ArticlePatterns (New York, N.Y.)2026
Node persistence from topological data analysis reveals changes in brain functional connectivity.
Article in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Node persistence from topological data analysis reveals changes in brain functional connectivity.Patterns (New York, N.Y.) · 2026Article
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4 authors.
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Abstract
Large-scale analyses of brain functional connectivity can uncover disruptions in regional activity and connectivity that are commonly associated with neurological disorders or cognitive decline associated with healthy aging. In our study, we employ persistent homology (PH), a prominent tool in topological data analysis, to investigate changes in resting-state functional connectivity in healthy aging and autism spectrum disorder (ASD). We analyze functional connectivity changes across three distinct scales: (1) global scale (brain-wide changes), (2) mesoscopic scale (resting-state-network-level changes), and (3) local scale (region-level changes). At the local scale, we introduce node persistence, a scalable PH-based measure that detects brain regions with significant differences in healthy aging or ASD. Notably, these regions overlap with regions whose non-invasive stimulation improves motor function in the elderly or alleviates ASD symptoms, suggesting the utility of node persistence in identifying clinically relevant brain regions affected by aging and ASD.
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