pathologic conditions. Previous studies demonstrated alterations in topologies of task-based functional connectivity pattern during memory encoding and recognition in older adults [75] and during episodic memory performance in Schizophrenia [76]. The GUI panel for investigating functional brain networks based on functional connectivity data is shown in Fig. 3. Input data. GAT accepts two types of input data to investigate the organization of functional correlation networks: First, GAT accepts the output from functional connectivity toolbox (http://www.nitrc.org/projects/conn) as input and ex- tract the individual correlation matrices (an N6N matrix) as well as the ROI names. The correlation outputs of functional connectivity toolbox are in the form of z-scores. Thus, the program first converts the z-scores to original correlation values. Network nodes are defined based on the same ROI scheme used for functional connectivity analysis within the functional connectivity toolbox. Alternatively, the user can input previously extracted correlation matrices, partial correlation values, coherence matrices, or mutual information matrices by putting them into an M6N6N Matlab array with M subjects and N ROIS. Construction of functional network. From the previous step, we have an N6N association matrix R for each individual with each entry rij represents the strength of functional connectivity between regions i and j. For each association matrix (individual), an N6N 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 constructed with n nodes (ROIs), with a network degree of E equal to number of edges (links), and a GAT: Graph Analysis Toolbox for Brain Networks network density (cost) of D = E/[(N6(N21) )/2] representing the fraction of present connections to all possible connections. Thresholding. Thresholding the association matrices of different individuals at an absolute threshold results in networks with a different number of nodes (and degrees) that might influence the network measures and reduce interpretation of between group results [29]. Thus, the individual association matrices are thresholded at a range of network densities. Network analysis. The network analysis step is similar to analysis of structural networks. The only difference is that for functional networks, a set of network measures are quantified for each individual network, rather than each group. Therefore, normalized clustering coefficient (C/Crand), normalized path length (L/Lrand), and small world index are quantified for each individual network. Benchmark random networks generated for functional graphs include topology randomization and correlation matrix randomization [53]. Similarly, the regional network measures such as nodal clustering, transitivity, path length, local efficiency, nodal be- tweenness and assortativity are quantified for each individual network, as described above. Covariates of nuisance. For between-group comparisons, the individual network measures might require correction for between-group differences in covariates of nuisance such as age and gender. GAT accepts the covariates of nuisance in a spreadsheet (Microsoft Excel.xls files) and performs a linear regression analysis at every individual network measure to remove the effects of covariates. The residuals of this regression are then substituted for the raw individual network measures for further between-group comparison. Comparing network measures between groups. Before performing statistical testing, the density range in which network Figure 3. GUI for graph theoretical analysis of functional data. The toolbox facilitates construction of functional networks, extraction of network measures, statistical analysis, regional network analysis, modularity analysis, AUC and FDA analyses and visualization. doi:10.1371/journal.pone.0040709.g003 PLoS ONE | www.plosone.org 6 July 2012 | Volume 7 | Issue 7 | e40709