geometric regularity of fiber systems. For topographic preservation, we propose a novel likelihood function that tests the match between parallel curves and fiber orientation distributions. For geometric regularity, we use Gaussian distributions of Frenet-Serret frames. Taken together, we develop a Bayesian framework for generating highly organized tracks that accurately follow neuroanatomy. Using multi-shell diffusion images of 56 subjects from Human Connectome Project, we compare our method with algorithms from MRtrix. By applying regression analysis between retinotopic eccentricity and tracks, we quantitatively demonstrate that our method achieves superior performance in preserving the retinotopic organization of optic radiation.

  • Riemannian Metric Optimization for Connectivity-driven Surface Mapping.

    Jin Kyu Gahm, Yonggang Shi
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    With the advance of human connectome research, there are great interests in computing diffeomorphic maps of brain surfaces with rich connectivity features. In this paper, we propose a novel framework for connectivity-driven surface mapping based on Riemannian metric optimization on surfaces (RMOS) in the Laplace-Beltrami (LB) embedding space. The mathematical foundation of our method is that we can use the pullback metric to define an isometry between surfaces for an arbitrary diffeomorphism, which in turn results in identical LB embeddings from the two surfaces. For connectivity-driven surface mapping, our goal is to compute a diffeomorphism that can match a set of connectivity features defined over anatomical surfaces. The proposed RMOS approach achieves this goal by iteratively optimizing the Riemannian metric on surfaces to match the connectivity features in the LB embedding space. At the core of our framework is an optimization approach that converts the cost function of connectivity features into a distance measure in the LB embedding space, and optimizes it using gradients of the LB eigen-system with respect to the Riemannian metric. We demonstrate our method on the mapping of thalamic surfaces according to connectivity to ten cortical regions, which we compute with the multi-shell diffusion imaging data from the Human Connectome Project (HCP). Comparisons with a state-of-the-art method show that the RMOS method can more effectively match anatomical features and detect thalamic atrophy due to normal aging.

  • Within brain area tractography suggests local modularity using high resolution connectomics.

    Peter N Taylor, Yujiang Wang, Marcus Kaiser
    Scientific reports, Jan 06, 2017 PMID: 28054634
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    Previous structural brain connectivity studies have mainly focussed on the macroscopic scale of around 1,000 or fewer brain areas (network nodes). However, it has recently been demonstrated that high resolution structural connectomes of around 50,000 nodes can be generated reproducibly. In this study, we infer high resolution brain connectivity matrices using diffusion imaging data from the Human Connectome Project. With such high resolution we are able to analyse networks within brain areas in a single subject. We show that the global network has a scale invariant topological organisation, which means there is a hierarchical organisation of the modular architecture. Specifically, modules within brain areas are spatially localised. We find that long range connections terminate between specific modules, whilst short range connections via highly curved association fibers terminate within modules. We suggest that spatial locations of white matter modules overlap with cytoarchitecturally distinct grey matter areas and may serve as the structural basis for function specialisation within brain areas. Future studies might elucidate how brain diseases change this modular architecture within brain areas.

  • Correspondence between evoked and intrinsic functional brain network configurations.

    Taylor Bolt, Jason S Nomi, Mikail Rubinov, Lucina Q Uddin
    Human brain mapping, Jan 05, 2017 PMID: 28052450
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    Much of the literature exploring differences between intrinsic and task-evoked brain architectures has examined changes in functional connectivity patterns between specific brain regions. While informative, this approach overlooks important overall functional changes in hub organization and network topology that may provide insights about differences in integration between intrinsic and task-evoked states. Examination of changes in overall network organization, such as a change in the concentration of hub nodes or a quantitative change in network organization, is important for understanding the underlying processes that differ between intrinsic and task-evoked brain architectures. The present study used graph-theoretical techniques applied to publicly available neuroimaging data collected from a large sample of individuals (N = 202), and a within-subject design where resting-state and several task scans were collected from each participant as part of the Human Connectome Project. We demonstrate that differences between intrinsic and task-evoked brain networks are characterized by a task-general shift in high-connectivity hubs from primarily sensorimotor/auditory processing areas during the intrinsic state to executive control/salience network areas during task performance. In addition, we demonstrate that differences between intrinsic and task-evoked architectures are associated with changes in overall network organization, such as increases in network clustering, global efficiency and integration between modules. These findings offer a new perspective on the principles guiding functional brain organization by identifying unique and divergent properties of overall network organization between the resting-state and task performance. Hum Brain Mapp 38:1992-2007, 2017. © 2017 Wiley Periodicals, Inc.

  • Sleep duration and resting fMRI functional connectivity: examination of short sleepers with and without perceived daytime dysfunction.

    Brian J Curtis, Paula G Williams, Christopher R Jones, Jeffrey S Anderson
    Brain and behavior, Dec 30, 2016 PMID: 28031999
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    Approximately 30% of the U.S. population reports recurrent short sleep; however, perceived sleep need varies widely among individuals. Some "habitual short sleepers" routinely sleep 4-6 hr/night without self-reported adverse consequences. Identifying neural mechanisms underlying individual differences in perceived sleep-related dysfunction has important implications for understanding associations between sleep duration and health.

  • Gradients of Connectivity in the Cerebral Cortex.

    Fenna M Krienen, Chet C Sherwood
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    The human neocortex is organized with distributed networks that connect distant regions together, but what determines their spatial layout? A recent study sheds light on the topological placement of regions along the cortical surface in relation to gradients of connectivity in both humans and macaques.

  • Alignment of Tractograms As Graph Matching.

    Emanuele Olivetti, Nusrat Sharmin, Paolo Avesani
    Frontiers in neuroscience, Dec 21, 2016 PMID: 27994537
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    The white matter pathways of the brain can be reconstructed as 3D polylines, called streamlines, through the analysis of diffusion magnetic resonance imaging (dMRI) data. The whole set of streamlines is called tractogram and represents the structural connectome of the brain. In multiple applications, like group-analysis, segmentation, or atlasing, tractograms of different subjects need to be aligned. Typically, this is done with registration methods, that transform the tractograms in order to increase their similarity. In contrast with transformation-based registration methods, in this work we propose the concept of tractogram correspondence, whose aim is to find which streamline of one tractogram corresponds to which streamline in another tractogram, i.e., a map from one tractogram to another. As a further contribution, we propose to use the relational information of each streamline, i.e., its distances from the other streamlines in its own tractogram, as the building block to define the optimal correspondence. We provide an operational procedure to find the optimal correspondence through a combinatorial optimization problem and we discuss its similarity to the graph matching problem. In this work, we propose to represent tractograms as graphs and we adopt a recent inexact sub-graph matching algorithm to approximate the solution of the tractogram correspondence problem. On tractograms generated from the Human Connectome Project dataset, we report experimental evidence that tractogram correspondence, implemented as graph matching, provides much better alignment than affine registration and comparable if not better results than non-linear registration of volumes.

  • Bursty properties revealed in large-scale brain networks with a point-based method for dynamic functional connectivity.

    William Hedley Thompson, Peter Fransson
    Scientific reports, Dec 20, 2016 PMID: 27991540
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    The brain is organized into large scale spatial networks that can be detected during periods of rest using fMRI. The brain is also a dynamic organ with activity that changes over time. We developed a method and investigated properties where the connections as a function of time are derived and quantified. The point based method (PBM) presented here derives covariance matrices after clustering individual time points based upon their global spatial pattern. This method achieved increased temporal sensitivity, together with temporal network theory, allowed us to study functional integration between resting-state networks. Our results show that functional integrations between two resting-state networks predominately occurs in bursts of activity. This is followed by varying intermittent periods of less connectivity. The described point-based method of dynamic resting-state functional connectivity allows for a detailed and expanded view on the temporal dynamics of resting-state connectivity that provides novel insights into how neuronal information processing is integrated in the human brain at the level of large-scale networks.

  • Dependence on b-value of the direction-averaged diffusion-weighted imaging signal in brain.

    Emilie T McKinnon, Jens H Jensen, G Russell Glenn, Joseph A Helpern
    Magnetic resonance imaging, Dec 19, 2016 PMID: 27989904
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    The dependence of the direction-averaged diffusion-weighted imaging (DWI) signal in brain was studied as a function of b-value in order to help elucidate the relationship between diffusion weighting and brain microstructure.

  • Convexity-constrained and nonnegativity-constrained spherical factorization in diffusion-weighted imaging.

    Daan Christiaens, Stefan Sunaert, Paul Suetens, Frederik Maes
    NeuroImage, Dec 19, 2016 PMID: 27989845
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    Diffusion-weighted imaging (DWI) facilitates probing neural tissue structure non-invasively by measuring its hindrance to water diffusion. Analysis of DWI is typically based on generative signal models for given tissue geometry and microstructural properties. In this work, we generalize multi-tissue spherical deconvolution to a blind source separation problem under convexity and nonnegativity constraints. This spherical factorization approach decomposes multi-shell DWI data, represented in the basis of spherical harmonics, into tissue-specific orientation distribution functions and corresponding response functions, without assuming the latter as known thus fully unsupervised. In healthy human brain data, the resulting components are associated with white matter fibres, grey matter, and cerebrospinal fluid. The factorization results are on par with state-of-the-art supervised methods, as demonstrated also in Monte-Carlo simulations evaluating accuracy and precision of the estimated response functions and orientation distribution functions of each component. In animal data and in the presence of oedema, the proposed factorization is able to recover unseen tissue structure, solely relying on DWI. As such, our method broadens the applicability of spherical deconvolution techniques to exploratory analysis of tissue structure in data where priors are uncertain or hard to define.

  • Hand classification of fMRI ICA noise components.

    Ludovica Griffanti, Gwenaëlle Douaud, Janine Bijsterbosch, Stefania Evangelisti, Fidel Alfaro-Almagro, Matthew F Glasser, Eugene P Duff, Sean Fitzgibbon, Robert Westphal, Davide Carone, Christian F Beckmann, Stephen M Smith
    NeuroImage, Dec 19, 2016 PMID: 27989777
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    We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.

  • Comparison of probabilistic and deterministic fiber tracking of cranial nerves.

    Amir Zolal, Stephan B Sobottka, Dino Podlesek, Jennifer Linn, Bernhard Rieger, Tareq A Juratli, Gabriele Schackert, Hagen H Kitzler
    Journal of neurosurgery, Dec 17, 2016 PMID: 27982771
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    OBJECTIVE The depiction of cranial nerves (CNs) using diffusion tensor imaging (DTI) is of great interest in skull base tumor surgery and DTI used with deterministic tracking methods has been reported previously. However, there are still no good methods usable for the elimination of noise from the resulting depictions. The authors have hypothesized that probabilistic tracking could lead to more accurate results, because it more efficiently extracts information from the underlying data. Moreover, the authors have adapted a previously described technique for noise elimination using gradual threshold increases to probabilistic tracking. To evaluate the utility of this new approach, a comparison is provided with this work between the gradual threshold increase method in probabilistic and deterministic tracking of CNs. METHODS Both tracking methods were used to depict CNs II, III, V, and the VII+VIII bundle. Depiction of 240 CNs was attempted with each of the above methods in 30 healthy subjects, which were obtained from 2 public databases: the Kirby repository (KR) and Human Connectome Project (HCP). Elimination of erroneous fibers was attempted by gradually increasing the respective thresholds (fractional anisotropy [FA] and probabilistic index of connectivity [PICo]). The results were compared with predefined ground truth images based on corresponding anatomical scans. Two label overlap measures (false-positive error and Dice similarity coefficient) were used to evaluate the success of both methods in depicting the CN. Moreover, the differences between these parameters obtained from the KR and HCP (with higher angular resolution) databases were evaluated. Additionally, visualization of 10 CNs in 5 clinical cases was attempted with both methods and evaluated by comparing the depictions with intraoperative findings. RESULTS Maximum Dice similarity coefficients were significantly higher with probabilistic tracking (p < 0.001; Wilcoxon signed-rank test). The false-positive error of the last obtained depiction was also significantly lower in probabilistic than in deterministic tracking (p < 0.001). The HCP data yielded significantly better results in terms of the Dice coefficient in probabilistic tracking (p < 0.001, Mann-Whitney U-test) and in deterministic tracking (p = 0.02). The false-positive errors were smaller in HCP data in deterministic tracking (p < 0.001) and showed a strong trend toward significance in probabilistic tracking (p = 0.06). In the clinical cases, the probabilistic method visualized 7 of 10 attempted CNs accurately, compared with 3 correct depictions with deterministic tracking. CONCLUSIONS High angular resolution DTI scans are preferable for the DTI-based depiction of the cranial nerves. Probabilistic tracking with a gradual PICo threshold increase is more effective for this task than the previously described deterministic tracking with a gradual FA threshold increase and might represent a method that is useful for depicting cranial nerves with DTI since it eliminates the erroneous fibers without manual intervention.

  • Microstructure Imaging of Crossing (MIX) White Matter Fibers from diffusion MRI.

    Hamza Farooq, Junqian Xu, Jung Who Nam, Daniel F Keefe, Essa Yacoub, Tryphon Georgiou, Christophe Lenglet
    Scientific reports, Dec 17, 2016 PMID: 27982056
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    Diffusion MRI (dMRI) reveals microstructural features of the brain white matter by quantifying the anisotropic diffusion of water molecules within axonal bundles. Yet, identifying features such as axonal orientation dispersion, density, diameter, etc., in complex white matter fiber configurations (e.g. crossings) has proved challenging. Besides optimized data acquisition and advanced biophysical models, computational procedures to fit such models to the data are critical. However, these procedures have been largely overlooked by the dMRI microstructure community and new, more versatile, approaches are needed to solve complex biophysical model fitting problems. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. Here, we introduce a new and versatile optimization technique (MIX), which enables microstructure imaging of crossing white matter fibers. We provide a MATLAB implementation of MIX, and demonstrate its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data.

  • Automated white matter fiber tract identification in patients with brain tumors.

    Lauren J O'Donnell, Yannick Suter, Laura Rigolo, Pegah Kahali, Fan Zhang, Isaiah Norton, Angela Albi, Olutayo Olubiyi, Antonio Meola, Walid I Essayed, Prashin Unadkat, Pelin Aksit Ciris, William M Wells, Yogesh Rathi, Carl-Fredrik Westin, Alexandra J Golby
    NeuroImage. Clinical, Dec 17, 2016 PMID: 27981029
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    We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.

  • Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling.

    Srikanth Ryali, Kaustubh Supekar, Tianwen Chen, John Kochalka, Weidong Cai, Jonathan Nicholas, Aarthi Padmanabhan, Vinod Menon
    PLoS computational biology, Dec 14, 2016 PMID: 27959921
    Show Summary

    Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of interactions between salience (SN), default mode (DMN), and central executive (CEN) networks-three brain systems that play a critical role in human cognition. In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN, CEN, and DMN. Furthermore, the three "static" networks occurred in a segregated state only intermittently. Findings were replicated in two adult cohorts from the Human Connectome Project. VB-HMM further revealed immature dynamic interactions between SN, CEN, and DMN in children, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our computational techniques provide new insights into human brain network dynamics and its maturation with development.

  • Let's Not Waste Time: Using Temporal Information in Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR) for Parcellating FMRI Data.

    Ronald J Janssen, Pasi Jylänki, Marcel A J van Gerven
    PloS one, Dec 10, 2016 PMID: 27935937
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    We have proposed a Bayesian approach for functional parcellation of whole-brain FMRI measurements which we call Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR). We use distance-dependent Chinese restaurant processes (dd-CRPs) to define a flexible prior which partitions the voxel measurements into clusters whose number and shapes are unknown a priori. With dd-CRPs we can conveniently implement spatial constraints to ensure that our parcellations remain spatially contiguous and thereby physiologically meaningful. In the present work, we extend CAESAR by using Gaussian process (GP) priors to model the temporally smooth haemodynamic signals that give rise to the measured FMRI data. A challenge for GP inference in our setting is the cubic scaling with respect to the number of time points, which can become computationally prohibitive with FMRI measurements, potentially consisting of long time series. As a solution we describe an efficient implementation that is practically as fast as the corresponding time-independent non-GP model with typically-sized FMRI data sets. We also employ a population Monte-Carlo algorithm that can significantly speed up convergence compared to traditional single-chain methods. First we illustrate the benefits of CAESAR and the GP priors with simulated experiments. Next, we demonstrate our approach by parcellating resting state FMRI data measured from twenty participants as taken from the Human Connectome Project data repository. Results show that CAESAR affords highly robust and scalable whole-brain clustering of FMRI timecourses.

  • The obese brain as a heritable phenotype: a combined morphometry and twin study.

    C M Weise, P Piaggi, M Reinhardt, K Chen, C R Savage, J Krakoff, B Pleger
    Show Summary

    Body weight and adiposity are heritable traits. To date, it remains unknown whether obesity-associated brain structural alterations are under a similar level of genetic control.

  • Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project.

    An T Vu, Keith Jamison, Matthew F Glasser, Stephen M Smith, Timothy Coalson, Steen Moeller, Edward J Auerbach, Kamil Uğurbil, Essa Yacoub
    NeuroImage, Nov 30, 2016 PMID: 27894889
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    Whole-brain functional magnetic resonance imaging (fMRI), in conjunction with multiband acceleration, has played an important role in mapping the functional connectivity throughout the entire brain with both high temporal and spatial resolution. Ultrahigh magnetic field strengths (7T and above) allow functional imaging with even higher functional contrast-to-noise ratios for improved spatial resolution and specificity compared to traditional field strengths (1.5T and 3T). High-resolution 7T fMRI, however, has primarily been constrained to smaller brain regions given the amount of time it takes to acquire the number of slices necessary for high resolution whole brain imaging. Here we evaluate a range of whole-brain high-resolution resting state fMRI protocols (0.9, 1.25, 1.5, 1.6 and 2mm isotropic voxels) at 7T, obtained with both in-plane and slice acceleration parallel imaging techniques to maintain the temporal resolution and brain coverage typically acquired at 3T. Using the processing pipeline developed by the Human Connectome Project, we demonstrate that high resolution images acquired at 7T provide increased functional contrast to noise ratios with significantly less partial volume effects and more distinct spatial features, potentially allowing for robust individual subject parcellations and descriptions of fine-scaled patterns, such as visuotopic organization.

  • Analysis of alcohol use disorders from the Nathan Kline Institute-Rockland Sample: Correlation of brain cortical thickness with neuroticism.

    Yihong Zhao, Zhi-Liang Zheng, F Xavier Castellanos
    Show Summary

    Although differences in both neuroanatomical measures and personality traits, in particular neuroticism, have been associated with alcohol use disorders (AUD), whether lifetime AUD diagnosis alters the relationship between neuroticism and neuroanatomical structures remains to be determined.

  • Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI.

    Sebastiano Stramaglia, Leonardo Angelini, Guorong Wu, Jesus M Cortes, Luca Faes, Daniele Marinazzo
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    We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network.

  • Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex.

    Lei Nie, Paul M Matthews, Yike Guo
    Show Summary

    Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual-or state-dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects.

  • The fiber-density-coreset for redundancy reduction in huge fiber-sets.

    Guy Alexandroni, Gali Zimmerman Moreno, Nir Sochen, Hayit Greenspan
    NeuroImage, Nov 20, 2016 PMID: 27856314
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    State of the art Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) protocols of white matter followed by advanced tractography techniques produce impressive reconstructions of White Matter (WM) pathways. These pathways often contain millions of trajectories (fibers). While for several applications the high number of fibers is essential, other applications (visualization, registration, some types of across-subject comparison) can achieve satisfying results using much smaller sets and may be overburdened by the computational load of the large fiber sets. In this paper we propose a novel, highly efficient algorithm for extracting a meaningful subset of fibers, which we term the Fiber-Density-Coreset (FDC). The reduced set is optimized to represent the main structures of the brain. FDC is based on an efficient geometric approximation paradigm named coresets, an optimization scheme showing much success in tasks requiring large computation time and/or memory. FDC was compared to two commonly used methods for selecting a reduced set of fibers: fiber-clustering and downsampling. The reduced sets were evaluated by several methods, including a novel structural comparison to the full sets called 3D indicator structure comparison (3D-ISC). The comparison was applied to High Angular Resolution Diffusion Imaging (HARDI) scans of 15 healthy individuals obtained from the Human Connectome Project. FDC produced the most satisfying subsets, consistently in all 15 subjects. It also displayed low memory usage and significantly lower running time than conventional fiber reduction schemes.

  • Multidimensional heritability analysis of neuroanatomical shape.

    Tian Ge, Martin Reuter, Anderson M Winkler, Avram J Holmes, Phil H Lee, Lee S Tirrell, Joshua L Roffman, Randy L Buckner, Jordan W Smoller, Mert R Sabuncu
    Nature communications, Nov 16, 2016 PMID: 27845344
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    In the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behaviour and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure.

  • Cingulo-opercular network efficiency mediates the association between psychotic-like experiences and cognitive ability in the general population.

    Julia M Sheffield, Sridhar Kandala, Gregory C Burgess, Michael P Harms, Deanna M Barch
    Show Summary

    Psychosis is hypothesized to occur on a spectrum between psychotic disorders and healthy individuals. In the middle of the spectrum are individuals who endorse psychotic-like experiences (PLEs) that may not impact daily functioning or cause distress. Individuals with PLEs show alterations in both cognitive ability and functional connectivity of several brain networks, but the relationship between PLEs, cognition, and functional networks remains poorly understood.

  • Two Distinct Scene-Processing Networks Connecting Vision and Memory.

    Christopher Baldassano, Andre Esteva, Li Fei-Fei, Diane M Beck
    eNeuro, Nov 09, 2016 PMID: 27822493
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    A number of regions in the human brain are known to be involved in processing natural scenes, but the field has lacked a unifying framework for understanding how these different regions are organized and interact. We provide evidence from functional connectivity and meta-analyses for a new organizational principle, in which scene processing relies upon two distinct networks that split the classically defined parahippocampal place area (PPA). The first network of strongly connected regions consists of the occipital place area/transverse occipital sulcus and posterior PPA, which contain retinotopic maps and are not strongly coupled to the hippocampus at rest. The second network consists of the caudal inferior parietal lobule, retrosplenial complex, and anterior PPA, which connect to the hippocampus (especially anterior hippocampus), and are implicated in both visual and nonvisual tasks, including episodic memory and navigation. We propose that these two distinct networks capture the primary functional division among scene-processing regions, between those that process visual features from the current view of a scene and those that connect information from a current scene view with a much broader temporal and spatial context. This new framework for understanding the neural substrates of scene-processing bridges results from many lines of research, and makes specific functional predictions.

  • Structure as cause and representation: Implications of descriptivist inference for structural modeling across multiple levels of analysis.

    Kristian E Markon, Katherine G Jonas
    Show Summary

    What does a structural model reflect? Different answers to this question implicitly underlie different nosological paradigms. Traditionally, structural analysis has been seen as a process of identifying true or causative values, states, or conditions. This paradigm has faced mounting challenges, however, as psychopathology theory and research has come to encompass different levels of analysis, with concomitant questions about what constructs are most "correct." Here, we discuss an alternative descriptivist paradigm, in which models are seen as the process of identifying optimally parsimonious, generalizable representations of observations. This paradigm allows for an integration of theoretical and methodological approaches that are often seen in mutual opposition, and recasts traditional measurement and structural models in a new light. In this article, we explain the descriptivist perspective, illustrating important concepts using empirical examples from the Human Connectome Project and this issue. We address structural theory within the context of varying levels of analysis, demonstrating how the descriptivist approach can elucidate the nature of hierarchical features and provide a framework for empirically delineating psychopathology structure. (PsycINFO Database Record

  • Situating the default-mode network along a principal gradient of macroscale cortical organization.

    Daniel S Margulies, Satrajit S Ghosh, Alexandros Goulas, Marcel Falkiewicz, Julia M Huntenburg, Georg Langs, Gleb Bezgin, Simon B Eickhoff, F Xavier Castellanos, Michael Petrides, Elizabeth Jefferies, Jonathan Smallwood
    Show Summary

    Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.

  • Activity flow over resting-state networks shapes cognitive task activations.

    Michael W Cole, Takuya Ito, Danielle S Bassett, Douglas H Schultz
    Nature neuroscience, Nov 01, 2016 PMID: 27723746
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    Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

  • Dynamic functional connectivity in bipolar disorder is associated with executive function and processing speed: A preliminary study.

    Tanya T Nguyen, Sanja Kovacevic, Sheena I Dev, Kun Lu, Thomas T Liu, Lisa T Eyler
    Neuropsychology, Oct 28, 2016 PMID: 27775400
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    Disturbances in functional connectivity have been suggested to contribute to cognitive and emotion processing deficits observed in bipolar disorder (BD). Functional connectivity between medial prefrontal cortex (mPFC) and other brain regions may be particularly abnormal. The goal of the present study was to characterize the temporal dynamics of the default mode network (DMN) connectivity in BD and examine its association with cognition.

  • Chronnectomic patterns and neural flexibility underlie executive function.

    Jason S Nomi, Shruti Gopal Vij, Dina R Dajani, Rosa Steimke, Eswar Damaraju, Srinivas Rachakonda, Vince D Calhoun, Lucina Q Uddin
    NeuroImage, Oct 26, 2016 PMID: 27777174
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    Despite extensive research into executive function (EF), the precise relationship between brain dynamics and flexible cognition remains unknown. Using a large, publicly available dataset (189 participants), we find that functional connections measured throughout 56min of resting state fMRI data comprise five distinct connectivity states. Elevated EF performance as measured outside of the scanner was associated with greater episodes of more frequently occurring connectivity states, and fewer episodes of less frequently occurring connectivity states. Frequently occurring states displayed metastable properties, where cognitive flexibility may be facilitated by attenuated correlations and greater functional connection variability. Less frequently occurring states displayed properties consistent with low arousal and low vigilance. These findings suggest that elevated EF performance may be associated with the propensity to occupy more frequently occurring brain configurations that enable cognitive flexibility, while avoiding less frequently occurring brain configurations related to low arousal/vigilance states. The current findings offer a novel framework for identifying neural processes related to individual differences in executive function.

  • Automated individual-level parcellation of Broca's region based on functional connectivity.

    Estrid Jakobsen, Franziskus Liem, Manousos A Klados, Şeyma Bayrak, Michael Petrides, Daniel S Margulies
    NeuroImage, Oct 25, 2016 PMID: 27693796
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    Broca's region can be subdivided into its constituent areas 44 and 45 based on established differences in connectivity to superior temporal and inferior parietal regions. The current study builds on our previous work manually parcellating Broca's area on the individual-level by applying these anatomical criteria to functional connectivity data. Here we present an automated observer-independent and anatomy-informed parcellation pipeline with comparable precision to the manual labels at the individual-level. The method first extracts individualized connectivity templates of areas 44 and 45 by assigning to each surface vertex within the ventrolateral frontal cortex the partial correlation value of its functional connectivity to group-level templates of areas 44 and 45, accounting for other template connectivity patterns. To account for cross-subject variability in connectivity, the partial correlation procedure is then repeated using individual-level network templates, including individual-level connectivity from areas 44 and 45. Each node is finally labeled as area 44, 45, or neither, using a winner-take-all approach. The method also incorporates prior knowledge of anatomical location by weighting the results using spatial probability maps. The resulting area labels show a high degree of spatial overlap with the gold-standard manual labels, and group-average area maps are consistent with cytoarchitectonic probability maps of areas 44 and 45. To facilitate reproducibility and to demonstrate that the method can be applied to resting-state fMRI datasets with varying acquisition and preprocessing parameters, the labeling procedure is applied to two open-source datasets from the Human Connectome Project and the Nathan Kline Institute Rockland Sample. While the current study focuses on Broca's region, the method is adaptable to parcellate other cortical regions with distinct connectivity profiles.

