Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in Epilepsy

Epilepsy patients with drug-resistant epilepsy are eligible for surgery aiming to remove the cerebral regions that generate seizures, the so-called epileptogenic zone network (EZN). Thus, the accurate delineation of the EZN is crucial. Personalized virtual brain models derived from patient-specific anatomical and functional data are used in Virtual Epileptic Patient (VEP) to estimate the EZN via optimization methods from Bayesian inference. The Bayesian inference approach used in previous VEP integrates priors, based on the features of stereotactic-electroencephalography (SEEG) seizures recordings. Here, we propose new priors, based on quantitative 23Na-MRI. The 23Na-MRI data were acquired at 7T and provided several features characterizing the sodium signal decay. The hypothesis is that the sodium features are biomarkers of neuronal excitability related to the EZN and will add additional information to VEP estimation. In this paper, we first proposed the mapping from 23Na-MRI features to predict the EZN via a machine learning approach. Then, exploiting these predictions as priors in the VEP pipeline, we demonstrated that 23Na-MRI prior based VEP estimation of the EZN improved the results in terms of balanced accuracy and as good as SEEG priors in terms of the weighted harmonic mean of the precision and recall.