ArticlePloS one2023
Topological data analysis of human brain networks through order statistics.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis.Nature communications · 2026Article
- Image complexity-based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning.Scientific reports · 2025Article
- Article
- A Q-analysis package for higher-order interactions analysis in Python and its application in network physiology.Frontiers in network physiology · 2025Article
- Dissecting glial scar formation by spatial point pattern and topological data analysis.Scientific reports · 2024Article
- Altered topological structure of the brain white matter in maltreated children through topological data analysis.Network neuroscience (Cambridge, Mass.) · 2024Article
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3 authors.
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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.
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