  • White matter integrity in brain networks relevant to anxiety and depression: evidence from the human connectome project dataset.

    Nele A J De Witte, Sven C Mueller
    Brain imaging and behavior, Oct 17, 2016 PMID: 27744495
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    Anxiety and depression are associated with altered communication within global brain networks and between these networks and the amygdala. Functional connectivity studies demonstrate an effect of anxiety and depression on four critical brain networks involved in top-down attentional control (fronto-parietal network; FPN), salience detection and error monitoring (cingulo-opercular network; CON), bottom-up stimulus-driven attention (ventral attention network; VAN), and default mode (default mode network; DMN). However, structural evidence on the white matter (WM) connections within these networks and between these networks and the amygdala is lacking. The current study in a large healthy sample (n = 483) observed that higher trait anxiety-depression predicted lower WM integrity in the connections between amygdala and specific regions of the FPN, CON, VAN, and DMN. We discuss the possible consequences of these anatomical alterations for cognitive-affective functioning and underscore the need for further theory-driven research on individual differences in anxiety and depression on brain structure.

  • Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index.

    Karen Hodgson, Russell A Poldrack, Joanne E Curran, Emma E Knowles, Samuel Mathias, Harald H H Göring, Nailin Yao, Rene L Olvera, Peter T Fox, Laura Almasy, Ravi Duggirala, Deanna M Barch, John Blangero, David C Glahn
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    Head movements are typically viewed as a nuisance to functional magnetic resonance imaging (fMRI) analysis, and are particularly problematic for resting state fMRI. However, there is growing evidence that head motion is a behavioral trait with neural and genetic underpinnings. Using data from a large randomly ascertained extended pedigree sample of Mexican Americans (n = 689), we modeled the genetic structure of head motion during resting state fMRI and its relation to 48 other demographic and behavioral phenotypes. A replication analysis was performed using data from the Human Connectome Project, which uses an extended twin design (n = 864). In both samples, head motion was significantly heritable (h2 = 0.313 and 0.427, respectively), and phenotypically correlated with numerous traits. The most strongly replicated relationship was between head motion and body mass index, which showed evidence of shared genetic influences in both data sets. These results highlight the need to view head motion in fMRI as a complex neurobehavioral trait correlated with a number of other demographic and behavioral phenotypes. Given this, when examining individual differences in functional connectivity, the confounding of head motion with other traits of interest needs to be taken into consideration alongside the critical important of addressing head motion artifacts.

  • Structural and functional connectivity of the precuneus and thalamus to the default mode network.

    Samantha I Cunningham, Dardo Tomasi, Nora D Volkow
    Human brain mapping, Oct 16, 2016 PMID: 27739612
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    Neuroimaging studies have identified functional interactions between the thalamus, precuneus, and default mode network (DMN) in studies of consciousness. However, less is known about the structural connectivity of the precuneus and thalamus to regions within the DMN. We used diffusion tensor imaging (DTI) to parcellate the precuneus and thalamus based on their probabilistic white matter connectivity to each other and DMN regions of interest (ROIs) in 37 healthy subjects from the Human Connectome Database. We further assessed resting-state functional connectivity (RSFC) among the precuneus, thalamus, and DMN ROIs. The precuneus was found to have the greatest structural connectivity with the thalamus, where connection fractional anisotropy (FA) increased with age. The precuneus also showed significant structural connectivity to the hippocampus and middle pre-frontal cortex, but minimal connectivity to the angular gyrus and midcingulate cortex. In contrast, the precuneus exhibited significant RSFC with the thalamus and the strongest RSFC with the AG. Significant symmetrical structural connectivity was found between the thalamus and hippocampus, mPFC, sFG, and precuneus that followed known thalamocortical pathways, while thalamic RSFC was strongest with the precuneus and hippocampus. Overall, these findings reveal high levels of structural and functional connectivity linking the thalamus, precuneus, and DMN. Differences between structural and functional connectivity (such as between the precuneus and AG) may be interpreted to reflect dynamic shifts in RSFC for cortical hub-regions involved with consciousness, but could also reflect the limitations of DTI to detect superficial white matter tracts that connect cortico-cortical regions. Hum Brain Mapp 38:938-956, 2017. © 2016 Wiley Periodicals, Inc.

  • Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging.

    Fang-Cheng Yeh, Timothy D Verstynen
    Frontiers in neuroscience, Sep 30, 2016 PMID: 27683539
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    Multi-shell and diffusion spectrum imaging (DSI) are becoming increasingly popular methods of acquiring diffusion MRI data in a research context. However, single-shell acquisitions, such as diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), still remain the most common acquisition schemes in practice. Here we tested whether multi-shell and DSI data have conversion flexibility to be interpolated into corresponding HARDI data. We acquired multi-shell and DSI data on both a phantom and in vivo human tissue and converted them to HARDI. The correlation and difference between their diffusion signals, anisotropy values, diffusivity measurements, fiber orientations, connectivity matrices, and network measures were examined. Our analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of the HARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9. The average angular error between converted and original HARDI was 20.7° at voxels with signal-to-noise ratios greater than 5. The network topology measures had less than 2% difference, whereas the average nodal measures had a percentage difference around 4~7%. In general, multi-shell and DSI acquisitions can be converted to their corresponding single-shell HARDI with high fidelity. This supports multi-shell and DSI acquisitions over HARDI acquisition as the scheme of choice for diffusion acquisitions.

  • Gamma-Rhythmic Gain Modulation.

    Jianguang Ni, Thomas Wunderle, Christopher Murphy Lewis, Robert Desimone, Ilka Diester, Pascal Fries
    Neuron, Sep 27, 2016 PMID: 27667008
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    Cognition requires the dynamic modulation of effective connectivity, i.e., the modulation of the postsynaptic neuronal response to a given input. If postsynaptic neurons are rhythmically active, this might entail rhythmic gain modulation, such that inputs synchronized to phases of high gain benefit from enhanced effective connectivity. We show that visually induced gamma-band activity in awake macaque area V4 rhythmically modulates responses to unpredictable stimulus events. This modulation exceeded a simple additive superposition of a constant response onto ongoing gamma-rhythmic firing, demonstrating the modulation of multiplicative gain. Gamma phases leading to strongest neuronal responses also led to shortest behavioral reaction times, suggesting functional relevance of the effect. Furthermore, we find that constant optogenetic stimulation of anesthetized cat area 21a produces gamma-band activity entailing a similar gain modulation. As the gamma rhythm in area 21a did not spread backward to area 17, this suggests that postsynaptic gamma is sufficient for gain modulation.

  • Segmentation of the Cingulum Bundle in the Human Brain: A New Perspective Based on DSI Tractography and Fiber Dissection Study.

    Yupeng Wu, Dandan Sun, Yong Wang, Yibao Wang, Shaowu Ou
    Frontiers in neuroanatomy, Sep 23, 2016 PMID: 27656132
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    The cingulum bundle (CB) is a critical white matter fiber tract in the brain, which forms connections between the frontal lobe, parietal lobe and temporal lobe. In non-human primates, the CB is actually divided into distinct subcomponents on the basis of corticocortical connections. However, at present, no study has verified similar distinct subdivisions in the human brain. In this study, we reconstructed these distinct subdivisions in the human brain, and determined their exact cortical connections using high definition fiber tracking (HDFT) technique on 10 healthy adults and a 488-subject template from the Human Connectome Project (HCP-488). Fiber dissections were performed to verify tractography results. Five CB segments were identified. CB-I ran from the subrostral areas to the precuneus and splenium, encircling the corpus callosum (CC). CB-II arched around the splenium and extended anteriorly above the CC to the medial aspect of the superior frontal gyrus (SFG). CB-III connected the superior parietal lobule (SPL) and precuneus with the medial aspect of the SFG. CB-IV was a relatively minor subcomponent from the SPL and precuneus to the frontal region. CB-V, the para-hippocampal cingulum, stemmed from the medial temporal lobe and fanned out to the occipital lobes. Our findings not only provide a more accurate and detailed description on the associated architecture of the subcomponents within the CB, but also offer new insights into the functional role of the CB in the human brain.

  • Active delineation of Meyer's loop using oriented priors through MAGNEtic tractography (MAGNET).

    Maxime Chamberland, Benoit Scherrer, Sanjay P Prabhu, Joseph Madsen, David Fortin, Kevin Whittingstall, Maxime Descoteaux, Simon K Warfield
    Human brain mapping, Sep 21, 2016 PMID: 27647682
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    Streamline tractography algorithms infer connectivity from diffusion MRI (dMRI) by following diffusion directions which are similarly aligned between neighboring voxels. However, not all white matter (WM) fascicles are organized in this manner. For example, Meyer's loop is a highly curved portion of the optic radiation (OR) that exhibits a narrow turn, kissing and crossing pathways, and changes in fascicle dispersion. From a neurosurgical perspective, damage to Meyer's loop carries a potential risk of inducing vision deficits to the patient, especially during temporal lobe resection surgery. To prevent such impairment, achieving an accurate delineation of Meyer's loop with tractography is thus of utmost importance. However, current algorithms tend to under-estimate the full extent of Meyer's loop, mainly attributed to the aforementioned rule for connectivity which requires a direction to be chosen across a field of orientations. In this article, it was demonstrated that MAGNEtic Tractography (MAGNET) can benefit Meyer's loop delineation by incorporating anatomical knowledge of the expected fiber orientation to overcome local ambiguities. A new ROI-mechanism was proposed which supplies additional information to streamline reconstruction algorithms by the means of oriented priors. Their results showed that MAGNET can accurately generate Meyer's loop in all of our 15 child subjects (8 males; mean age 10.2 years ± 3.1). It effectively improved streamline coverage when compared with deterministic tractography, and significantly reduced the distance between the anterior-most portion of Meyer's loop and the temporal pole by 16.7 mm on average, a crucial landmark used for preoperative planning of temporal lobe surgery. Hum Brain Mapp 38:509-527, 2017. © 2016 Wiley Periodicals, Inc.

  • Individual-specific features of brain systems identified with resting state functional correlations.

    Evan M Gordon, Timothy O Laumann, Babatunde Adeyemo, Adrian W Gilmore, Steven M Nelson, Nico U F Dosenbach, Steven E Petersen
    NeuroImage, Sep 20, 2016 PMID: 27640749
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    Recent work has made important advances in describing the large-scale systems-level organization of human cortex by analyzing functional magnetic resonance imaging (fMRI) data averaged across groups of subjects. However, new findings have emerged suggesting that individuals' cortical systems are topologically complex, containing small but reliable features that cannot be observed in group-averaged datasets, due in part to variability in the position of such features along the cortical sheet. This previous work has reported only specific examples of these individual-specific system features; to date, such features have not been comprehensively described. Here we used fMRI to identify cortical system features in individual subjects within three large cross-subject datasets and one highly sampled within-subject dataset. We observed system features that have not been previously characterized, but 1) were reliably detected across many scanning sessions within a single individual, and 2) could be matched across many individuals. In total, we identified forty-three system features that did not match group-average systems, but that replicated across three independent datasets. We described the size and spatial distribution of each non-group feature. We further observed that some individuals were missing specific system features, suggesting individual differences in the system membership of cortical regions. Finally, we found that individual-specific system features could be used to increase subject-to-subject similarity. Together, this work identifies individual-specific features of human brain systems, thus providing a catalog of previously unobserved brain system features and laying the foundation for detailed examinations of brain connectivity in individuals.

  • Connectivity-based change point detection for large-size functional networks.

    Seok-Oh Jeong, Chongwon Pae, Hae-Jeong Park
    NeuroImage, Sep 14, 2016 PMID: 27622394
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    Recent understanding that the brain at rest does not remain in a single state but transiently visits multiple states emphasizes the importance of state changes embedded in the brain network. Due to the effectiveness of larger networks in characterizing brain states, there is an increasing need for a network-based change point detection method that is applicable to large-size networks, particularly those with longer time series. This paper presents a fast and efficient method for detecting change points in the large-size functional networks of resting-state fMRI. To detect change points, a statistic for the covariance change at each time point is tested by a local false discovery rate, estimated based on the empirical null principle using a semiparametric mixture model. We present simulations and empirical analyses of task-based and resting-state fMRI data sets with various network sizes up to 300 nodes selected from the Human Connectome Project database. We demonstrate that the proposed method is not only efficient in detecting change points in large samples of large-size networks but also is less sensitive to the window size selection and provides the consequent identification of the changed edges. The covariance-based change point detection method in this study would be very useful in exploring characteristics of dynamic states in long-term large-size resting-state brain networks.

  • Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex.

    Xi Jiang, Xiang Li, Jinglei Lv, Shijie Zhao, Shu Zhang, Wei Zhang, Tuo Zhang, Junwei Han, Lei Guo, Tianming Liu
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    Various studies in the brain mapping field have demonstrated that there exist multiple concurrent functional networks that are spatially overlapped and interacting with each other during specific task performance to jointly realize the total brain function. Assessing such spatial overlap patterns of functional networks (SOPFNs) based on fMRI has thus received increasing interest for brain function studies. However, there are still two crucial issues to be addressed. First, the SOPFNs are assessed over the entire fMRI scan assuming the temporal stationarity, while possibly time-dependent dynamics of the SOPFNs is not sufficiently explored. Second, the SOPFNs are assessed within individual subjects, while group-wise consistency of the SOPFNs is largely unknown.

  • Real-time estimation of dynamic functional connectivity networks.

    Ricardo Pio Monti, Romy Lorenz, Rodrigo M Braga, Christoforos Anagnostopoulos, Robert Leech, Giovanni Montana
    Human brain mapping, Sep 08, 2016 PMID: 27600689
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    Two novel and exciting avenues of neuroscientific research involve the study of task-driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real-time. While the former is a well-established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real-time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real-time. In this work, we propose a novel methodology with which to accurately track changes in time-varying functional connectivity networks in real-time. The proposed method is shown to perform competitively when compared to state-of-the-art offline algorithms using both synthetic as well as real-time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task-related changes in network structure in real-time. Hum Brain Mapp 38:202-220, 2017. © 2016 Wiley Periodicals, Inc.

  • The "Hub Disruption Index," a Reliable Index Sensitive to the Brain Networks Reorganization. A Study of the Contralesional Hemisphere in Stroke.

    Maite Termenon, Sophie Achard, Assia Jaillard, Chantal Delon-Martin
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    Stroke, resulting in focal structural damage, induces changes in brain function at both local and global levels. Following stroke, cerebral networks present structural, and functional reorganization to compensate for the dysfunctioning provoked by the lesion itself and its remote effects. As some recent studies underlined the role of the contralesional hemisphere during recovery, we studied its role in the reorganization of brain function of stroke patients using resting state fMRI and graph theory. We explored this reorganization using the "hub disruption index" (κ), a global index sensitive to the reorganization of nodes within the graph. For a given graph metric, κ of a subject corresponds to the slope of the linear regression model between the mean local network measures of a reference group, and the difference between that reference and the subject under study. In order to translate the use of κ in clinical context, a prerequisite to achieve meaningful results is to investigate the reliability of this index. In a preliminary part, we studied the reliability of κ by computing the intraclass correlation coefficient in a cohort of 100 subjects from the Human Connectome Project. Then, we measured intra-hemispheric κ index in the contralesional hemisphere of 20 subacute stroke patients compared to 20 age-matched healthy controls. Finally, due to the small number of patients, we tested the robustness of our results repeating the experiment 1000 times by bootstrapping on the Human Connectome Project database. Statistical analysis showed a significant reduction of κ for the contralesional hemisphere of right stroke patients compared to healthy controls. Similar results were observed for the right contralesional hemisphere of left stroke patients. We showed that κ, is more reliable than global graph metrics and more sensitive to detect differences between groups of patients as compared to healthy controls. Using new graph metrics as κ allows us to show that stroke induces a network-wide pattern of reorganization in the contralesional hemisphere whatever the side of the lesion. Graph modeling combined with measure of reorganization at the level of large-scale networks can become a useful tool in clinic.

  • Auditory and visual connectivity gradients in frontoparietal cortex.

    Rodrigo M Braga, Peter J Hellyer, Richard J S Wise, Robert Leech
    Human brain mapping, Aug 30, 2016 PMID: 27571304
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    A frontoparietal network of brain regions is often implicated in both auditory and visual information processing. Although it is possible that the same set of multimodal regions subserves both modalities, there is increasing evidence that there is a differentiation of sensory function within frontoparietal cortex. Magnetic resonance imaging (MRI) in humans was used to investigate whether different frontoparietal regions showed intrinsic biases in connectivity with visual or auditory modalities. Structural connectivity was assessed with diffusion tractography and functional connectivity was tested using functional MRI. A dorsal-ventral gradient of function was observed, where connectivity with visual cortex dominates dorsal frontal and parietal connections, while connectivity with auditory cortex dominates ventral frontal and parietal regions. A gradient was also observed along the posterior-anterior axis, although in opposite directions in prefrontal and parietal cortices. The results suggest that the location of neural activity within frontoparietal cortex may be influenced by these intrinsic biases toward visual and auditory processing. Thus, the location of activity in frontoparietal cortex may be influenced as much by stimulus modality as the cognitive demands of a task. It was concluded that stimulus modality was spatially encoded throughout frontal and parietal cortices, and was speculated that such an arrangement allows for top-down modulation of modality-specific information to occur within higher-order cortex. This could provide a potentially faster and more efficient pathway by which top-down selection between sensory modalities could occur, by constraining modulations to within frontal and parietal regions, rather than long-range connections to sensory cortices. Hum Brain Mapp 38:255-270, 2017. © 2016 Wiley Periodicals, Inc.

  • Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project.

    Gregory C Burgess, Sridhar Kandala, Dan Nolan, Timothy O Laumann, Jonathan D Power, Babatunde Adeyemo, Michael P Harms, Steven E Petersen, Deanna M Barch
    Brain connectivity, Aug 30, 2016 PMID: 27571276
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    Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR.

  • The Human Connectome Project's neuroimaging approach.

    Matthew F Glasser, Stephen M Smith, Daniel S Marcus, Jesper L R Andersson, Edward J Auerbach, Timothy E J Behrens, Timothy S Coalson, Michael P Harms, Mark Jenkinson, Steen Moeller, Emma C Robinson, Stamatios N Sotiropoulos, Junqian Xu, Essa Yacoub, Kamil Ugurbil, David C Van Essen
    Nature neuroscience, Aug 30, 2016 PMID: 27571196
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    Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.

  • Functional brain networks related to individual differences in human intelligence at rest.

    Luke J Hearne, Jason B Mattingley, Luca Cocchi
    Scientific reports, Aug 27, 2016 PMID: 27561736
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    Intelligence is a fundamental ability that sets humans apart from other animal species. Despite its importance in defining human behaviour, the neural networks responsible for intelligence are not well understood. The dominant view from neuroimaging work suggests that intelligent performance on a range of tasks is underpinned by segregated interactions in a fronto-parietal network of brain regions. Here we asked whether fronto-parietal interactions associated with intelligence are ubiquitous, or emerge from more widespread associations in a task-free context. First we undertook an exploratory mapping of the existing literature on functional connectivity associated with intelligence. Next, to empirically test hypotheses derived from the exploratory mapping, we performed network analyses in a cohort of 317 unrelated participants from the Human Connectome Project. Our results revealed a novel contribution of across-network interactions between default-mode and fronto-parietal networks to individual differences in intelligence at rest. Specifically, we found that greater connectivity in the resting state was associated with higher intelligence scores. Our findings highlight the need to broaden the dominant fronto-parietal conceptualisation of intelligence to encompass more complex and context-specific network dynamics.

  • Data Quality Influences Observed Links Between Functional Connectivity and Behavior.

    Joshua S Siegel, Anish Mitra, Timothy O Laumann, Benjamin A Seitzman, Marcus Raichle, Maurizio Corbetta, Abraham Z Snyder
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    A growing field of research explores links between behavioral measures and functional connectivity (FC) assessed using resting-state functional magnetic resonance imaging. Recent studies suggest that measurement of these relationships may be corrupted by head motion artifact. Using data from the Human Connectome Project (HCP), we find that a surprising number of behavioral, demographic, and physiological measures (23 of 122), including fluid intelligence, reading ability, weight, and psychiatric diagnostic scales, correlate with head motion. We demonstrate that "trait" (across-subject) and "state" (across-day, within-subject) effects of motion on FC are remarkably similar in HCP data, suggesting that state effects of motion could potentially mimic trait correlates of behavior. Thus, head motion is a likely source of systematic errors (bias) in the measurement of FC:behavior relationships. Next, we show that data cleaning strategies reduce the influence of head motion and substantially alter previously reported FC:behavior relationship. Our results suggest that spurious relationships mediated by head motion may be widespread in studies linking FC to behavior.

  • Heterogeneity of trans-callosal structural connectivity and effects on resting state subnetwork integrity may underlie both wanted and unwanted effects of therapeutic corpus callostomy.

    Peter Neal Taylor, Rob Forsyth
    NeuroImage. Clinical, Aug 23, 2016 PMID: 27547729
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    The corpus callosum (CC) is the primary structure supporting interhemispheric connectivity in the brain. Partial or complete surgical callosotomy may be performed for the palliation of intractable epilepsy. A variety of disconnection syndromes are recognised after injury to or division of the CC however their mechanisms are poorly understood and their occurrence difficult to predict. We use novel high resolution structural connectivity analyses to demonstrate reasons for this poor predictability.

  • Artifact removal in the context of group ICA: A comparison of single-subject and group approaches.

    Yuhui Du, Elena A Allen, Hao He, Jing Sui, Lei Wu, Vince D Calhoun
    Human brain mapping, Feb 10, 2016 PMID: 26859308
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    Independent component analysis (ICA) has been widely applied to identify intrinsic brain networks from fMRI data. Group ICA computes group-level components from all data and subsequently estimates individual-level components to recapture intersubject variability. However, the best approach to handle artifacts, which may vary widely among subjects, is not yet clear. In this work, we study and compare two ICA approaches for artifacts removal. One approach, recommended in recent work by the Human Connectome Project, first performs ICA on individual subject data to remove artifacts, and then applies a group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA based artifacts Removal Plus Group ICA (IRPG). A second proposed approach, called Group Information Guided ICA (GIG-ICA), performs ICA on group data, then removes the group-level artifact components, and finally performs subject-specific ICAs using the group-level non-artifact components as spatial references. We used simulations to evaluate the two approaches with respect to the effects of data quality, data quantity, variable number of sources among subjects, and spatially unique artifacts. Resting-state test-retest datasets were also employed to investigate the reliability of functional networks. Results from simulations demonstrate GIG-ICA has greater performance compared with IRPG, even in the case when single-subject artifacts removal is perfect and when individual subjects have spatially unique artifacts. Experiments using test-retest data suggest that GIG-ICA provides more reliable functional networks. Based on high estimation accuracy, ease of implementation, and high reliability of functional networks, we find GIG-ICA to be a promising approach. Hum Brain Mapp 37:1005-1025, 2016. © 2015 Wiley Periodicals, Inc.
  • Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion.

    Georg Langs, Polina Golland, Satrajit S Ghosh
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    The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available.
  • Training shortest-path tractography: Automatic learning of spatial priors.

    Niklas Kasenburg, Matthew Liptrot, Nina Linde Reislev, Silas N Ørting, Mads Nielsen, Ellen Garde, Aasa Feragen
    NeuroImage, Jan 26, 2016 PMID: 26804779
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    Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we demonstrate that our framework also retains support for conventional interactive constraints such as waypoint regions. We apply our approach to the open access, high quality Human Connectome Project data, as well as a dataset acquired on a typical clinical scanner. Our results show that the use of a learned prior substantially increases the overlap of tractography output with a reference atlas on both populations, and this is confirmed by visual inspection. Furthermore, we demonstrate how a prior learned on the high quality dataset significantly increases the overlap with the reference for the more typical yet lower quality data acquired on a clinical scanner. We hope that such automatic incorporation of prior knowledge and the obviation of expert interactive tract delineation on every subject, will improve the feasibility of large clinical tractography studies.
  • Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas.

    Georgios Michalareas, Julien Vezoli, Stan van Pelt, Jan-Mathijs Schoffelen, Henry Kennedy, Pascal Fries
    Neuron, Jan 19, 2016 PMID: 26777277
    Show Summary
    Primate visual cortex is hierarchically organized. Bottom-up and top-down influences are exerted through distinct frequency channels, as was recently revealed in macaques by correlating inter-areal influences with laminar anatomical projection patterns. Because this anatomical data cannot be obtained in human subjects, we selected seven homologous macaque and human visual areas, and we correlated the macaque laminar projection patterns to human inter-areal directed influences as measured with magnetoencephalography. We show that influences along feedforward projections predominate in the gamma band, whereas influences along feedback projections predominate in the alpha-beta band. Rhythmic inter-areal influences constrain a functional hierarchy of the seven homologous human visual areas that is in close agreement with the respective macaque anatomical hierarchy. Rhythmic influences allow an extension of the hierarchy to 26 human visual areas including uniquely human brain areas. Hierarchical levels of ventral- and dorsal-stream visual areas are differentially affected by inter-areal influences in the alpha-beta band.
  • Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project.

    Roland N Boubela, Klaudius Kalcher, Wolfgang Huf, Christian Našel, Ewald Moser
    Frontiers in neuroscience, Jan 19, 2016 PMID: 26778951
    Show Summary
    Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units (GPUs) to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets.
  • Insula reactivity to negative stimuli is associated with daily cigarette use: A preliminary investigation using the Human Connectome Database.

    N R Dias, A L Peechatka, A C Janes
    Show Summary
    Individuals who smoke more cigarettes per day are at greater risk for developing smoking-related illness and have more difficulty quitting. Withdrawal-related negative mood is one factor thought to motivate drug use. However, heavy smokers are generally more sensitive to negative affect, not just negative emotion stemming from withdrawal. One possibility is that individual differences in how the brain processes negative stimuli may impact smoking use. Given the wealth of data implicating the insula in nicotine dependence and affective processing we hypothesize that the number of cigarettes an individual smokes per day will relate to insula reactivity to negative stimuli.A functional magnetic resonance imaging (fMRI) emotional processing task collected by the Human Connectome Project was assessed in 21 daily tobacco smokers who reported smoking between 5 and 20 cigarettes per day. The number of cigarettes smoked per day was correlated with right and left anterior insula reactivity to faces expressing a negative emotion relative to a control. This anterior insula region of interest has been associated with treatment outcome and smoking cue-reactivity in our prior work.Those who smoked more daily cigarettes showed greater right insula reactivity to negative stimuli (r=0.564, p=0.008). Left insula reactivity was not associated with cigarettes smoked per day.Smokers who use more cigarettes per day have greater insula reactivity to negative stimuli, furthering the field's understanding of the insula's involvement in nicotine use. This preliminary work also suggests a mechanism contributing to higher rates of daily smoking.
  • Connections of the dorsolateral prefrontal cortex with the thalamus: a probabilistic tractography study.

