Topological analysis of brain dynamical signals indicates signatures of seizure susceptibility

TL;DR

This study uses topological data analysis to identify signatures of seizure susceptibility in brain signals from epileptic zebrafish, revealing detectable topological markers even outside seizure periods.

Contribution

It introduces a novel application of Persistent Homology to distinguish epileptic states and susceptibility in brain dynamics across different genetic conditions.

Findings

Ictal and non-ictal periods have distinct topological signatures.

Topological differences can predict seizure susceptibility within a single fishline.

TDA effectively identifies markers of epileptic susceptibility in brain signals.

Abstract

Epilepsy is known to drastically alter brain dynamics during seizures (ictal periods), but its effects on background (non-ictal) brain dynamics remain poorly understood. To investigate this, we analyzed an in-house dataset of brain activity recordings from epileptic zebrafish, focusing on two controlled genetic conditions across two fishlines. After using machine learning to segment and label recordings, we applied time-delay embedding and Persistent Homology -- a noise-robust method from Topological Data Analysis (TDA) -- to uncover topological patterns in brain activity. We find that ictal and non-ictal periods can be distinguished based on the topology of their dynamics, independent of genetic condition or fishline, which validates our approach. Remarkably, within a single wild-type fishline, we identified topological differences in non-ictal periods between seizure-prone and seizure-free individuals. These findings suggest that epilepsy leaves detectable topological signatures in brain dynamics even outside of ictal periods. Overall, this study demonstrates the utility of TDA as a quantitative framework to screen for topological markers of epileptic susceptibility, with potential applications across species.