Executive Summary : | This project aims to understand the propagation patterns of seizures and related synchronization phenomena in evolving large-scale epileptic brain networks. It will investigate structural changes of functional networks from multichannel intracranial electroencephalograms (iEEG) recorded from patients with medically refractory epilepsy. The study will focus on two types of seizures with a clinically well-defined origin, which either remain local focal seizures or spread to other brain areas. Network- and nonlinear dynamics-based analyses will be pursued to differentiate synchronization phenomena related to these clinically different categories of seizure. The study is crucial as the seizure onset zone is thought to be the best surgical target, but poor surgical long-term outcomes suggest that the large-scale epileptic brain network is involved in seizure propagation and termination.
Epilepsy is a brain disorder characterized by recurrent unprovoked seizures, affecting around 50 million people annually. One-third of subjects do not respond to antiepileptic drugs, making understanding the initiation, propagation, and termination of seizure-related brain dynamics crucial for developing alternative treatments and surgical interventions. Current research focuses on modeling seizure dynamics and complex-network-based analyses of seizures. Two main strategies have been proposed: a neural network-based biophysically-constrained model and a computational nonlinear dynamical model. These models have shown that seizure dynamics occur naturally at high-degree nodes and that network synchronization may be a self-organized mechanism for seizures to stop.
However, the propagation of seizure activity from one part of the brain to another remains poorly understood. A self-consistent linear response theory has been suggested to understand spatiotemporal signal propagation for nonlinear perturbed systems, but it has not been used to study the spatial-temporal propagation of seizures in the human brain. The objective is to provide mathematical tools for predicting propagation patterns in the epileptic brain for both types of seizures in light of network and nonlinear dynamics.
The proposed research will investigate iEEG recordings of seizures, derive evolving functional brain networks using a time-series-analysis-based approach, and study structural characterization of networks for both seizure types. |