Every year, 1,000 people in the UK die directly from epilepsy, and 40,000 receive a confirmed diagnosis only after an average delay of a year. This research tackles the problem that treatment is currently a game of "watchful waiting"—doctors prescribe drugs and wait to see if seizures stop, with no clear indicators of which medication or dose will work. For the 50% of patients whose seizures remain uncontrolled a year after starting treatment, this uncertainty means continued seizures that strike apparently at random, severely reducing quality of life. The researcher will build mathematical models that describe how different brain regions interact and how internal and external stimuli—like stress—can trigger seizures. By feeding clinical brain recordings into these models, the algorithms become personalised to each patient. Working with a software engineer and people with epilepsy, the team will translate this understanding into prototype tools: one to help clinical teams pinpoint where seizures originate for surgical planning, and another—a smartphone app paired with a wireless headset—that could forecast when a seizure is likely. If successful, these tools could replace guesswork with prediction, giving patients warning and clinicians a basis for choosing treatments.
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Accurate diagnosis, prognosis and management of epilepsy is a significant unmet medical need. Epilepsy is a serious brain condition whereby susceptible individuals have recurrent seizures. It affects almost 1% of the UK population at some point in their lives. Every year 125,000 people attend first seizure clinics across the UK. Of these, 40,000 eventually receive a confirmed diagnosis of epilepsy, typically following an average delay of a year (Joint Epilepsy Council, September 2011). Whilst two-thirds of these confirmed cases can ultimately be controlled by anti-epilepsy drugs (AED), approximately 50% remain uncontrolled a year after commencing treatment, with no clear indicators of choice of AED or dose. Treatment response is currently a case of "watchful waiting" to see whether further seizures occur and adjusting choice of medication and dose accordingly. For those who do not respond they continue to have seizures, apparently at random, which leads to a significant reduction in quality and quantity of life. Every year over 1,000 people die in the UK as a direct consequence of epilepsy. I propose an exciting programme of research in which mathematical models and computer algorithms will be developed to improve our understanding of how seizures occur. These models will describe how different regions of the brain interact with each other and how internal and external stimuli can influence these interactions. The interplay between neural activity within brain regions and the connections between regions critically determines whether seizures can occur and how frequently they are likely to happen. Having developed this fundamental understanding, computer algorithms can be developed to inform key parameters of these models directly from clinical recordings collected from people with epilepsy. This makes the models personalised and therefore suitable to address key questions for people with (suspected) epilepsy: - is the diagnosis accurate? - are the drugs being prescribed effective? - will exposure to stress make seizures more likely? - can I know when my seizures are most likely to happen? - will surgery stop my seizures happening? I will work with a software engineer and people with epilepsy to translate this understanding into a prototype tools to address these questions. For example, developing a tool that can aid a multi-disciplinary clinical team in determining where in the brain seizures originate from and using this information to better plan for surgery. Alternatively, a smart-phone based app that receives electrical brain recordings from a wireless headset to provide a forecast of seizure risk. Co-designing and developing these prototypes with people with lived experience and clinicians will maximise the likelihood of their leading to impact of the fundamental research.
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