Topic: hybrid brain models

Advancing therapies for neurological disorders relies on developing efficient, personalized whole-brain models that can capture mechanistic insights and pathological features. Current challenges in creating such computational models arise from managing a considerable number of adjustable parameters. The conventional manual exploration of this parameter space is a serious limitation for personalizing models to individual subject data [1]. This work aims to address this by approaching the problem as an optimization challenge in the context of developing brain models capable of characterizing in