    Pierre-Jean Le Reste, C Haegelen, B Gibaud, T Moreau, X Morandi
    Show Summary
    The dorsolateral prefrontal cortex (DLPFC) is a cortical area involved in higher cognitive functions, and at the center of the pathophysiology of mental disorders such as depression and schizophrenia. Considering these major roles and the development of deep brain stimulation, the object of this study was to assess the patterns of connectivity of the DLPFC with its main subcortical relay, the thalamus, with the help of probabilistic tractography.We used T1-weighted imaging and diffusion data from 18 subjects from the Human Connectome Project. The DLPFC and the thalamic nuclear groups were defined using the combination of atlases, sulcogyral anatomy and cytoarchitectonic data. Probabilistic tractography was performed from the DLPFC to the thalamus. The patterns of connectivity were assessed using two indexes: (1) a connectivity index (CI) which evaluate the strength of connection (2) an asymmetry index (AI) which explores the inter-hemispheric variability.The analysis of CI showed significant connections between the DLPFC and the dorsomedial nuclei (p < 0.05), the anterior nuclear groups (p < 0.05) and the right centromedian nucleus (p < 0.05). No link was found between handedness and AI (p > 0.05). Most of subjects (15/18) had a right predominance of the thalamo cortical connections of the DLPFC.Probabilistic tractography appears as a valuable non-invasive tool for the exploration of the thalamocortical connections between the dorsolateral prefrontal cortex and thalamic nuclei. It allowed to show different inter-hemispheric patterns of connectivity, and highlighted the centromedian nucleus as a key subcortical relay of executive functions.
  • Abnormal striatal resting-state functional connectivity in adolescents with obsessive-compulsive disorder.

    Gail A Bernstein, Bryon A Mueller, Melinda Westlund Schreiner, Sarah M Campbell, Emily K Regan, Peter M Nelson, Alaa K Houri, Susanne S Lee, Alexandra D Zagoloff, Kelvin O Lim, Essa S Yacoub, Kathryn R Cullen
    Psychiatry research, Dec 18, 2015 PMID: 26674413
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    Neuroimaging research has implicated abnormalities in cortico-striatal-thalamic-cortical (CSTC) circuitry in pediatric obsessive-compulsive disorder (OCD). In this study, resting-state functional magnetic resonance imaging (R-fMRI) was used to investigate functional connectivity in the CSTC circuitry in adolescents with OCD. Imaging was obtained with the Human Connectome Project (HCP) scanner using newly developed pulse sequences which allow for higher spatial and temporal resolution. Fifteen adolescents with OCD and 13 age- and gender-matched healthy controls (ages 12-19) underwent R-fMRI on the 3T HCP scanner. Twenty-four minutes of resting-state scans (two consecutive 12-min scans) were acquired. We investigated functional connectivity of the striatum using a seed-based, whole brain approach with anatomically-defined seeds placed in the bilateral caudate, putamen, and nucleus accumbens. Adolescents with OCD compared with controls exhibited significantly lower functional connectivity between the left putamen and a single cluster of right-sided cortical areas including parts of the orbitofrontal cortex, inferior frontal gyrus, insula, and operculum. Preliminary findings suggest that impaired striatal connectivity in adolescents with OCD in part falls within the predicted CSTC network, and also involves impaired connections between a key CSTC network region (i.e., putamen) and key regions in the salience network (i.e., insula/operculum). The relevance of impaired putamen-insula/operculum connectivity in OCD is discussed.
  • Phase-cycled simultaneous multislice balanced SSFP imaging with CAIPIRINHA for efficient banding reduction.

    Yi Wang, Xingfeng Shao, Thomas Martin, Steen Moeller, Essa Yacoub, Danny J J Wang
    Show Summary
    To present a time-efficient technique for banding reduction in balanced steady-state free precession (bSSFP) imaging using phase-cycled simultaneous multislice (SMS) acquisition with CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration).The proposed technique exploits the inherent phase modulation of SMS imaging with CAIPIRINHA to acquire multiple phase-cycled images, which can be combined for efficient banding reduction within the same scan time of a single-band bSSFP scan.Bloch equation simulation, phantom and in vivo brain, abdominal and cardiac imaging experiments were performed on healthy volunteers at 3T using multi-channel head and body array coils with SMS acceleration factors of two to four. The performance of banding reduction was quantitatively evaluated based on the percent ripple of signal distribution and signal-to-noise ratio (SNR) efficiency in both phantom and human studies.The banding artifact was successfully removed or suppressed using phase-cycled SMS bSSFP imaging across SMS factors of two to four. The performance of banding reduction improved with higher SMS factors along with increased SNR efficiency.Phase-cycled SMS bSSFP with CAIPIRINHA is a promising technique for efficient band reduction in bSSFP without prolonged scan time. Further evaluation of this technique in clinical applications is warranted. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.
  • Waiting with purpose: A reliable but small association between purpose in life and impulsivity.

    Anthony L Burrow, R Nathan Spreng
    Show Summary
    Purpose in life contributes to health and wellbeing. We examine the link between purpose and behavioral impulsivity that may account for these benefits. In a community sample of 503 adults, we found a small yet reliable positive association between purpose and valuing future rewards on a delayed discounting task, a behavioral index of impulsivity. This bootstrapped correlation remained after accounting for Big-5 personality traits, positive affect, and demographic characteristics, suggesting a unique and robust link between purpose and impulsivity (r = .1). We interpret this connection as evidence that purpose enables a broader life view, which serves to inhibit impulsive distractions.
  • Subdivision of Broca's region based on individual-level functional connectivity.

    Estrid Jakobsen, Joachim Böttger, Pierre Bellec, Stefan Geyer, Rudolf Rübsamen, Michael Petrides, Daniel S Margulies
    Show Summary
    Broca's region is composed of two adjacent cytoarchitectonic areas, 44 and 45, which have distinct connectivity to superior temporal and inferior parietal regions in both macaque monkeys and humans. The current study aimed to make use of prior knowledge of sulcal anatomy and resting-state functional connectivity, together with a novel visualization technique, to manually parcellate areas 44 and 45 in individual brains in vivo. One hundred and one resting-state functional magnetic resonance imaging datasets from the Human Connectome Project were used. Left-hemisphere surface-based correlation matrices were computed and visualized in brainGL. By observation of differences in the connectivity patterns of neighbouring nodes, areas 44 and 45 were manually parcellated in individual brains, and then compared at the group-level. Additionally, the manual labelling approach was compared with parcellation results based on several data-driven clustering techniques. Areas 44 and 45 could be clearly distinguished from each other in all individuals, and the manual segmentation method showed high test-retest reliability. Group-level probability maps of areas 44 and 45 showed spatial consistency across individuals, and corresponded well to cytoarchitectonic probability maps. Group-level connectivity maps were consistent with previous studies showing distinct connectivity patterns of areas 44 and 45. Data-driven parcellation techniques produced clusters with varying degrees of spatial overlap with the manual labels, indicating the need for further investigation and validation of machine learning cortical segmentation approaches. The current study provides a reliable method for individual-level cortical parcellation that could be applied to regions distinguishable by even the most subtle differences in patterns of functional connectivity.
  • An integrated framework for targeting functional networks via transcranial magnetic stimulation.

    Alexander Opitz, Michael D Fox, R Cameron Craddock, Stan Colcombe, Michael P Milham
    NeuroImage, Nov 27, 2015 PMID: 26608241
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    Transcranial magnetic stimulation (TMS) is a powerful investigational tool for in vivo manipulation of regional or network activity, with a growing number of potential clinical applications. Unfortunately, the vast majority of targeting strategies remain limited by their reliance on non-realistic brain models and assumptions that anatomo-functional relationships are 1:1. Here, we present an integrated framework that combines anatomically realistic finite element models of the human head with resting functional MRI to predict functional networks targeted via TMS at a given coil location and orientation. Using data from the Human Connectome Project, we provide an example implementation focused on dorsolateral prefrontal cortex (DLPFC). Three distinct DLPFC stimulation zones were identified, differing with respect to the network to be affected (default, frontoparietal) and sensitivity to coil orientation. Network profiles generated for DLPFC targets previously published for treating depression revealed substantial variability across studies, highlighting a potentially critical technical issue.
  • Fiber tracts of the dorsal language stream in the human brain.

    Kaan Yagmurlu, Erik H Middlebrooks, Necmettin Tanriover, Albert L Rhoton
    Journal of neurosurgery, Nov 21, 2015 PMID: 26587654
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    OBJECT The aim of this study was to examine the arcuate (AF) and superior longitudinal fasciculi (SLF), which together form the dorsal language stream, using fiber dissection and diffusion imaging techniques in the human brain. METHODS Twenty-five formalin-fixed brains (50 hemispheres) and 3 adult cadaveric heads, prepared according to the Klingler method, were examined by the fiber dissection technique. The authors' findings were supported with MR tractography provided by the Human Connectome Project, WU-Minn Consortium. The frequencies of gyral distributions were calculated in segments of the AF and SLF in the cadaveric specimens. RESULTS The AF has ventral and dorsal segments, and the SLF has 3 segments: SLF I (dorsal pathway), II (middle pathway), and III (ventral pathway). The AF ventral segment connects the middle (88%; all percentages represent the area of the named structure that is connected to the tract) and posterior (100%) parts of the superior temporal gyri and the middle part (92%) of the middle temporal gyrus to the posterior part of the inferior frontal gyrus (96% in pars opercularis, 40% in pars triangularis) and the ventral premotor cortex (84%) by passing deep to the lower part of the supramarginal gyrus (100%). The AF dorsal segment connects the posterior part of the middle (100%) and inferior temporal gyri (76%) to the posterior part of the inferior frontal gyrus (96% in pars opercularis), ventral premotor cortex (72%), and posterior part of the middle frontal gyrus (56%) by passing deep to the lower part of the angular gyrus (100%). CONCLUSIONS This study depicts the distinct subdivision of the AF and SLF, based on cadaveric fiber dissection and diffusion imaging techniques, to clarify the complicated language processing pathways.
  • Effects of thresholding on correlation-based image similarity metrics.

    Vanessa V Sochat, Krzysztof J Gorgolewski, Oluwasanmi Koyejo, Joke Durnez, Russell A Poldrack
    Frontiers in neuroscience, Nov 19, 2015 PMID: 26578875
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    The computation of image similarity is important for a wide range of analyses in neuroimaging, from decoding to meta-analysis. In many cases the images being compared have empty voxels, but the effects of such empty voxels on image similarity metrics are poorly understood. We present a detailed investigation of the influence of different degrees of image thresholding on the outcome of pairwise image comparison. Given a pair of brain maps for which one of the maps is thresholded, we show that an analysis using the intersection of non-zero voxels across images at a threshold of Z = ±1.0 maximizes accuracy for retrieval of a list of maps of the same contrast, and thresholding up to Z = ±2.0 can increase accuracy as compared to comparison using unthresholded maps. Finally, maps can be thresholded up to to Z = ±3.0 (corresponding to 25% of voxels non-empty within a standard brain mask) and still maintain a lower bound of 90% accuracy. Our results suggest that a small degree of thresholding may improve the accuracy of image similarity computations, and that robust meta-analytic image similarity comparisons can be obtained using thresholded images.
  • Canonical genetic signatures of the adult human brain.

    Michael Hawrylycz, Jeremy A Miller, Vilas Menon, David Feng, Tim Dolbeare, Angela L Guillozet-Bongaarts, Anil G Jegga, Bruce J Aronow, Chang-Kyu Lee, Amy Bernard, Matthew F Glasser, Donna L Dierker, Jörg Menche, Aaron Szafer, Forrest Collman, Pascal Grange, Kenneth A Berman, Stefan Mihalas, Zizhen Yao, Lance Stewart, Albert-László Barabási, Jay Schulkin, John Phillips, Lydia Ng, Chinh Dang, David R Haynor, Allan Jones, David C Van Essen, Christof Koch, Ed Lein
    Nature neuroscience, Nov 17, 2015 PMID: 26571460
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    The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.
  • Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis.

    Alexandra Kammen, Meng Law, Bosco S Tjan, Arthur W Toga, Yonggang Shi
    NeuroImage, Nov 10, 2015 PMID: 26551261
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    Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic organization of the optic radiation including a successful reconstruction of Meyer's loop. Then, using the reconstructed optic radiation bundle from the HCP cohort, we construct a probabilistic atlas and demonstrate its consistency with a post-mortem atlas. Finally, we generate a shape-based representation of the optic radiation for morphometry analysis.
  • Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity.

    Bo-yong Park, Jongbum Seo, Juneho Yi, Hyunjin Park
    PloS one, Nov 05, 2015 PMID: 26536135
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    Obesity is a medical condition affecting billions of people. Various neuroimaging methods including magnetic resonance imaging (MRI) have been used to obtain information about obesity. We adopted a multi-modal approach combining diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) to incorporate complementary information and thus better investigate the brains of non-healthy weight subjects. The objective of this study was to explore multi-modal neuroimaging and use it to predict a practical clinical score, body mass index (BMI). Connectivity analysis was applied to DTI and rs-fMRI. Significant regions and associated imaging features were identified based on group-wise differences between healthy weight and non-healthy weight subjects. Six DTI-driven connections and 10 rs-fMRI-driven connectivities were identified. DTI-driven connections better reflected group-wise differences than did rs-fMRI-driven connectivity. We predicted BMI values using multi-modal imaging features in a partial least-square regression framework (percent error 15.0%). Our study identified brain regions and imaging features that can adequately explain BMI. We identified potentially good imaging biomarker candidates for obesity-related diseases.
  • Measuring macroscopic brain connections in vivo.

    Saad Jbabdi, Stamatios N Sotiropoulos, Suzanne N Haber, David C Van Essen, Timothy E Behrens
    Nature neuroscience, Oct 28, 2015 PMID: 26505566
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    Decades of detailed anatomical tracer studies in non-human animals point to a rich and complex organization of long-range white matter connections in the brain. State-of-the art in vivo imaging techniques are striving to achieve a similar level of detail in humans, but multiple technical factors can limit their sensitivity and fidelity. In this review, we mostly focus on magnetic resonance imaging of the brain. We highlight some of the key challenges in analyzing and interpreting in vivo connectomics data, particularly in relation to what is known from classical neuroanatomy in laboratory animals. We further illustrate that, despite the challenges, in vivo imaging methods can be very powerful and provide information on connections that is not available by any other means.
  • Different Interaction Modes for the Default Mode Network Revealed by Resting State Functional MRI.

    Nianming Zuo, Ming Song, Lingzhong Fan, Simon B Eickhoff, Tianzi Jiang
    Show Summary
    The default mode network (DMN), which, in the resting state, is in charge of both the brain's intrinsic mentation and its reflexive responses to external stimuli, is recognized as an essential network in the human brain. These two roles of mentation and reflexive response recruit the DMN nodes and other task networks differently. Existing research has revealed that the interactions inside the DMN (between nodes within the DMN) and outside the DMN (between nodes in the DMN and ones in task networks) have different modes, in terms of both strength and timing. These findings raise interesting questions. For example, are the internal and external interactions of the DMN equally linear or nonlinear? This study examined these interaction patterns using datasets from the Human Connectome Project. A maximal information-based nonparametric exploration statistics strategy was utilized to characterize the full correlations (FC), and the Pearson correlation was used to capture the linear component of the FC. Then we contrasted the level of linearity/nonlinearity with respect to the internal and external interactions of the DMN. After a brain-wide exploration, we found that the interactions between the DMN and the sensorimotor-related networks (including the sensorimotor, sensory association, and integration areas) showed more nonlinearity, whereas the ones between the intra-DMN nodes were comparably less nonlinear. These findings may provide a clue for understanding the underlying neuronal principles of the internal and external roles of the DMN. This article is protected by copyright. All rights reserved.
  • The common genetic influence over processing speed and white matter microstructure: Evidence from the Old Order Amish and Human Connectome Projects.

    Peter Kochunov, Paul M Thompson, Anderson Winkler, Mary Morrissey, Mao Fu, Thomas R Coyle, Xiaoming Du, Florian Muellerklein, Anya Savransky, Christopher Gaudiot, Hemalatha Sampath, George Eskandar, Neda Jahanshad, Binish Patel, Laura Rowland, Thomas E Nichols, Jeffrey R O'Connell, Alan R Shuldiner, Braxton D Mitchell, L Elliot Hong
    NeuroImage, Oct 27, 2015 PMID: 26499807
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    Speed with which brain performs information processing influences overall cognition and is dependent on the white matter fibers. To understand genetic influences on processing speed and white matter FA, we assessed processing speed and diffusion imaging fractional anisotropy (FA) in related individuals from two populations. Discovery analyses were performed in 146 individuals from large Old Order Amish (OOA) families and findings were replicated in 485 twins and siblings of the Human Connectome Project (HCP). The heritability of processing speed was h(2)=43% and 49% (both p<0.005), while the heritability of whole brain FA was h(2)=87% and 88% (both p<0.001), in the OOA and HCP, respectively. Whole brain FA was significantly correlated with processing speed in the two cohorts. Quantitative genetic analysis demonstrated a significant degree to which common genes influenced joint variation in FA and brain processing speed. These estimates suggested common sets of genes influencing variation in both phenotypes, consistent with the idea that common genetic variations contributing to white matter may also support their associated cognitive behavior.
  • An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging.

    Jesper L R Andersson, Stamatios N Sotiropoulos
    NeuroImage, Oct 21, 2015 PMID: 26481672
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    In this paper we describe a method for retrospective estimation and correction of eddy current(EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal-Tanner gradients on a 3T Siemens Verio, a 3T Siemens Connectome Skyra or a 7T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement.
  • Bridging Cytoarchitectonics and Connectomics in Human Cerebral Cortex.

    Martijn P van den Heuvel, Lianne H Scholtens, Lisa Feldman Barrett, Claus C Hilgetag, Marcel A de Reus
    Show Summary
    The rich variation in cytoarchitectonics of the human cortex is well known to play an important role in the differentiation of cortical information processing, with functional multimodal areas noted to display more branched, more spinous, and an overall more complex cytoarchitecture. In parallel, connectome studies have suggested that also the macroscale wiring profile of brain areas may have an important contribution in shaping neural processes; for example, multimodal areas have been noted to display an elaborate macroscale connectivity profile. However, how these two scales of brain connectivity are related-and perhaps interact-remains poorly understood. In this communication, we combined data from the detailed mappings of early twentieth century cytoarchitectonic pioneers Von Economo and Koskinas (1925) on the microscale cellular structure of the human cortex with data on macroscale connectome wiring as derived from high-resolution diffusion imaging data from the Human Connectome Project. In a cross-scale examination, we show evidence of a significant association between cytoarchitectonic features of human cortical organization-in particular the size of layer 3 neurons-and whole-brain corticocortical connectivity. Our findings suggest that aspects of microscale cytoarchitectonics and macroscale connectomics are related.One of the most widely known and perhaps most fundamental properties of the human cortex is its rich variation in cytoarchitectonics. At the same time, neuroimaging studies have also revealed cortical areas to vary in their level of macroscale connectivity. Here, we provide evidence that aspects of local cytoarchitecture are associated with aspects of global macroscale connectivity, providing insight into the question of how the scales of micro-organization and macro-organization of the human cortex are related.
  • Sparse representation of HCP grayordinate data reveals novel functional architecture of cerebral cortex.

    Xi Jiang, Xiang Li, Jinglei Lv, Tuo Zhang, Shu Zhang, Lei Guo, Tianming Liu
    Human brain mapping, Oct 15, 2015 PMID: 26466353
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    The recently publicly released Human Connectome Project (HCP) grayordinate-based fMRI data not only has high spatial and temporal resolution, but also offers group-corresponding fMRI signals across a large population for the first time in the brain imaging field, thus significantly facilitating mapping the functional brain architecture with much higher resolution and in a group-wise fashion. In this article, we adopt the HCP grayordinate task-based fMRI (tfMRI) data to systematically identify and characterize task-based heterogeneous functional regions (THFRs) on cortical surface, i.e., the regions that are activated during multiple tasks conditions and contribute to multiple task-evoked systems during a specific task performance, and to assess the spatial patterns of identified THFRs on cortical gyri and sulci by applying a computational framework of sparse representations of grayordinate brain tfMRI signals. Experimental results demonstrate that both consistent task-evoked networks and intrinsic connectivity networks across all subjects and tasks in HCP grayordinate data are effectively and robustly reconstructed via the proposed sparse representation framework. Moreover, it is found that there are relatively consistent THFRs locating at bilateral parietal lobe, frontal lobe, and visual association cortices across all subjects and tasks. Particularly, those identified THFRs locate significantly more on gyral regions than on sulcal regions. These results based on sparse representation of HCP grayordinate data reveal novel functional architecture of cortical gyri and sulci, and might provide a foundation to better understand functional mechanisms of the human cerebral cortex in the future. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
  • Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity.

    Emily S Finn, Xilin Shen, Dustin Scheinost, Monica D Rosenberg, Jessica Huang, Marvin M Chun, Xenophon Papademetris, R Todd Constable
    Nature neuroscience, Oct 13, 2015 PMID: 26457551
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    Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.
  • The nondecussating pathway of the dentatorubrothalamic tract in humans: human connectome-based tractographic study and microdissection validation.

    Antonio Meola, Ayhan Comert, Fang-Cheng Yeh, Sananthan Sivakanthan, Juan C Fernandez-Miranda
    Journal of neurosurgery, Oct 10, 2015 PMID: 26452117
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    OBJECT The dentatorubrothalamic tract (DRTT) is the major efferent cerebellar pathway arising from the dentate nucleus (DN) and decussating to the contralateral red nucleus (RN) and thalamus. Surprisingly, hemispheric cerebellar output influences bilateral limb movements. In animals, uncrossed projections from the DN to the ipsilateral RN and thalamus may explain this phenomenon. The aim of this study was to clarify the anatomy of the dentatorubrothalamic connections in humans. METHODS The authors applied advanced deterministic fiber tractography to a template of 488 subjects from the Human Connectome Project (Q1-Q3 release, WU-Minn HCP consortium) and validated the results with microsurgical dissection of cadaveric brains prepared according to Klingler's method. RESULTS The authors identified the "classic" decussating DRTT and a corresponding nondecussating path (the nondecussating DRTT, nd-DRTT). Within each of these 2 tracts some fibers stop at the level of the RN, forming the dentatorubro tract and the nondecussating dentatorubro tract. The left nd-DRTT encompasses 21.7% of the tracts and 24.9% of the volume of the left superior cerebellar peduncle, and the right nd-DRTT encompasses 20.2% of the tracts and 28.4% of the volume of the right superior cerebellar peduncle. CONCLUSIONS The connections of the DN with the RN and thalamus are bilateral, not ipsilateral only. This affords a potential anatomical substrate for bilateral limb motor effects originating in a single cerebellar hemisphere under physiological conditions, and for bilateral limb motor impairment in hemispheric cerebellar lesions such as ischemic stroke and hemorrhage, and after resection of hemispheric tumors and arteriovenous malformations. Furthermore, when a lesion is located on the course of the dentatorubrothalamic system, a careful preoperative tractographic analysis of the relationship of the DRTT, nd-DRTT, and the lesion should be performed in order to tailor the surgical approach properly and spare all bundles.
  • The controversial existence of the human superior fronto-occipital fasciculus: Connectome-based tractographic study with microdissection validation.

    Antonio Meola, Ayhan Comert, Fang-Cheng Yeh, Lucia Stefaneanu, Juan C Fernandez-Miranda
    Human brain mapping, Oct 06, 2015 PMID: 26435158
    Show Summary
    The superior fronto-occipital fasciculus (SFOF), a long association bundle that connects frontal and occipital lobes, is well-documented in monkeys but is controversial in human brain. Its assumed role is in visual processing and spatial awareness. To date, anatomical and neuroimaging studies on human and animal brains are not in agreement about the existence, course, and terminations of SFOF. To clarify the existence of the SFOF in human brains, we applied deterministic fiber tractography to a template of 488 healthy subjects and to 80 individual subjects from the Human Connectome Project (HCP) and validated the results with white matter microdissection of post-mortem human brains. The imaging results showed that previous reconstructions of the SFOF were generated by two false continuations, namely between superior thalamic peduncle (STP) and stria terminalis (ST), and ST and posterior thalamic peduncle. The anatomical microdissection confirmed this finding. No other fiber tracts in the previously described location of the SFOF were identified. Hence, our data suggest that the SFOF does not exist in the human brain. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
  • Medial Demons Registration Localizes The Degree of Genetic Influence Over Subcortical Shape Variability: An N= 1480 Meta-Analysis.

    Boris A Gutman, Neda Jahanshad, Christopher R K Ching, Yalin Wang, Peter V Kochunov, Thomas E Nichols, Paul M Thompson
    Show Summary
    We present a multi-cohort shape heritability study, extending the fast spherical demons registration to subcortical shapes via medial modeling. A multi-channel demons registration based on vector spherical harmonics is applied to medial and curvature features, while controlling for metric distortion. We registered and compared seven subcortical structures of 1480 twins and siblings from the Queensland Twin Imaging Study and Human Connectome Project: Thalamus, Caudate, Putamen, Pallidum, Hippocampus, Amygdala, and Nucleus Accumbens. Radial distance and tensor-based morphometry (TBM) features were found to be highly heritable throughout the entire basal ganglia and limbic system. Surface maps reveal subtle variation in heritability across functionally distinct parts of each structure. Medial Demons reveals more significantly heritable regions than two previously described surface registration methods. This approach may help to prioritize features and measures for genome-wide association studies.
  • A positive-negative mode of population covariation links brain connectivity, demographics and behavior.

    Stephen M Smith, Thomas E Nichols, Diego Vidaurre, Anderson M Winkler, Timothy E J Behrens, Matthew F Glasser, Kamil Ugurbil, Deanna M Barch, David C Van Essen, Karla L Miller
    Nature neuroscience, Sep 29, 2015 PMID: 26414616
    Show Summary
    We investigated the relationship between individual subjects' functional connectomes and 280 behavioral and demographic measures in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation: subjects were predominantly spread along a single 'positive-negative' axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.
  • Stable Overlapping Replicator Dynamics for Brain Community Detection.

