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Published on: August 20, 2019
Energy-Based Phase-Locking State Analysis in Brain State Identification
Chenfei Ye1, Ziyan Deng2, Shiqing Cong1
1School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Human Brain Mapping
|June 5, 2026
Summary
A new method, Energy-based Phase-Locking State Analysis (EPLSA), analyzes brain dynamics by integrating phase synchronization and energy principles. It shows superior reliability and clinical utility for studying brain states in health and disease.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- The human brain exhibits multistability, studied using Energy Landscape Analysis (ELA) with BOLD signals.
- Traditional methods neglect phase synchronization dynamics, while existing phase-based methods lack thermodynamic rigor for state stability.
- Limitations exist in current frameworks for quantifying brain state dynamics and stability.
Purpose of the Study:
- Introduce Energy-based Phase-Locking State Analysis (EPLSA), a novel computational framework.
- Integrate instantaneous phase-coupling dynamics with energy landscape principles to overcome limitations of conventional methods.
- Validate EPLSA's superiority in reliability, state differentiation, and classification across neuroimaging datasets.
Main Methods:
- Developed Energy-based Phase-Locking State Analysis (EPLSA) integrating phase-coupling and energy landscape principles.
- Validated EPLSA against Leading Eigenvector Dynamic Analysis (LEiDA) and conventional ELA using HCP and Natural Sleep datasets.
- Applied EPLSA to analyze sleep-wake transitions and Alzheimer's disease patient data (OASIS-3).
Main Results:
- EPLSA demonstrated superior test-retest reliability, task-specific brain state differentiation, and individual classification compared to LEiDA and ELA.
- Sleep-wake analysis revealed EPLSA's sensitivity to consciousness transitions, showing altered state occupancy and reduced direct transition probabilities during sleep.
- In Alzheimer's disease, EPLSA identified altered co-activation dynamics (FPCN-DMN, VIS-LMN) correlating with cognitive impairment.
Conclusions:
- EPLSA offers a unified approach to phase-coupling and thermodynamic principles for analyzing brain dynamics.
- The method provides novel insights into neurodynamic mechanisms across cognitive tasks, consciousness states, and neurodegenerative conditions.
- EPLSA is a transformative tool for investigating brain function, with potential for early detection and monitoring of neurological disorders.
