GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks S. M. Hadi Hosseini1*, Fumiko Hoeft1,2, Shelli R. Kesler1,3 1 Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, United States of America, 2 Division of Child and Adolescent Psychiatry, Department of Psychiatry, University of California San Francisco, San Francisco, California, United States of America, 3 Stanford Cancer Center, Palo Alto, California, United States of America Abstract In recent years, graph theoretical analyses of neuroimaging data have increased our understanding of the organization of large-scale structural and functional brain networks. However, tools for pipeline application of graph theory for analyzing topology of brain networks is still lacking. In this report, we describe the development of a graph-analysis toolbox (GAT) that facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process. We demonstrated the capabilities of GAT by investigating the differences in the organization of regional gray- matter correlation networks in survivors of acute lymphoblastic leukemia (ALL) and healthy matched Controls (CON). The results revealed an alteration in small-world characteristics of the brain networks in the ALL survivors; an observation that confirm our hypothesis suggesting widespread neurobiological injury in ALL survivors. Along with demonstration of the capabilities of the GAT, this is the first report of altered large-scale structural brain networks in ALL survivors. Citation: Hosseini SMH, Hoeft F, Kesler SR (2012) GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks. PLoS ONE 7(7): e40709. doi:10.1371/journal.pone.0040709 Editor: Renaud Lambiotte, University of Namur, Belgium Received April 2, 2012; Accepted June 12, 2012; Published July 13, 2012 Copyright: 2012 Hosseini et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the National Institutes of Health Director’s New Innovator Award 1 DP2 OD004445-01 to SK and K23 HD054720 to FH and National Cancer Institute (K07 CA134639 to SK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: hosseiny@stanford.edu Introduction Brain structural and functional connectivity plays an impor- tant role in neuroanatomy, neurodevelopment, electrophysiolo- gy, functional brain imaging, and neural basis of cognition [1]. Brain networks, along with other biological networks, have been shown to follow a specific topology known as small-world. A small-world network architecture facilitates rapid synchroniza- tion and efficient information transfer with minimal wiring cost through an optimal balance between local processing and global interaction [2]. Since small-world characteristics were described quantitatively for brain networks, there have been multiple graph-theoretical studies seeking to assess the organization of structural and functional brain networks in healthy individuals and patient population [3–22]. The unique feature of graph-theoretical analysis, compared with the more traditional univariate neuroimaging approaches, is that it can directly test the differences in topological parameters of the brain network such as small-worldness, modularity, highly connected regions (hubs), and regional network parameters. [23,24] Additionally, graph theoretical analysis is potentially applicable to any modality, scale, or volume of neuroscientific data [25]. Graph theoretical analyses have been applied to regional gray matter volume, cortical thickness, surface area, and diffusion weighted imaging data to analyze topology of structural brain networks and to resting state and task-related functional connectivity data to analyze the topology of functional brain networks. These studies have illustrated an alteration of arrange- ments in structural and functional brain networks associated with normal aging, multiple sclerosis, Alzheimer’s disease, schizophre- nia, depression, and epilepsy [4,5,9,12,14,15,20,22,26]. In recent years, a number of freely available software packages have been introduced to apply graph theory for analyzing topology of brain networks (e.g. Brain Connectivity Toolbox [27]; eConnectome [28]; NetworkX (http://networkx.lanl.gov/ overview.html); and Brainwaiver (http://cran.r-project.org/web/ packages/brainwaver). The focus of these packages is mainly on extracting network measures and/or visualization of networks. However, the methodology of comparing network topologies of different groups (or systems) is challenging [29]. In this report, we describe the development a graph analysis toolbox (GAT) that facilitates analysis and comparison of structural and functional brain networks. GAT is an open-source Matlab-based package with graphical user interface that integrates the Brain Connectivity Toolbox [27] for quantification of network measures and the REX toolbox (http://web.mit.edu/swg/software.htm) for region of PLoS ONE | www.plosone.org 1 July 2012 | Volume 7 | Issue 7 | e40709