{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T21:53:03Z","timestamp":1792101183788,"version":"4.4.0"},"reference-count":118,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Else-Kr\u00f6ner-Fresenius-Stiftung"},{"name":"EKFS-Translational Psychiatry"},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01MH124639-01"],"award-info":[{"award-number":["U01MH124639-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"German Innovation Fund"},{"name":"German Federal Ministry of Education and Research"},{"name":"German Federal Ministry of Education and Research","award":["01ZX1904E"],"award-info":[{"award-number":["01ZX1904E"]}]},{"name":"German Science Foundation"},{"name":"German Ministry of Science"},{"name":"German Ministry of Health"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Electroencephalography (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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Hypothesis<\/jats:title>\n                    <jats:p>Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>From a single-center cohort (N\u2005=\u2005735, 51.6% male) comprising healthy control individuals (HC, N\u2005=\u2005245) and inpatients suffering from SCZ (N\u2005=\u2005250) or MDD (N\u2005=\u2005240), 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Results<\/jats:title>\n                    <jats:p>The classification models robustly discriminated SCZ from HC (balanced accuracy, BAC\u2005=\u200572.7%, P\u2005&amp;lt;\u2005.001), MDD from HC (BAC\u2005=\u200567.0%, P\u2005&amp;lt;\u2005.001), and SCZ from MDD individuals (BAC\u2005=\u200563.2%, P\u2005&amp;lt;\u2005.001). Notably, central alpha (8\u201311 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 (\u03c1HC\u2005=\u20050.23, P\u2005&amp;lt;\u2005.001; \u03c1MDD\u2005=\u20050.17, P\u2005=\u2005.01).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/schbul\/sbae150","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T11:55:52Z","timestamp":1724154952000},"page":"804-817","source":"Crossref","is-referenced-by-count":21,"title":["EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation"],"prefix":"10.1093","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3736-2462","authenticated-orcid":false,"given":"Elif","family":"Sarisik","sequence":"first","affiliation":[{"name":"Max Planck Fellow Group Precision Psychiatry,\u00a0Max Planck Institute of Psychiatry , Munich ,","place":["Germany"]},{"name":"Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich , Munich 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