    Burak Yoldemir, Bernard Ng, Rafeef Abugharbieh
    Show Summary
    A fundamental means for understanding the brain's organizational structure is to group its spatially disparate regions into functional subnetworks based on their interactions. Most community detection techniques are designed for generating partitions, but certain brain regions are known to interact with multiple subnetworks. Thus, the brain's underlying subnetworks necessarily overlap. In this paper, we propose a technique for identifying overlapping subnetworks from weighted graphs with statistical control over false node inclusion. Our technique improves upon the replicator dynamics formulation by incorporating a graph augmentation strategy to enable subnetwork overlaps, and a graph incrementation scheme for merging subnetworks that might be falsely split by replicator dynamics due to its stringent mutual similarity criterion in defining subnetworks. To statistically control for inclusion of false nodes into the detected subnetworks, we further present a procedure for integrating stability selection into our subnetwork identification technique. We refer to the resulting technique as stable overlapping replicator dynamics (SORD). Our experiments on synthetic data show significantly higher accuracy in subnetwork identification with SORD than several state-of-the-art techniques. We also demonstrate higher test-retest reliability in multiple network measures on the Human Connectome Project data. Further, we illustrate that SORD enables identification of neuroanatomically-meaningful subnetworks and network hubs.
  • A probabilistic atlas of the cerebellar white matter.

    K M van Baarsen, M Kleinnijenhuis, S Jbabdi, S N Sotiropoulos, J A Grotenhuis, A M van Cappellen van Walsum
    NeuroImage, Sep 20, 2015 PMID: 26385011
    Show Summary
    Imaging of the cerebellar cortex, deep cerebellar nuclei and their connectivity are gaining attraction, due to the important role the cerebellum plays in cognition and motor control. Atlases of the cerebellar cortex and nuclei are used to locate regions of interest in clinical and neuroscience studies. However, the white matter that connects these relay stations is of at least similar functional importance. Damage to these cerebellar white matter tracts may lead to serious language, cognitive and emotional disturbances, although the pathophysiological mechanism behind it is still debated. Differences in white matter integrity between patients and controls might shed light on structure-function correlations. A probabilistic parcellation atlas of the cerebellar white matter would help these studies by facilitating automatic segmentation of the cerebellar peduncles, the localization of lesions and the comparison of white matter integrity between patients and controls. In this work a digital three-dimensional probabilistic atlas of the cerebellar white matter is presented, based on high quality 3T, 1.25mm resolution diffusion MRI data from 90 subjects participating in the Human Connectome Project. The white matter tracts were estimated using probabilistic tractography. Results over 90 subjects were symmetrical and trajectories of superior, middle and inferior cerebellar peduncles resembled the anatomy as known from anatomical studies. This atlas will contribute to a better understanding of cerebellar white matter architecture. It may eventually aid in defining structure-function correlations in patients with cerebellar disorders.
  • Structural and functional connectivity of visual and auditory attentional networks: insights from the Human Connectome Project.

    David Osher, Sean Tobyne, Keith Congden, Samantha Michalka, David Somers
    Journal of vision, Sep 02, 2015 PMID: 26325911
    Show Summary
    Recent work in our laboratory has suggested that human caudal lateral frontal cortex contains four interleaved regions in each hemisphere that exhibit strong sensory-specific biases in attention tasks (Michalka et al, 2014). Two visually-biased attention regions, superior and inferior pre-central sulcus (sPCS, iPCS), anatomically alternate with two auditory-biased attention regions, caudal inferior frontal sulcus (cIFS) and the transverse gyrus intersection the precentral sulcus (tgPCS). These small regions were identified in fMRI studies in a small number of individual subjects. Here, we have investigated these regions and their putative networks by mining the WashU-Minn Human Connectome Project (HCP) dataset. We used data from the 482 HCP participants with both diffusion-weighted imaging and resting-state fMRI. We defined seed regions from our individual subject data in a task that contrasted auditory and visual spatial attention. Probabilistic activation maps were constructed and thresholded to generate ROIs. These ROIs served as seed regions for resting state and tractography analyses of the HCP dataset. Stronger functional connectivity was observed for the sPCS and iPCS than for tgPCS and cIFS with superior parietal lobule visual attention regions, and conversely stronger connectivity was observed for the tgPCS and cIFS than for sPCS and iPCS with superior temporal lobe auditory attention regions. A similar pattern was observed with tractography for all ROIs, except for tgPCS. We next analyzed the whole-brain connectivity patterns of these ROIs using a multivariate approach; we found that the modality of sensory-bias can be predicted well above chance in both hemispheres at a voxelwise scale (L:71%, R:80%), using only the connectivity pattern of an individual voxel. A long-term goal of this analysis is to develop reliable methods for identifying fine-scale brain networks in large population datasets, which could have important clinical applications. Our preliminary results reveal both successes and challenges of these efforts. Meeting abstract presented at VSS 2015.
  • Shared Predisposition in the Association Between Cannabis Use and Subcortical Brain Structure.

    David Pagliaccio, Deanna M Barch, Ryan Bogdan, Phillip K Wood, Michael T Lynskey, Andrew C Heath, Arpana Agrawal
    JAMA psychiatry, Aug 27, 2015 PMID: 26308883
    Show Summary
    Prior neuroimaging studies have suggested that alterations in brain structure may be a consequence of cannabis use. Siblings discordant for cannabis use offer an opportunity to use cross-sectional data to disentangle such causal hypotheses from shared effects of genetics and familial environment on brain structure and cannabis use.To determine whether cannabis use is associated with differences in brain structure in a large sample of twins/siblings and to examine sibling pairs discordant for cannabis use to separate potential causal and predispositional factors linking lifetime cannabis exposure to volumetric alterations.Cross-sectional diagnostic interview, behavioral, and neuroimaging data were collected from community sampling and established family registries from August 2012 to September 2014. This study included data from 483 participants (22-35 years old) enrolled in the ongoing Human Connectome Project, with 262 participants reporting cannabis exposure (ie, ever used cannabis in their lifetime).Cannabis exposure was measured with the Semi-Structured Assessment for the Genetics of Alcoholism. Whole-brain, hippocampus, amygdala, ventral striatum, and orbitofrontal cortex volumes were related to lifetime cannabis use (ever used, age at onset, and frequency of use) using linear regressions. Genetic (ρg) and environmental (ρe) correlations between cannabis use and brain volumes were estimated. Linear mixed models were used to examine volume differences in sex-matched concordant unexposed (n = 71 pairs), exposed (n = 81 pairs), or exposure discordant (n = 89 pairs) sibling pairs.Among 483 study participants, cannabis exposure was related to smaller left amygdala (approximately 2.3%; P = .007) and right ventral striatum (approximately 3.5%; P < .005) volumes. These volumetric differences were within the range of normal variation. The association between left amygdala volume and cannabis use was largely owing to shared genetic factors (ρg = -0.43; P = .004), while the origin of the association with right ventral striatum volumes was unclear. Importantly, brain volumes did not differ between sex-matched siblings discordant for use (fixed effect = -7.43; t = -0.93, P = .35). Both the exposed and unexposed siblings in pairs discordant for cannabis exposure showed reduced amygdala volumes relative to members of concordant unexposed pairs (fixed effect = 12.56; t = 2.97; P = .003).In this study, differences in amygdala volume in cannabis users were attributable to common predispositional factors, genetic or environmental in origin, with little support for causal influences. Causal influences, in isolation or in conjunction with predispositional factors, may exist for other brain regions (eg, ventral striatum) or at more severe levels of cannabis involvement and deserve further study.
  • Inter-individual differences in the experience of negative emotion predict variations in functional brain architecture.

    Raluca Petrican, Cristina Saverino, R Shayna Rosenbaum, Cheryl Grady
    NeuroImage, Aug 26, 2015 PMID: 26302674
    Show Summary
    Current evidence suggests that two spatially distinct neuroanatomical networks, the dorsal attention network (DAN) and the default mode network (DMN), support externally and internally oriented cognition, respectively, and are functionally regulated by a third, frontoparietal control network (FPC). Interactions among these networks contribute to normal variations in cognitive functioning and to the aberrant affective profiles present in certain clinical conditions, such as major depression. Nevertheless, their links to non-clinical variations in affective functioning are still poorly understood. To address this issue, we used fMRI to measure the intrinsic functional interactions among these networks in a sample of predominantly younger women (N=162) from the Human Connectome Project. Consistent with the previously documented dichotomous motivational orientations (i.e., withdrawal versus approach) associated with sadness versus anger, we hypothesized that greater sadness would predict greater DMN (rather than DAN) functional dominance, whereas greater anger would predict the opposite. Overall, there was evidence of greater DAN (rather than DMN) functional dominance, but this pattern was modulated by current experience of specific negative emotions, as well as subclinical depressive and anxiety symptoms. Thus, greater levels of currently experienced sadness and subclinical depression independently predicted weaker DAN functional dominance (i.e., weaker DAN-FPC functional connectivity), likely reflecting reduced goal-directed attention towards the external perceptual environment. Complementarily, greater levels of currently experienced anger and subclinical anxiety predicted greater DAN functional dominance (i.e., greater DAN-FPC functional connectivity and, for anxiety only, also weaker DMN-FPC coupling). Our findings suggest that distinct affective states and subclinical mood symptoms have dissociable neural signatures, reflective of the symbiotic relationship between cognitive processes and emotional states.
  • Mapping the mouse brain with rs-fMRI: An optimized pipeline for functional network identification.

    Valerio Zerbi, Joanes Grandjean, Markus Rudin, Nicole Wenderoth
    NeuroImage, Aug 23, 2015 PMID: 26296501
    Show Summary
    The use of resting state fMRI (rs-fMRI) in translational research is a powerful tool to assess brain connectivity and investigate neuropathology in mouse models. However, despite encouraging initial results, the characterization of consistent and robust resting state networks in mice remains a methodological challenge. One key reason is that the quality of the measured MR signal is degraded by the presence of structural noise from non-neural sources. Notably, in the current pipeline of the Human Connectome Project, a novel approach has been introduced to clean rs-fMRI data, which involves automatic artifact component classification and data cleaning (FIX). FIX does not require any external recordings of physiology or the segmentation of CSF and white matter. In this study, we evaluated the performance of FIX for analyzing mouse rs-fMRI data. Our results showed that FIX can be easily applied to mouse datasets and detects true signals with 100% accuracy and true noise components with very high accuracy (>98%), thus reducing both within- and between-subject variability of rs-fMRI connectivity measurements. Using this improved pre-processing pipeline, maps of 23 resting state circuits in mice were identified including two networks that displayed default mode network-like topography. Hierarchical clustering grouped these neural networks into meaningful larger functional circuits. These mouse resting state networks, which are publicly available, might serve as a reference for future work using mouse models of neurological disorders.
  • High resolution whole brain diffusion imaging at 7T for the Human Connectome Project.

    A T Vu, E Auerbach, C Lenglet, S Moeller, S N Sotiropoulos, S Jbabdi, J Andersson, E Yacoub, K Ugurbil
    NeuroImage, Aug 12, 2015 PMID: 26260428
    Show Summary
    Mapping structural connectivity in healthy adults for the Human Connectome Project (HCP) benefits from high quality, high resolution, multiband (MB)-accelerated whole brain diffusion MRI (dMRI). Acquiring such data at ultrahigh fields (7T and above) can improve intrinsic signal-to-noise ratio (SNR), but suffers from shorter T2 and T2(⁎) relaxation times, increased B1(+) inhomogeneity (resulting in signal loss in cerebellar and temporal lobe regions), and increased power deposition (i.e. specific absorption rate (SAR)), thereby limiting our ability to reduce the repetition time (TR). Here, we present recent developments and optimizations in 7T image acquisitions for the HCP that allow us to efficiently obtain high quality, high resolution whole brain in-vivo dMRI data at 7T. These data show spatial details typically seen only in ex-vivo studies and complement already very high quality 3T HCP data in the same subjects. The advances are the result of intensive pilot studies aimed at mitigating the limitations of dMRI at 7T. The data quality and methods described here are representative of the datasets that will be made freely available to the community in 2015.
  • Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes.

    Jesper L R Andersson, Stamatios N Sotiropoulos
    NeuroImage, Aug 04, 2015 PMID: 26236030
    Show Summary
    Diffusion MRI offers great potential in studying the human brain microstructure and connectivity. However, diffusion images are marred by technical problems, such as image distortions and spurious signal loss. Correcting for these problems is non-trivial and relies on having a mechanism that predicts what to expect. In this paper we describe a novel way to represent and make predictions about diffusion MRI data. It is based on a Gaussian process on one or several spheres similar to the Geostatistical method of "Kriging". We present a choice of covariance function that allows us to accurately predict the signal even from voxels with complex fibre patterns. For multi-shell data (multiple non-zero b-values) the covariance function extends across the shells which means that data from one shell is used when making predictions for another shell.
  • Surface-Based Display of Volume-Averaged Cerebellar Imaging Data.

    Jörn Diedrichsen, Ewa Zotow
    PloS one, Aug 01, 2015 PMID: 26230510
    Show Summary
    The paper presents a flat representation of the human cerebellum, useful for visualizing functional imaging data after volume-based normalization and averaging across subjects. Instead of reconstructing individual cerebellar surfaces, the method uses a white- and grey-matter surface defined on volume-averaged anatomical data. Functional data can be projected along the lines of corresponding vertices on the two surfaces. The flat representation is optimized to yield a roughly proportional relationship between the surface area of the 2D-representation and the volume of the underlying cerebellar grey matter. The map allows users to visualize the activation state of the complete cerebellar grey matter in one concise view, equally revealing both the anterior-posterior (lobular) and medial-lateral organization. As examples, published data on resting-state networks and task-related activity are presented on the flatmap. The software and maps are freely available and compatible with most major neuroimaging packages.
  • High-Resolution Functional Connectivity Density: Hub Locations, Sensitivity, Specificity, Reproducibility, and Reliability.

    Dardo Tomasi, Ehsan Shokri-Kojori, Nora D Volkow
    Show Summary
    Brain regions with high connectivity have high metabolic cost and their disruption is associated with neuropsychiatric disorders. Prior neuroimaging studies have identified at the group-level local functional connectivity density ( L: FCD) hubs, network nodes with high degree of connectivity with neighboring regions, in occipito-parietal cortices. However, the individual patterns and the precision for the location of the hubs were limited by the restricted spatiotemporal resolution of the magnetic resonance imaging (MRI) measures collected at rest. In this work, we show that MRI datasets with higher spatiotemporal resolution (2-mm isotropic; 0.72 s), collected under the Human Connectome Project (HCP), provide a significantly higher precision for hub localization and for the first time reveal L: FCD patterns with gray matter (GM) specificity >96% and sensitivity >75%. High temporal resolution allowed effective 0.01-0.08 Hz band-pass filtering, significantly reducing spurious L: FCD effects in white matter. These high spatiotemporal resolution L: FCD measures had high reliability [intraclass correlation, ICC(3,1) > 0.6] but lower reproducibility (>67%) than the low spatiotemporal resolution equivalents. GM sensitivity and specificity benchmarks showed the robustness of L: FCD to changes in model parameter and preprocessing steps. Mapping individual's brain hubs with high sensitivity, specificity, and reproducibility supports the use of L: FCD as a biomarker for clinical applications in neuropsychiatric disorders.
  • Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI.

    Salim Arslan, Sarah Parisot, Daniel Rueckert
    Information processing in medical imaging : proceedings of the ... conference Jul 30, 2015 PMID: 26221668
    Show Summary
    Identification of functional connections within the human brain has gained a lot of attention due to its potential to reveal neural mechanisms. In a whole-brain connectivity analysis, a critical stage is the computation of a set of network nodes that can effectively represent cortical regions. To address this problem, we present a robust cerebral cortex parcellation method based on spectral graph theory and resting-state fMRI correlations that generates reliable parcellations at the single-subject level and across multiple subjects. Our method models the cortical surface in each hemisphere as a mesh graph represented in the spectral domain with its eigenvectors. We connect cortices of different subjects with each other based on the similarity of their connectivity profiles and construct a multi-layer graph, which effectively captures the fundamental properties of the whole group as well as preserves individual subject characteristics. Spectral decomposition of this joint graph is used to cluster each cortical vertex into a subregion in order to obtain whole-brain parcellations. Using rs-fMRI data collected from 40 healthy subjects, we show that our proposed algorithm computes highly reproducible parcellations across different groups of subjects and at varying levels of detail with an average Dice score of 0.78, achieving up to 9% better reproducibility compared to existing approaches. We also report that our group-wise parcellations are functionally more consistent, thus, can be reliably used to represent the population in network analyses.
  • Measuring Asymmetric Interactions in Resting State Brain Networks.

    Anand A Joshi, Ronald Salloum, Chitresh Bhushan, Richard M Leahy
    Information processing in medical imaging : proceedings of the ... conference Jul 30, 2015 PMID: 26221690
    Show Summary
    Directed graph representations of brain networks are increasingly being used to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network.
  • Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex.

    Sarah Parisot, Salim Arslan, Jonathan Passerat-Palmbach, William M Wells, Daniel Rueckert
    Information processing in medical imaging : proceedings of the ... conference Jul 30, 2015 PMID: 26221706
    Show Summary
    The analysis of the connectome of the human brain provides key insight into the brain's organisation and function, and its evolution in disease or ageing. Parcellation of the cortical surface into distinct regions in terms of structural connectivity is an essential step that can enable such analysis. The estimation of a stable connectome across a population of healthy subjects requires the estimation of a groupwise parcellation that can capture the variability of the connectome across the population. This problem has solely been addressed in the literature via averaging of connectivity profiles or finding correspondences between individual parcellations a posteriori. In this paper, we propose a groupwise parcellation method of the cortex based on diffusion MR images (dMRI). We borrow ideas from the area of cosegmentation in computer vision and directly estimate a consistent parcellation across different subjects and scales through a spectral clustering approach. The parcellation is driven by the tractography connectivity profiles, and information between subjects and across scales. Promising qualitative and quantitative results on a sizeable data-set demonstrate the strong potential of the method.
  • Coupled Stable Overlapping Replicator Dynamics for Multimodal Brain Subnetwork Identification.

    Burak Yoldemir, Bernard Ng, Rafeef Abugharbieh
    Information processing in medical imaging : proceedings of the ... conference Jul 30, 2015 PMID: 26221717
    Show Summary
    Combining imaging modalities to synthesize their inherent strengths provides a promising means for improving brain subnetwork identification. We propose a multimodal integration technique based on a sex-differentiated formulation of replicator dynamics for identifying subnetworks of brain regions that exhibit high inter-connectivity both functionally and structurally. Our method has a number of desired properties, namely, it can operate on weighted graphs derived from functional magnetic resonance imaging (tMRI) and diffusion MRI (dMRI) data, allows for subnetwork overlaps, has an intrinsic criterion for setting the number of subnetworks, and provides statistical control on false node inclusion in the identified subnetworks via the incorporation of stability selection. We thus refer to our technique as coupled stable overlapping replicator dynamics (CSORD). On synthetic data, We demonstrate that CSORD achieves significantly higher subnetwork identification accuracy than state-of-the-art techniques. On real. data from the Human Connectome Project (HCP), we show that CSORD attains improved test-retest reliability on multiple network measures and superior task classification accuracy.
  • In vivo characterization of the connectivity and subcomponents of the human globus pallidus.

    Patrick Beukema, Fang-Cheng Yeh, Timothy Verstynen
    NeuroImage, Jul 22, 2015 PMID: 26196668
    Show Summary
    Projections from the substantia nigra and striatum traverse through the pallidum on the way to their targets. To date, in vivo characterization of these pathways remains elusive. Here we used high angular resolution diffusion imaging (N=138) to study the characteristics and structural subcompartments of the human pallidum. Our central result shows that the diffusion orientation distribution functions within the pallidum are asymmetrically oriented in a dorsal to dorsolateral direction, consistent with the orientation of underlying fiber systems. We also observed systematic differences in the diffusion signal between the two pallidal segments. Compared to the outer pallidal segment, the internal segment has more peaks in the diffusion orientation distribution and stronger anisotropy in the primary fiber direction, consistent with known cellular differences between the underlying nuclei. These differences in orientation, complexity, and degree of anisotropy are sufficiently robust to automatically segment the pallidal nuclei using diffusion properties. We characterize these patterns in one data set using diffusion spectrum imaging and replicate in a separate sample of subjects imaged using multi-shell imaging, highlighting the reliability of these diffusion patterns within pallidal nuclei. Thus the gray matter diffusion signal can be useful as an in vivo measure of the collective efferent pathways running through the human pallidum.
  • Graph Theoretical Analysis Reveals: Women's Brains Are Better Connected than Men's.

    Balázs Szalkai, Bálint Varga, Vince Grolmusz
    PloS one, Jul 02, 2015 PMID: 26132764
    Show Summary
    Deep graph-theoretic ideas in the context with the graph of the World Wide Web led to the definition of Google's PageRank and the subsequent rise of the most popular search engine to date. Brain graphs, or connectomes, are being widely explored today. We believe that non-trivial graph theoretic concepts, similarly as it happened in the case of the World Wide Web, will lead to discoveries enlightening the structural and also the functional details of the animal and human brains. When scientists examine large networks of tens or hundreds of millions of vertices, only fast algorithms can be applied because of the size constraints. In the case of diffusion MRI-based structural human brain imaging, the effective vertex number of the connectomes, or brain graphs derived from the data is on the scale of several hundred today. That size facilitates applying strict mathematical graph algorithms even for some hard-to-compute (or NP-hard) quantities like vertex cover or balanced minimum cut. In the present work we have examined brain graphs, computed from the data of the Human Connectome Project, recorded from male and female subjects between ages 22 and 35. Significant differences were found between the male and female structural brain graphs: we show that the average female connectome has more edges, is a better expander graph, has larger minimal bisection width, and has more spanning trees than the average male connectome. Since the average female brain weighs less than the brain of males, these properties show that the female brain has better graph theoretical properties, in a sense, than the brain of males. It is known that the female brain has a smaller gray matter/white matter ratio than males, that is, a larger white matter/gray matter ratio than the brain of males; this observation is in line with our findings concerning the number of edges, since the white matter consists of myelinated axons, which, in turn, roughly correspond to the connections in the brain graph. We have also found that the minimum bisection width, normalized with the edge number, is also significantly larger in the right and the left hemispheres in females: therefore, the differing bisection widths are independent from the difference in the number of edges.
  • Multi-level block permutation.

    Anderson M Winkler, Matthew A Webster, Diego Vidaurre, Thomas E Nichols, Stephen M Smith
    NeuroImage, Jun 16, 2015 PMID: 26074200
    Show Summary
    Under weak and reasonable assumptions, mainly that data are exchangeable under the null hypothesis, permutation tests can provide exact control of false positives and allow the use of various non-standard statistics. There are, however, various common examples in which global exchangeability can be violated, including paired tests, tests that involve repeated measurements, tests in which subjects are relatives (members of pedigrees) - any dataset with known dependence among observations. In these cases, some permutations, if performed, would create data that would not possess the original dependence structure, and thus, should not be used to construct the reference (null) distribution. To allow permutation inference in such cases, we test the null hypothesis using only a subset of all otherwise possible permutations, i.e., using only the rearrangements of the data that respect exchangeability, thus retaining the original joint distribution unaltered. In a previous study, we defined exchangeability for blocks of data, as opposed to each datum individually, then allowing permutations to happen within block, or the blocks as a whole to be permuted. Here we extend that notion to allow blocks to be nested, in a hierarchical, multi-level definition. We do not explicitly model the degree of dependence between observations, only the lack of independence; the dependence is implicitly accounted for by the hierarchy and by the permutation scheme. The strategy is compatible with heteroscedasticity and variance groups, and can be used with permutations, sign flippings, or both combined. We evaluate the method for various dependence structures, apply it to real data from the Human Connectome Project (HCP) as an example application, show that false positives can be avoided in such cases, and provide a software implementation of the proposed approach.
  • Linking contemporary high resolution magnetic resonance imaging to the von Economo legacy: A study on the comparison of MRI cortical thickness and histological measurements of cortical structure.

    Lianne H Scholtens, Marcel A de Reus, Martijn P van den Heuvel
    Human brain mapping, May 20, 2015 PMID: 25988402
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    The cerebral cortex is a distinctive part of the mammalian nervous system, displaying a spatial variety in cyto-, chemico-, and myelinoarchitecture. As part of a rich history of histological findings, pioneering anatomists von Economo and Koskinas provided detailed mappings on the cellular structure of the human cortex, reporting on quantitative aspects of cytoarchitecture of cortical areas. Current day investigations into the structure of human cortex have embraced technological advances in Magnetic Resonance Imaging (MRI) to assess macroscale thickness and organization of the cortical mantle in vivo. However, direct comparisons between current day MRI estimates and the quantitative measurements of early anatomists have been limited. Here, we report on a simple, but nevertheless important cross-analysis between the histological reports of von Economo and Koskinas on variation in thickness of the cortical mantle and MRI derived measurements of cortical thickness. We translated the von Economo cortical atlas to a subdivision of the commonly used Desikan-Killiany atlas (as part of the FreeSurfer Software package and a commonly used parcellation atlas in studies examining MRI cortical thickness). Next, values of "width of the cortical mantle" as provided by the measurements of von Economo and Koskinas were correlated to cortical thickness measurements derived from high-resolution anatomical MRI T1 data of 200+ subjects of the Human Connectome Project (HCP). Cross-correlation revealed a significant association between group-averaged MRI measurements of cortical thickness and histological recordings (r = 0.54, P < 0.001). Further validating such a correlation, we manually segmented the von Economo parcellation atlas on the standardized Colin27 brain dataset and applied the obtained three-dimensional von Economo segmentation atlas to the T1 data of each of the HCP subjects. Highly consistent with our findings for the mapping to the Desikan-Killiany regions, cross-correlation between in vivo MRI cortical thickness and von Economo histology-derived values of cortical mantle width revealed a strong positive association (r = 0.62, P < 0.001). Linking today's state-of-the-art T1-weighted imaging to early histological examinations our findings indicate that MRI technology is a valid method for in vivo assessment of thickness of human cortex.
  • Quantitative mapping of the per-axon diffusion coefficients in brain white matter.

    Enrico Kaden, Frithjof Kruggel, Daniel C Alexander
    Show Summary
    This article presents a simple method for estimating the effective diffusion coefficients parallel and perpendicular to the axons unconfounded by the intravoxel fiber orientation distribution. We also call these parameters the per-axon or microscopic diffusion coefficients.Diffusion MR imaging is used to probe the underlying tissue material. The key observation is that for a fixed b-value the spherical mean of the diffusion signal over the gradient directions does not depend on the axon orientation distribution. By exploiting this invariance property, we propose a simple, fast, and robust estimator of the per-axon diffusion coefficients, which we refer to as the spherical mean technique.We demonstrate quantitative maps of the axon-scale diffusion process, which has factored out the effects due to fiber dispersion and crossing, in human brain white matter. These microscopic diffusion coefficients are estimated in vivo using a widely available off-the-shelf pulse sequence featuring multiple b-shells and high-angular gradient resolution.The estimation of the per-axon diffusion coefficients is essential for the accurate recovery of the fiber orientation distribution. In addition, the spherical mean technique enables us to discriminate microscopic tissue features from fiber dispersion, which potentially improves the sensitivity and/or specificity to various neurological conditions. Magn Reson Med, 2015.
  • Fiber Orientation and Compartment Parameter Estimation from Multi-Shell Diffusion Imaging.

