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Topological data analysis of human brain networks through order statistics

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  • Soumya Das
  • D Vijay Anand
  • Moo K Chung

Abstract

Understanding the common topological characteristics of the human brain network across a population is central to understanding brain functions. The abstraction of human connectome as a graph has been pivotal in gaining insights on the topological properties of the brain network. The development of group-level statistical inference procedures in brain graphs while accounting for the heterogeneity and randomness still remains a difficult task. In this study, we develop a robust statistical framework based on persistent homology using the order statistics for analyzing brain networks. The use of order statistics greatly simplifies the computation of the persistent barcodes. We validate the proposed methods using comprehensive simulation studies and subsequently apply to the resting-state functional magnetic resonance images. We found a statistically significant topological difference between the male and female brain networks.

Suggested Citation

  • Soumya Das & D Vijay Anand & Moo K Chung, 2023. "Topological data analysis of human brain networks through order statistics," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-26, March.
    • Handle: RePEc:plo:pone00:0276419
    DOI: 10.1371/journal.pone.0276419
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    References listed on IDEAS

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    1. Chad M Topaz & Lori Ziegelmeier & Tom Halverson, 2015. "Topological Data Analysis of Biological Aggregation Models," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-26, May.
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