High-resolution Bayesian Virtual Epileptic Patient using neural field models.

Epilepsy remains a significant medical challenge, particularly in drug-resistant cases where surgical intervention may be the only viable treatment option. Identifying the epileptogenic zone, the brain region responsible for seizure initiation, is a critical step in surgical planning. Combining dynamical system models, machine learning, and the neuroimaging data of epileptic patients in the so-called Bayesian Virtual Epileptic Patient (VEP) framework has previously been shown to be a promising approach for identifying the epileptogenic zone. However, previous studies employed coupled neural mass models to describe the whole-brain seizure dynamics and, hence, could only provide a highly coarse spatial estimate of the epileptogenic zone. In this study, we propose an extension of the Bayesian VEP to a neural field model, which can improve the spatial resolution by several orders. Performing model inversion using neural field models is a challenging task as the parameter space is very high dimensional, and it becomes computationally expensive to compute gradients. We demonstrate that by using pseudospectral methods and spherical harmonic transforms, it is feasible to perform model inversion on a neural field extension. We found that the high-resolution Bayesian VEP not only improves the spatial resolution but also significantly reduces the number of false positives.