Virtual Epileptic Patient (VEP): Data-driven probabilistic personalized brain modeling in drug-resistant epilepsy
Wang, H. E.; Woodman, M.; Triebkorn, P.; Lemarechal, J.-D.; Jha, J.; Dollomaja, B.; Vattikonda, A. N.; Sip, V.; Medina Villalon, S.; Hashemi, M.; Guye, M.; Scholly, J.; Bartolomei, F.; Jirsa, V.
Show abstract
One-third of 50 million epilepsy patients worldwide suffer from drug resistant epilepsy and are candidates for surgery. Precise estimates of the epileptogenic zone networks (EZNs) are crucial for planning intervention strategies. Here, we present the Virtual Epileptic Patient (VEP), a multimodal probabilistic modeling framework for personalized end-to-end analysis of brain imaging data of drug resistant epilepsy patients. The VEP uses data-driven, personalized virtual brain models derived from patient-specific anatomical (such as T1-MRI, DW-MRI, and CT scan) and functional data (such as stereo-EEG). It employs Markov Chain Monte Carlo (MCMC) and optimization methods from Bayesian inference to estimate a patients EZN while considering robustness, convergence, sensor sensitivity, and identifiability diagnostics. We describe both high-resolution neural field simulations and a low-resolution neural mass model inversion. The VEP workflow was evaluated retrospectively with 53 epilepsy patients and is now being used in an ongoing clinical trial (EPINOV).
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