Assessing and COntrolling seizuRe-modulating fluctuatioNs (ACORN)

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**Background**Epilepsy is one of the most common and serious neurological diseases characterised by recurrent, unprovoked seizures affecting around 50 million people globally and 500,000 patients in the UK. In a third of patients, seizure control is inadequate, and the side effects of treatment are detrimental to quality of life. Novel treatment approaches are needed.Seizures vary greatly in their severity, ranging from minor experiential phenomena or involuntary movements through to seizures with loss of awareness, falling to the ground and a generalized convulsion. All seizures adversely affect quality of life and more severe forms carry risk of physical and cerebral injury and of fatality.Individuals with focal epilepsy typically experience 2-3 types of seizures, ranging from experiential phenomena in isolation, to convulsive seizures. In some individuals, lack of sleep, stress, menstrual cycle or other factors may precipitate more severe types of seizures. To date, the factors driving the types of seizure appears to be patient-specific, and generally not well-understood.In agreement with the clinical observations, my research has recently shown that most patients display variability in brain activity patterns from one seizure to the next. Intriguingly, this variability was not random; some patients showed circadian fluctuations in their seizure activity patterns, whilst others showed longer-term fluctuations. Our observations also agree with a growing body of evidence that seizure generating processes appear to follow daily, weekly, monthly, or even seasonal fluctuations in most patients. It is additionally suspected that these fluctuations are also related to other physiological changes such as respiration and heart rate, and influenced by environmental factors such as air temperature, humidity and pressure. **Motivation**Together, these findings suggest that seizure-modulating processes exist, which influence brain activity patterns during a seizure, and in turn determine the severity of a seizure. These seizure-modulating processes appear to fluctuate over time, as they are influenced by other physiological and environmental processes, but they are most likely patient-specific, and currently not well-characterised or understood. **Goal**The goal of this fellowship is to capture and analyse the fluctuations in seizure-modulating processes to predict the type or severity of seizures in individual patients. I propose to do so using long-term EEG recordings, combined with wearable sensors and environmental sensor data. On an individual patient level, I will first characterise key seizure features that fluctuate over time and determine seizure severity, and associate them with the continuous fluctuations in physiological and environmental factors. Using the associations, I will build and validate patient-specific predictive models of seizure severity. Finally, I will explore interacting with some of the seizure-modulating processes (e.g. through exercise, diet or changing medication levels) in a closed-loop manner.**Impact**If successful, this fellowship will lead to a better understanding of how seizure activity is modulated in individual patients. In the first instance, this would result in an individualised predictive model that can forecast the severity of upcoming seizures. In the longer term, I will develop novel time-adaptive treatment strategies which minimise side effects by interacting with seizure-modulating processes in a targeted and timely manner. These treatment strategies could be for example the automated controlled release of a drug, or real-time feedback to the patient to change drug dosage, or diet.