interest extraction (REX). For structural network analysis, GAT accepts gray matter volume/surface area/cortical thickness data of groups, extracts structural correlation networks, applies different thresholding schemes for comparing networks between groups, calculates network measures for different thresholding schemes, estimates between-group differences in network measures using functional data analysis (FDA) [30,31] and area under the curve (AUC) analysis, tests the significance of between-group differences in global and regional network measures using nonparametric permutation testing, and performs hub analysis, random failure and targeted attack analysis and modularity analysis. For functional networks, GAT accepts the output from functional connectivity toolbox (http://www.nitrc.org/projects/conn), ex- tracts the network measures, finds the range of network densities where individual networks are not fragmented, performs both parametric and non-parametric statistical tests to test the significance of between-group differences in global and regional network measures at each densities as well as on FDA and AUC estimates, and the above-mentioned analyses as for structural graphs. To demonstrate the capabilities of GAT, we investigated the differences in organization of structural brain networks in survivors of acute lymphoblastic leukemia (ALL), the most common childhood cancer, and healthy matched controls. There are several lines of evidence suggesting that ALL may involve widespread neurobiologic injury. First, while the mechanism by which cancer and its treatments affect cognitive function are largely unknown, possible candidates include neurotoxic effects of chemotherapy, oxidative damage and cytokine dysregulation [32,33]. These candidate mechanisms might have diffuse effects on brain structure. Second, structural neuroimaging studies, including our own [34] have shown diffuse changes in white matter and gray matter structure associated with ALL [35–38]. Third, meta-analyses of neuropsychological studies on ALL survivors have indicated decline in a wide range of cognitive functions including executive functioning, processing speed and memory [39,40] (see [41,42] for a review). These functions are known to be subserved by distributed, integrated neural networks [43]. We investigated whether topological properties of large-scale structural brain networks are altered in ALL survivors. Materials and Methods Overview of How to Use the GAT GAT is an open-source Matlab (The MathWorks Inc., Natick) package that provides a GUI framework to facilitate the investigation of organization of brain networks. The GUI allows users to interact with the toolbox easily without requiring knowledge of Matlab or programming. GAT provides an interactive platform for conducting graph theoretical analysis on various types of data including morphometry, functional, diffusion weighted and behavioral data (Fig. 1). The main focus of the toolbox is on analyzing the between-group differences in brain network topology. The toolbox facilitates investigation of brain networks by constructing binary undirected graphs (i.e. two regions in brain network are either connected or not connected and the connection does not have any weight or direction). However, we are extending the toolbox to be able to analyze weighted and directed networks. In the following sections, we describe the detailed procedure for analyzing structural and functional network. The procedure for analyzing between-group differences in behavioral networks/white matter networks is quite similar to the procedure described for comparison of structural morphometry/functional networks. GAT: Graph Analysis Toolbox for Brain Networks Figure 1. Main GUI of the graph analysis toolbox (GAT). The toolbox allows topological assessment of morphometry, functional, diffusion, and behavioral data. doi:10.1371/journal.pone.0040709.g001 Structural Graphs Based on Morphometry Data Coordinated variations in brain morphology have been proposed as a valid measure to infer large-scale structural brain networks [13,44–46]. This approach depends upon the assump- tion that positive correlations between morphometric parameters of different brain regions indicate connectivity [45]. Previous study revealed that some of the tractography maps obtained from diffusion tensor imaging are strikingly similar to pattern of correlations in cortical thickness [44]. In addition, the structural networks constructed from morphometric correlations of cortical volume, thickness, and surface data [13,44–46] have been shown to follow small-world characteristics in healthy individuals [12,17,19,47]. The GUI panel for investigating structural corre- lation networks based on morphometry data is shown in Fig. 2. Input data. GAT accepts two types of morphometry data to investigate the organization of structural correlation networks: first, GAT accepts the gray matter images of individuals as input (e.g. modulated, normalized gray matter maps from voxel-based morphometry analysis). Having the gray matter images, the first step is to define the network nodes that are identified by the regional parcellation scheme that will be chosen. There are different nodal definition methods in brain network analysis. While the results might be affected by the choice of parcellation scheme, recent evidence indicates that the results of between- group comparison remain intact regardless of the applied parcellation scheme [48]. The ROI scheme that is available in GAT is the 90 cortical and subcortical regions of interest (ROIs) from the Automated Anatomical Labeling (AAL) atlas, extracted using the WFU PickAtlas Toolbox [49]. The ROIs are identical to those used in a previous graph analysis study of typical brain development by Fan and colleagues [19,22,50]. Note that the gray matter images should be resliced to the same dimension as that of the provided AAL ROIs. The ROIs are subsequently used to mask the individual modulated, normalized gray matter images and extract the average volume within each ROI using the REX code (http://web.mit.edu/swg/software.htm). Alterna- tively, a customized ROI scheme can be implemented to extract PLoS ONE | www.plosone.org 2 July 2012 | Volume 7 | Issue 7 | e40709