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BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics

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PLoS ONE

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      Abstract

      The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website ( www.nitrc.org/projects/bnv/).

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            Author and article information

            Affiliations
            State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
            Semmelweis University, Hungary
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: MX JW YH. Performed the experiments: MX. Analyzed the data: MX. Contributed reagents/materials/analysis tools: MX JW YH. Wrote the paper: MX JW YH.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2013
            4 July 2013
            : 8
            : 7
            23861951 3701683 PONE-D-13-17256 10.1371/journal.pone.0068910

            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.

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            Pages: 15
            Funding
            This study was supported by the Natural Science Foundation (Grant Nos. 81030028 and 30870667), the National Science Fund for Distinguished Young Scholars (Grant No. 81225012, YH), and Beijing Natural Science Foundation (Grant No. Z111107067311036 and 7102090). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Biology
            Neuroscience
            Cognitive Neuroscience
            Computational Neuroscience

            Uncategorized

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