GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks

By S. M. Hadi Hosseini, Fumiko Hoeft, Shelli R. Kesler. PLoS ONE, 2012

353 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/2bc264f08a48d36bdeeea470bee1e5d00f119d73

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In Cognitive Neuroscience

Citations in the corpus

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “In addition, there are several image repositories for pairwise studies in the area of brain network connectivity that can be explored by various packages based on graph theory (Rubinov and Sporns, 2010; Hosseini et al., 2012; Kruschwitz et al., 2015; Wang et al., 2015a; Mijalkov et al., 2017; Waller”
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, methodology): “, 2011), CONN (Whitfield-Gabrieli and Nieto-Castanon, 2012), Graph-Analysis Toolbox (GAT; Hosseini et al., 2012) and GraphVar (Kruschwitz et al.”
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, neutral): “The modularity of the learned features in each hidden layer was further analyzed via a graph-theoretical measurement (Girvan and Newman, 2002; Newman and Girvan, 2004) implemented in a graphtheoretical analysis toolbox (Hosseini et al., 2012 ).”
Proportional thresholding in resting‐state fMRI functional connectivity networks and consequences for patient‐control connectome studies: Issues and recommendations (2017, neutral): “…that work by integrating e ﬀ ects across a wide range of thresholds, such as so-called Area Under the Curve (AUC) methods (Ginestet et al., 2011; Hosseini et al., 2012) (see also (Langer et al., 2013) for discussion) and multi-threshold permutation correction (MTPC) methods (Drakesmith et…”
Resting-state EEG connectome topology and social norm processing in healthy young adults (2026, unknown): “…for each of which the graph-theoretical metrics of interest are extracted, and calculates the area under the curve (AUC), thereby defining an aver - age value as a threshold-independent measure for each selected metric (Ginestet et al. 2011; Hosseini et al. 2012; Van Den Heuvel et al. 2017 ).”