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

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.

Who will benefit from this research and how:
+ Clinical neurologists: This project will develop software that could be used to supplement current clinical decision making in
- performing epilepsy surgery (e.g. by suggesting alternative surgery strategies that have a better outcome in terms of seizure freedom or side effects), or
- patient selection - i.e. identification of patients who are most at risk of persistent post-operative seizures

+ Patients & carers: Improved and personalised predictions of patient outcomes can be used to improve patient counselling and better manage patient expectations of the outcomes from
- epilepsy surgery in terms of seizure freedom or other side effects.

+ Employers seeking a skilled interdisciplinary workforce: This project will train five people (2x PDRAs, 2x PhDs, 1x RSE) in applying data analytics and modelling to clinical datasets. All five will additionally undergo skills training in project management, leadership, and communication.

+ Academic (modelling): Other researchers performing computational modelling will benefit from the publication of my models of brain network changes, which could be applied to other application domains. Models will be published open access and source code released publicly.

+ Academic (neuroscience): A deeper understanding of how network change relates to functional change on a subject-specific level will have wide-ranging implications. Open access publication of results will disseminate information to this community.

+ Academic (complex network analysis): The development of tools to predict and improve changes after network damage may be of benefit in other application domains (e.g. power, transport, telecommunications networks).

+ Public: Members of the public will benefit from this fellowship through outreach activities (public lectures, talks at local secondary state school).

+ Local researchers: Other research groups in Newcastle will benefit from the skilled personnel in my group, additional to myself. For example, Dr Dennis Prangle's group works on parameter estimation and dimensionality reduction which is directly relevant to Objective 7.

+ Collaborating partners: The key external partners all have complementary expertise to my own. By spending time working closely with them I will be able to initiate new ideas and training opportunities outside of my own team.

+ Healthcare sector (industry): The software developed in this fellowship may have commercial value through licensing as part of existing software, or development as a stand-alone tools. Through my collaborators there is contact with a large medical device companies in the context of epilepsy surgery already.

Expanded explanations of impact is provided in the pathways to impact document.