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arXiv · 2603.17086

Topological inference on brain networks with application to lesion symptom mapping

Yuan Wang·Jian Yin·Nicholas Riccardi·Drik-Bart Den Ouden·Julius Fridriksson·Rutvik H. Desai
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Abstract

Persistent homology (PH) characterizes the shape of brain networks through persistence features. Group comparison of persistence features from brain networks can be challenging as they are inherently heterogeneous. A recent scale-space representation of persistence diagrams (PDs) through heat diffusion reparameterizes them using a finite number of Fourier coefficients with respect to the Laplace--Beltrami (LB) eigenfunction expansion of the domain, providing a powerful vectorized algebraic representation for group comparisons. In this study, we develop a transposition-based permutation test for comparing multiple groups of PDs using heat-diffusion estimates. We evaluate the empirical performance of the spectral transposition test in capturing within- and between-group similarity and dissimilarity under varying levels of topological noise and cycle location variability. In application, we propose a topological lesion symptom mapping (TLSM) method based on the proposed framework. The method is applied to resting-state functional brain networks of individuals with post-stroke aphasia to identify characteristic cycles associated with varying levels of speech-language impairment.

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Yuan Wang, Jian Yin, Nicholas Riccardi, Drik-Bart Den Ouden, Julius Fridriksson, Rutvik H. Desai. 2026-03-17. Topological inference on brain networks with application to lesion symptom mapping. https://arxiv.org/abs/2603.17086

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