MultiscalE dynamics on high-order NeTworks applied to Epilepsy

Objetivo

"Neurological disorders such as epilepsy (affecting about 5 MILL people worldwide) are associated with deviations from the optimal network structure and dynamics. Epilepsy surgery (costing 1000-3000 EUR per surgery in EU countries) consists in the removal of the minimum amount of tissue needed to stop seizure propagation, but it is only successful in aprox. 2/3 of the cases. Analyses of brain network structure, and computational studies simulating seizure dynamics over it, have related aspects of the patients' brain network (e.g. hub distribution, excitability) with surgery outcome. However, results are not always replicable, and have not been able to surpass the current clinical standard. In the MENTE (which stands for ""mind"" in Spanish) project, I will explicitly consider the previously neglected effect of high-order (HO) brain structure on seizure dynamics and epilepsy surgery. I will characterize the HO brain structure of epilepsy patients (via Topological Data Analysis), in relation to surgery outcome. I will then create a HO computational model of seizure dynamics, by extending pairwise multiscale (MS) neuronal models of ictal activity. I will study the emergent behavior of the system via theoretico-computational analysis to characterize the healthy-to-ictal transition. Finally, I will fit the model to intracranial EEG data of ictal activity, and use it to model epilepsy surgery, with the goal of predicting surgery outcome and finding alternative resection strategies (smaller of with better outcome), to reduce the societal and economical burden of epilepsy. MENTE will validate the role of HO coupling on brain activity and promote the theoretico-computational modeling, analysis and application to neuroscience of network dynamics including HO and MS coupling. The fellowship will strengthen mine and the host's expertise these topics, and allow me develop my academic career to the point of scientific maturity.
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