During recent years, epilepsy and seizures are increasingly understood as a disorder of large-scale brain networks. There are two key components to these networks: the activity within brain regions and the pattern of connections between them. The interplay between these, and in particular the dynamic time-scales over which these interactions occur, is crucial for understanding how likely it is for seizures to emerge. In turn, this may then help us understand and model the impact of treatments on this emergent seizure-likelihood.
This project involves the development and evaluation of a model framework that combines the introduction of a new perturbation to a dynamic network model. By building on small network structures with known variations, perturbations can be introduced and their efficacy assessed over time using numerical analysis and non-linear dynamics. It will then focus on using these novel insights with respect to actual clinical data (e.g. EEG) from people with epilepsy and undergoing new medications.
This is an inherently mathematical and computational PhD research project, which will utilise tools from nonlinear and complex systems, ultimately applied to clinical data. It builds naturally on the research expertise of the supervisors who have significant experience in developing computational tools aimed at improving diagnosis, management and treatment of epilepsy.