Brain structural and functional connectivity plays an important role in neuroanatomy, neurodevelopment, electrophysiology, 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 synchronization 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 arrangements in structural and functional brain networks associated with normal aging, multiple sclerosis, Alzheimer’s disease, schizophrenia, 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 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), extracts 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.