    Giang Tran, Yonggang Shi
    Show Summary
    Diffusion MRI offers the unique opportunity of assessing the structural connections of human brains in vivo.With the advance of diffusion MRI technology, multi-shell imaging methods are becoming increasingly practical for large scale studies and clinical application. In this work, we propose a novel method for the analysis of multi-shell diffusion imaging data by incorporating compartment models into a spherical deconvolution framework for fiber orientation distribution (FOD) reconstruction. For numerical implementation, we develop an adaptively constrained energy minimization approach to efficiently compute the solution. On simulated and real data from Human Connectome Project (HCP), we show that our method not only reconstructs sharp and clean FODs for the modeling of fiber crossings, but also generates reliable estimation of compartment parameters with great potential for clinical research of neurological diseases. In comparisons with publicly available DSI-Studio and BEDPOSTX of FSL, we demonstrate that our method reconstructs sharper FODs with more precise estimation of fiber directions. By applying probabilistic tractography to the FODs computed by our method, we show that more complete reconstruction of the corpus callosum bundle can be achieved. On a clinical, two-shell diffusion imaging data, we also demonstrate the feasibility of our method in analyzing white matter lesions.
  • ConnectomeDB-Sharing human brain connectivity data.

    Michael R Hodge, William Horton, Timothy Brown, Rick Herrick, Timothy Olsen, Michael E Hileman, Michael McKay, Kevin A Archie, Eileen Cler, Michael P Harms, Gregory C Burgess, Matthew F Glasser, Jennifer S Elam, Sandra W Curtiss, Deanna M Barch, Robert Oostenveld, Linda J Larson-Prior, Kamil Ugurbil, David C Van Essen, Daniel S Marcus
    NeuroImage, May 03, 2015 PMID: 25934470
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    ConnectomeDB is a database for housing and disseminating data about human brain structure, function, and connectivity, along with associated behavioral and demographic data. It is the main archive and dissemination platform for data collected under the WU-Minn consortium Human Connectome Project. Additional connectome-style study data is and will be made available in the database under current and future projects, including the Connectome Coordination Facility. The database currently includes multiple modalities of magnetic resonance imaging (MRI) and magnetoencephalograpy (MEG) data along with associated behavioral data. MRI modalities include structural, task, resting state and diffusion. MEG modalities include resting state and task. Imaging data includes unprocessed, minimally preprocessed and analysis data. Imaging data and much of the behavioral data are publicly available, subject to acceptance of data use terms, while access to some sensitive behavioral data is restricted to qualified investigators under a more stringent set of terms. ConnectomeDB is the public side of the WU-Minn HCP database platform. As such, it is geared towards public distribution, with a web-based user interface designed to guide users to the optimal set of data for their needs and a robust backend mechanism based on the commercial Aspera fasp service to enable high speed downloads. HCP data is also available via direct shipment of hard drives and Amazon S3.
  • A symmetric multivariate leakage correction for MEG connectomes.

    G L Colclough, M J Brookes, S M Smith, M W Woolrich
    NeuroImage, Apr 12, 2015 PMID: 25862259
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    Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections.
  • The Budapest Reference Connectome Server v2.0.

    Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince Grolmusz
    Neuroscience letters, Apr 12, 2015 PMID: 25862487
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    The connectomes of different human brains are pairwise distinct: we cannot talk about an abstract "graph of the brain". Two typical connectomes, however, have quite a few common graph edges that may describe the same connections between the same cortical areas. The Budapest Reference Connectome Server v2.0 generates the common edges of the connectomes of 96 distinct cortexes, each with 1015 vertices, computed from 96 MRI data sets of the Human Connectome Project. The user may set numerous parameters for the identification and filtering of common edges, and the graphs are downloadable in both csv and GraphML formats; both formats carry the anatomical annotations of the vertices, generated by the FreeSurfer program. The resulting consensus graph is also automatically visualized in a 3D rotating brain model on the website. The consensus graphs, generated with various parameter settings, can be used as reference connectomes based on different, independent MRI images, therefore they may serve as reduced-error, low-noise, robust graph representations of the human brain. The webserver is available at http://connectome.pitgroup.org.
  • Supervised Dictionary Learning for Inferring Concurrent Brain Networks.

    Shijie Zhao, Junwei Han, Jinglei Lv, Xi Jiang, Xintao Hu, Yu Zhao, Bao Ge, Lei Guo, Tianming Liu
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    Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.
  • Synchronization, non-linear dynamics and low-frequency fluctuations: analogy between spontaneous brain activity and networked single-transistor chaotic oscillators.

    Ludovico Minati, Pietro Chiesa, Davide Tabarelli, Ludovico D'Incerti, Jorge Jovicich
    Chaos (Woodbury, N.Y.), Apr 03, 2015 PMID: 25833429
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    In this paper, the topographical relationship between functional connectivity (intended as inter-regional synchronization), spectral and non-linear dynamical properties across cortical areas of the healthy human brain is considered. Based upon functional MRI acquisitions of spontaneous activity during wakeful idleness, node degree maps are determined by thresholding the temporal correlation coefficient among all voxel pairs. In addition, for individual voxel time-series, the relative amplitude of low-frequency fluctuations and the correlation dimension (D2), determined with respect to Fourier amplitude and value distribution matched surrogate data, are measured. Across cortical areas, high node degree is associated with a shift towards lower frequency activity and, compared to surrogate data, clearer saturation to a lower correlation dimension, suggesting presence of non-linear structure. An attempt to recapitulate this relationship in a network of single-transistor oscillators is made, based on a diffusive ring (n = 90) with added long-distance links defining four extended hub regions. Similarly to the brain data, it is found that oscillators in the hub regions generate signals with larger low-frequency cycle amplitude fluctuations and clearer saturation to a lower correlation dimension compared to surrogates. The effect emerges more markedly close to criticality. The homology observed between the two systems despite profound differences in scale, coupling mechanism and dynamics appears noteworthy. These experimental results motivate further investigation into the heterogeneity of cortical non-linear dynamics in relation to connectivity and underline the ability for small networks of single-transistor oscillators to recreate collective phenomena arising in much more complex biological systems, potentially representing a future platform for modelling disease-related changes.
  • Subcomponents and connectivity of the superior longitudinal fasciculus in the human brain.

    Xuhui Wang, Sudhir Pathak, Lucia Stefaneanu, Fang-Cheng Yeh, Shiting Li, Juan C Fernandez-Miranda
    Brain structure & function, Mar 19, 2015 PMID: 25782434
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    The subcomponents of the human superior longitudinal fasciculus (SLF) are disputed. The objective of this study was to investigate the segments, connectivity and asymmetry of the SLF. We performed high angular diffusion spectrum imaging (DSI) analysis on ten healthy adults. We also conducted fiber tracking on a 30-subject DSI template (CMU-30) and 488-subject template from the Human Connectome Project (HCP-488). In addition, five normal brains obtained at autopsy were microdissected. Based on tractography and microdissection results, we show that the human SLF differs significantly from that of monkey. The fibers corresponding to SLF-I found in 6 out of 20 hemispheres proved to be part of the cingulum fiber system in all cases and confirmed on both DSI and HCP-488 template. The most common patterns of connectivity bilaterally were as follows: from angular gyrus to caudal middle frontal gyrus and dorsal precentral gyrus representing SLF-II (or dorsal SLF), and from supramarginal gyrus to ventral precentral gyrus and pars opercularis to form SLF-III (or ventral SLF). Some connectivity features were, however, clearly asymmetric. Thus, we identified a strong asymmetry of the dorsal SLF (SLF-II), where the connectivity between the supramarginal gyrus with the dorsal precentral gyrus and the caudal middle frontal gyrus was only present in the left hemisphere. Contrarily, the ventral SLF (SLF-III) showed fairly constant connectivity with pars triangularis only in the right hemisphere. The results provide a novel neuroanatomy of the SLF that may help to better understand its functional role in the human brain.
  • Magnetoencephalography in the study of brain dynamics.

    Vittorio Pizzella, Laura Marzetti, Stefania Della Penna, Francesco de Pasquale, Filippo Zappasodi, Gian Luca Romani
    Functional neurology Mar 13, 2015 PMID: 25764254
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    To progress toward understanding of the mechanisms underlying the functional organization of the human brain, either a bottom-up or a top-down approach may be adopted. The former starts from the study of the detailed functioning of a small number of neuronal assemblies, while the latter tries to decode brain functioning by considering the brain as a whole. This review discusses the top-down approach and the use of magnetoencephalography (MEG) to describe global brain properties. The main idea behind this approach is that the concurrence of several areas is required for the brain to instantiate a specific behavior/functioning. A central issue is therefore the study of brain functional connectivity and the concept of brain networks as ensembles of distant brain areas that preferentially exchange information. Importantly, the human brain is a dynamic device, and MEG is ideally suited to investigate phenomena on behaviorally relevant timescales, also offering the possibility of capturing behaviorally-related brain connectivity dynamics.
  • Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data.

    Peter Kochunov, Neda Jahanshad, Daniel Marcus, Anderson Winkler, Emma Sprooten, Thomas E Nichols, Susan N Wright, L Elliot Hong, Binish Patel, Timothy Behrens, Saad Jbabdi, Jesper Andersson, Christophe Lenglet, Essa Yacoub, Steen Moeller, Eddie Auerbach, Kamil Ugurbil, Stamatios N Sotiropoulos, Rachel M Brouwer, Bennett Landman, Hervé Lemaitre, Anouk den Braber, Marcel P Zwiers, Stuart Ritchie, Kimm van Hulzen, Laura Almasy, Joanne Curran, Greig I deZubicaray, Ravi Duggirala, Peter Fox, Nicholas G Martin, Katie L McMahon, Braxton Mitchell, Rene L Olvera, Charles Peterson, John Starr, Jessika Sussmann, Joanna Wardlaw, Margie Wright, Dorret I Boomsma, Rene Kahn, Eco J C de Geus, Douglas E Williamson, Ahmad Hariri, Dennis van 't Ent, Mark E Bastin, Andrew McIntosh, Ian J Deary, Hilleke E Hulshoff Pol, John Blangero, Paul M Thompson, David C Glahn, David C Van Essen
    NeuroImage, Mar 10, 2015 PMID: 25747917
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    The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h(2)=0.53-0.90, p<10(-5)), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application.
  • Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations.

    Shu Zhang, Xiang Li, Jinglei Lv, Xi Jiang, Lei Guo, Tianming Liu
    Brain imaging and behavior, Mar 04, 2015 PMID: 25732072
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    A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. Specifically, in the first stage, the whole-brain tfMRI or rsfMRI signals of each subject were composed into a big data matrix, which was then factorized into a subject-specific dictionary matrix and a weight coefficient matrix for sparse representation. In the second stage, all of the dictionary matrices from both tfMRI/rsfMRI data across multiple subjects were composed into another big data-matrix, which was further sparsely represented by a cross-subjects common dictionary and a weight matrix. This framework has been applied on the recently publicly released Human Connectome Project (HCP) fMRI data and experimental results revealed that there are distinctive and descriptive atoms in the cross-subjects common dictionary that can effectively characterize and differentiate tfMRI and rsfMRI signals, achieving 100 % classification accuracy. Moreover, our methods and results can be meaningfully interpreted, e.g., the well-known default mode network (DMN) activities can be recovered from the very noisy and heterogeneous aggregated big-data of tfMRI and rsfMRI signals across all subjects in HCP Q1 release.
  • White matter lesional predictors of chronic visual neglect: a longitudinal study.

    Marine Lunven, Michel Thiebaut De Schotten, Clémence Bourlon, Christophe Duret, Raffaella Migliaccio, Gilles Rode, Paolo Bartolomeo
    Show Summary
    Chronic visual neglect prevents brain-damaged patients from returning to an independent and active life. Detecting predictors of persistent neglect as early as possible after the stroke is therefore crucial to plan the relevant interventions. Neglect signs do not only depend on focal brain lesions, but also on dysfunction of large-scale brain networks connected by white matter bundles. We explored the relationship between markers of axonal degeneration occurring after the stroke and visual neglect chronicity. A group of 45 patients with unilateral strokes in the right hemisphere underwent cognitive testing for neglect twice, first at the subacute phase (<3 months after onset) and then at the chronic phase (>1 year). For each patient, magnetic resonance imaging including diffusion sequences was performed at least 4 months after the stroke. After masking each patient's lesion, we used tract-based spatial statistics to obtain a voxel-wise statistical analysis of the fractional anisotropy data. Twenty-seven patients had signs of visual neglect at initial testing. Only 10 of these patients had recovered from neglect at follow-up. When compared with patients without neglect, the group including all subacute neglect patients had decreased fractional anisotropy in the second (II) and third (III) branches of the right superior longitudinal fasciculus, as well as in the splenium of the corpus callosum. The subgroup of chronic patients showed reduced fractional anisotropy in a portion the splenium, the forceps major, which provides interhemispheric communication between regions of the occipital lobe and of the superior parietal lobules. The severity of neglect correlated with fractional anisotropy values in superior longitudinal fasciculus II/III for subacute patients and in its caudal portion for chronic patients. Our results confirm a key role of fronto-parietal disconnection in the emergence and chronic persistence of neglect, and demonstrate an implication of caudal interhemispheric disconnection in chronic neglect. Splenial disconnection may prevent fronto-parietal networks in the left hemisphere from resolving the activity imbalance with their right hemisphere counterparts, thus leading to persistent neglect.
  • Large-scale probabilistic functional modes from resting state fMRI.

    Samuel J Harrison, Mark W Woolrich, Emma C Robinson, Matthew F Glasser, Christian F Beckmann, Mark Jenkinson, Stephen M Smith
    NeuroImage, Jan 20, 2015 PMID: 25598050
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    It is well established that it is possible to observe spontaneous, highly structured, fluctuations in human brain activity from functional magnetic resonance imaging (fMRI) when the subject is 'at rest'. However, characterising this activity in an interpretable manner is still a very open problem. In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function. This is presented as a probabilistic generative model within a variational framework that allows Bayesian inference, even on voxelwise rfMRI data. Furthermore, using this approach it becomes possible to infer distinct extended modes that are correlated with each other in space and time, a property which we believe is neuroscientifically desirable. We assess the performance of our model on both simulated data and high quality rfMRI data from the Human Connectome Project, and contrast its properties with those of both spatial and temporal independent component analysis (ICA). We show that our method is able to stably infer sets of modes with complex spatio-temporal interactions and spatial differences between subjects.
  • Brain functional networks extraction based on fMRI artifact removal: Single subject and group approaches.

    Yuhui Du, Elena A Allen, Hao He, Jing Sui, Vince D Calhoun
    Show Summary
    Independent component analysis (ICA) has been widely applied to identify brain functional networks from multiple-subject fMRI. However, the best approach to handle artifacts is not yet clear. In this work, we study and compare two ICA approaches for artifact removal using simulations and real fMRI data. The first approach, recommended by the human connectome project, performs ICA on individual data to remove artifacts, and then applies group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA artifact Removal Plus Group ICA (TRPG). A second approach, Group Information Guided ICA (GIG-ICA), performs ICA on group data, and then removes the artifact group independent components (ICs), followed by individual subject ICA using the remaining group ICs as spatial references. Experiments demonstrate that GIG-ICA is more accurate in estimation of sources and time courses, more robust to data quality and quantity, and more reliable for identifying networks than IRPG.
  • Simultaneous multi-scale diffusion estimation and tractography guided by entropy spectrum pathways.

    Vitaly L Galinsky, Lawrence R Frank
    Show Summary
    We have developed a method for the simultaneous estimation of local diffusion and the global fiber tracts based upon the information entropy flow that computes the maximum entropy trajectories between locations and depends upon the global structure of the multi-dimensional and multi-modal diffusion field. Computation of the entropy spectrum pathways requires only solving a simple eigenvector problem for the probability distribution for which efficient numerical routines exist, and a straight forward integration of the probability conservation through ray tracing of the convective modes guided by a global structure of the entropy spectrum coupled with a small scale local diffusion. The intervoxel diffusion is sampled by multi b-shell multi q-angle diffusion weighted imaging data expanded in spherical waves. This novel approach to fiber tracking incorporates global information about multiple fiber crossings in every individual voxel and ranks it in the most scientifically rigorous way. This method has potential significance for a wide range of applications, including studies of brain connectivity.
  • Group-wise optimization of common brain landmarks with joint structural and functional regulations.

    Dajiang Zhu, Jinglei Lv, Hanbo Chen, Tianming Liu
    Med Image Comput Comput Assist Interv Dec 17, 2014 PMID: 25513575
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    An unrelenting human quest regarding the brain science is: what is the intrinsic relationship between the brain's structural and functional architectures, which partly defines what we are and who we are. Recent studies suggest that each brain's cytoarchitectonic region has a unique set of extrinsic inputs and outputs, named as "connectional fingerprint", which largely determines the functions that each brain area performs. However, their explicit connections are largely unknown. For example, in what extent they are inclined to be coherent with each other and otherwise they will intend to show more heterogeneity? In this work, based on a widely used brain structural atlas which represents the most consistent structural connectome across different populations, we proposed a novel group-wise optimization framework to computationally model the functional homogeneity behind them. The optimization procedure is conducted under the joint structural and functional regulations and therefore the achieved common brain landmarks reflect the consistency of brain structure and function simultaneously. The Human Connectome Project (HCP) Q1 dataset, which includes 68 subjects with high quality imaging data, was used as test bed and the results imply that there exists extraordinary accordance between brain structural and functional architectures.
  • Exploring spatiotemporal network transitions in task functional MRI.

    Gregory Scott, Peter J Hellyer, Adam Hampshire, Robert Leech
    Human brain mapping, Dec 16, 2014 PMID: 25504834
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    A critical question for cognitive neuroscience regards how transitions between cognitive states emerge from the dynamic activity of functional brain networks. Here we combine a simple data reorganization with spatial independent component analysis (ICA), enabling a spatiotemporal ICA (stICA) which captures the consistent evolution of networks during the onset and offset of a task. The technique was applied to functional magnetic resonance imaging (MRI) (FMRI) datasets involving alternating between rest and task, and to simple synthetic data. Starting and finishing time-points of periods of interest (anchors) were defined at task block onsets and offsets. For each subject, the 10 volumes following each anchor were extracted and concatenated spatially, producing a single 3D sample. Samples for all anchors and subjects were concatenated along the fourth dimension. This 4D dataset was decomposed using ICA into spatiotemporal components. One component exhibited the transition with task onset from a default mode network (DMN) becoming less active to a frontoparietal control network becoming more active. We observed other changes with relevance to understanding network dynamics, for example, the DMN showed a changing spatial distribution, shifting to an anterior/superior pattern of deactivation during task from a posterior/inferior pattern during rest. By anchoring analyses to periods associated with the onsets and offsets of task, our approach reveals novel aspects of the dynamics of network activity accompanying these transitions. Importantly, these findings were observed without specifying a priori either the spatial networks or the task time courses.
  • Theoretical and experimental evaluation of multi-band EPI for high-resolution whole brain pCASL Imaging.

    Xiufeng Li, Dingxin Wang, Edward J Auerbach, Steen Moeller, Kamil Ugurbil, Gregory J Metzger
    NeuroImage, Dec 03, 2014 PMID: 25462690
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    Multi-band echo planar imaging (MB-EPI), a new approach to increase data acquisition efficiency and/or temporal resolution, has the potential to overcome critical limitations of standard acquisition strategies for obtaining high-resolution whole brain perfusion imaging using arterial spin labeling (ASL). However, the use of MB also introduces confounding effects, such as spatially varying amplified thermal noise and leakage contamination, which have not been evaluated to date as to their effect on cerebral blood flow (CBF) estimation. In this study, both the potential benefits and confounding effects of MB-EPI were systematically evaluated through both simulation and experimentally using a pseudo-continuous arterial spin labeling (pCASL) strategy. These studies revealed that the amplified noise, given by the geometry factor (g-factor), and the leakage contamination, assessed by the total leakage factor (TLF), have a minimal impact on CBF estimation. Furthermore, it is demonstrated that MB-EPI greatly benefits high-resolution whole brain pCASL studies in terms of improved spatial and temporal signal-to-noise ratio efficiencies, and increases compliance with the assumptions of the commonly used single blood compartment model, resulting in improved CBF estimates.
  • Evaluation of highly accelerated simultaneous multi-slice EPI for fMRI.

    L Chen, A T Vu, J Xu, S Moeller, K Ugurbil, E Yacoub, D A Feinberg
    NeuroImage, Dec 03, 2014 PMID: 25462696
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    Echo planar imaging (EPI) is the MRI technique that is most widely used for blood oxygen level-dependent (BOLD) functional MRI (fMRI). Recent advances in EPI speed have been made possible with simultaneous multi-slice (SMS) methods which combine acceleration factors M from multiband (MB) radiofrequency pulses and S from simultaneous image refocusing (SIR) to acquire a total of N=S×M images in one echo train, providing up to N times speed-up in total acquisition time over conventional EPI. We evaluated accelerations as high as N=48 using different combinations of S and M which allow for whole brain imaging in as little as 100ms at 3T with a 32 channel head coil. The various combinations of acceleration parameters were evaluated by tSNR as well as BOLD contrast-to-noise ratio (CNR) and information content from checkerboard and movie clips in fMRI experiments. We found that at low acceleration factors (N≤6), setting S=1 and varying M alone yielded the best results in all evaluation metrics, while at acceleration N=8 the results were mixed using both S=1 and S=2 sequences. At higher acceleration factors (N>8), using S=2 yielded maximal BOLD CNR and information content as measured by classification of movie clip frames. Importantly, we found significantly greater BOLD information content using relatively fast TRs in the range of 300ms-600ms compared to a TR of 2s, suggesting that faster TRs capture more information per unit time in task based fMRI.
  • Holistic Atlases of Functional Networks and Interactions Reveal Reciprocal Organizational Architecture of Cortical Function.

    Jinglei Lv, Xi Jiang, Xiang Li, Dajiang Zhu, Shu Zhang, Shijie Zhao, Hanbo Chen, Tuo Zhang, Xintao Hu, Junwei Han, Jieping Ye, Lei Guo, Tianming Liu
    Show Summary
    For decades, it has been largely unknown to what extent multiple functional networks spatially overlap/interact with each other and jointly realize the total cortical function. Here, by developing novel sparse representation of whole-brain fMRI signals and by using the recently publicly released large-scale Human Connectome Project (HCP) high-quality fMRI data, we show that a number of reproducible and robust functional networks, including both task-evoked and resting state networks, are simultaneously distributed in distant neuroanatomic areas and substantially spatially overlapping with each other, thus forming an initial collection of holistic atlases of functional networks and interactions (HAFNI). More interestingly, the HAFNIs revealed two distinct patterns of highly overlapped regions and highly-specialized regions and exhibited that these two patterns of areas are reciprocally localized, revealing a novel organizational principle of cortical function.
  • Toward a multisubject analysis of neural connectivity.

    C J Oates, L Costa, T E Nichols
    Neural computation, Nov 08, 2014 PMID: 25380333
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    Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected from multiple subjects whose connectivities may differ but are likely to share many features. In such circumstances, it is natural to leverage similarity among subjects to improve statistical efficiency. The first exact algorithm for estimation of multiple related DAGs was recently proposed by Oates, Smith, Mukherjee, and Cussens ( 2014 ). In this letter we present examples and discuss implications of the methodology as applied to the analysis of fMRI data from a multisubject experiment. Elicitation of tuning parameters requires care, and we illustrate how this may proceed retrospectively based on technical replicate data. In addition to joint learning of subject-specific connectivity, we allow for heterogeneous collections of subjects and simultaneously estimate relationships between the subjects themselves. This letter aims to highlight the potential for exact estimation in the multisubject setting.
  • Normal relationship of the cervicomedullary junction with the obex and olivary bodies: a comparison of cadaveric dissection and in vivo diffusion tensor imaging.

    Erik H Middlebrooks, Kaan Yagmurlu, Jeffrey A Bennett, Sharatchandra Bidari
    Show Summary
    The purpose of our study is to compare cadaver dissections with in vivo diffusion tensor imaging (DTI) to determine the position of the cervicomedullary junction (CMJ) relative to the readily identified anatomic landmarks, namely the obex and olivary bodies (olives), in normal subjects. The information gained from this study would allow further investigation into abnormalities of the CMJ, such as Chiari malformation, without the need for time-intensive tractography studies.Six formalin-fixed human cadaver brains were compared with DTI studies in 15 normal controls. Measurements were made from the upper border of the crossing fibers of the pyramidal decussation to both the obex and the inferior margin of the olive.For the cadaver specimens, the average distance from the inferior border of the olive to the upper border of the decussation measured 3.7 mm (±1.2 mm). The average distance from the obex to the upper decussation was 6.7 mm (±2.1 mm). In the DTI subjects, the inferior olive to the upper decussation averaged 3.4 mm (±0.9 mm). The distance from the obex to the decussation averaged 6.4 mm (±1.3 mm).The CMJ reliably lies 3.4 mm (±0.9 mm) caudal to the inferior border of the olive and 6.4 mm (±1.3 mm) caudal to the obex. Awareness of this anatomic relationship readily allows recognition of abnormalities of the position of the CMJ with routine imaging.
  • Deriving a multi-subject functional-connectivity atlas to inform connectome estimation.

    Ronald Phlypo, Bertrand Thirion, Gaël Varoquaux
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Oct 17, 2014 PMID: 25320798
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    The estimation of functional connectivity structure from functional neuroimaging data is an important step toward understanding the mechanisms of various brain diseases and building relevant biomarkers. Yet, such inferences have to deal with the low signal-to-noise ratio and the paucity of the data. With at our disposal a steadily growing olume of publicly available neuroimaging data, it is however possible to improve the estimation procedures involved in connectome mapping. In this work, we propose a novel learning scheme for functional connectivity based on sparse Gaussian graphical models that aims at minimizing the bias induced by the regularization used in the estimation, by carefully separating the estimation of the model support from the coefficients. Moreover, our strategy makes it possible to include new data with a limited computational cost. We illustrate the physiological relevance of he learned prior, that can be identified as a functional connectivity atlas, based on an experiment on 46 subjects of the Human Connectome Dataset.
  • Image quality transfer via random forest regression: applications in diffusion MRI.

    Daniel C Alexander, Darko Zikic, Jiaying Zhang, Hui Zhang, Antonio Criminisi
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Oct 17, 2014 PMID: 25320803
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    This paper introduces image quality transfer. The aim is to learn the fine structural detail of medical images from high quality data sets acquired with long acquisition times or from bespoke devices and transfer that information to enhance lower quality data sets from standard acquisitions. We propose a framework for solving this problem using random forest regression to relate patches in the low-quality data set to voxel values in the high quality data set. Two examples in diffusion MRI demonstrate the idea. In both cases, we learn from the Human Connectome Project (HCP) data set, which uses an hour of acquisition time per subject, just for diffusion imaging, using custom built scanner hardware and rapid imaging techniques. The first example, super-resolution of diffusion tensor images (DTIs), enhances spatial resolution of standard data sets with information from the high-resolution HCP data. The second, parameter mapping, constructs neurite orientation density and dispersion imaging (NODDI) parameter maps, which usually require specialist data sets with two b-values, from standard single-shell high angular resolution diffusion imaging (HARDI) data sets with b = 1000 smm-2. Experiments quantify the improvement against alternative image reconstructions in comparison to ground truth from the HCP data set in both examples and demonstrate efficacy on a standard data set.
  • Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers.

