Localization of the epileptogenic network from scalp EEG using a patient-specific whole-brain model

• Published in: Harvard data science review 2024-11, p.1-21
• Main Authors: Maliia, Mihai Dragos, Köksal-Ersöz, Elif, Benard, Adrien, Calas, Tristan, Nica, Anca, Denoyer, Yves, Yochum, Maxime, Wendling, Fabrice, Benquet, Pascal
• Format: Article
• Language: English
• Subjects: Bioengineering; Life Sciences
• ISSN: 2472-1751; 2472-1751; 2644-2353
• DOI: 10.1162/netn_a_00418

Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion’s removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies.