[SurgeryNet] Epilepsy surgery induced brain network changes: relation to patient outcomes

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Epilepsy is a serious neurological condition affecting over 600,000 patients in the UK. Patients have seizures which can result in loss of consciousness, convulsions, and even increased risk of sudden death. Drugs are only effective for around two thirds of patients, motivating the need for alternative treatments. Brain surgery, where the part of the brain thought to be causing seizures is removed is a serious option for many. However, even after the invasive removal of brain tissue, seizures still recur in up to half of patients. The past decades have seen a revolution in our thinking of how to study the brain. Computational, and methodological advances have allowed us to think of the brain as a complex network of interacting regions - and epilepsy as a disorder of abnormal interactions within and between regions. Given that brain networks can be measured in many different ways, and given that epilepsy surgery can be thought of as a change to a network, it soon becomes apparent that this challenge is extremely complex. In short, we need new ways to improve the surgical treatment of patients with epilepsy by leveraging the complexity of the derived brain networks, rather than being hampered by them.In this fellowship I will develop and use advanced computational techniques to analyse pre-surgery data acquired from over 500 patients with epilepsy. I will use these techniques to generate personalised brain networks, then use computer models to predict patient outcomes. Since this group of >500 patients already underwent surgery I will compare the predictions to the actual patient outcomes. In the second phase of the fellowship I will apply these models to new patient data from hospitals around the world to test if the predictions are robust. Finally, I plan to conduct prospective analysis to evaluate patient benefit in real-life clinical settings. If successful this fellowship will lead to predictive and mechanistic models to inform clinical decision making for surgery.