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GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

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      Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website.1

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          The structure and function of complex networks

           M. Newman (2003)
          Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

            Author and article information

            1State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
            2Center for Cognition and Brain Disorders, Hangzhou Normal University Hangzhou, China
            3Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou, China
            4McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC, Canada
            Author notes

            Edited by: Wei Gao, University of North Carolina at Chapel Hill, USA

            Reviewed by: Qingbao Yu, The Mind Research Network, USA; Fumihiko Taya, National University of Singapore, Singapore

            *Correspondence: Alan Evans, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC H3A2B4, Canada alan@ ; Yong He, State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China yong.he@

            These authors have contributed equally to this work.

            Front Hum Neurosci
            Front Hum Neurosci
            Front. Hum. Neurosci.
            Frontiers in Human Neuroscience
            Frontiers Media S.A.
            30 June 2015
            : 9
            Copyright © 2015 Wang, Wang, Xia, Liao, Evans and He.

            This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

            Figures: 9, Tables: 1, Equations: 2, References: 121, Pages: 16, Words: 10811
            Funded by: National Science Fund for Distinguished Young Scholars
            Award ID: 81225012
            Funded by: National Key Basic Research Program of China
            Award ID: 2014CB846102
            Funded by: Natural Science Foundation
            Award ID: 81030028
            Award ID: 31221003
            Award ID: 30870667
            Award ID: 81401479
            Funded by: Beijing Funding for Training Talents
            Award ID: 2012D009012000003
            Funded by: Beijing Natural Science Foundation
            Award ID: Z111107067311036
            Award ID: 7102090
            Funded by: Zhejiang Provincial Natural Science Foundation of China
            Award ID: LZ13C090001
            Funded by: Open Research Fund of Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments
            Award ID: PD11001005002013


            hub, network, small-world, resting fmri, connectome, graph theory


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