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      The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture

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          Abstract

          The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.

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          Most cited references60

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          A whole brain fMRI atlas generated via spatially constrained spectral clustering.

          Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/. Copyright © 2011 Wiley Periodicals, Inc.
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            Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.

            During rest, multiple cortical brain regions are functionally linked forming resting-state networks. This high level of functional connectivity within resting-state networks suggests the existence of direct neuroanatomical connections between these functionally linked brain regions to facilitate the ongoing interregional neuronal communication. White matter tracts are the structural highways of our brain, enabling information to travel quickly from one brain region to another region. In this study, we examined both the functional and structural connections of the human brain in a group of 26 healthy subjects, combining 3 Tesla resting-state functional magnetic resonance imaging time-series with diffusion tensor imaging scans. Nine consistently found functionally linked resting-state networks were retrieved from the resting-state data. The diffusion tensor imaging scans were used to reconstruct the white matter pathways between the functionally linked brain areas of these resting-state networks. Our results show that well-known anatomical white matter tracts interconnect at least eight of the nine commonly found resting-state networks, including the default mode network, the core network, primary motor and visual network, and two lateralized parietal-frontal networks. Our results suggest that the functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.
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              The anatomical basis of functional localization in the cortex.

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                Author and article information

                Journal
                Cereb Cortex
                Cereb. Cortex
                cercor
                cercor
                Cerebral Cortex (New York, NY)
                Oxford University Press
                1047-3211
                1460-2199
                August 2016
                25 July 2016
                25 July 2016
                : 26
                : 8
                : 3508-3526
                Affiliations
                [1 ]Brainnetome Center
                [2 ]National Laboratory of Pattern Recognition and
                [3 ]CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences , Beijing 100190, China
                [4 ]Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China , Chengdu 625014, China
                [5 ]The Queensland Brain Institute, University of Queensland , Brisbane, QLD 4072, Australia
                [6 ]Department of Radiology, Tianjin Medical University General Hospital , Tianjin, China
                [7 ]Research Imaging Institute, University of Texas Health Science Center , San Antonio, TX, USA
                [8 ]Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich , Juelich 52425, Germany
                [9 ]Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine-University Düsseldorf , Düsseldorf 40225, Germany
                [10 ]Department of Physics, Florida International University , Miami, FL, USA
                Author notes
                Address correspondence to Tianzi Jiang, Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. Email: jiangtz@ 123456nlpr.ia.ac.cn

                Lingzhong Fan and Hai Li equally contributed to this work.

                Article
                bhw157
                10.1093/cercor/bhw157
                4961028
                27230218
                983bf290-4205-48b1-9c95-dc4cf4a269cc
                © The Author 2016. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 19
                Funding
                Funded by: National Key Basic Research and Development Program
                Award ID: 2011CB707801
                Award ID: 2012CB720702
                Funded by: Strategic Priority Research Program of the Chinese Academy of Sciences
                Award ID: XDB02030300
                Funded by: Natural Science Foundation of China
                Award ID: 91432302
                Award ID: 91132301
                Award ID: 81501179
                Award ID: 81270020
                Funded by: Deutsche Forschungsgemeinschaft, http://dx.doi.org/10.13039/501100001659;
                Award ID: EI 816/4-1
                Award ID: EI 816/6-1
                Funded by: National Institute of Mental Health, http://dx.doi.org/10.13039/100000025;
                Award ID: R01-MH074457
                Funded by: Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain”
                Funded by: European Union Seventh Framework Programme
                Award ID: FP7/2007-2013
                Award ID: 604102
                Funded by: Open Project Funding of National Key Laboratory of Cognitive Neuroscience and Learning-Beijing Normal University
                Award ID: CNLYB1410
                Categories
                Original Articles

                Neurology
                brain atlas,connectivity-based parcellation,diffusion tensor imaging,functional characterization,resting-state functional connectivity

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