Identifying Optimal Neurostimulation for Epilepsy using Computational Approaches (IONECA) (360G-Wellcome-208940_Z_17_Z)

Epilepsy is a debilitating disease characterised by unpredictable recurrent seizures. Continuous electric brain stimulation is a promising treatment option for 35% of patients that are drug-resistant. However, our understanding of its mechanisms of action is still limited, the success rates vary, and there is no clear strategy regarding stimulation location or parameters. We propose to apply network analysis and computational modelling approaches for identifying optimal stimulation settings on a patient-specific basis. In a retrospective study, we will compare the functional networks of patients with focal epilepsy during different stimulation settings, and relate these changes to the stimulation effect on seizures. We will then use computational modelling and inference to simulate patient-specific functional networks that predict the stimulation effect for settings which have not been tested in the patient. Finally, combining our simulations with optimisation methods will allow us to identify optimal stimulation parameters for each patient. In summary, we aim to develop a comprehensive network analysis and modelling framework that will help identify the "where" and "how" of continuous electric brain stimulation in focal epilepsies. This will result in an analysis software package for prospective use, which would significantly contribute towards making neurostimulation a reliable treatment option in epilepsy.