GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.

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 re...

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Autores principales:	S M Hadi Hosseini, Fumiko Hoeft, Shelli R Kesler
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d01b4b43cb7343a7a59bc5082714b12f
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spelling	oai:doaj.org-article:d01b4b43cb7343a7a59bc5082714b12f2021-11-18T07:12:32ZGAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.1932-620310.1371/journal.pone.0040709https://doaj.org/article/d01b4b43cb7343a7a59bc5082714b12f2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22808240/?tool=EBIhttps://doaj.org/toc/1932-6203In 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.S M Hadi HosseiniFumiko HoeftShelli R KeslerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 7, p e40709 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q S M Hadi Hosseini Fumiko Hoeft Shelli R Kesler GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.
description	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.
format	article
author	S M Hadi Hosseini Fumiko Hoeft Shelli R Kesler
author_facet	S M Hadi Hosseini Fumiko Hoeft Shelli R Kesler
author_sort	S M Hadi Hosseini
title	GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.
title_short	GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.
title_full	GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.
title_fullStr	GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.
title_full_unstemmed	GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.
title_sort	gat: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/d01b4b43cb7343a7a59bc5082714b12f
work_keys_str_mv	AT smhadihosseini gatagraphtheoreticalanalysistoolboxforanalyzingbetweengroupdifferencesinlargescalestructuralandfunctionalbrainnetworks AT fumikohoeft gatagraphtheoreticalanalysistoolboxforanalyzingbetweengroupdifferencesinlargescalestructuralandfunctionalbrainnetworks AT shellirkesler gatagraphtheoreticalanalysistoolboxforanalyzingbetweengroupdifferencesinlargescalestructuralandfunctionalbrainnetworks
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GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.

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