Identifying optimal neurostimulation for epilepsy using computational approaches (IONECA)

Epilepsy is a debilitating disease characterised by unpredictable recurrent seizures. Continuous electrical brain stimulation is a promising treatment option for patients who are resistant to drug treatments. However, we are unsure how the treatment works and its success rate varies. There is also no clear strategy on how it should be applied.

We will use network analysis and computational modelling to identify 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 effect on seizures. We will then use computational modelling and inference to simulate patient-specific functional networks that predict the stimulation effect for settings that have not been tested in the patient. We will combine our simulations with optimisation methods that will allow us to identify optimal stimulation parameters for individual patients.

We aim to develop a comprehensive network analysis and modelling framework that will help identify where and how continuous electrical brain stimulation can prevent focal epilepsies. This will result in an analysis software package which would help make neurostimulation a reliable treatment option for patients with epilepsy.