HCP Young Adult

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    David A Feinberg, Steen Moeller, Stephen M. Smith, Edward Auerbach, Sudhir Ramanna, Matt Glasser, Kamil Ugurbil, Essa Yacoub
    PLoS ONE 5(12): e15710, Dec 20, 2010
    Show 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.
  • Deciphering the human-brain connectome

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