ArticleSchizophrenia bulletin2025
EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.
Article in Schizophrenia bulletin, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Beyond the label "major depressive disorder"-detailed characterization of study population matters for EEG-biomarker research.Frontiers in neuroscience · 2025Pooled it
- Peripheral magnetic stimulation effects on brain activity in older adults with schizophrenia - a randomized controlled pilot study.Frontiers in psychiatry · 2026Trial
- Accelerated aging in schizophrenia: integrating epigenetic clocks, telomere dynamics, senescence-associated secretory phenotype, and oxidative stress.European archives of psychiatry and clinical neuroscience · 2026Review
- Stability-Driven Selection of EEG Connectivity Features for Psychosis Classification: A Network-Based Machine Learning Approach.CNS neuroscience & therapeutics · 2026Article
- MDEFusion: A Multi-Domain EEG Feature Fusion Network With Bidirectional Attention LSTM and Half-Split Crossover SAE for Schizophrenia Recognition.Brain and behavior · 2026Article
- Enhancing Diagnostic Accuracy of Neurological Disorders Through Feature-Driven Multi-Class Classification with Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundElectroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders. HYPOTHESIS: Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD). STUDY
designFrom a single-center cohort (N = 735, 51.6% male) comprising healthy control individuals (HC, N = 245) and inpatients suffering from SCZ (N = 250) or MDD (N = 240), we acquired resting-state 19 channel-EEG recordings. Using repeated nested cross-validation, support vector machine models were trained to (1) classify patients with SCZ or MDD and HC individuals and (2) predict age in HC individuals. The age model was applied to patient groups to calculate Electrophysiological Age Gap Estimation (EphysAGE) as the difference between predicted and chronological age. The links between EphysAGE, diagnosis, and medication were then further explored. STUDY
resultsThe classification models robustly discriminated SCZ from HC (balanced accuracy, BAC = 72.7%, P < .001), MDD from HC (BAC = 67.0%, P < .001), and SCZ from MDD individuals (BAC = 63.2%, P < .001). Notably, central alpha (8-11 Hz) power decrease was the most consistently predictive feature for SCZ and MDD. Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC = 0.23, P < .001; ρMDD = 0.17, P = .01).
conclusionsML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.
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