Energy-Based Phase-Locking State Analysis in Brain State Identification.
Human Brain Mapping June 1, 2026 Chenfei Ye, Ziyan Deng, Shiqing Cong et al.
A new computational framework called Energy-based Phase-Locking State Analysis (EPLSA) integrates phase-coupling dynamics with energy landscape principles to analyze brain network states from fMRI data. Across two independent datasets, EPLSA outperformed existing methods in reliability, task-specific state differentiation, and individual classification. Sleep-wake analysis showed decreased primary state occupancy and increased minor state prevalence during sleep, with reduced direct transition probabilities. In Alzheimer's disease patients, the frontoparietal control network-default mode network co-activation state showed shortened dwell time and occurrence frequency, while the visual network-limbic network co-activation state showed prolonged dwell time and occurrence frequency, with these metrics correlating with cognitive impairment.