NIH Blueprint: The Human Connectome Project

HCP 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.

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Natural Scenes Viewing Alters the Dynamics of Functional Connectivity in the Human Brain | Neuron
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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.

Authors: Viviana Betti, Stefania Della Penna, Francesco de Pasquale, Dante Mantini, Laura Marzetti, Gian Luca Romani, and Maurizio Corbetta.

Neuron, 21 Aug 2013, doi: 10.1016/j.neuron.2013.06.022
An approach for parcellating human cortical areas using resting-state correlations | NeuroImage
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Summary: The parcellation of brain areas relies on distinctions related to function, architectonics, connectivity and topography. This paper provides a critical discussion of techniques using Resting State Functional Connectivity (RSFC) to identify boundaries between (and centers of) cortical areas in individuals and across independent groups of subjects. These RSFC parcellation methods are compared and contrasted with task-activation maps, architechtonic divisions, and methods to identify RSFC-defined system boundaries, such as clustering and independent component analysis. The authors highlight the limitations of the RSFC-Boundary Mapping methods and outline evidence to suggest that patterns of RSFC provide confirmatory and complementary information for the purposes of parcellating cortical areas and subcortical divisions of the brain.

Authors: Gagan Wig, Timothy Laumann, and Steven Petersen.

NeuroImage, 19 July 2013, doi: 10.1016/j.neuroimage.2013.07.035
The WU-Minn Human Connectome Project: An overview | NeuroImage
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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.

Authors: David C. Van Essen, Stephen M. Smith, Deanna M. Barch, Timothy E.J. Behrens, Essa Yacoub, and Kamil Ugurbil.

NeuroImage, doi: 10.1016/j.neuroimage.2013.05.041
Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project | NeuroImage
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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.

Authors: 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, and Essa Yacoub.

NeuroImage, doi: 10.1016/j.neuroimage.2013.05.012
The minimal preprocessing pipelines for the Human Connectome Project | NeuroImage
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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.

Authors: 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, and Mark Jenkinson.

NeuroImage, doi: 10.1016/j.neuroimage.2013.04.127
Advances in diffusion MRI acquisition and processing in the Human Connectome Project | NeuroImage
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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.

Authors: 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, and Timothy E.J. Behrens.

NeuroImage, doi: 10.1016/j.neuroimage.2013.05.057
Resting-state fMRI in the Human Connectome Project | NeuroImage
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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.

Authors: 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 Ugurbil, David C. Van Essen, and Matthew F. Glasser.

NeuroImage, doi: 10.1016/j.neuroimage.2013.05.039
Function in the human connectome: Task-fMRI and individual differences in behavior | NeuroImage
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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.

Authors: 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, and David C. Van Essen.

NeuroImage, doi: 10.1016/j.neuroimage.2013.05.033
Adding dynamics to the Human Connectome Project with MEG | NeuroImage
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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.

Authors: 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, and A.Z. Snyder.

NeuroImage, doi: 10.1016/j.neuroimage.2013.05.056
Human Connectome Project informatics: Quality control, database services, and data visualization | NeuroImage
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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. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.

Authors: 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, and David C. Van Essen.

NeuroImage, doi: 10.1016/j.neuroimage.2013.05.077
Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure | NeuroImage
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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.

Authors: L. Marzetti, S. Della Penna, A.Z. Snyder, V. Pizzella, G. Nolte, F. de Pasquale, G.L. Romani, M. Corbetta.

NeuroImage, doi: 10.1016/j.neuroimage.2013.04.062
RubiX: Combining Spatial Resolutions for Bayesian Interference of Crossing Fibres in Diffusion MRI | IEEE Transactions on Medical Imaging
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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.

Authors: Stamatios N. Sotiropoulos, Saad Jbabdi, Jesper L. Andersson, Mark W. Woolrich, Kamil Ugurbil and Timothy E. J. Behrens.

IEEE Trans Med Imaging, doi: 10.1109/TMI.2012.2231873
Parcellating an Individual Subject's Cortical and Subcortical Brain Structures Using Snowball Sampling of Resting-State Correlations | Cerebral Cortex
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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.

For more information on functional correlations and resting state fMRI data see Components of the HCP: Resting State fMRI.

Authors: 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, and Steven E. Petersen.

