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Test-retest reliability of modular-relevant analysis in brain functional network.

Xuyun Wen1,2, Mengting Yang1,2, Liming Hsu3

  • 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China.

Frontiers in Neuroscience
|December 26, 2022
PubMed
Summary

This study assessed the reliability of brain network modules using functional magnetic resonance imaging (fMRI). Results show moderate-to-good reliability for key metrics, guiding future clinical applications.

Keywords:
brain functional networkmodular structuremodule detectionnetwork metrictest-retest reliability

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Area of Science:

  • Neuroscience
  • Network Science
  • Graph Theory

Background:

  • Human brain function is modeled as complex networks using functional magnetic resonance imaging (fMRI).
  • Module detection is a key mesoscale analysis for understanding brain organization, but its reliability needs assessment for clinical use.

Purpose of the Study:

  • To systematically explore the reliability of popular network metrics derived from modular structures in brain functional networks.
  • To provide guidance on selecting reliable modular-relevant metrics and analysis strategies for future brain network studies.

Main Methods:

  • Utilized resting-state test-retest fMRI data.
  • Constructed brain functional networks using coarse-to-fine atlases.
  • Applied four single-subject and twelve group-level module detection methods.

Main Results:

  • Reported moderate-to-good reliability for modularity, intra- and inter-modular functional connectivities, within-modular degree, and participation coefficient.
  • Identified significant influence of module detection algorithms and node definition on partition reliability and analysis outcomes.

Conclusions:

  • Modular-relevant network metrics demonstrate robust evaluation potential.
  • Reliability is influenced by the choice of module detection algorithm and node definition.