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Comparison method for community detection on brain networks from neuroimaging data.

Fumihiko Taya1,2, Joshua de Souza1, Nitish V Thakor1,3,4,5

  • 11Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, 28 Medical Drive, #05-Cor, 117456 Singapore, Singapore.

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|December 12, 2018
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Summary

This study introduces a novel method to compare brain network community detection algorithms using neuroimaging data from multiple subjects. It helps identify the best algorithm for revealing group-level brain network structures without needing a "ground truth".

Keywords:
Brain atlasesBrain networkCommunity detectionFunctional Magnetic Resonance Imaging (fMRI)Multiple-subject dataNormalized Mutual Information (NMI)Permutation test“Ground truth”

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Higher cognitive functions rely on information flow between distant brain areas.
  • Brain network analysis offers deeper insights than traditional functional mapping.
  • Existing graph theory metrics lack intermediate-scale network information, necessitating community structure analysis.

Purpose of the Study:

  • To propose and validate a method for comparing different community detection schemes in neuroimaging data from multiple subjects.
  • To evaluate community detection algorithms without relying on "ground truth" or prior assumptions about network features.
  • To identify the most representative group-based community structure for brain networks.

Main Methods:

  • A novel method was developed to compare community detection algorithms on multi-subject neuroimaging data.
  • Three community detection algorithms and three brain atlases were tested using resting-state functional magnetic resonance imaging (fMRI) networks.
  • A non-parametric permutation test assessed similarity between group and individual community structures, with Normalized Mutual Information (NMI) quantifying this similarity.

Main Results:

  • The proposed method successfully evaluated and compared different community detection algorithms and brain atlases.
  • The study identified which algorithm/atlas combinations best represent group-level brain network community structures.
  • Feasibility was demonstrated using resting-state fMRI data, highlighting the method's utility in neuroscience research.

Conclusions:

  • The developed method offers a robust framework for evaluating community detection schemes in multi-subject neuroimaging studies.
  • It facilitates the selection of optimal algorithms and atlases for uncovering consistent group-level brain network organization.
  • This work advances the understanding of brain network mesoscale organization and provides a standardized approach for comparative analysis.