Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance. Neuroimage 2023 Dec 15;284:120436
Date
11/07/2023Pubmed ID
37931870Pubmed Central ID
PMC11074922DOI
10.1016/j.neuroimage.2023.120436Scopus ID
2-s2.0-85176243738 (requires institutional sign-in at Scopus site) 2 CitationsAbstract
Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.
Author List
Chung MK, Ramos CG, De Paiva FB, Mathis J, Prabhakaran V, Nair VA, Meyerand ME, Hermann BP, Binder JR, Struck AFAuthor
Jeffrey R. Binder MD Professor in the Neurology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
BrainEpilepsy, Temporal Lobe
Humans
Magnetic Resonance Imaging
Models, Statistical
Nerve Net
