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

      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 11 , 11 , 13 , 9 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 16 , 21 , 14 , 22 , 16 , 23 , 24 , 25 , 23 , 15 , 9 , 26 , 4 , 11 , 25 , 18 , 27 , 21 , 12 , 21 , 25 , 11 , 28 , 27 , 29 , 30 , 14 , 31 , 19 , 9 , 6 , 32 , 16 , 2 , 14 , 29 , 33 , 27 , 11 , 34 , 14 , 35 , 36 , 37 , 16 , 11 , 25 , 12 , 31 , 38 , 16 , 25 , 12 , 16 , 11 , 16 , 28 , 25 , 21 , 25 , 16 , 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 references74

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          Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization.

          Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.
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            Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy.

            Functional homotopy, the high degree of synchrony in spontaneous activity between geometrically corresponding interhemispheric (i.e., homotopic) regions, is a fundamental characteristic of the intrinsic functional architecture of the brain. However, despite its prominence, the lifespan development of the homotopic resting-state functional connectivity (RSFC) of the human brain is rarely directly examined in functional magnetic resonance imaging studies. Here, we systematically investigated age-related changes in homotopic RSFC in 214 healthy individuals ranging in age from 7 to 85 years. We observed marked age-related changes in homotopic RSFC with regionally specific developmental trajectories of varying levels of complexity. Sensorimotor regions tended to show increasing homotopic RSFC, whereas higher-order processing regions showed decreasing connectivity (i.e., increasing segregation) with age. More complex maturational curves were also detected, with regions such as the insula and lingual gyrus exhibiting quadratic trajectories and the superior frontal gyrus and putamen exhibiting cubic trajectories. Sex-related differences in the developmental trajectory of functional homotopy were detected within dorsolateral prefrontal cortex (Brodmann areas 9 and 46) and amygdala. Evidence of robust developmental effects in homotopic RSFC across the lifespan should serve to motivate studies of the physiological mechanisms underlying functional homotopy in neurodegenerative and psychiatric disorders.
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              Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective.

              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.
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                Author and article information

                Journal
                Sci Data
                Scientific data
                Springer Nature
                2052-4463
                2014
                : 1
                Affiliations
                [1 ] Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China ; Faculty of Psychology, Southwest University , Beibei, Chongqing 400715, China.
                [2 ] Division of Neuroradiology, University of Utah , Salt Lake City, Utah 84132, USA.
                [3 ] Unité de neuroimagerie fonctionnelle, Centre de recherche de l'institut universitaire de gériatrie de Montréal, Université de Montréal , Montreal, Quebec, Canada H3W 1W5.
                [4 ] Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin 53719, USA.
                [5 ] Department of Biomedical Engineering, New Jersey Institute of Technology , Newark, New Jersey 07102, USA.
                [6 ] Institute of Clinical Radiology, Ludwig-Maximilians-University , 80336 Munich, Germany.
                [7 ] Centre for Studies on Prevention of Alzheimer's Disease, Department of Psychiatry, Douglas Institute, McGill University Faculty of Medicine , Montreal, Quebec, Canada H4H 1R3.
                [8 ] Department of Psychology, Harvard University , Cambridge, Massachussetts 02138, USA.
                [9 ] Mind Research Network , Albuquerque, New Mexico 87106, USA.
                [10 ] Nathan S. Kline Institute for Psychiatric Research , Orangeburg, New York 10962, USA ; Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, the Child Study Center at NYU Langone Medical Center , New York, New York 10016, USA.
                [11 ] Faculty of Psychology, Southwest University , Beibei, Chongqing 400715, China.
                [12 ] Center for Cognition and Brain Disorders, Hangzhou Normal University, Gongshu , Hangzhou, Zhejiang 311121, China.
                [13 ] Nathan S. Kline Institute for Psychiatric Research , Orangeburg, New York 10962, USA.
                [14 ] Nathan S. Kline Institute for Psychiatric Research , Orangeburg, New York 10962, USA ; Center for the Developing Brain, Child Mind Institute , New York, New York 10022, USA.
                [15 ] Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, the Child Study Center at NYU Langone Medical Center , New York, New York 10016, USA.
                [16 ] Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China ; University of Chinese Academy of Sciences , Shijingshan, Beijing 100049, China.
                [17 ] Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China ; State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China.
                [18 ] Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Wuhou , Chengdu, Sichuan 610041, China.
                [19 ] Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany.
                [20 ] Department of Neurology, Xuanwu Hospital, Capital Medical University , Xicheng, Beijing 100053, China.
                [21 ] State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University , Haidian, Beijing 100875, China.
                [22 ] Department of Psychology, Yale University , New Haven, Connecticut 06511, USA.
                [23 ] Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College , Hanover, New Hampshire 03755, USA.
                [24 ] Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences , Haidian, Beijing 100190, China.
                [25 ] Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China.
                [26 ] Virginia Tech Carilion Research Institute , Roanoke, Virginia 24016, USA.
                [27 ] Department of Radiology, Xuanwu Hospital, Capital Medical University , Xicheng, Beijing 100053, China.
                [28 ] Department of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Xuanwu , Nanjing, Jiangsu 210002, China.
                [29 ] Department of Psychiatry, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania 15213, USA.
                [30 ] Beijing Key Laboratory of Learning and Cognition, Department of Psychology, Capital Normal University , Haidian, Beijing 100048, China.
                [31 ] Department of Neurosurgery, Huashan Hospital, Fudan University , Jingan, Shanghai 200040, China.
                [32 ] Department of Biomedical Engineering, University of Wisconsin-Madison , Madison, Wisconsin 53705, USA.
                [33 ] Department of Radiology, University of Wisconsin-Madison , Madison, Wisconsin 53705, USA.
                [34 ] Department of Psychiatry, Huashan Hospital, Fudan University , Jingan, Shanghai 200021, China.
                [35 ] Department of Radiology, Huashan Hospital, Fudan University , Jingan, Shanghai 200040, China.
                [36 ] Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig 04103, Germany.
                [37 ] Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei 430060, China.
                [38 ] Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China ; Nathan S. Kline Institute for Psychiatric Research , Orangeburg, New York 10962, USA ; Center for the Developing Brain, Child Mind Institute , New York, New York 10022, USA.
                Article
                10.1038/sdata.2014.49
                4421932
                25977800
                987f04c9-da7d-4ec2-a5f0-73d85e0a4225
                History

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