    Michael Schober, Niklas Kasenburg, Aasa Feragen, Philipp Hennig, Soren Hauberg
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Oct 17, 2014 PMID: 25320808
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    Tractography in diffusion tensor imaging estimates connectivity in the brain through observations of local diffusivity. These observations are noisy and of low resolution and, as a consequence, connections cannot be found with high precision. We use probabilistic numerics to estimate connectivity between regions of interest and contribute a Gaussian Process tractography algorithm which allows for both quantification and visualization of its posterior uncertainty. We use the uncertainty both in visualization of individual tracts as well as in heat maps of tract locations. Finally, we provide a quantitative evaluation of different metrics and algorithms showing that the adjoint metric (8] combined with our algorithm produces paths which agree most often with experts.
  • Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations.

    Evan M Gordon, Timothy O Laumann, Babatunde Adeyemo, Jeremy F Huckins, William M Kelley, Steven E Petersen
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    The cortical surface is organized into a large number of cortical areas; however, these areas have not been comprehensively mapped in the human. Abrupt transitions in resting-state functional connectivity (RSFC) patterns can noninvasively identify locations of putative borders between cortical areas (RSFC-boundary mapping; Cohen et al. 2008). Here we describe a technique for using RSFC-boundary maps to define parcels that represent putative cortical areas. These parcels had highly homogenous RSFC patterns, indicating that they contained one unique RSFC signal; furthermore, the parcels were much more homogenous than a null model matched for parcel size when tested in two separate datasets. Several alternative parcellation schemes were tested this way, and no other parcellation was as homogenous as or had as large a difference compared with its null model. The boundary map-derived parcellation contained parcels that overlapped with architectonic mapping of areas 17, 2, 3, and 4. These parcels had a network structure similar to the known network structure of the brain, and their connectivity patterns were reliable across individual subjects. These observations suggest that RSFC-boundary map-derived parcels provide information about the location and extent of human cortical areas. A parcellation generated using this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html.
  • Brain activity: connectivity, sparsity, and mutual information.

    Ben Cassidy, Caroline Rae, Victor Solo
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    We develop a new approach to functional brain connectivity analysis, which deals with four fundamental aspects of connectivity not previously jointly treated. These are: temporal correlation, spurious spatial correlation, sparsity, and network construction using trajectory (as opposed to marginal) Mutual Information. We call the new method Sparse Conditional Trajectory Mutual Information (SCoTMI). We demonstrate SCoTMI on simulated and real fMRI data, showing that SCoTMI gives more accurate and more repeatable detection of network links than competing network estimation methods.
  • Neuroanatomic and cognitive abnormalities in attention-deficit/hyperactivity disorder in the era of 'high definition' neuroimaging.

    Argelinda Baroni, F Xavier Castellanos
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    The ongoing release of the Human Connectome Project (HCP) data is a watershed event in clinical neuroscience. By attaining a quantum leap in spatial and temporal resolution within the framework of a twin/sibling design, this open science resource provides the basis for delineating brain-behavior relationships across the neuropsychiatric landscape. Here we focus on attention-deficit/hyperactivity disorder (ADHD), which is at least partly continuous across the population, highlighting constructs that have been proposed for ADHD and which are included in the HCP phenotypic battery. We review constructs implicated in ADHD (reward-related processing, inhibition, vigilant attention, reaction time variability, timing and emotional lability) which can be examined in the HCP data and in future 'high definition' clinical datasets.
  • Evaluation and statistical inference for human connectomes.

    Franco Pestilli, Jason D Yeatman, Ariel Rokem, Kendrick N Kay, Brian A Wandell
    Nature methods, Sep 08, 2014 PMID: 25194848
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    Diffusion-weighted imaging coupled with tractography is currently the only method for in vivo mapping of human white-matter fascicles. Tractography takes diffusion measurements as input and produces the connectome, a large collection of white-matter fascicles, as output. We introduce a method to evaluate the evidence supporting connectomes. Linear fascicle evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to quantify the prediction error. We use the prediction error to evaluate the evidence that supports the properties of the connectome, to compare tractography algorithms and to test hypotheses about tracts and connections.
  • Semiautomatic segmentation of brain subcortical structures from high-field MRI.

    Jinyoung Kim, Christophe Lenglet, Yuval Duchin, Guillermo Sapiro, Noam Harel
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    Volumetric segmentation of subcortical structures, such as the basal ganglia and thalamus, is necessary for noninvasive diagnosis and neurosurgery planning. This is a challenging problem due in part to limited boundary information between structures, similar intensity profiles across the different structures, and low contrast data. This paper presents a semiautomatic segmentation system exploiting the superior image quality of ultrahigh field (7 T) MRI. The proposed approach utilizes the complementary edge information in the multiple structural MRI modalities. It combines optimally selected two modalities from susceptibility-weighted, T2-weighted, and diffusion MRI, and introduces a tailored new edge indicator function. In addition to this, we employ prior shape and configuration knowledge of the subcortical structures in order to guide the evolution of geometric active surfaces. Neighboring structures are segmented iteratively, constraining oversegmentation at their borders with a nonoverlapping penalty. Several experiments with data acquired on a 7 T MRI scanner demonstrate the feasibility and power of the approach for the segmentation of basal ganglia components critical for neurosurgery applications such as deep brain stimulation surgery.
  • Effective connectivity during animacy perception--dynamic causal modelling of Human Connectome Project data.

    Hauke Hillebrandt, Karl J Friston, Sarah-Jayne Blakemore
    Scientific reports, Sep 02, 2014 PMID: 25174814
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    Biological agents are the most complex systems humans have to model and predict. In predictive coding, high-level cortical areas inform sensory cortex about incoming sensory signals, a comparison between the predicted and actual sensory feedback is made, and information about unpredicted sensory information is passed forward to higher-level areas. Predictions about animate motion - relative to inanimate motion - should result in prediction error and increase signal passing from lower level sensory area MT+/V5, which is responsive to all motion, to higher-order posterior superior temporal sulcus (pSTS), which is selectively activated by animate motion. We tested this hypothesis by investigating effective connectivity in a large-scale fMRI dataset from the Human Connectome Project. 132 participants viewed animations of triangles that were designed to move in a way that appeared animate (moving intentionally), or inanimate (moving in a mechanical way). We found that forward connectivity from V5 to the pSTS increased, and inhibitory self-connection in the pSTS decreased, when viewing intentional motion versus inanimate motion. These prediction errors associated with animate motion may be the cause for increased attention to animate stimuli found in previous studies.
  • Behavioral relevance of the dynamics of the functional brain connectome.

    Hao Jia, Xiaoping Hu, Gopikrishna Deshpande
    Brain connectivity, Aug 29, 2014 PMID: 25163490
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    Abstract While many previous studies assumed the functional connectivity (FC) between brain regions to be stationary, recent studies have demonstrated that FC dynamically varies across time. However, two challenges have limited the interpretability of dynamic FC information. First, a principled framework for selecting the temporal extent of the window used to examine the dynamics is lacking and this has resulted in ad-hoc selections of window lengths and subsequent divergent results. Second, it is unclear whether there is any behavioral relevance to the dynamics of the functional connectome in addition to that obtained from conventional static FC (SFC). In this work, we address these challenges by first proposing a principled framework for selecting the extent of the temporal windows in a dynamic and data-driven fashion based on statistical tests of the stationarity of time series. Further, we propose a method involving three levels of clustering-across space, time, and subjects-which allow for group-level inferences of the dynamics. Next, using a large resting-state functional magnetic resonance imaging and behavioral dataset from the Human Connectome Project, we demonstrate that metrics derived from dynamic FC can explain more than twice the variance in 75 behaviors across different domains (alertness, cognition, emotion, and personality traits) as compared with SFC in healthy individuals. Further, we found that individuals with brain networks exhibiting greater dynamics performed more favorably in behavioral tasks. This indicates that the ease with which brain regions engage or disengage may provide potential biomarkers for disorders involving altered neural circuitry.
  • Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data.

    Ben Jeurissen, Jacques-Donald Tournier, Thijs Dhollander, Alan Connelly, Jan Sijbers
    NeuroImage, Aug 12, 2014 PMID: 25109526
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    Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates. The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the precision of the fODF fibre orientations and reduce the presence of spurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD.
  • Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI.

    Jian Cheng, Rachid Deriche, Tianzi Jiang, Dinggang Shen, Pew-Thian Yap
    NeuroImage, Aug 10, 2014 PMID: 25108182
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    Spherical Deconvolution (SD) is commonly used for estimating fiber Orientation Distribution Functions (fODFs) from diffusion-weighted signals. Existing SD methods can be classified into two categories: 1) Continuous Representation based SD (CR-SD), where typically Spherical Harmonic (SH) representation is used for convenient analytical solutions, and 2) Discrete Representation based SD (DR-SD), where the signal profile is represented by a discrete set of basis functions uniformly oriented on the unit sphere. A feasible fODF should be non-negative and should integrate to unity throughout the unit sphere S(2). However, to our knowledge, most existing SH-based SD methods enforce non-negativity only on discretized points and not the whole continuum of S(2). Maximum Entropy SD (MESD) and Cartesian Tensor Fiber Orientation Distributions (CT-FOD) are the only SD methods that ensure non-negativity throughout the unit sphere. They are however computational intensive and are susceptible to errors caused by numerical spherical integration. Existing SD methods are also known to overestimate the number of fiber directions, especially in regions with low anisotropy. DR-SD introduces additional error in peak detection owing to the angular discretization of the unit sphere. This paper proposes a SD framework, called Non-Negative SD (NNSD), to overcome all the limitations above. NNSD is significantly less susceptible to the false-positive peaks, uses SH representation for efficient analytical spherical deconvolution, and allows accurate peak detection throughout the whole unit sphere. We further show that NNSD and most existing SD methods can be extended to work on multi-shell data by introducing a three-dimensional fiber response function. We evaluated NNSD in comparison with Constrained SD (CSD), a quadratic programming variant of CSD, MESD, and an L1-norm regularized non-negative least-squares DR-SD. Experiments on synthetic and real single-/multi-shell data indicate that NNSD improves estimation performance in terms of mean difference of angles, peak detection consistency, and anisotropy contrast between isotropic and anisotropic regions.
  • Group-PCA for very large fMRI datasets.

    Stephen M Smith, Aapo Hyvärinen, Gaël Varoquaux, Karla L Miller, Christian F Beckmann
    NeuroImage, Aug 06, 2014 PMID: 25094018
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    Increasingly-large datasets (for example, the resting-state fMRI data from the Human Connectome Project) are demanding analyses that are problematic because of the sheer scale of the aggregate data. We present two approaches for applying group-level PCA; both give a close approximation to the output of PCA applied to full concatenation of all individual datasets, while having very low memory requirements regardless of the number of datasets being combined. Across a range of realistic simulations, we find that in most situations, both methods are more accurate than current popular approaches for analysis of multi-subject resting-state fMRI studies. The group-PCA output can be used to feed into a range of further analyses that are then rendered practical, such as the estimation of group-averaged voxelwise connectivity, group-level parcellation, and group-ICA.
  • Data dictionary services in XNAT and the Human Connectome Project.

    Rick Herrick, Michael McKay, Timothy Olsen, William Horton, Mark Florida, Charles J Moore, Daniel S Marcus
    Show Summary
    The XNAT informatics platform is an open source data management tool used by biomedical imaging researchers around the world. An important feature of XNAT is its highly extensible architecture: users of XNAT can add new data types to the system to capture the imaging and phenotypic data generated in their studies. Until recently, XNAT has had limited capacity to broadcast the meaning of these data extensions to users, other XNAT installations, and other software. We have implemented a data dictionary service for XNAT, which is currently being used on ConnectomeDB, the Human Connectome Project (HCP) public data sharing website. The data dictionary service provides a framework to define key relationships between data elements and structures across the XNAT installation. This includes not just core data representing medical imaging data or subject or patient evaluations, but also taxonomical structures, security relationships, subject groups, and research protocols. The data dictionary allows users to define metadata for data structures and their properties, such as value types (e.g., textual, integers, floats) and valid value templates, ranges, or field lists. The service provides compatibility and integration with other research data management services by enabling easy migration of XNAT data to standards-based formats such as the Resource Description Framework (RDF), JavaScript Object Notation (JSON), and Extensible Markup Language (XML). It also facilitates the conversion of XNAT's native data schema into standard neuroimaging vocabularies and structures.
  • Fast computation of voxel-level brain connectivity maps from resting-state functional MRI using l₁-norm as approximation of Pearson's temporal correlation: proof-of-concept and example vector hardware implementation.

    Ludovico Minati, Domenico Zacà, Ludovico D'Incerti, Jorge Jovicich
    Show Summary
    An outstanding issue in graph-based analysis of resting-state functional MRI is choice of network nodes. Individual consideration of entire brain voxels may represent a less biased approach than parcellating the cortex according to pre-determined atlases, but entails establishing connectedness for 1(9)-1(11) links, with often prohibitive computational cost. Using a representative Human Connectome Project dataset, we show that, following appropriate time-series normalization, it may be possible to accelerate connectivity determination replacing Pearson correlation with l1-norm. Even though the adjacency matrices derived from correlation coefficients and l1-norms are not identical, their similarity is high. Further, we describe and provide in full an example vector hardware implementation of l1-norm on an array of 4096 zero instruction-set processors. Calculation times <1000 s are attainable, removing the major deterrent to voxel-based resting-sate network mapping and revealing fine-grained node degree heterogeneity. L1-norm should be given consideration as a substitute for correlation in very high-density resting-state functional connectivity analyses.
  • Intrinsic and task-evoked network architectures of the human brain.

    Michael W Cole, Danielle S Bassett, Jonathan D Power, Todd S Braver, Steven E Petersen
    Neuron, Jul 04, 2014 PMID: 24991964
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    Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an "intrinsic," standard architecture of functional brain organization. Furthermore, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain's functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity-areas of neuroscientific inquiry typically considered separately.
  • Time-resolved resting-state brain networks.

    Andrew Zalesky, Alex Fornito, Luca Cocchi, Leonardo L Gollo, Michael Breakspear
    Show Summary
    Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.
  • Correspondences between retinotopic areas and myelin maps in human visual cortex.

    Rouhollah O Abdollahi, Hauke Kolster, Matthew F Glasser, Emma C Robinson, Timothy S Coalson, Donna Dierker, Mark Jenkinson, David C Van Essen, Guy A Orban
    NeuroImage, Jun 28, 2014 PMID: 24971513
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    We generated probabilistic area maps and maximum probability maps (MPMs) for a set of 18 retinotopic areas previously mapped in individual subjects (Georgieva et al., 2009 and Kolster et al., 2010) using four different inter-subject registration methods. The best results were obtained using a recently developed multimodal surface matching method. The best set of MPMs had relatively smooth borders between visual areas and group average area sizes that matched the typical size in individual subjects. Comparisons between retinotopic areas and maps of estimated cortical myelin content revealed the following correspondences: (i) areas V1, V2, and V3 are heavily myelinated; (ii) the MT cluster is heavily myelinated, with a peak near the MT/pMSTv border; (iii) a dorsal myelin density peak corresponds to area V3D; (iv) the phPIT cluster is lightly myelinated; and (v) myelin density differs across the four areas of the V3A complex. Comparison of the retinotopic MPM with cytoarchitectonic areas, including those previously mapped to the fs_LR cortical surface atlas, revealed a correspondence between areas V1-3 and hOc1-3, respectively, but little correspondence beyond V3. These results indicate that architectonic and retinotopic areal boundaries are in agreement in some regions, and that retinotopy provides a finer-grained parcellation in other regions. The atlas datasets from this analysis are freely available as a resource for other studies that will benefit from retinotopic and myelin density map landmarks in human visual cortex.
  • Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project.

    Ian M McDonough, Kaoru Nashiro
    Show Summary
    An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity-a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity.
  • MSM: a new flexible framework for Multimodal Surface Matching.

    Emma C Robinson, Saad Jbabdi, Matthew F Glasser, Jesper Andersson, Gregory C Burgess, Michael P Harms, Stephen M Smith, David C Van Essen, Mark Jenkinson
    NeuroImage, Jun 19, 2014 PMID: 24939340
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    Surface-based cortical registration methods that are driven by geometrical features, such as folding, provide sub-optimal alignment of many functional areas due to variable correlation between cortical folding patterns and function. This has led to the proposal of new registration methods using features derived from functional and diffusion imaging. However, as yet there is no consensus over the best set of features for optimal alignment of brain function. In this paper we demonstrate the utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity. The versatility of the framework originates from adapting the discrete Markov Random Field (MRF) registration method to surface alignment. This has the benefit of being very flexible in the choice of a similarity measure and relatively insensitive to local minima. The method offers significant flexibility in the choice of feature set, and we demonstrate the advantages of this by performing registrations using univariate descriptors of surface curvature and myelination, multivariate feature sets derived from resting fMRI, and multimodal descriptors of surface curvature and myelination. We compare the results with two state of the art surface registration methods that use geometric features: FreeSurfer and Spherical Demons. In the future, the MSM technique will allow explorations into the best combinations of features and alignment strategies for inter-subject alignment of cortical functional areas for a wide range of neuroimaging data sets.
  • Toward neurobiological characterization of functional homogeneity in the human cortex: regional variation, morphological association and functional covariance network organization.

    Lili Jiang, Ting Xu, Ye He, Xiao-Hui Hou, Jinhui Wang, Xiao-Yan Cao, Gao-Xia Wei, Zhi Yang, Yong He, Xi-Nian Zuo
    Brain structure & function, Jun 07, 2014 PMID: 24903825
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    Local functional homogeneity of the human cortex indicates the boundaries between functionally heterogeneous regions and varies remarkably across the cortical mantle. It is unclear whether these variations have the neurobiological and structural basis. We employed structural and resting-state functional magnetic resonance imaging scans from 482 healthy subjects and computed the vertex-wise regional homogeneity of low-frequency fluctuations (2dReHo) and five measures of cortical morphology. We then used these metrics to examine regional variation, morphological association and functional covariance network of 2dReHo. Within the ventral visual stream, increases of 2dReHo reflect reduced complexity of information processing or functional hierarchies. Along the divisions of the prefrontal cortex and posteromedial cortex, the gradients of 2dReHo revealed the hierarchical organization within the two association areas, respectively. Individual differences in 2dReHo are associated with those of cortical morphology, and their whole-brain inter-regional covariation is organized into a functional covariance network, comprising five hierarchically organized modules with hubs of both primary sensory and high-order association areas. These highly reproducible observations suggest that local functional homogeneity has neurobiological relevance that is likely determined by anatomical, developmental and neurocognitive factors and should serve as a neuroimaging marker to investigate the human brain function.
  • Study protocol: The Whitehall II imaging sub-study.

    Nicola Filippini, Enikő Zsoldos, Rita Haapakoski, Claire E Sexton, Abda Mahmood, Charlotte L Allan, Anya Topiwala, Vyara Valkanova, Eric J Brunner, Martin J Shipley, Edward Auerbach, Steen Moeller, Kâmil Uğurbil, Junqian Xu, Essa Yacoub, Jesper Andersson, Janine Bijsterbosch, Stuart Clare, Ludovica Griffanti, Aaron T Hess, Mark Jenkinson, Karla L Miller, Gholamreza Salimi-Khorshidi, Stamatios N Sotiropoulos, Natalie L Voets, Stephen M Smith, John R Geddes, Archana Singh-Manoux, Clare E Mackay, Mika Kivimäki, Klaus P Ebmeier
    BMC psychiatry, Jun 03, 2014 PMID: 24885374
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    The Whitehall II (WHII) study of British civil servants provides a unique source of longitudinal data to investigate key factors hypothesized to affect brain health and cognitive ageing. This paper introduces the multi-modal magnetic resonance imaging (MRI) protocol and cognitive assessment designed to investigate brain health in a random sample of 800 members of the WHII study.A total of 6035 civil servants participated in the WHII Phase 11 clinical examination in 2012-2013. A random sample of these participants was included in a sub-study comprising an MRI brain scan, a detailed clinical and cognitive assessment, and collection of blood and buccal mucosal samples for the characterisation of immune function and associated measures. Data collection for this sub-study started in 2012 and will be completed by 2016. The participants, for whom social and health records have been collected since 1985, were between 60-85 years of age at the time the MRI study started. Here, we describe the pre-specified clinical and cognitive assessment protocols, the state-of-the-art MRI sequences and latest pipelines for analyses of this sub-study.The integration of cutting-edge MRI techniques, clinical and cognitive tests in combination with retrospective data on social, behavioural and biological variables during the preceding 25 years from a well-established longitudinal epidemiological study (WHII cohort) will provide a unique opportunity to examine brain structure and function in relation to age-related diseases and the modifiable and non-modifiable factors affecting resilience against and vulnerability to adverse brain changes.
  • Spatial dependencies between large-scale brain networks.

    Robert Leech, Gregory Scott, Robin Carhart-Harris, Federico Turkheimer, Simon D Taylor-Robinson, David J Sharp
    PloS one, Jun 03, 2014 PMID: 24887067
    Show Summary
    Functional neuroimaging reveals both increases (task-positive) and decreases (task-negative) in neural activation with many tasks. Many studies show a temporal relationship between task positive and task negative networks that is important for efficient cognitive functioning. Here we provide evidence for a spatial relationship between task positive and negative networks. There are strong spatial similarities between many reported task negative brain networks, termed the default mode network, which is typically assumed to be a spatially fixed network. However, this is not the case. The spatial structure of the DMN varies depending on what specific task is being performed. We test whether there is a fundamental spatial relationship between task positive and negative networks. Specifically, we hypothesize that the distance between task positive and negative voxels is consistent despite different spatial patterns of activation and deactivation evoked by different cognitive tasks. We show significantly reduced variability in the distance between within-condition task positive and task negative voxels than across-condition distances for four different sensory, motor and cognitive tasks--implying that deactivation patterns are spatially dependent on activation patterns (and vice versa), and that both are modulated by specific task demands. We also show a similar relationship between positively and negatively correlated networks from a third 'rest' dataset, in the absence of a specific task. We propose that this spatial relationship may be the macroscopic analogue of microscopic neuronal organization reported in sensory cortical systems, and that this organization may reflect homeostatic plasticity necessary for efficient brain function.
  • Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective.

    Xi-Nian Zuo, Xiu-Xia Xing
    Show Summary
    Resting-state functional magnetic resonance imaging (RFMRI) enables researchers to monitor fluctuations in the spontaneous brain activities of thousands of regions in the human brain simultaneously, representing a popular tool for macro-scale functional connectomics to characterize normal brain function, mind-brain associations, and the various disorders. However, the test-retest reliability of RFMRI remains largely unknown. We review previously published papers on the test-retest reliability of voxel-wise metrics and conduct a meta-summary reliability analysis of seven common brain networks. This analysis revealed that the heteromodal associative (default, control, and attention) networks were mostly reliable across the seven networks. Regarding examined metrics, independent component analysis with dual regression, local functional homogeneity and functional homotopic connectivity were the three mostly reliable RFMRI metrics. These observations can guide the use of reliable metrics and further improvement of test-retest reliability for other metics in functional connectomics. We discuss the main issues with low reliability related to sub-optimal design and the choice of data processing options. Future research should use large-sample test-retest data to rectify both the within-subject and between-subject variability of RFMRI measurements and accelerate the application of functional connectomics.
  • A high performance 3D cluster-based test of unsmoothed fMRI data.

    Huanjie Li, Lisa D Nickerson, Jinhu Xiong, Qihong Zou, Yang Fan, Yajun Ma, Tingqi Shi, Jianqiao Ge, Jia-Hong Gao
    NeuroImage, May 20, 2014 PMID: 24836011
    Show Summary
    Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity. Recently, a threshold-free cluster enhancement technique was proposed which does not require spatial smoothing, but this method can be used only for group level analysis. Advances in imaging technology now yield high quality high spatial resolution imaging data in single subjects and an inference approach that retains the benefits of greater spatial resolution is called for. In this work, we present a new CST with a correction for voxelation to address this problem. The theoretical formulation of the new approach based on Gaussian random fields is developed to estimate statistical significance using 3D statistical parametric maps without assuming spatial smoothness. Simulated phantom and resting-state fMRI experimental data are then used to compare the voxelation-corrected procedure to the widely used standard random field theory. Unlike standard random field theory approaches, which require heavy spatial smoothing, the new approach has a higher sensitivity for localizing activation regions without the requirement of spatial smoothness.
  • White matter connections of the supplementary motor area in humans.

    Francesco Vergani, Luis Lacerda, Juan Martino, Johannes Attems, Christopher Morris, Patrick Mitchell, Michel Thiebaut de Schotten, Flavio Dell'Acqua
    Show Summary
    The supplementary motor area (SMA) is frequently involved by brain tumours (particularly WHO grade II gliomas). Surgery in this area can be followed by the 'SMA syndrome', characterised by contralateral akinesia and mutism. Knowledge of the connections of the SMA can provide new insights on the genesis of the SMA syndrome, and a better understanding of the challenges related to operating in this region.White matter connections of the SMA were studied with both postmortem dissection and advance diffusion imaging tractography. Postmortem dissections were performed according to the Klingler technique. 12 specimens were fixed in 10% formalin and frozen at -15°C for 2 weeks. After thawing, dissection was performed with blunt dissectors. For diffusion tractography, high-resolution diffusion imaging datasets from 10 adult healthy controls from the Human Connectome Project database were used. Whole brain tractography was performed using a spherical deconvolution approach.Five main connections were identified in both postmortem dissections and tractography reconstructions: (1) U-fibres running in the precentral sulcus, connecting the precentral gyrus and the SMA; (2) U-fibres running in the cingulate sulcus, connecting the SMA with the cingulate gyrus; (3) frontal 'aslant' fascicle, directly connecting the SMA with the pars opercularis of the inferior frontal gyrus; (4) medial fibres connecting the SMA with the striatum; and (5) SMA callosal fibres. Good concordance was observed between postmortem dissections and diffusion tractography.The SMA shows a wide range of white matter connections with motor, language and lymbic areas. Features of the SMA syndrome (akinesia and mutism) can be better understood on the basis of these findings.
  • Magnetic resonance imaging at ultrahigh fields.

