GraphVar: A user-friendly toolbox for comprehensive graph analyses of functional brain connectivity

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.

[1]  Jing Li,et al.  Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation , 2010, NeuroImage.

[2]  C. Stam,et al.  Small‐world properties of nonlinear brain activity in schizophrenia , 2009, Human brain mapping.

[3]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[4]  Theiler,et al.  Generating surrogate data for time series with several simultaneously measured variables. , 1994, Physical review letters.

[5]  K. Worsley,et al.  Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. , 2009, Brain : a journal of neurology.

[6]  Hae-Jeong Park,et al.  MNET: network analysis toolbox for integrating structural-functional human brain connectome , 2014 .

[7]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[8]  Alan C. Evans,et al.  Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease , 2008, The Journal of Neuroscience.

[9]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[10]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[11]  Andreas Daffertshofer,et al.  Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.

[12]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[13]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[14]  Philipp Schwartenbeck,et al.  Abnormalities of functional brain networks in pathological gambling: a graph-theoretical approach , 2013, Front. Hum. Neurosci..

[15]  Yong He,et al.  Disrupted small-world networks in schizophrenia. , 2008, Brain : a journal of neurology.

[16]  Edward T. Bullmore,et al.  On the use of correlation as a measure of network connectivity , 2012, NeuroImage.

[17]  Liang Wang,et al.  Altered small‐world brain functional networks in children with attention‐deficit/hyperactivity disorder , 2009, Human brain mapping.

[18]  Fumiko Hoeft,et al.  GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks , 2012, PloS one.

[19]  Jimin Liang,et al.  Dysfunctional connectivity patterns in chronic heroin users: An fMRI study , 2009, Neuroscience Letters.