Lopez-Sola, E.; Mercadal, B.; Lleal-Custey, E.; Salvador, R.; Sanchez-Todo, R.; Bartolomei, F.; Wendling, F.; Ruffini, G.

Computational modeling has recently emerged as a powerful tool to better understand seizure dynamics and guide new treatment strategies. This work presents a method for personalizing whole-brain computational models in epilepsy, integrating SEEG, MRI, and diffusion MRI data to enhance therapeutic approaches. The objective of this method is to construct a mechanistic model replicating seizure propagation from the epileptogenic network to the entire brain, which can then be used to simulate and evaluate patient-specific therapeutic interventions. The pipeline uses neural mass models for each node in the network, simulating whole-brain dynamics. Model personalization involves adjusting global and local parameters representing the excitability of individual brain areas, using an evolutionary algorithm that aims to maximize the correlation between empirical and synthetic functional connectivity matrices derived from SEEG data. The resulting personalized models successfully reproduce individual seizure propagation patterns and can be used to simulate therapeutic interventions like surgery, stimulation, or pharmacological interventions within a unified physiological framework. Notably, model predictions reveal distinct patient-specific responses across interventions, highlighting the potential of whole-brain modeling to guide individualized treatment by identifying accessible and functionally relevant targets.