Epilepsy as a dynamic disease

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This project will build on a mathematical neuronal population activity model, recently developed at Nottingham (using a mixture of statistical physics and nonlinear dynamics), and extend this to include physiologically important components for the diffusion of ions in the extracellular space, and the resulting effect on electrical communication within cortical tissue. This is vitally important for the mechanistic interpretation of clinical EEG signals, against which the model will be validated, and will draw down from world leading biomedical expertise at Birmingham. Tools from machine learning will be juxtaposed with the mechanistic model to create a whitebox for predicting future brain states of individual patients using new observational data. This will further be augmented with routines for the prediction of critical transitions, giving the ability to avert or manage disastrous consequences of downstream seizures.