GAT: Graph Analysis Toolbox for Brain Networks Figure 2. GUI for graph theoretical analysis of morphometry data. The toolbox facilitates construction of structural correlation networks, extraction of network measures, statistical analysis, regional network analysis, network failure and attack analysis, modularity analysis, AUC and FDA analyses and visualization. doi:10.1371/journal.pone.0040709.g002 the regional volume data. REX accepts predefined ROIs as NIFTI-1 image mask files (*.img or.nii formats) or text files (.tal format). In addition, user can input previously extracted regional morphometry data (e.g. surface area, cortical thickness, regional gray matter volume data) via spreadsheets (Microsoft Excel.xls files) into GAT. Covariates of nuisance. For between-group comparisons, the regional morphometry data might require correction for between-group differences in covariates of nuisance such as total brain volume, age and gender. GAT accepts the covariates of nuisance in a spreadsheet (Microsoft Excel.xls files ) and performs a linear regression analysis at every ROI to remove the effects of covariates. The residuals of this regression are then substituted for raw regional morphometry values [13,19,20]. Construction of structural correlation network. GAT uses the extracted regional morphometry data (or residuals) of all the N ROIs for construction of structural correlation networks. For each group, an N6N association matrix R is generated with each entry rij defined as the Pearson correlation coefficient (or partial correlation coefficient) between regional values of regions i and j, across subjects. The user can specify what type of correlation analysis (Pearson vs. partial correlation) to be done on the data. From each association matrix, a binary adjacency matrix A is derived where aij is considered 1 if rij is greater than a specific threshold and zero otherwise. The diagonal elements of the constructed association matrix are set to zero. The resultant adjacency matrix represents a binary undirected graph G in which regions i and j are connected if gij is unity. Therefore, a graph is PLoS ONE | www.plosone.org 3 July 2012 | Volume 7 | Issue 7 | e40709