Automating region selection with genetic algorithms for energy landscape analyses of brain dynamics

Understanding brain dynamics is essential in cognitive neuroscience. Energy landscape analysis (ELA), which characterizes brain activity using a pairwise maximum entropy model, is a powerful tool for analyzing these dynamics but has traditionally relied on the subjective, manual selection of small regions of interest (ROIs) to satisfy mathematical constraints. We developed ELA/GAopt, a framework that uses genetic algorithms to automate ROI selection from whole-brain atlases in a data-driven manner. ELA/GAopt's consistent identification of reproducible ROI subsets was validated across three independent datasets. In multi-site clinical data, the framework identified and replicated autism-specific dynamics characterized by global co-activation within sensory-motor and visual networks. These results demonstrate that ELA/GAopt provides a systematic, objective approach for characterizing condition-specific brain dynamics, thereby establishing a methodological foundation for future externally validated biomarker studies and the systematic exploration of brain state transitions.