    Kamil Ugurbil
    Show Summary
    Since the introduction of 4 T human systems in three academic laboratories circa 1990, rapid progress in imaging and spectroscopy studies in humans at 4 T and animal model systems at 9.4 T have led to the introduction of 7 T and higher magnetic fields for human investigation at about the turn of the century. Work conducted on these platforms has demonstrated the existence of significant advantages in SNR and biological information content at these ultrahigh fields, as well as the presence of numerous challenges. Primary difference from lower fields is the deviation from the near field regime; at the frequencies corresponding to hydrogen resonance conditions at ultrahigh fields, the RF is characterized by attenuated traveling waves in the human body, which leads to image nonuniformities for a given sample-coil configuration because of interferences. These nonuniformities were considered detrimental to the progress of imaging at high field strengths. However, they are advantageous for parallel imaging for signal reception and parallel transmission, two critical technologies that account, to a large extend, for the success of ultrahigh fields. With these technologies, and improvements in instrumentation and imaging methods, ultrahigh fields have provided unprecedented gains in imaging of brain function and anatomy, and started to make inroads into investigation of the human torso and extremities. As extensive as they are, these gains still constitute a prelude to what is to come given the increasingly larger effort committed to ultrahigh field research and development of ever better instrumentation and techniques.
  • ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.

    Ludovica Griffanti, Gholamreza Salimi-Khorshidi, Christian F Beckmann, Edward J Auerbach, Gwenaëlle Douaud, Claire E Sexton, Enikő Zsoldos, Klaus P Ebmeier, Nicola Filippini, Clare E Mackay, Steen Moeller, Junqian Xu, Essa Yacoub, Giuseppe Baselli, Kamil Ugurbil, Karla L Miller, Stephen M Smith
    NeuroImage, Mar 25, 2014 PMID: 24657355
    Show Summary
    The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.
  • Identifying group-wise consistent white matter landmarks via novel fiber shape descriptor.

    Hanbo Chen, Tuo Zhang, Tianming Liu
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Feb 08, 2014 PMID: 24505650
    Show Summary
    Identification of common and corresponding white matter (WM) regions of interest (ROI) across human brains has attracted growing interest because it not only facilitates comparison among individuals and populations, but also enables the assessment of structural/functional connectivity in populations. However, due to the complexity and variability of the WM structure and a lack of effective white matter streamline descriptors, establishing accurate correspondences of WM ROIs across individuals and populations has been a challenging open problem. In this paper, a novel fiber shape descriptor which can facilitate quantitative measurement of fiber bundle profile including connection complexity and similarity has been proposed. A novel framework was then developed using the descriptor to identify group-wise consistent connection hubs in WM regions a s landmarks. 1 2 group-wiseconsistent WMhave been identified in our experiment. These WM landmarks are found highly reproducible across individuals and accurately predictable on new individual subjects by our fiber shape descriptor. Therefore, these landmarks, as well as proposed fiber shape descriptor has shown great potential to human brain mapping.
  • Adaptively constrained convex optimization for accurate fiber orientation estimation with high order spherical harmonics.

    Giang Tran, Yonggang Shi
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Feb 08, 2014 PMID: 24505797
    Show Summary
    Diffusion imaging data from the Human Connectome Project (HCP) provides a great opportunity to map the whole brain white matter connectivity to unprecedented resolution in vivo. In this paper we develop a novel method for accurately reconstruct fiber orientation distribution from cutting-edge diffusion data by solving the spherical deconvolution problem as a constrained convex optimization problem. With a set of adaptively selected constraints, our method allows the use of high order spherical harmonics to reliably resolve crossing fibers with small separation angles. In our experiments, we demonstrate on simulated data that our algorithm outperforms a popular spherical deconvolution method in resolving fiber crossings. We also successfully applied our method to the multi-shell and diffusion spectrum imaging (DSI) data from HCP to demonstrate its ability in using state-of-the-art diffusion data to study complicated fiber structures.
  • Functional connectivity-based parcellation of the human sensorimotor cortex.

    Xiangyu Long, Dominique Goltz, Daniel S Margulies, Till Nierhaus, Arno Villringer
    Show Summary
    Task-based functional magnetic resonance imaging (fMRI) has been successfully employed to obtain somatotopic maps of the human sensorimotor cortex. Here, we showed through direct comparison that a similar functional map can be obtained, independently of a task, by performing a connectivity-based parcellation of the sensorimotor cortex based on resting-state fMRI. Cortex corresponding to two adjacent Brodmann areas (BA 3 and BA 4) was selected as the sensorimotor area. Parcellation was obtained along a medial-lateral axis, which was confirmed to be somatotopic (corresponding roughly to an upper, middle and lower limb, respectively) by comparing it with maps obtained using motoric task-based fMRI in the same participants. Interestingly, the resting-state parcellation map demonstrated higher correspondence to the task-based divisions after individuals performed the motor task. Using the resting-state fMRI data, we also observed higher functional correlations between the centrally located hand region and the other two regions, than between the foot and tongue. The functional relevance of these somatosensory parcellation results indicates the feasibility of a wide range of potential applications to brain mapping.
  • Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG.

    F Chella, L Marzetti, V Pizzella, F Zappasodi, G Nolte
    NeuroImage, Jan 15, 2014 PMID: 24418509
    Show Summary
    We present a novel approach to the third order spectral analysis, commonly called bispectral analysis, of electroencephalographic (EEG) and magnetoencephalographic (MEG) data for studying cross-frequency functional brain connectivity. The main obstacle in estimating functional connectivity from EEG and MEG measurements lies in the signals being a largely unknown mixture of the activities of the underlying brain sources. This often constitutes a severe confounder and heavily affects the detection of brain source interactions. To overcome this problem, we previously developed metrics based on the properties of the imaginary part of coherency. Here, we generalize these properties from the linear to the nonlinear case. Specifically, we propose a metric based on an antisymmetric combination of cross-bispectra, which we demonstrate to be robust to mixing artifacts. Moreover, our metric provides complex-valued quantities that give the opportunity to study phase relationships between brain sources. The effectiveness of the method is first demonstrated on simulated EEG data. The proposed approach shows a reduced sensitivity to mixing artifacts when compared with a traditional bispectral metric. It also exhibits a better performance in extracting phase relationships between sources than the imaginary part of the cross-spectrum for delayed interactions. The method is then applied to real EEG data recorded during resting state. A cross-frequency interaction is observed between brain sources at 10Hz and 20Hz, i.e., for alpha and beta rhythms. This interaction is then projected from signal to source level by using a fit-based procedure. This approach highlights a 10-20Hz dominant interaction localized in an occipito-parieto-central network.
  • Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers.

    Gholamreza Salimi-Khorshidi, Gwenaëlle Douaud, Christian F Beckmann, Matthew F Glasser, Ludovica Griffanti, Stephen M Smith
    NeuroImage, Jan 07, 2014 PMID: 24389422
    Show Summary
    Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL.
  • BOLD Granger causality reflects vascular anatomy.

    J Taylor Webb, Michael A Ferguson, Jared A Nielsen, Jeffrey S Anderson
    PloS one, Dec 19, 2013 PMID: 24349569
    Show Summary
    A number of studies have tried to exploit subtle phase differences in BOLD time series to resolve the order of sequential activation of brain regions, or more generally the ability of signal in one region to predict subsequent signal in another region. More recently, such lag-based measures have been applied to investigate directed functional connectivity, although this application has been controversial. We attempted to use large publicly available datasets (FCON 1000, ADHD 200, Human Connectome Project) to determine whether consistent spatial patterns of Granger Causality are observed in typical fMRI data. For BOLD datasets from 1,240 typically developing subjects ages 7-40, we measured Granger causality between time series for every pair of 7,266 spherical ROIs covering the gray matter and 264 seed ROIs at hubs of the brain's functional network architecture. Granger causality estimates were strongly reproducible for connections in a test and replication sample (n=620 subjects for each group), as well as in data from a single subject scanned repeatedly, both during resting and passive video viewing. The same effect was even stronger in high temporal resolution fMRI data from the Human Connectome Project, and was observed independently in data collected during performance of 7 task paradigms. The spatial distribution of Granger causality reflected vascular anatomy with a progression from Granger causality sources, in Circle of Willis arterial inflow distributions, to sinks, near large venous vascular structures such as dural venous sinuses and at the periphery of the brain. Attempts to resolve BOLD phase differences with Granger causality should consider the possibility of reproducible vascular confounds, a problem that is independent of the known regional variability of the hemodynamic response.
  • Estimation of the CSA-ODF using Bayesian compressed sensing of multi-shell HARDI.

    Julio M Duarte-Carvajalino, Christophe Lenglet, Junqian Xu, Essa Yacoub, Kamil Ugurbil, Steen Moeller, Lawrence Carin, Guillermo Sapiro
    Show Summary
    Diffusion MRI provides important information about the brain white matter structures and has opened new avenues for neuroscience and translational research. However, acquisition time needed for advanced applications can still be a challenge in clinical settings. There is consequently a need to accelerate diffusion MRI acquisitions.A multi-task Bayesian compressive sensing (MT-BCS) framework is proposed to directly estimate the constant solid angle orientation distribution function (CSA-ODF) from under-sampled (i.e., accelerated image acquisition) multi-shell high angular resolution diffusion imaging (HARDI) datasets, and accurately recover HARDI data at higher resolution in q-space. The proposed MT-BCS approach exploits the spatial redundancy of the data by modeling the statistical relationships within groups (clusters) of diffusion signal. This framework also provides uncertainty estimates of the computed CSA-ODF and diffusion signal, directly computed from the compressive measurements. Experiments validating the proposed framework are performed using realistic multi-shell synthetic images and in vivo multi-shell high angular resolution HARDI datasets.Results indicate a practical reduction in the number of required diffusion volumes (q-space samples) by at least a factor of four to estimate the CSA-ODF from multi-shell data.This work presents, for the first time, a multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions. It demonstrates improvement of the quality of acquired datasets by means of CS de-noising, and accurate estimation of the CSA-ODF, as well as enables a reduction in the acquisition time by a factor of two to four, especially when "staggered" q-space sampling schemes are used. The proposed MT-BCS framework can naturally be combined with parallel MR imaging to further accelerate HARDI acquisitions.
  • Functional connectomics from resting-state fMRI.

    Stephen M Smith, Diego Vidaurre, Christian F Beckmann, Matthew F Glasser, Mark Jenkinson, Karla L Miller, Thomas E Nichols, Emma C Robinson, Gholamreza Salimi-Khorshidi, Mark W Woolrich, Deanna M Barch, Kamil Uğurbil, David C Van Essen
    Show Summary
    Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI (rfMRI) for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image-processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project and highlight some upcoming challenges in functional connectomics.
  • Cartography and connectomes.

    David C Van Essen
    Neuron, Nov 05, 2013 PMID: 24183027
    Show Summary
    The past 25 years have seen great progress in parcellating the cerebral cortex into a mosaic of many distinct areas in mice, monkeys, and humans. Quantitative studies of interareal connectivity have revealed unexpectedly many pathways and a wide range of connection strengths in mouse and macaque cortex. In humans, advances in analyzing "structural" and "functional" connectivity using powerful but indirect noninvasive neuroimaging methods are yielding intriguing insights about brain circuits, their variability across individuals, and their relationship to behavior.
  • Methods to detect, characterize, and remove motion artifact in resting state fMRI.

    Jonathan D Power, Anish Mitra, Timothy O Laumann, Abraham Z Snyder, Bradley L Schlaggar, Steven E Petersen
    NeuroImage, Sep 03, 2013 PMID: 23994314
    Show Summary
    Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.
  • Natural scenes viewing alters the dynamics of functional connectivity in the human brain.

    Viviana Betti, Stefania Della Penna, Francesco de Pasquale, Dante Mantini, Laura Marzetti, Gian Luca Romani, Maurizio Corbetta
    Neuron, Aug 21, 2013 PMID: 23891400
    Show Summary
    This study uses magnetoencephalography (MEG) to measure slow (0.1Hz) coherent fluctuations of band-limited power (BLP) during rest and movie observation and compares them to functional MRI (fMRI)-defined resting state networks (RSNs). MEG BLP correlations were measured within/between fMRI-defined RSN to examine whether and how their strength and dynamics were influenced by going from restful fixation to an active task, i.e., watching a movie. In the same subjects, RSN topography was compared at rest and during movie watching using two measures of connectivity: BOLD fMRI connectivity and MEG BLP correlation.

    There were three main findings: first, RSN topography, both MEG and fMRI, did not change when watching a movie as compared to fixation. However, movie watching did cause robust decrements of ongoing resting-state correlation in the α/β frequency BLP within/across multiple networks (the main MEG correlate of fMRI RSNs) and the formation of more focal task-dependent temporal correlation in θ, β, and γ band BLP between networks. Finally, transient (non-stationary) decrements in α BLP correlation in the occipital visual cortex RSN were correlated with “event boundaries” (breaks between discrete actions or segments) in the movie.
  • Evaluation of slice accelerations using multiband echo planar imaging at 3 T.

    Junqian Xu, Steen Moeller, Edward J Auerbach, John Strupp, Stephen M Smith, David A Feinberg, Essa Yacoub, Kâmil Uğurbil
    NeuroImage, Aug 01, 2013 PMID: 23899722
    Show Summary
    We evaluate residual aliasing among simultaneously excited and acquired slices in slice accelerated multiband (MB) echo planar imaging (EPI). No in-plane accelerations were used in order to maximize and evaluate achievable slice acceleration factors at 3 T. We propose a novel leakage (L-) factor to quantify the effects of signal leakage between simultaneously acquired slices. With a standard 32-channel receiver coil at 3 T, we demonstrate that slice acceleration factors of up to eight (MB=8) with blipped controlled aliasing in parallel imaging (CAIPI), in the absence of in-plane accelerations, can be used routinely with acceptable image quality and integrity for whole brain imaging. Spectral analyses of single-shot fMRI time series demonstrate that temporal fluctuations due to both neuronal and physiological sources were distinguishable and comparable up to slice-acceleration factors of nine (MB=9). The increased temporal efficiency could be employed to achieve, within a given acquisition period, higher spatial resolution, increased fMRI statistical power, multiple TEs, faster sampling of temporal events in a resting state fMRI time series, increased sampling of q-space in diffusion imaging, or more quiet time during a scan.
  • An approach for parcellating human cortical areas using resting-state correlations.

    Gagan S Wig, Timothy O Laumann, Steven E Petersen
    NeuroImage, Jul 24, 2013 PMID: 23876247
    Show Summary
    Resting State Functional Connectivity (RSFC) reveals properties related to the brain's underlying organization and function. Features related to RSFC signals, such as the locations where the patterns of RSFC exhibit abrupt transitions, can be used to identify putative boundaries between cortical areas (RSFC-Boundary Mapping). The locations of RSFC-based area boundaries are consistent across independent groups of subjects. RSFC-based parcellation converges with parcellation information from other modalities in many locations, including task-evoked activity and probabilistic estimates of cellular architecture, providing evidence for the ability of RSFC to parcellate brain structures into functionally meaningful units. We not only highlight a collection of these observations, but also point out several limitations and observations that mandate careful consideration in using and interpreting RSFC for the purposes of parcellating the brain's cortical and subcortical structures.
  • Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex.

    B T Thomas Yeo, Fenna M Krienen, Michael W L Chee, Randy L Buckner
    NeuroImage, Jun 03, 2013 PMID: 24185018
    Show Summary
    The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP.
  • Human Connectome Project informatics: quality control, database services, and data visualization.

    Daniel S Marcus, Michael P Harms, Abraham Z Snyder, Mark Jenkinson, J Anthony Wilson, Matthew F Glasser, Deanna M Barch, Kevin A Archie, Gregory C Burgess, Mohana Ramaratnam, Michael Hodge, William Horton, Rick Herrick, Timothy Olsen, Michael McKay, Matthew House, Michael Hileman, Erin Reid, John Harwell, Timothy Coalson, Jon Schindler, Jennifer S Elam, Sandra W Curtiss, David C Van Essen, WU-Minn HCP Consortium
    NeuroImage, May 28, 2013 PMID: 23707591
    Show Summary
    The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.
  • Genetics of the connectome.

    Paul M Thompson, Tian Ge, David C Glahn, Neda Jahanshad, Thomas E Nichols
    NeuroImage, May 28, 2013 PMID: 23707675
    Show Summary
    Connectome genetics attempts to discover how genetic factors affect brain connectivity. Here we review a variety of genetic analysis methods--such as genome-wide association studies (GWAS), linkage and candidate gene studies--that have been fruitfully adapted to imaging data to implicate specific variants in the genome for brain-related traits. Studies that emphasized the genetic influences on brain connectivity. Some of these analyses of brain integrity and connectivity using diffusion MRI, and others have mapped genetic effects on functional networks using resting state functional MRI. Connectome-wide genome-wide scans have also been conducted, and we review the multivariate methods required to handle the extremely high dimension of the genomic and network data. We also review some consortium efforts, such as ENIGMA, that offer the power to detect robust common genetic associations using phenotypic harmonization procedures and meta-analysis. Current work on connectome genetics is advancing on many fronts and promises to shed light on how disease risk genes affect the brain. It is already discovering new genetic loci and even entire genetic networks that affect brain organization and connectivity.
  • Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs.

    Moisés Hernández, Ginés D Guerrero, José M Cecilia, José M García, Alberto Inuggi, Saad Jbabdi, Timothy E J Behrens, Stamatios N Sotiropoulos
    PloS one, May 10, 2013 PMID: 23658616
    Show Summary
    With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation.
  • In vivo architectonics: a cortico-centric perspective.

    David C Van Essen, Matthew F Glasser
    NeuroImage, May 08, 2013 PMID: 23648963
    Show Summary
    Recent advances in noninvasive structural imaging have opened up new approaches to cortical parcellation, many of which are described in this special issue on In Vivo Brodmann Mapping. In this introductory article, we focus on the emergence of cortical myelin maps as a valuable way to assess cortical organization in humans and nonhuman primates. We demonstrate how myelin maps are useful in three general domains: (i) as a way to identify cortical areas and functionally specialized regions in individuals and group averages; (ii) as a substrate for improved intersubject registration; and (iii) as a basis for interspecies comparisons. We also discuss how myelin-based cortical parcellation is complementary in important ways to connectivity-based parcellation using functional MRI or diffusion imaging and tractography. These observations and perspectives provide a useful background and context for other articles in this special issue.
  • Design of multishell sampling schemes with uniform coverage in diffusion MRI.

    Emmanuel Caruyer, Christophe Lenglet, Guillermo Sapiro, Rachid Deriche
    Show Summary
    In diffusion MRI, a technique known as diffusion spectrum imaging reconstructs the propagator with a discrete Fourier transform, from a Cartesian sampling of the diffusion signal. Alternatively, it is possible to directly reconstruct the orientation distribution function in q-ball imaging, providing so-called high angular resolution diffusion imaging. In between these two techniques, acquisitions on several spheres in q-space offer an interesting trade-off between the angular resolution and the radial information gathered in diffusion MRI. A careful design is central in the success of multishell acquisition and reconstruction techniques.The design of acquisition in multishell is still an open and active field of research, however. In this work, we provide a general method to design multishell acquisition with uniform angular coverage. This method is based on a generalization of electrostatic repulsion to multishell.We evaluate the impact of our method using simulations, on the angular resolution in one and two bundles of fiber configurations. Compared to more commonly used radial sampling, we show that our method improves the angular resolution, as well as fiber crossing discrimination.We propose a novel method to design sampling schemes with optimal angular coverage and show the positive impact on angular resolution in diffusion MRI.
  • Trends and properties of human cerebral cortex: correlations with cortical myelin content.

    Matthew F Glasser, Manu S Goyal, Todd M Preuss, Marcus E Raichle, David C Van Essen
    NeuroImage, Apr 10, 2013 PMID: 23567887
    Show Summary
    "In vivo Brodmann mapping" or non-invasive cortical parcellation using MRI, especially by measuring cortical myelination, has recently become a popular research topic, though myeloarchitectonic cortical parcellation in humans previously languished in favor of cytoarchitecture. We review recent in vivo myelin mapping studies and discuss some of the different methods for estimating myelin content. We discuss some ways in which myelin maps may improve surface registration and be useful for cross-modal and cross-species comparisons, including some preliminary cross-species results. Next, we consider neurobiological aspects of why some parts of cortex are more myelinated than others. Myelin content is inversely correlated with intracortical circuit complexity - in general, more myelin content means simpler and perhaps less dynamic intracortical circuits. Using existing PET data and functional network parcellations, we examine metabolic differences in the differently myelinated cortical functional networks. Lightly myelinated cognitive association networks tend to have higher aerobic glycolysis than heavily myelinated early sensory-motor ones, perhaps reflecting greater ongoing dynamic anabolic cortical processes. This finding is consistent with the hypothesis that intracortical myelination may stabilize intracortical circuits and inhibit synaptic plasticity. Finally, we discuss the future of the in vivo myeloarchitectural field and cortical parcellation--"in vivo Brodmann mapping"--in general.
  • Spatially constrained hierarchical parcellation of the brain with resting-state fMRI.

    Thomas Blumensath, Saad Jbabdi, Matthew F Glasser, David C Van Essen, Kamil Ugurbil, Timothy E J Behrens, Stephen M Smith
    NeuroImage, Mar 26, 2013 PMID: 23523803
    Show Summary
    We propose a novel computational strategy to partition the cerebral cortex into disjoint, spatially contiguous and functionally homogeneous parcels. The approach exploits spatial dependency in the fluctuations observed with functional Magnetic Resonance Imaging (fMRI) during rest. Single subject parcellations are derived in a two stage procedure in which a set of (~1000 to 5000) stable seeds is grown into an initial detailed parcellation. This parcellation is then further clustered using a hierarchical approach that enforces spatial contiguity of the parcels. A major challenge is the objective evaluation and comparison of different parcellation strategies; here, we use a range of different measures. Our single subject approach allows a subject-specific parcellation of the cortex, which shows high scan-to-scan reproducibility and whose borders delineate clear changes in functional connectivity. Another important measure, on which our approach performs well, is the overlap of parcels with task fMRI derived clusters. Connectivity-derived parcellation borders are less well matched to borders derived from cortical myelination and from cytoarchitectonic atlases, but this may reflect inherent differences in the data.
  • The WU-Minn Human Connectome Project: an overview.

    David C Van Essen, Stephen M Smith, Deanna M Barch, Timothy E J Behrens, Essa Yacoub, Kamil Ugurbil, WU-Minn HCP Consortium
    NeuroImage, Mar 18, 2013 PMID: 23684880
    Show Summary
    This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
  • Multiband accelerated spin-echo echo planar imaging with reduced peak RF power using time-shifted RF pulses.

    Edward J Auerbach, Junqian Xu, Essa Yacoub, Steen Moeller, Kâmil Uğurbil
    Show Summary
    To evaluate an alternative method for generating multibanded radiofrequency (RF) pulses for use in multiband slice-accelerated imaging with slice-GRAPPA unaliasing, substantially reducing the required peak power without bandwidth compromises. This allows much higher accelerations for spin-echo methods such as SE-fMRI and diffusion-weighted MRI where multibanded slice acceleration has been limited by available peak power.Multibanded "time-shifted" RF pulses were generated by inserting temporal shifts between the applications of RF energy for individual bands, avoiding worst-case constructive interferences. Slice profiles and images in phantoms and human subjects were acquired at 3 T.For typical sinc pulses, time-shifted multibanded RF pulses were generated with little increase in required peak power compared to single-banded pulses. Slice profile quality was improved by allowing for higher pulse bandwidths, and image quality was improved by allowing for optimum flip angles to be achieved.A simple approach has been demonstrated that significantly alleviates the restrictions imposed on achievable slice acceleration factors in multiband spin-echo imaging due to the power requirements of multibanded RF pulses. This solution will allow for increased accelerations in diffusion-weighted MRI applications where data acquisition times are normally very long and the ability to accelerate is extremely valuable.
  • Parcellating an individual subject's cortical and subcortical brain structures using snowball sampling of resting-state correlations.

    Gagan S Wig, Timothy O Laumann, Alexander L Cohen, Jonathan D Power, Steven M Nelson, Matthew F Glasser, Francis M Miezin, Abraham Z Snyder, Bradley L Schlaggar, Steven E Petersen
    Show Summary
    A key part of the HCP effort is defining regions of the brain that act together to perform its many functions. Much like the lots of land that make up a map of a neighborhood, we call these brain regions “parcels”, and the overall division of the brain a “parcellation map”.

    This study describes a new way to divide, or parcellate, the brain using BOLD functional correlations from data collected from subjects at rest (resting state functional correlations, or RSFC) called “snowball sampling” and compares its performance with other parcellation methods. The “Snowball sampling” method (RSFC-Snowballing), here being applied to define brain networks, is based on a concept developed by social network science that describes how shared relationships (e.g. a shared interest, such as tennis) can be used to define a network (e.g. groups of people in a community that play tennis).

    RSFC-Snowballing is an iterative method to map functional correlations starting from a single “seed” location on the brain surface. Locations that share functional connectivity with the starting seed are then used as seeds themselves, and so on in consecutive rounds, to further map and build a functional connectivity network. When the results of RSFC-Snowballing from many seeds scattered throughout the brain are put together, hotspots, or peaks, emerge that indicate brain area centers.
  • Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project.

    Kamil Ugurbil, Junqian Xu, Edward J Auerbach, Steen Moeller, An T Vu, Julio M Duarte-Carvajalino, Christophe Lenglet, Xiaoping Wu, Sebastian Schmitter, Pierre Francois Van de Moortele, John Strupp, Guillermo Sapiro, Federico De Martino, Dingxin Wang, Noam Harel, Michael Garwood, Liyong Chen, David A Feinberg, Stephen M Smith, Karla L Miller, Stamatios N Sotiropoulos, Saad Jbabdi, Jesper L R Andersson, Timothy E J Behrens, Matthew F Glasser, David C Van Essen, Essa Yacoub, WU-Minn HCP Consortium
    NeuroImage, Mar 07, 2013 PMID: 23702417
    Show Summary
    This article describes technical improvements and optimization of resting state functional MR imaging (rfMRI), diffusion imaging (dMRI), and task based fMRI (tfMRI) as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 T, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 s for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.
  • Resting-state fMRI in the Human Connectome Project.

    Stephen M Smith, Christian F Beckmann, Jesper Andersson, Edward J Auerbach, Janine Bijsterbosch, Gwenaëlle Douaud, Eugene Duff, David A Feinberg, Ludovica Griffanti, Michael P Harms, Michael Kelly, Timothy Laumann, Karla L Miller, Steen Moeller, Steve Petersen, Jonathan Power, Gholamreza Salimi-Khorshidi, Abraham Z Snyder, An T Vu, Mark W Woolrich, Junqian Xu, Essa Yacoub, Kamil Uğurbil, David C Van Essen, Matthew F Glasser, WU-Minn HCP Consortium
    NeuroImage, Mar 02, 2013 PMID: 23702415
    Show Summary
    A key objective of the HCP is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects). In this paper we outline the work behind, and rationale for, decisions taken regarding the HCP rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.
  • The minimal preprocessing pipelines for the Human Connectome Project.

    Matthew F Glasser, Stamatios N Sotiropoulos, J Anthony Wilson, Timothy S Coalson, Bruce Fischl, Jesper L Andersson, Junqian Xu, Saad Jbabdi, Matthew Webster, Jonathan R Polimeni, David C Van Essen, Mark Jenkinson, WU-Minn HCP Consortium
    NeuroImage, Feb 26, 2013 PMID: 23668970
    Show Summary
    This article describes the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. Finally, we discuss some potential future improvements to the pipelines.
  • Adding dynamics to the Human Connectome Project with MEG.

