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However, traditional amplitude\u2010based approaches fundamentally neglect critical phase synchronization dynamics that mediate large\u2010scale neural coordination, while existing phase\u2010based methods like Leading Eigenvector Dynamic Analysis (LEiDA) lack thermodynamic formalism for state stability quantification. Here, we introduce Energy\u2010based Phase\u2010Locking State Analysis (EPLSA), a transformative computational framework that synergistically integrates instantaneous phase\u2010coupling dynamics with rigorous energy landscape principles, addressing fundamental limitations of conventional methodologies. Comprehensive validation across two independent neuroimaging datasets (HCP and Natural Sleep) demonstrated EPLSA's marked superiority over LEiDA and conventional ELA in terms of test\u2013retest reliability, task\u2010specific brain state differentiation, and individual\u2010level classification performance. To demonstrate the physiological and clinical utility of the proposed method, sleep\u2013wake analysis was performed to reveal EPLSA's enhanced sensitivity to consciousness state transitions, identifying decreased primary state occupancy and increased minor state prevalence during sleep, with significantly reduced direct transition probabilities. Furthermore, application to patients with Alzheimer's disease using the OASIS\u20103 dataset identified shortened dwell time and occurrence frequency for the frontoparietal control network\u2010default mode network (FPCN\u2010DMN) co\u2010activation state, and prolonged dwell time and occurrence frequency for the visual network\u2010limbic network (VIS\u2010LMN) co\u2010activation state, with these metrics significantly correlating with cognitive impairment. By unifying phase\u2010coupling and thermodynamic principles, EPLSA provides novel insights into neurodynamic mechanisms across cognitive tasks, consciousness states, and neurodegenerative conditions, offering a transformative analytical tool for investigating brain function in health and disease with particular promise for early detection and monitoring of neurological disorders.<\/jats:p>","DOI":"10.1002\/hbm.70558","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T09:15:41Z","timestamp":1780650941000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Energy\u2010Based Phase\u2010Locking State Analysis in Brain State Identification"],"prefix":"10.1002","volume":"47","author":[{"given":"Chenfei","family":"Ye","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyan","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Harbin Institute of Technology (Shenzhen)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiqing","family":"Cong","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Ran","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Harbin Institute of Technology (Shenzhen)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Radiology Shandong Provincial Hospital Affiliated to Shandong First Medical University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoulin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Guangxi Normal University  Guilin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8819-6228","authenticated-orcid":false,"given":"Ting","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhs352"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2588046"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.01867"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.033"},{"key":"e_1_2_10_6_1","first-page":"738","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. 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