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GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

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      Abstract

      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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      Most cited references 121

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      Collective dynamics of 'small-world' networks.

      Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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        Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

        An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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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.
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            Author and article information

            Affiliations
            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@ 123456bic.mni.mcgill.ca ; 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@ 123456bnu.edu.cn

            These authors have contributed equally to this work.

            Contributors
            Journal
            Front Hum Neurosci
            Front Hum Neurosci
            Front. Hum. Neurosci.
            Frontiers in Human Neuroscience
            Frontiers Media S.A.
            1662-5161
            30 June 2015
            2015
            : 9
            4485071 10.3389/fnhum.2015.00386
            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.

            Counts
            Figures: 9, Tables: 1, Equations: 2, References: 121, Pages: 16, Words: 10811
            Funding
            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
            Categories
            Neuroscience
            Methods

            Neurosciences

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

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