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

S M Hadi Hosseini1, Fumiko Hoeft, Shelli R Kesler

  • 1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, United States of America. hosseiny@stanford.edu

Plos One
|July 19, 2012
PubMed
Summary

Researchers developed a graph-analysis toolbox (GAT) to study brain networks. This tool revealed altered brain network organization in acute lymphoblastic leukemia (ALL) survivors, indicating neurobiological injury.

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Area of Science:

  • Neuroimaging
  • Graph Theory
  • Network Neuroscience

Background:

  • Graph theoretical analyses enhance understanding of brain network organization.
  • A lack of comprehensive tools hinders pipeline application of graph theory in neuroimaging.
  • Existing methods may be sensitive to thresholding in network analysis.

Purpose of the Study:

  • To introduce the Graph Analysis Toolbox (GAT), a novel software facilitating brain network analysis.
  • To enable comparison of structural and functional brain networks using graph theory.
  • To demonstrate GAT's utility in identifying neurobiological alterations in clinical populations.

Main Methods:

  • Development of GAT with a graphical user interface (GUI) for network construction and analysis.
  • Utilizing Area Under the Curve (AUC) and Functional Data Analysis (FDA) with permutation testing for robust topology comparisons.
  • Investigating differences in regional gray-matter correlation networks between acute lymphoblastic leukemia (ALL) survivors and healthy controls (CON).

Main Results:

  • GAT successfully facilitated the analysis and comparison of brain network topologies.
  • Significant alterations in small-world characteristics were observed in the brain networks of ALL survivors compared to controls.
  • The findings support the hypothesis of widespread neurobiological injury in ALL survivors.

Conclusions:

  • GAT provides a valuable tool for analyzing and comparing brain network structures and functions.
  • Altered large-scale structural brain networks are evident in ALL survivors.
  • This study represents the first report of such network alterations in ALL survivors, highlighting potential neurobiological impacts.