癫痫
立体脑电图
癫痫外科
抗药性癫痫
工作流程
医学
磁共振成像
计算机科学
放射科
数据库
精神科
作者
Huifang Wang,Marmaduke Woodman,Paul Triebkorn,Jean-Didier Lemaréchal,Jayant Jha,Borana Dollomaja,Anirudh Nihalani Vattikonda,Viktor Sip,Samuel Médina Villalon,Meysam Hashemi,Maxime Guye,Julia Scholly,Fabricē Bartolomei,Viktor K. Jirsa
出处
期刊:Science Translational Medicine
[American Association for the Advancement of Science (AAAS)]
日期:2023-01-25
卷期号:15 (680)
被引量:26
标识
DOI:10.1126/scitranslmed.abp8982
摘要
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 to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. 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 stereoelectroencephalography recordings of patients' seizures. These key parameters together with their personalized model determine a given patient's EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs 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 non-seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy.
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