Codes: Delineating epileptogenic networks using brain imaging data and personalized modelling in drug-resistant epilepsy

Codes: Delineating epileptogenic networks using brain imaging data and personalized modelling in drug-resistant epilepsy Precise estimates of the epileptogenic zone networks (EZN) are crucial for planning intervention strategies to treat drug resistant focal epilepsy. Here, we present the Virtual Epileptic Patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate the EZN and to aid the surgical strategy. The structural scaffold of the patient-specific whole brain network model is constructed from anatomical T1 and diffusion weighted MRI. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereo-EEG recordings of patients’ seizures. These key parameters together with their personalized model determine a given patient’s EZN. The personalized model can further be used to predict the outcome of surgical intervention using virtual surgeries. The VEP workflow was evaluated retrospectively using 53 patients with drug resistant focal epilepsy. VEP reproduced the clinical definition with precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZN was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean: 0.028) than in not seizure-free patients (mean: 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with expected 356 prospective epilepsy patients