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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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Graph-to-signal transformation based classification of functional connectivity brain networks.

Tamanna Tabassum Khan Munia1, Selin Aviyente1

  • 1Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States of America.

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|August 23, 2019
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Summary

This study introduces a new graph-to-signal transformation for analyzing brain functional connectivity networks (FCNs). The method extracts more discriminative features than traditional graph measures, improving network characterization.

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Area of Science:

  • Neuroscience
  • Network Science
  • Data Analysis

Background:

  • Complex network theory reveals brain topology and alterations linked to disease and cognition.
  • Functional connectivity networks (FCNs) model brain regions as nodes and connections as edges.
  • Current graph theoretic measures for FCNs are limited to binary networks and network size.

Purpose of the Study:

  • To develop a novel graph-to-signal transformation for extracting features from functional connectivity networks.
  • To overcome limitations of existing graph theoretic measures, such as network size dependency and binary constraints.
  • To enhance the characterization and discrimination of FCNs across different conditions.

Main Methods:

  • Proposed a novel graph-to-signal transformation based on classical multidimensional scaling (CMDS).
  • Utilized the resistance distance matrix to transform weighted FCNs into signals, preserving Euclidean distances between nodes.
  • Applied standard signal feature extraction techniques to the transformed graph signals.

Main Results:

  • Demonstrated that distinct network structures transform into unique signals using the proposed method.
  • Extracted signal features showed improved discriminative power compared to traditional graph theoretic measures.
  • The graph-to-signal transformation effectively characterizes FCNs across different experimental conditions.

Conclusions:

  • The novel graph-to-signal transformation provides a powerful tool for analyzing functional connectivity networks.
  • Extracted signal features offer enhanced discrimination capabilities for characterizing brain networks.
  • This approach advances the analysis of complex brain network data.