Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy

• DOI: 10.1126/scitranslmed.abp8982
• Publication Date: 2023-01-25T18:58:12Z
• Authors: Huifang E. Wang; Marmaduke Woodman; Paul Triebkorn; Jean-Didier Lemar...; Jayant Jha; Borana Dollomaja; Anirudh Nihalani ...; Viktor Sip; Samuel Medina Vil...; Meysam Hashemi; Maxime Guye; Julia Makhalova; Fabrice Bartolomei; Viktor Jirsa
• Keywords: Drug Resistant Epilepsy

Precise estimates of epileptogenic zone networks (EZNs) 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 estimate EZNs aid surgical strategies. The structural scaffold patient-specific whole-brain network model is constructed from anatomical T1 diffusion-weighted magnetic resonance imaging. Each node equipped with mathematical dynamical simulate seizure activity. Bayesian inference sample optimize key parameters using functional stereoelectroencephalography recordings patients’ seizures. These together their determine given patient’s EZN. Personalized were further used predict outcome surgeries. We evaluated VEP retrospectively 53 patients VEPs reproduced clinically defined precision 0.6, where physical distance between regions identified by was small. Compared resected 25 who underwent surgery, showed lower false discovery rates in seizure-free (mean, 0.028) than non–seizure-free 0.407). now being an ongoing clinical trial (EPINOV) expected 356 prospective