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An open science resource for establishing reliability and reproducibility in functional connectomics

a , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 2 , 13 , 2 , 2 , 11 , 10 , 11 , 14 , 12 , 1 , 15 , 1 , 16 , 17 , 18 , 19 , 1 , 15 , 20 , 11 , 14 , 21 , 1 , 15 , 22 , 23 , 1 , 22 , 12 , 10 , 24 , 5 , 2 , 1 , 17 , 25 , 20 , 13 , 20 , 1 , 2 , 26 , 25 , 27 , 28 , 11 , 14 , 29 , 18 , 10 , 7 , 30 , 1 , 15 , 3 , 11 , 14 , 27 , 31 , 25 , 2 , 32 , 11 , 14 , 33 , 34 , 35 , 1 , 15 , 2 , 1 , 13 , 29 , 1 , 11 , 14 , 1 , 15 , 1 , 13 , 1 , 15 , 2 , 1 , 15 , 26 , 1 , 20 , 1 , 1 , 15 , b , 11 , 14

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      Abstract

      Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

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      Most cited references 78

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      Complex network measures of brain connectivity: uses and interpretations.

      Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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        Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.

        An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
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          Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

          Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Author and article information

            Affiliations
            [1 ] Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China
            [2 ] Faculty of Psychology, Southwest University , Beibei, Chongqing 400715, China
            [3 ] Division of Neuroradiology, University of Utah , Salt Lake City, Utah 84132, USA
            [4 ] Unité de neuroimagerie fonctionnelle, Centre de recherche de l’institut universitaire de gériatrie de Montréal, Université de Montréal , Montreal, Quebec, Canada H3W 1W5
            [5 ] Department of Psychiatry, University of Wisconsin—Madison , Madison, Wisconsin 53719, USA
            [6 ] Department of Biomedical Engineering, New Jersey Institute of Technology , Newark, New Jersey 07102, USA
            [7 ] Institute of Clinical Radiology, Ludwig-Maximilians-University , 80336 Munich, Germany
            [8 ] Centre for Studies on Prevention of Alzheimer’s Disease, Department of Psychiatry, Douglas Institute, McGill University Faculty of Medicine , Montreal, Quebec, Canada H4H 1R3
            [9 ] Department of Psychology, Harvard University , Cambridge, Massachussetts 02138, USA
            [10 ] Mind Research Network , Albuquerque, New Mexico 87106, USA
            [11 ] Nathan S. Kline Institute for Psychiatric Research , Orangeburg, New York 10962, USA
            [12 ] Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, the Child Study Center at NYU Langone Medical Center , New York, New York 10016, USA
            [13 ] Center for Cognition and Brain Disorders, Hangzhou Normal University, Gongshu , Hangzhou, Zhejiang 311121, China
            [14 ] Center for the Developing Brain, Child Mind Institute , New York, New York 10022, USA
            [15 ] University of Chinese Academy of Sciences , Shijingshan, Beijing 100049, China
            [16 ] State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China
            [17 ] Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Wuhou , Chengdu, Sichuan 610041, China
            [18 ] Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
            [19 ] Department of Neurology, Xuanwu Hospital, Capital Medical University , Xicheng, Beijing 100053, China
            [20 ] State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University , Haidian, Beijing 100875, China
            [21 ] Department of Psychology, Yale University , New Haven, Connecticut 06511, USA
            [22 ] Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College , Hanover, New Hampshire 03755, USA
            [23 ] Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences , Haidian, Beijing 100190, China
            [24 ] Virginia Tech Carilion Research Institute , Roanoke, Virginia 24016, USA
            [25 ] Department of Radiology, Xuanwu Hospital, Capital Medical University , Xicheng, Beijing 100053, China
            [26 ] Department of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Xuanwu , Nanjing, Jiangsu 210002, China
            [27 ] Department of Psychiatry, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania 15213, USA
            [28 ] Beijing Key Laboratory of Learning and Cognition, Department of Psychology, Capital Normal University , Haidian, Beijing 100048, China
            [29 ] Department of Neurosurgery, Huashan Hospital, Fudan University , Jingan, Shanghai 200040, China
            [30 ] Department of Biomedical Engineering, University of Wisconsin—Madison , Madison, Wisconsin 53705, USA
            [31 ] Department of Radiology, University of Wisconsin—Madison , Madison, Wisconsin 53705, USA
            [32 ] Department of Psychiatry, Huashan Hospital, Fudan University , Jingan, Shanghai 200021, China
            [33 ] Department of Radiology, Huashan Hospital, Fudan University , Jingan, Shanghai 200040, China
            [34 ] Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig 04103, Germany
            [35 ] Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei 430060, China
            Author notes
            [a ] X.-N.Z. (email: zuoxn@ 123456psych.ac.cn )
            []

            Consortium Leadership: Michael P. Milham, Xi-Nian Zuo. COINS Database Organization: Vince Calhoun, William Courtney, Margaret King. Data Organization: Hao-Ming Dong, Ye He, Erica Ho, Xiao-Hui Hou, Dan Lurie, David O’Connor, Ting Xu, Ning Yang, Lei Zhang, Zhe Zhang, Xing-Ting Zhu. Quality Control Protocol Development: R. Cameron Craddock, Zarrar Shehzad. Data Analysis: Ting Xu, Dan Lurie, Zarrar Shehzad, Michael P. Milham, Xi-Nian Zuo. Data Contribution: Jeffrey S. Anderson, Pierre Bellec, Rasmus M. Birn, Bharat Biswal, Janusch Blautzik, John C.S. Breitner, Randy L. Buckner, Vince D. Calhoun, F. Xavier Castellanos, Antao Chen, Bing Chen, Jiangtao Chen, Xu Chen, Stanley J. Colcombe, R. Cameron Craddock, Adriana Di Martino, Xiaolan Fu, Qiyong Gong, Krzyszotof J. Gorgolewski, Ying Han, Yong He, Avram Holmes, Jeremy Huckins, Tianzi Jiang, Yi Jiang, William Kelley, Clare Kelly, Stephen M. LaConte, Janet E. Lainhart, Xu Lei, Hui-Jie Li, Kaiming Li, Kuncheng Li, Qixiang Lin, Dongqiang Liu, Jia Liu, Xun Liu, Yijun Liu, Guangming Lu, Jie Lu, Beatriz Luna, Jing Luo, Ying Mao, Daniel S. Margulies, Andrew R. Mayer, Thomas Meindl, Mary E. Meyerand, Michael P. Milham, Weizhi Nan, Jared A. Nielsen, David Paulsen, Vivek Prabhakaran, Zhigang Qi, Jiang Qiu, Chunhong Shao, Weijun Tang, Arno Villringer, Huiling Wang, Kai Wang, Dongtao Wei, Gao-Xia Wei, Xu-Chu Weng, Xuehai Wu, Zhi Yang, Yu-Feng Zang, Qinglin Zhang, Zhiqiang Zhang, Ke Zhao, Zonglei Zhen, Yuan Zhou, Xi-Nian Zuo.

            Journal
            Sci Data
            Sci Data
            Scientific Data
            Nature Publishing Group
            2052-4463
            09 December 2014
            2014
            : 1
            25977800 4421932 sdata201449 10.1038/sdata.2014.49
            Copyright © 2014, Macmillan Publishers Limited

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