Cerebral Cortex, doi: 10.1093/cercor/bht056
Effects of Image Reconstruction on Fibre Orientation Mapping from Multichannel Diffusion MRI: Reducing the Noise Floor Using SENSE | Magnetic Resonance in Medicine
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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.

Authors: 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, and C. Lenglet.

Magnetic Resonance Medicine, doi: 10.1002/mrm.24623
Human and Monkey Ventral Prefrontal Fibers Use the Same Organizational Principles to Reach Their Targets: Tracing versus Tractography | J. Neuroscience
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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.

Authors: Saad Jbabdi, Julia F. Lehman, Suzanne N. Haber, and Timothy E. Behrens.

J Neuroscience, doi: 10.1523/​JNEUROSCI.2457-12.2013
Obscuring Surface Anatomy in Volumetric Imaging Data | Neuroinformatics
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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.

Authors: Mikhail Milchenko and Daniel Marcus.

Neuroimage 2012, DOI: 10.1007/s12021-012-9160-3
A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain | Neuron
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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.

Authors: Francesco de Pasquale, Stefania Della Penna, Abraham Z. Snyder, Laura Marzetti, Vittorio Pizzella, Gian Luca Romani, and Maurizio Corbetta.

Neuron, doi: 10.1016/j.neuron.2012.03.031
The Human Connectome Project: A data acquisition perspective | Neuroimage
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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.

Authors: David Van Essen, Kamil Ugurbil, Edward Auerbach, Deanna Barch, Timothy E. J. Behrens, Richard Bucholz, Audrey Chang, Liyong Chen, Maurizio Corbetta, Sandra W. Curtiss, Stefania Della Penna, David Feinberg, Matthew F. Glasser, Noam Harel, Andrew C. Heath, Linda Larson-Prior, Daniel Marcus, George Michalareas, Steen Moeller, Robert Oostenveld, Steven E. Petersen, Fred Prior, Bradley L. Schlaggar, Stephen M. Smith, Abraham Z. Snyder, Junquian Xu, Essa Yacoub and WU-Minn HCP Consortium.

NeuroImage, doi: 10.1016/j.neuroimage.2012.02.018
Model-based analysis of Multishell diffusion MR data for tractography: How to get over fitting problems | Magnetic Resonance in Medicine
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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.

Authors: Saad Jbabdi, Stamatios N. Sotiropoulos, Alexander M. Savio, Manuel Graña, and Timothy E. J. Behrens.

Magnetic Resonance in Medicine, 14 Feb 2012, doi: 10.1002/mrm.24204
Temporally-independent functional modes
of spontaneous brain activity
| PNAS
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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.

Authors: 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, and Kamil Ugurbil.

PNAS, 7 Feb 2012 (online), doi:10.1073/pnas.1121329109
Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI | Neuroimage
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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.

Authors: Stamatios Sotiropoulos, Timothy Behrens, and Saad Jbabdi.

NeuroImage, 14 Jan 2012, doi: 10.1016/j.neuroimage.2012.01.056
The future of the human connectome | Neuroimage
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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.

Authors: David Van Essen and Kamil Ugurbil.

Neuroimage, 10 Jan 2012, doi: 10.1016/j.neuroimage.2012.01.032
Comprehensive in vivo Mapping of the Human Basal Ganglia and Thalamic Connectome in Individuals Using 7T MRI | PLoS One
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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.

Authors: Christophe Lenglet, Aviva Abosch, Essa Yacoub, Federico De Martino, Guillermo Sapiro and Noam Harel.

PLoS One, 3 Jan 2012, doi: 10.1371/journal.pone.0029153
Parcellations and Hemispheric Asymmetries of Human Cerebral Cortex Analyzed on Surface-Based Atlases | Cerebral Cortex
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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.

Authors: David Van Essen, Matthew F. Glasser, Donna L. Dierker, John Harwell and Timothy Coalson.

Cerebral Cortex, 2 Nov 2011, doi: 10.1093/cercor/bhr29
Functional network organization of the human brain | Neuron
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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.

Authors: Jonathan Power, Alexander Cohen, Steven Nelson, Gagan Wig, Kelly Barnes, Jessica Church, Alecia Vogel, Timothy Laumann, Fran Miezin, Bradley Schlaggar, Steven Petersen.

