Network mOdel for MechAnism of epilepsy Dynamics

Epilepsy, characterized by recurrent seizures, is one of the most common neurological diseases globally affecting around 5 per 1000 people in Europe. Yet little is known about the exact mechanism of seizure and its relationship to pathological brain structure.

From the clinical perspective, proposing the optimal surgical strategies and predicting the outcomes of surgeries are difficult due to the dynamic nature of epilepsy.

The NOMAD project proposes novel network models of seizure propagation based on statistical physics, aiming at understanding the mechanism of the interplay between the brain network changes and seizure propagation, and improving the optimal surgical strategies.Numerous studies have focused on characterizing the changes in the epileptic brain network via network analysis.

However, due to the bias from different measurements and the arbitrariness of choices in diverse types of network analysis, there is considerable controversy in the conclusions.

Recently, spreading models built on statistical physics for seizure have been proposed which are promising in predicting epileptic surgical outcomes, yet they are inefficient in surpassing clinical criteria.Integrating recent advances in statistical physics and network science, NOMAD studies the fundamental roles of inhibitory regulations and plasticity in shaping seizure dynamics, which are neglected in previous studies.

I will first study the critical dynamics of seizure on a theoretical model taking into account the role of inhibitory regulations and secondly, a computational framework considering in addition the effect of plasticity via large-scale Monte Carlo simulations.

Lastly, I will apply the models to clinical data by predicting the seizure dynamics on brain networks constructed from clinical data and proposing optimal surgical strategies.The project will provide new insights into understanding the mechanism of epilepsy and potentially new tools to enhance epilepsy surgical outcomes.