    L J Larson-Prior, R Oostenveld, S Della Penna, G Michalareas, F Prior, A Babajani-Feremi, J-M Schoffelen, L Marzetti, F de Pasquale, F Di Pompeo, J Stout, M Woolrich, Q Luo, R Bucholz, P Fries, V Pizzella, G L Romani, M Corbetta, A Z Snyder,
    NeuroImage, Feb 25, 2013 PMID: 23702419
    Show Summary
    Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP. High quality MEG data will be collected from 50 twin pairs both in the resting state and during performance of motor, working memory and language tasks. Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency.
  • Function in the human connectome: task-fMRI and individual differences in behavior.

    Deanna M Barch, Gregory C Burgess, Michael P Harms, Steven E Petersen, Bradley L Schlaggar, Maurizio Corbetta, Matthew F Glasser, Sandra Curtiss, Sachin Dixit, Cindy Feldt, Dan Nolan, Edward Bryant, Tucker Hartley, Owen Footer, James M Bjork, Russ Poldrack, Steve Smith, Heidi Johansen-Berg, Abraham Z Snyder, David C Van Essen, WU-Minn HCP Consortium
    NeuroImage, Feb 18, 2013 PMID: 23684877
    Show Summary
    The HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels.
  • Human and monkey ventral prefrontal fibers use the same organizational principles to reach their targets: tracing versus tractography.

    Saad Jbabdi, Julia F Lehman, Suzanne N Haber, Timothy E Behrens
    Show Summary
    This unique study combines exquisite data from macaque chemical tract tracing with diffusion MRI data from both macaques and humans. This study answers two questions that are central to the Human Connectome effort. First, can detailed connectional organization be measured precisely with diffusion imaging? Second, to what extent do the precise organizational principles, derived in the macaque monkey, translate into the human?

    This study is unusual for several reasons. First, the chemical tracer data afford comparisons of exquisite detail, because the precise trajectory of each connection in the macaque has been mapped. Second, the diffusion data afford direct comparison between species, in white matter pathways where ground truth is known. Third, we studied ventral prefrontal cortex connections, a brain region where white matter pathways are both extremely complex, and highly evolved from macaque to human. Successes both for tractography and for macaque-predictions in this region are likely to generalize to less complex regions of white matter that are more conserved in evolution.

    We suggest that this cross-technique, cross-species approach to brain connectomics has great power for future studies. The chemical tracer data provides firm grounding for understanding the diffusion imaging results, and for suggesting relevant and testable predictions in the human data.
  • Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE.

    S N Sotiropoulos, S Moeller, S Jbabdi, J Xu, J L Andersson, E J Auerbach, E Yacoub, D Feinberg, K Setsompop, L L Wald, T E J Behrens, K Ugurbil, C Lenglet
    Show Summary
    Multichannel receiver coils are used in MRI to speed up data collection and reduce noise relative to signal in MR images. However, the use of multichannel MRI requires methods to combine information from the different channels to “reconstruct” images. The image reconstruction method that is used to combine the signal from the different coils can raise the amount of noise (the “noise floor”) and dramatically change the amount of true signal that is left in the resulting images. This is particularly problematic for diffusion-weighted MRI (dMRI), where any elevation in the noise floor limits the ability to quantify the true signal attenuation (due to limits on diffusion by the brain’s structure, see Components of the HCP: tractography).

    This study explores the impact of image reconstruction methods on the estimation of fiber orientations in dMRI. We utilize, as an alternative to the traditionally-used root-sum-of-squares (RSoS) method, a sensitivity encoding (SENSE) image reconstruction for multichannel MRI data, aimed particularly for diffusion-weighted data we call SENSE1. We compare the performance of the RSoS and SENSE1 reconstruction methods on the same k-space data for the purpose of fiber orientation mapping at various b-values, both for model-free and model-based approaches. We illustrate the artifacts caused by the RSoS elevated noise floor and demonstrate the advantages of the SENSE1 approach. These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multichannel receiver coils for diffusion MRI acquisition.
  • Advances in diffusion MRI acquisition and processing in the Human Connectome Project.

    Stamatios N Sotiropoulos, Saad Jbabdi, Junqian Xu, Jesper L Andersson, Steen Moeller, Edward J Auerbach, Matthew F Glasser, Moises Hernandez, Guillermo Sapiro, Mark Jenkinson, David A Feinberg, Essa Yacoub, Christophe Lenglet, David C Van Essen, Kamil Ugurbil, Timothy E J Behrens, WU-Minn HCP Consortium
    NeuroImage, Jan 27, 2013 PMID: 23702418
    Show Summary
    In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the HCP. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects.
  • RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI.

    Stamatios N Sotiropoulos, Saad Jbabdi, Jesper L Andersson, Mark W Woolrich, Kamil Ugurbil, Timothy E J Behrens
    Show Summary
    When choosing a spatial resolution for collection in magnetic resonance imaging (MRI), one must balance the desire for a high signal to noise ratio (SNR) and that for highly detailed images, a.k.a. high spatial specificity. In diffusion-weighted MRI (dMRI), high SNR imaging at lower resolution allows researchers to estimate white matter fiber microstructure with greater accuracy. Images of lower resolution have higher SNR, but the larger voxel size blurs the detail available at higher resolutions.

    This study presents a new approach that combines data from both high and low spatial resolution dMRI into a single model to estimate underlying fiber patterns at the highest available resolution. The RubiX (Resolutions Unified for Bayesian Inference of Crossings) generative model represents a data-fusion framework, where data from all resolutions are combined through a spatial and a local model. In simulations and in vivo human brain data, we show RubiX can estimate crossing patterns more accurately and with less uncertainty compared to a ball-and-stick model that utilizes only high-resolution data, matched for image acquisition time with the multi-resolution protocol. For the in vivo data, RubiX estimates agree more with our prior anatomical knowledge for regions such as the centrum semiovale and the pons. The current study illustrates the value of spending some of the acquisition time for collecting data at a lower (than desired) spatial resolution, rather than collecting more, but noisier data only at high resolution.
  • Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure.

    L Marzetti, S Della Penna, A Z Snyder, V Pizzella, G Nolte, F de Pasquale, G L Romani, M Corbetta
    NeuroImage, Oct 31, 2012 PMID: 23631996
    Show Summary
    Resting state networks (RSNs) are sets of brain regions exhibiting temporally coherent activity fluctuations in the absence of imposed task structure. RSNs have been extensively studied with fMRI in the infra-slow frequency range (nominally < 10−1 Hz). The topography of fMRI RSNs reflects stationary temporal correlation over minutes. However, neuronal communication occurs on a much faster time scale, at frequencies nominally in the range of 100–102 Hz.

    The authors examined phase-shifted interactions in the delta (2–3.5 Hz), theta (4–7 Hz), alpha (8–12 Hz) and beta (13–30 Hz) frequency bands of resting-state source space MEG signals. These analyses were conducted between nodes of the dorsal attention network (DAN), one of the most robust RSNs, and between the DAN and other networks. Phase shifted interactions were mapped by the multivariate interaction measure (MIM), a measure of true interaction constructed from the maximization of imaginary coherency in the virtual channels comprised of voxel signals in source space. Non-zero-phase interactions occurred between homologous left and right hemisphere regions of the DAN in the delta and alpha frequency bands. Even stronger non-zero-phase interactions were detected between networks. Visual regions bilaterally showed phase-shifted interactions in the alpha band with regions of the DAN. Bilateral somatomotor regions interacted with DAN nodes in the beta band.

    These results demonstrate the existence of consistent, frequency specific phase-shifted interactions on a millisecond time scale between cortical regions within RSN as well as across RSNs.
  • Obscuring surface anatomy in volumetric imaging data.

    Mikhail Milchenko, Daniel Marcus
    Neuroinformatics, Sep 13, 2012 PMID: 22968671
    Show Summary
    The identifying or sensitive anatomical features in MR and CT images used in research raise patient privacy concerns when such data are shared. In order to protect human subject privacy, we developed a method of anatomical surface modification and investigated the effects of such modification on image statistics and common neuroimaging processing tools. Common approaches to obscuring facial features typically remove large portions of the voxels. The approach described here focuses on blurring the anatomical surface instead, to avoid impinging on areas of interest and hard edges that can confuse processing tools.
  • The future of the human connectome.

    D C Van Essen, K Ugurbil
    NeuroImage, Aug 15, 2012 PMID: 22245355
    Show Summary
    This review by David Van Essen and Kamil Ugurbil, co-PIs of the Washington University-University of Minnesota Consortium of the Human Connectome Project, provides a forward looking perspective on the prospects and daunting challenges of this extraordinary undertaking.

    An account of the imaging advances leading up to the NIH Request for Applications and early days of the HCP gives the reader perspective on the original ideas and aspirations of the effort. Neurobiological complexities and technical limitations are discussed, ranging from the enormous scale of a comprehensively defined connectome to the challenge of defining standard cortical areas considering the high variability in brain structure among individuals. The authors then detail how each of the challenges is being addressed using advances in imaging and analysis being developed by the members of the consortium and others. The authors end with a vision for what the HCP endeavor will achieve and the new questions about brain structure and function its results will raise for future work.
  • A cortical core for dynamic integration of functional networks in the resting human brain.

    Francesco de Pasquale, Stefania Della Penna, Abraham Z Snyder, Laura Marzetti, Vittorio Pizzella, Gian Luca Romani, Maurizio Corbetta
    Neuron, May 24, 2012 PMID: 22632732
    Show Summary
    Despite our understanding of the segregation of human brain function through methods such as functional magnetic resonance imaging (fMRI), studying dynamic integration of brain functions requires the temporal resolution and wide coverage of electrophysiological methods such as magnetoencephalography (MEG). This paper examines the temporal dynamics of correlations derived from MEG band-limited power (BLP) in healthy subjects at rest, within and across six brain networks defined by previous fMRI studies.

    The authors find that resting-state networks (RSNs) can be recovered from MEG BLP, each exhibiting a unique pattern of periods (temporal epochs) of high and low within-network correlation (or coherence) and different tendencies to participate in between-network interactions (cross-correlation). For example, some RSNs show high coherence in over 50% of the total recording time, while others, including the default mode network (DMN), are strongly coherent only 20-35% of the time.

    Cross-correlation is transient, limited to times in which one of the participating networks is strongly coherent internally while the other is only loosely coherent. Among all RSNs, the DMN exhibited the strongest interaction with other networks. The dorsal attention network (DAN) and the somatomotor network also showed significant cross-network interactions.
  • Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.

    Stamatios N Sotiropoulos, Timothy E J Behrens, Saad Jbabdi
    NeuroImage, Apr 02, 2012 PMID: 22270351
    Show Summary
    Diffusion weighted MRI allows for in vivo reconstruction of the brain’s white matter fiber bundles via tractography approaches. To date, few methods have been developed to model complex fiber geometries, such as fiber kissing, bending, or fanning.

    Building upon their earlier work on the “ball and stick” model developed to define fiber orientations, the authors of this paper propose a new method, called the “ball and rackets” model, to define fiber fanning extent, orientation, and anisotropy (fanning in one direction more than another) in single-shell diffusion data.

    To illustrate the potential of the ball and rackets approach, the paper presents whole-brain spatial maps of the fanning (dispersion) extent using post-mortem macaque data. In addition, several computer simulations were used to determine data acquisition conditions that allow for fiber fanning modeling, separate dispersion due to tissue structure from noise-induced dispersion, and to compare various approaches to extract dispersion-related information. The author’s simulations showed that a higher signal to noise ratio (SNR ≥ 30) in the diffusion data than that needed to estimate crossing fibers (10 ≤ SNR ≤ 30) is necessary to adequately resolve robust fanning patterns.

    Incorporation of fiber fanning information using approaches such as those outlined in this article will improve tractography methods by allowing researchers to distinguish real fiber architecture from noise-induced uncertainty and by increasing the ability to tell the direction (polarity) of a fiber tract, thus increasing both sensitivity and specificity.
  • Temporally-independent functional modes of spontaneous brain activity.

    Stephen M Smith, Karla L Miller, Steen Moeller, Junqian Xu, Edward J Auerbach, Mark W Woolrich, Christian F Beckmann, Mark Jenkinson, Jesper Andersson, Matthew F Glasser, David C Van Essen, David A Feinberg, Essa S Yacoub, Kamil Ugurbil
    Show Summary
    In this report, investigators from the Human Connectome Project (HCP) identify brain networks in a new way, taking advantage of recent improvements in fast FMRI data acquisition achieved by the HCP team. Existing methods of network modelling often only consider the average functional connectivity between regions, but this average is less meaningful for brain regions that are part of overlapping networks.

    One ideally wants a network model that explicitly allows overlap, for example, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. This report identifies functionally distinct networks by virtue of their temporal independence, revealing multiple "temporal functional modes", including several that subdivide the default-mode network. These functionally-distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.
  • Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems.

    Saad Jbabdi, Stamatios N Sotiropoulos, Alexander M Savio, Manuel Graña, Timothy E J Behrens
    Show Summary
    State-of-the art MRI technology, such as pioneered by the HCP consortium, enables us to acquire bleeding-edge diffusion MRI data of unprecedented quality. In particular, we can now acquire high-quality data using varying levels of diffusion sensitization (multiple shells, or b­-values) in relatively short time.

    This multi-shell type of data will be very beneficial for tractography, the reconstruction of white matter pathways from diffusion MRI data. Multiple shells allow for increased sensitivity to white matter orientation (from the outer shells) and increased signal-to-noise (from the inner shells).

    However, current models for tracking brain connections using diffusion MRI are not compatible with multi-shell data, as they do not account for the complex signal behaviour seen in experimental data. If not accounted for, this leads to a significant amount of over-fitting, i.e. creating fictional fiber orientations that can result in artifactual connections in the brain.

    This article proposes a simple extension to the current model that accounts for this complex signal behavior. This model may be helpful for future data acquisition strategies that attempt to combine multiple shells to improve estimates of fiber orientations in white matter and near the cortex.
  • Human connectomics.

    Timothy E J Behrens, Olaf Sporns
    Show Summary
    Macro-connectomics is providing systematic approaches for identifying functional brain subunits and for mapping the connections between them. This article reviews the current state-of-the-art in macro-scale human connectomics and the potential it has to fundamentally advance our understanding of neural processing and the ways in which brain regions interact to produce coherent experiences and behavior. The authors detail techniques used for mapping brain connectivity, the use of connectivity data to delineate functionally specialized regions, the relation of structural to functional connections, and the use of network analysis measures to quantitatively characterize the architecture of the human connectome.
  • Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

    Jonathan D Power, Kelly A Barnes, Abraham Z Snyder, Bradley L Schlaggar, Steven E Petersen
    NeuroImage, Feb 01, 2012 PMID: 22019881
    Show Summary
    This article demonstrates that subject head movement during resting state functional connectivity MRI (rs-fcMRI) scans produces significant changes in the intensity of the BOLD timecourse signal across the brain that cannot be accounted for using most current approaches to motion correction. Instead, the authors propose a process they call data "scrubbing" to identify and remove motion-corrupted frames using two indices of data quality: framewise head displacement and framewise rate of change of BOLD signal across the entire brain. Motion scrubbing the data significantly improves seed correlation maps, generally revealing an increase in medium- to long-range correlations and a decrease in many short-range correlations. The findings strongly suggest the need for many rs-fcMRI results to be critically revisited and for future functional MRI studies to account for this substantial artifact.
  • Comprehensive in vivo mapping of the human basal ganglia and thalamic connectome in individuals using 7T MRI.

    Christophe Lenglet, Aviva Abosch, Essa Yacoub, Federico De Martino, Guillermo Sapiro, Noam Harel
    PloS one, Jan 03, 2012 PMID: 22235267
    Show Summary
    Deep brain stimulation (DBS) surgery targeting various brain nuclei has become standard-of-care for the treatment of movement disorders, such as PD, essential tremor, and dystonia. In interventions such as DBS, a comprehensive, high-resolution, three-dimensional model of a patient's own brain anatomy and connectivity might significantly improve surgical planning and outcome, shedding new light on factors and mechanisms that affect therapeutic results.

    This paper presents a new imaging and computational protocol to build a subject-specific model of the structure and connections of the basal ganglia and thalamus, exploiting the enhanced signal-to-noise ratio, contrast, and resolution attainable by using high-field 7T MRI. The study provides new information regarding (i) subject-specific in-vivo visualization and segmentation of basal ganglia and thalamus, (ii) comprehensive reconstructions of white matter pathways connecting these structures, (iii) quantification of the probability of each pathway, and (iv) identification of subdivisions of the basal ganglia and thalamus based on their anatomical connectivity patterns.

    This work demonstrates new capabilities for studying basal ganglia circuitry, and opens new avenues of investigation into the movement and neuropsychiatric disorders, in individual human subjects.
  • Functional network organization of the human brain.

    Jonathan D Power, Alexander L Cohen, Steven M Nelson, Gagan S Wig, Kelly Anne Barnes, Jessica A Church, Alecia C Vogel, Timothy O Laumann, Fran M Miezin, Bradley L Schlaggar, Steven E Petersen
    Neuron, Nov 17, 2011 PMID: 22099467
    Show Summary
    Real‐world complex systems, such as the functional organization of the brain, may be mathematically modeled as graphs, revealing properties of the system. In this report, the authors study graphs of brain organization in healthy adults using resting state functional connectivity MRI. The brain is a complex network with macroscopic organization at the level of functional areas, but the number and locations of these areas is largely unknown. Here, the authors have developed methods to define brain areas and propose two novel brain-wide graphs: one built by defining a current best set of 264 putative functional areas and the ties between them; the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. By examining multiple network definitions within a single dataset, the authors show how network definition profoundly affects a network’s properties, and therefore the conclusions one would draw from it about the brain.
  • Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases.

    David C Van Essen, Matthew F Glasser, Donna L Dierker, John Harwell, Timothy Coalson
    Show Summary
    In humans, comparison and analysis of neuroimaging results obtained in different individuals is hampered by the dramatic variability in the pattern of cortical convolutions and in the location of cortical areas relative to these folds. Surface-based registration (SBR) offers an attractive general approach for addressing these problems by aligning individuals to a common template brain: an atlas target.

    This report builds upon 2 existing surface-based atlases (FreeSurfer's "fsaverage" and Caret's "PALS-B12" atlases) to generate the "fs_LR" hybrid atlas, which brings the left and right hemispheres into geographic correspondence, and creates registrations between the fs_LR and PALS-B12 atlases to enable migration of data between atlases. The refinements enabled analyses revealing hemispheric similarities and differences in the folding pattern of population-average midthickness surfaces and unexpected hemispheric asymmetries in cortical surface area.

    The authors also map numerous cortical parcellations derived from published studies onto these human atlas surfaces to identify discrepancies and convergence among current parcellation schemes and provide valuable reference data sets for comparison with other studies. The study estimates the total number of human neocortical areas to be ∼150 to 200 areas per hemisphere, which is modestly larger than a recent estimate for the macaque. Finally, the new fs_LR atlas will provide reference surfaces and a registration target for many surface-based analyses to be carried out as part of the Human Connectome Project.
  • Optimal Design of Multiple Q-shells experiments for Diffusion MRI

    Emmanuel Caryer, Jian Cheng, Christophe Lenglet, Guillermo Sapiro, Tianzi Jiang and Rachid Deriche
    CDMRI'11 Proceedings Aug 30, 2011
    Show Summary
    Diffusion MRI (dMRI) utilizes the measurement of Brownian motion of water molecules to obtain information about tissue structure and orientation inside the brain.

    Using dMRI to infer the three dimensional diffusion orientation distribution function (ODF) of water molecules requires the acquisition of many diffusion images sensitized to different orientations in the sampling space. Several high angular resolution diffusion imaging techniques, such as Q-ball imaging, have been used to map the orientation distribution function and more accurately describe complex fiber structure in the brain. Recently, the Q-ball approach to fiber reconstruction has been expanded to a multiple q-shell approach that makes use of the diffusion signal in the whole Fourier space, instead of on a unique sphere, to more fully tease apart complex structures such as crossing white matter fiber bundles. These techniques require intensive sampling and lengthy scan times. Therefore, it is critical to design a dMRI sampling strategy that optimizes the ordering of gradient direction sampling so that, should the acquisition be corrupted or terminated before completion, orientation information can be derived from partial scans.

    This article presents an original method to design multiple Q-shell sampling schemes for diffusion imaging acquisition. The fast method can provide incremental diffusion gradient sampling schemes for any number of total acquisitions, any number of shells, and any number of points per shell. Different sampling strategies were tested for the reconstruction of Spherical Polar Fourier (SPF) coefficients. The authors found an advantage of using separate diffusion directions between shells, instead of reusing the same directions. Also, they found the optimal number of shells to be equal to 3, whatever the diffusion model or number of measurements, when the reconstruction in the SPF basis is truncated at radial order 3.
  • Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI.

    Matthew F Glasser, David C Van Essen
    Show Summary
    Modern neuroimaging methods reveal an enormous amount of information about the functional organization and structural connectivity of human cerebral cortex. However, interpretation of these findings is seriously impeded by inadequacies of existing cortical parcellations. This article describes a new method of mapping cortical areas based on myelin content as revealed by standard T1 and T2-weighted MRI, producing an observer-independent, non-invasive measure of sharp transitions in myelin content across the surface—i.e., putative cortical areal borders.
  • Whole brain high-resolution functional imaging at ultra high magnetic fields: an application to the analysis of resting state networks.

    Federico De Martino, Fabrizio Esposito, Pierre-Francois van de Moortele, Noam Harel, Elia Formisano, Rainer Goebel, Kamil Ugurbil, Essa Yacoub
    NeuroImage, Aug 01, 2011 PMID: 21600293
    Show Summary
    Ultrahigh magnetic fields (7T and above) allow functional imaging with high contrast-to-noise ratios and improved spatial resolution and specificity compared to clinical fields (1.5T and 3T), facilitating the imaging of human brain function down to the columnar and layer levels. This article presents the use of whole-brain high-resolution (1, 1.5 and 2 mm isotropic voxels) resting state fMRI at 7T, obtained with parallel imaging technology and analyzed using a conventional, lower field analysis pipeline, for the reliable extraction of typical resting state brain networks, such as the default-mode network (DMN). The higher resolution data available at 7T results in reduced partial volume effects, permitting separations of detailed spatial features within the DMN patterns as well as a better function to anatomy correspondence.
  • Informatics and data mining tools and strategies for the human connectome project.

    Daniel S Marcus, John Harwell, Timothy Olsen, Michael Hodge, Matthew F Glasser, Fred Prior, Mark Jenkinson, Timothy Laumann, Sandra W Curtiss, David C Van Essen
    Show Summary
    A description of the proposed informatics platform, made up of the ConnectomeDB and the Connectome Workbench, that will handle: 1) storage of primary and processed data, 2) systematic processing and analysis of the data, 3) open access data sharing, and 4) mining and exploration of the data.
  • The Human Connectome Project: a data acquisition perspective.

    D C Van Essen, K Ugurbil, E Auerbach, D Barch, T E J Behrens, R Bucholz, A Chang, L Chen, M Corbetta, S W Curtiss, S Della Penna, D Feinberg, M F Glasser, N Harel, A C Heath, L Larson-Prior, D Marcus, G Michalareas, S Moeller, R Oostenveld, S E Petersen, F Prior, B L Schlaggar, S M Smith, A Z Snyder, J Xu, E Yacoub, WU-Minn HCP Consortium
    NeuroImage, Jun 26, 2011 PMID: 22366334
    Show Summary
    After almost 2 years of methods development, optimization, and pilot data collection, the WU-UMinn HCP consortium is launching the data acquisition Phase II of the project in July 2012. This review summarizes the data acquisition plans for the study of a population of 1200 healthy subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data.

    The authors outline the efforts of the last 2 years to improve and refine data acquisition for the modalities, which include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2- weighted MRI for structural and myelin mapping, and combined magnetoencephalography and electroencephaolography (MEG/EEG). The article ends with a discussion of the current limits of in vivo human imaging in deciphering the human connectome, the value and privacy challenges of sharing data acquired from twin-sibship families, and the importance of coordinating data and methods with other large-scale imaging projects.
  • Weight-conserving characterization of complex functional brain networks.

    Mikail Rubinov, Olaf Sporns
    NeuroImage, Jun 15, 2011 PMID: 21459148
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    Complex functional brain networks are large and extensive networks of nontrivially interacting brain regions that often serve as maps of global brain activity. Current characterizations of complex functional networks are based on methods optimized for simple functional networks and are associated with several methodological problems. This article describes a set of methods to overcome these problems and illustrates their use in resting-state functional magnetic resonance imaging networks from the 1000 Functional Connectomes Project.
  • Concepts and principles in the analysis of brain networks.

    Gagan S Wig, Bradley L Schlaggar, Steven E Petersen
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    An exploration of network modeling concepts and graph theory, and how they apply to the process of mapping the human brain. This article urges readers to understand the assumptions, constraints and principles of both graph theory mathematics and the underlying neurobiology of the brain, to avoid findings that mischaracterize the brain's network structure and function.
  • Challenges and Opportunities in Mining Neuroscience Data

    Huda Akil, Maryann Martone, David Van Essen
    Science, Feb 11, 2011
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    An examination of the proliferation of data-rich neuroscience projects and resources, including the Human Connectome Project and the Neuroscience Information Framework. This article elucidates how data mining is leading to a new breed of research based not on hypotheses explored by individual labs, but on data-intensive discovery performed by the community at large.
  • The human connectome: a complex network.

    Olaf Sporns
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    A review of the application of network concepts and network thinking to the human brain, and what it means for connectome science. This article outlines the study of the brain beyond the anatomical level, and presents a new picture of the human brain that "views cognitive processes as the result of collective and coordinate phenomena unfolding within a complex network." This article explores current and future models and theories of the brain in this light.
  • Multiplexed echo planar imaging for sub-second whole brain fMRI and fast diffusion imaging

    David A Feinberg, Steen Moeller, Stephen M. Smith, Edward Auerbach, Sudhir Ramanna, Matt Glasser, Kamil Ugurbil, Essa Yacoub
    PLoS ONE 5(12): e15710, Dec 20, 2010
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    While Echo Planar Imaging (EPI) takes only a fraction of a second to image one "slice" of the brain, it takes 2–3 seconds to acquire multi-slice whole brain coverage for fMRI, and longer for diffusion imaging.

    A "multiplexed" technique is reported to significantly reduce EPI whole brain scan time, without significantly sacrificing spatial resolution, and while gaining functional sensitivity.
  • Deciphering the human-brain connectome

    Christophe Lenglet, Michael Garwood, Noam Harel, Guillermo Sapiro, Essa Yacoub, David Van Essen, Kamil Ugurbil
    SPIE Newsroom, Dec 07, 2010
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    The Human Connectome Project aims to reveal and understand the complex neural pathways supporting brain function.
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