Neuron, 17 Nov 2011, doi 10.1016/j.neuron.2011.09.006
Video Abstract
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion | Neuroimage
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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.

Authors: Jonathan Power, Kelly Barnes, Abraham Snyder, Bradley Schlaggar, Steven Petersen.

NeuroImage, 14 October 2011; doi: 10.1016/j.neuroimage.2011.10.018
Human Connectomics | Current Opinion in Neurobiology
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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.

Authors: Timothy EJ Behrens, Olaf Sporns.

Current Opinion in Neurobiology, 9 September 2011; doi:10.1016/j.conb.2011.08.005
Optimal Design of Multiple Q-shells experiments for Diffusion MRI | CDMRI'11 Proceedings
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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.

Authors: Emmanuel Caryer, Jian Cheng, Christophe Lenglet, Guillermo Sapiro, Tianzi Jiang and Rachid Deriche.

Related: Build and download a sampling scheme for multiple Q-shell diffusion fMRI.

MICCAI Workshop on Computational Diffusion MRI - CDMRI'11
CDMRI'11 Proceedings, 30 Aug 2011
Whole brain high-resolution functional imaging at ultra high magnetic fields: An application to the analysis of resting state networks | Neuroimage
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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.

Authors: Federico De Martino, Fabrizio Esposito, Pierre-Francois van de Moortele, Noam Harel, Elia Formisano, Rainer Goebel, Kamil Ugurbil, Essa Yacoub.

Neuroimage, 1 August 2011; doi:10.1016/j.neuroimage.2011.05.008
Mapping Human Cortical Areas In Vivo Based on Myelin
Content as Revealed by T1- and T2-Weighted MRI
| The Journal of Neuroscience
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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.

Authors: Matthew Glasser, David Van Essen.

The Journal of Neuroscience, 10 August 2011, 31(32): 11597-11616; doi: 10.1523/​JNEUROSCI.2180-11.2011
Informatics and Data Mining Tools and Strategies for the Human Connectome Project | Frontiers in Neuroinformatics
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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.

Authors: Daniel Marcus, John Harwell, Timothy Olsen, Michael Hodge, Matthew Glasser, Fred Prior, Mark Jenkinson, Timothy Laumann, Sandra Curtiss, David Van Essen.

Front Neurosci, 27 Jun 2011; DOI: 10.3389/fninf.2011.00004
Weight-conserving characterization of complex functional brain networks | Neuroimage
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Summary: 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.

Author: Mikail Rubinov, Olaf Sporns.

Neuroimage, 15 Jun 2011; doi:10.1016/j.neuroimage.2011.03.069
Concepts and Principles in the Analysis of Brain Networks | Annals of the New York Academy of Science
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Summary: 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.

Authors: Gagan Wig, Bradley Schlaggar, Steve Petersen.

Ann N Y Acad Sci., 12 Apr 2011; DOI: 0.1111/j.1749-6632.2010.05947.x
The Human Connectome: A Complex Network | Annals of the New York Academy of Science
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Summary: 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.

Author: Olaf Sporns.

4 Jan 2011, Ann N Y Acad Sci.; DOI: 10.1111/j.1749-6632.2010.05888.x
Challenges and Opportunities for Mining Neuroscience Data | Science Magazine
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Summary: 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.

Authors: Huda Akil, Maryann Martone, David Van Essen.

11 February 2011, Science 331, 708 (2011); DOI: 10.1126/science.1199305
Multiplexed echo planar imaging for sub-second whole brain fMRI and fast diffusion imaging | PLoS
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Summary: 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.

Authors: David A Feinberg, Steen Moeller, Stephen M. Smith, Edward Auerbach, Sudhir Ramanna, Matt Glasser, Kamil Ugurbil, Essa Yacoub.

20 December 2010, PLoS ONE 5(12): e15710. DOI:10.1371/journal.pone.0015710
Deciphering the human-brain connectome | SPIE Newsroom
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Summary: The Human Connectome Project aims to reveal and understand the complex neural pathways supporting brain function.

Authors: Christophe Lenglet, Michael Garwood, Noam Harel, Guillermo Sapiro, Essa Yacoub, David Van Essen, Kamil Ugurbil.

7 December 2010, SPIE Newsroom. DOI: 10.1117/2.1201011.003395