Large-scale EEG reveals shared and distinct neural oscillatory features across major psychiatric disorders

Major psychiatric disorders are leading contributors to global disability, yet robust biomarkers to inform diagnosis and treatment remain elusive. Electroencephalography (EEG) offers a scalable, noninvasive approach to capture neural dysfunction, but prior findings have been inconsistent due to small, single-disorder cohorts. Here, we report a large-scale, two-site comparative study of resting-state EEG in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) compared with healthy controls (HC).

Across 9751 participants’ EEG recordings, standardized preprocessing and feature generation yielded spectral power and functional connectivity (FC) measures tested with both statistical and machine-learning approaches. We identified convergent transdiagnostic alterations—reduced alpha power and increased gamma-band FC, implicating shared disruptions in inhibitory control and large-scale network coordination. Additional spectral differences included more pronounced delta-theta elevation in SCZ and a graded delta-theta continuum (SCZ > BD > MDD), suggesting a dimensional progression of neural dysregulation.

Machine-learning models robustly distinguished individuals with psychiatric diagnoses from healthy controls within sites, but cross-site generalization and inter-disorder discrimination remained limited, underscoring challenges of heterogeneity. These findings identify reproducible EEG features that capture both shared and disorder-distinct neural alterations, advancing efforts towards early detection and individualized treatment in major psychiatric disorders. This work was supported by STI2030-Major Projects (Grant No. 2022ZD0212400, 2021ZD0200404), National Natural Science Foundation of China (Grant No. 82371453), Zhejiang Key Laboratory of Clinical and Basic Research for Psychiatric Diseases (Grant No. 2024ZY01010, 2024E10107), “Pioneer” and “Leading Goose” R&D Program of Zhejiang (Grant No. 2024C03006, 2024C04024, 2026C01013), Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (Grant No.

JYB2025XDXM605), Leading Innovation and Entrepreneurship Team of Zhejiang Province (Grant No. 2023R01005), Fundamental Research Funds for the Central Universities (Grant No. 226-2026-00003), the Leading Innovation and Entrepreneurship Team of Hangzhou City (Grant No. TD2024003) and the Construction Fund of Key Medical Disciplines of Hangzhou (Grant No. 2025HZGF10). Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, School of Brain Science and Brain Medicine and Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China Shuying Rao, Junyi Xie, Mengfan Niu, Yue Pan, Yue Li, Hua Yu, Jingyi Xie, Wanjun Guo, Tao Li & Haiteng Jiang College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, 311121, China Shuying Rao, Sha Zhao, Gang Pan, Tao Li & Haiteng Jiang School of Biomedical Informatics, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080, USA College of Computer Science and Technology, Zhejiang University, Hangzhou, 310013, China Zhejiang Key Laboratory of Clinical and Basic Research for Psychiatric Diseases, Hangzhou, 310058, China Correspondence to Tao Li or Haiteng Jiang.

The authors declare no competing interest. The study was carried out in accordance with ethical principles for medical research involving humans (WMA, Declaration of Helsinki). The study protocol was approved by the Ethical Committee of the West China Hospital (No. 2026-1972) and the Hangzhou Seventh People’s Hospital (No. 2023-053-02).

Informed consent was obtained from all participants before enrollment. All data were collected anonymously. The study was registered with the Chinese Clinical Trial Registry (No.