Optimising clinical data for predicting the outcome of epilepsy surgery

Principal investigator(s)Co-InvestigatorPhD student or Research Fellow(s)Project title

Dr Marc Goodfellow

Professor Mark Richardson, Dr Eugenio Abela Dr Leandro Junges, Dr Jen Creaser, Rhys Lloyd

Optimising clinical data for predicting the outcome of epilepsy surgery


Lay summary:

Epilepsy is one of the most common brain disorders. It causes sudden, unpredictable seizures and can therefore be quite disabling. One third of patients cannot be treated with drugs, but they could benefit enormously from brain surgery that targets brain areas causing seizures – if those areas can be clearly identified. Current methods, however, are limited in their accuracy, meaning that multiple (and costly) investigations are needed to find the target, and that epilepsy surgery sill fails in a considerable number of patients.

To improve this, we propose a radically new way of looking at brain activity recordings from epilepsy patients (so-called electroencephalogram, EEG). Our method uses the patient’s own EEG combined with advanced mathematics to simulate seizures, and to test what would happen if certain brain areas were operated on. This “computational operation theatre” could be used to safely test different operation strategies, determine which tests are further needed to refine them, and predict how successful an operation might be.

If successful, this approach could dramatically increase the information gained from standard clinical EEG studies, and lead to highly personalised treatment decisions for people with difficult to treat epilepsies.