Personalized Brain Network Models

This lecture explores the applications of personalized brain network models in medicine, focusing on the use of mean fields to characterize network nodes, bifurcations in fast discharge trains, and the taxonomy of brain state changes. It delves into the refinement of network pathology in epilepsy patients, the organization of dynamics in large-scale networks through time delays, and the validation of virtual epileptic patient models. The lecture also covers the fitting of SEEG data using Hamiltonian Monte Carlo, the impact of drug-resistant focal epilepsies on young individuals, and the evolution of epilepsy surgery outcomes over the past 30 years. It concludes by emphasizing the importance of multi-scale modeling and simulation in understanding brain function and dysfunction, with a focus on personalizing brain network models for various neurological disorders.