NIH Blueprint: The Human Connectome Project

HCP Consortium Publications

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.

For a list of publications by consortium and external investigators that use released HCP data (post initial HCP Q1 Release, March 2013), please see: Publications using HCP Data.

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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, 2015-01-20 | PMID: 25598050

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.

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, 2014-12-03 | PMID: 25462696

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.

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, 2014-12-03 | PMID: 25462690

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.

Toward a multisubject analysis of neural connectivity.

C J Oates, L Costa, T E Nichols

Neural computation, 2014-11-08 | PMID: 25380333

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.

Semiautomatic segmentation of brain subcortical structures from high-field MRI.

Jinyoung Kim, Christophe Lenglet, Yuval Duchin, Guillermo Sapiro, Noam Harel

IEEE journal of biomedical and health informatics, 2014-09-06 | PMID: 25192576

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.

Group-PCA for very large fMRI datasets.

Stephen M Smith, Aapo Hyvärinen, Gaël Varoquaux, Karla L Miller, Christian F Beckmann

NeuroImage, 2014-08-06 | PMID: 25094018

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

Frontiers in neuroinformatics, 2014-07-30 | PMID: 25071542

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.

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, 2014-07-04 | PMID: 24991964

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.

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, 2014-06-28 | PMID: 24971513

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.

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, 2014-06-19 | PMID: 24939340

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.

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, 2014-06-03 | PMID: 24885374

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.

Magnetic resonance imaging at ultrahigh fields.

Kamil Ugurbil

IEEE transactions on bio-medical engineering, 2014-04-02 | PMID: 24686229

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, 2014-03-25 | PMID: 24657355

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.

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, 2014-01-15 | PMID: 24418509

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, 2014-01-07 | PMID: 24389422

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.

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

Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 2013-12-17 | PMID: 24338816

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

Trends in cognitive sciences, 2013-11-19 | PMID: 24238796

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, 2013-11-05 | PMID: 24183027

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, 2013-09-03 | PMID: 23994314

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, 2013-08-21 | PMID: 23891400

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, 2013-08-01 | PMID: 23899722

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, 2013-07-24 | PMID: 23876247

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.

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, 2013-05-28 | PMID: 23707591

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, 2013-05-28 | PMID: 23707675

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, 2013-05-10 | PMID: 23658616

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, 2013-05-08 | PMID: 23648963

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

Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 2013-04-30 | PMID: 23625329

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, 2013-04-10 | PMID: 23567887

"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, 2013-03-26 | PMID: 23523803

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, 2013-03-18 | PMID: 23684880

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.

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

Cerebral cortex (New York, N.Y. : 1991), 2013-03-08 | PMID: 23476025

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.

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

Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 2013-03-08 | PMID: 23468087

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.

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, 2013-03-07 | PMID: 23702417

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, 2013-03-02 | PMID: 23702415

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, 2013-02-26 | PMID: 23668970

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, 2013-02-25 | PMID: 23702419

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, 2013-02-18 | PMID: 23684877

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

The Journal of neuroscience : the official journal of the Society for Neuroscience, 2013-02-13 | PMID: 23407972

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

Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 2013-02-07 | PMID: 23401137

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, 2013-01-27 | PMID: 23702418

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

IEEE transactions on medical imaging, 2013-01-25 | PMID: 23362247

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, 2012-10-31 | PMID: 23631996

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, 2012-09-13 | PMID: 22968671

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, 2012-08-15 | PMID: 22245355

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, 2012-05-24 | PMID: 22632732

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, 2012-04-02 | PMID: 22270351

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

Proceedings of the National Academy of Sciences of the United States of America, 2012-02-21 | PMID: 22323591

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

Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 2012-02-14 | PMID: 22334356

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

Current opinion in neurobiology, 2012-02-01 | PMID: 21908183

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, 2012-02-01 | PMID: 22019881

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, 2012-01-03 | PMID: 22235267

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, 2011-11-17 | PMID: 22099467

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

Cerebral cortex (New York, N.Y. : 1991), 2011-11-02 | PMID: 22047963

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 2011-08-30

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

The Journal of neuroscience : the official journal of the Society for Neuroscience, 2011-08-10 | PMID: 21832190

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, 2011-08-01 | PMID: 21600293

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

Frontiers in neuroinformatics, 2011-06-27 | PMID: 21743807

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, 2011-06-26 | PMID: 22366334

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, 2011-06-15 | PMID: 21459148

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

Annals of the New York Academy of Sciences, 2011-04-12 | PMID: 21486299

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, 2011-02-11

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

Annals of the New York Academy of Sciences, 2011-01-04 | PMID: 21251014

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, 2010-12-20

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, 2010-12-07

The Human Connectome Project aims to reveal and understand the complex neural pathways supporting brain function.