Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy

TL;DR

This paper presents a new topological data analysis method for embedding brain networks into a lower-dimensional space, enabling visualization and statistical analysis of brain dynamics in temporal lobe epilepsy from rs-fMRI data.

Contribution

It introduces a novel TDA-based embedding technique for brain networks, allowing for detailed comparison and analysis of topological differences in TLE patients versus controls.

Findings

TLE brains show increased rigidity in 0D topology.

TLE brains exhibit more rapid fluctuations in 1D topology.

The method enables effective visualization and statistical inference.

Abstract

We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology.