Inference-based time-resolved chaos analysis of brain models: Application to focal epilepsy

This paper introduces a new inference-based framework for time-resolved chaos analysis of brain models and demonstrates its application to focal epileptic seizures. The intermittent nature of epileptic seizures exhibits an unpredictable behavior that shares some characteristics with chaotic systems. Epilepsy research often uses concepts from chaos theory and nonlinear dynamics to better understand the mechanisms of seizure initiation, propagation, and termination. Traditional methods estimate the degree of chaos in brain dynamics directly from time series data. This provides neither an accurate estimate of the chaos nor insights into the key neurophysiological processes driving brain dynamics during epileptic seizures. Therefore, this study proposes a new method to calculate Lyapunov spectra by combining time series data with neurophysiological brain models and a specialised nonlinear Kalman filter. This study thereby provides insights into the temporal evolution of chaos in epileptogenic regions during epileptic seizures and identifies external inputs from adjacent and distant brain regions as major drivers of altered levels of chaoticity. This paper underscores the importance of fusion of neurophysiological computational models and clinical time series data in understanding the dynamic and chaotic aspects of epilepsy to develop more effective diagnostic and treatment strategies.