Topological Analysis of Brain Dynamical Signals Indicates Signatures of Seizure Susceptibility

Epilepsy is known to drastically alter brain dynamics during seizures (ictal periods), but its effects on background (nonictal) 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 nonictal 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 nonictal 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.