J D Kruschwitz1, D List2, L Waller3, M Rubinov4, H Walter3. 1. Charité - Universitätsmedizin Berlin, Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Berlin, Germany; Technische Universität Dresden, Institute of General Psychology, Dresden, Germany. Electronic address: johann.kruschwitz@charite.de. 2. Charité - Universitätsmedizin Berlin, Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Berlin, Germany; Technische Universität Dresden, Institute of General Psychology, Dresden, Germany. Electronic address: david.list@charite.de. 3. Charité - Universitätsmedizin Berlin, Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Berlin, Germany. 4. University of Cambridge, Cambridge, United Kingdom.
Abstract
BACKGROUND: Graph theory provides a powerful and comprehensive formalism of global and local topological network properties of complex structural or functional brain connectivity. Software packages such as the Brain-Connectivity-Toolbox have contributed to graph theory's increasing popularity for characterization of brain networks. However, comparably comprehensive packages are command-line based and require programming experience; this precludes their use by users without a computational background, whose research would otherwise benefit from graph-theoretical methods. NEW METHOD: "GraphVar" is a user-friendly GUI-based toolbox for comprehensive graph-theoretical analyses of brain connectivity, including network construction and characterization, statistical analysis on network topological measures, network based statistics, and interactive exploration of results. RESULTS: GraphVar provides a comprehensive collection of graph analysis routines for analyses of functional brain connectivity in one single toolbox by combining features across multiple currently available toolboxes, such as the Brain Connectivity Toolbox, the Graph Analysis Toolbox, and the Network Based Statistic Toolbox (BCT, Rubinov and Sporns, 2010; GAT, Hosseini et al., 2012; NBS, Zalesky et al., 2010). GraphVar was developed under the GNU General Public License v3.0 and can be downloaded at www.rfmri.org/graphvar or www.nitrc.org/projects/graphvar. COMPARISON WITH EXISTING METHODS: By combining together features across multiple toolboxes, GraphVar will allow comprehensive graph-theoretical analyses in one single toolbox without resorting to code. CONCLUSIONS: GraphVar will make graph theoretical methods more accessible for a broader audience of neuroimaging researchers.
BACKGROUND: Graph theory provides a powerful and comprehensive formalism of global and local topological network properties of complex structural or functional brain connectivity. Software packages such as the Brain-Connectivity-Toolbox have contributed to graph theory's increasing popularity for characterization of brain networks. However, comparably comprehensive packages are command-line based and require programming experience; this precludes their use by users without a computational background, whose research would otherwise benefit from graph-theoretical methods. NEW METHOD: "GraphVar" is a user-friendly GUI-based toolbox for comprehensive graph-theoretical analyses of brain connectivity, including network construction and characterization, statistical analysis on network topological measures, network based statistics, and interactive exploration of results. RESULTS: GraphVar provides a comprehensive collection of graph analysis routines for analyses of functional brain connectivity in one single toolbox by combining features across multiple currently available toolboxes, such as the Brain Connectivity Toolbox, the Graph Analysis Toolbox, and the Network Based Statistic Toolbox (BCT, Rubinov and Sporns, 2010; GAT, Hosseini et al., 2012; NBS, Zalesky et al., 2010). GraphVar was developed under the GNU General Public License v3.0 and can be downloaded at www.rfmri.org/graphvar or www.nitrc.org/projects/graphvar. COMPARISON WITH EXISTING METHODS: By combining together features across multiple toolboxes, GraphVar will allow comprehensive graph-theoretical analyses in one single toolbox without resorting to code. CONCLUSIONS: GraphVar will make graph theoretical methods more accessible for a broader audience of neuroimaging researchers.
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