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BRAPH: A graph theory software for the analysis of brain connectivity

1 , 1 , 2 , 2 , 1 , 3 , * , for the Alzheimer's Disease Neuroimaging Initiative

PLoS ONE

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      Abstract

      The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment.

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      Most cited references 55

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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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          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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            Author and article information

            Affiliations
            [1 ] UNAM—National Nanotechnology Research Center & Department of Physics, Bilkent University, Ankara, Turkey
            [2 ] Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
            [3 ] Department of Physics, Goteborg University, Goteborg, Sweden
            University of Texas at Austin, UNITED STATES
            Author notes

            Competing Interests: While commercial funding was obtained by the ADNI and PPMI initiatives, the authors have not directly received commercial funding. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

            • Conceptualization: JBP EW GV.

            • Data curation: MM EK JBP.

            • Formal analysis: MM EK JBP.

            • Funding acquisition: JBP EW GV.

            • Investigation: MM EK JBP EW GV.

            • Methodology: MM EK JBP EW GV.

            • Project administration: GV.

            • Resources: JBP EW.

            • Software: MM EK GV.

            • Supervision: JBP EW GV.

            • Validation: MM EK JBP EW GV.

            • Visualization: MM EK JBP GV.

            • Writing – original draft: MM EK JBP.

            • Writing – review & editing: MM EK JBP EW GV.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            1 August 2017
            2017
            : 12
            : 8
            28763447
            5538719
            10.1371/journal.pone.0178798
            PONE-D-17-05125
            (Editor)
            © 2017 Mijalkov et al

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Counts
            Figures: 8, Tables: 2, Pages: 23
            Product
            Funding
            Funded by: Stiftelsen för Strategisk Forskning
            Award ID: 4-3193/2014
            Award Recipient :
            Funded by: funder-id http://dx.doi.org/10.13039/501100003792, Hjärnfonden;
            Award ID: FO2015-0173
            Award Recipient :
            Funded by: funder-id http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
            Award ID: 2016-02282 2017-2020
            Award Recipient :
            Funded by: Tübitak
            Award ID: 2215
            Award Recipient :
            Funded by: KI Stratneuro
            Award ID: na
            Award Recipient :
            Funded by: Sten/Birgitta Westerberg
            Award ID: na
            Award Recipient :
            Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Parkinson's Progression Markers Initiative (PPMI). Data collection and sharing of ADNI was funded by the National Institutes of Health Grant U01 AG024904 and Department of Defense award number W81XWH-12-2-0012. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. PPMI—a public-private partnership—is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including Abbott, Avid Radiopharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Covance, Elan, GE Healthcare, Genentech, GSK-GlaxoSmithKline, Lilly, Merck, MSD-Meso Scale Discovery,Pfizer, Roche, UCB ( www.ppmi-info.org/fundingpartners). For up-to-date information on the PPMI database visit www.ppmi-info.org. We would also like to thank the Swedish Foundation for Strategic Research (SSF) grant number 4-3193/2014, the Strategic Research Programme in Neuroscience at Karolinska Institutet (StratNeuro), Hjärnfonden grant number FO2015-0173, Vetenskapsrådet grant number 2016-02282 2017-2020, and Birgitta och Sten Westerberg for additional financial support. We would also like to thank TÜBİTAK 2215 Graduate Programme for their support.
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            MRI Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) and the Parkinson’s Progression Markers Initiative (PPMI) ( http://www.ppmi-info.org/). Since we do not own the ADNI or PPMI data used in this study, we do not have permission to redistribute these data ourselves, as is stated in the data use agreements from ADNI ( http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Data_Use_Agreement.pdf) and PPMI ( https://ida.loni.usc.edu/collaboration/access/appLicense.jsp). However, the data can be obtained through procedures and under conditions as described on the ADNI ( http://adni.loni.usc.edu/data-samples/access-data/) and PPMI ( http://www.ppmi-info.org/access-data-specimens/download-data/) websites.

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