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Consensus clustering approach to group brain connectivity matrices.

Javier Rasero1,2,3, Mario Pellicoro2, Leonardo Angelini2,3,4

  • 1Biocruces Health Research Institute. Hospital Universitario de Cruces, Barakaldo, Spain.

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Summary
This summary is machine-generated.

This study introduces a novel consensus clustering approach to analyze complex network heterogeneity in connectivity matrices. The method effectively identifies subject groups by combining node-specific connectivity patterns for improved data analysis.

Keywords:
Consensus clusteringResting fMRIStructural DTIUnsupervised learning

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Area of Science:

  • Complex systems analysis
  • Network science
  • Computational biology

Background:

  • Connectivity matrices in health and disease exhibit significant heterogeneity.
  • Existing methods struggle to effectively capture this complex variability.
  • Community detection in complex networks offers potential solutions.

Purpose of the Study:

  • To introduce a novel consensus clustering approach for analyzing heterogeneous connectivity matrices.
  • To develop a method for identifying subject groups based on connectivity patterns.
  • To provide an unsupervised pretraining step for supervised classification.

Main Methods:

  • A consensus clustering strategy is adapted for community detection.
  • Distance matrices are computed for each node across subjects.
  • Consensus networks are built from node-specific partitions.
  • Subject groups are extracted via community detection on the consensus network.

Main Results:

  • The proposed methodology effectively represents subject heterogeneity in a weighted consensus matrix.
  • Applications on toy and real datasets demonstrate the approach's efficacy.
  • The method successfully identifies distinct groups of subjects based on connectivity.

Conclusions:

  • The novel consensus clustering approach offers a robust method for handling heterogeneity in connectivity data.
  • This technique can serve as an exploratory tool or a valuable pretraining step for machine learning models.
  • The approach advances the analysis of complex network data in biological and medical contexts.