EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

Autor:	Sarisik E; Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.; Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany.; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany., Popovic D; Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.; Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany.; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany.; German Center for Mental Health (DZPG), Partner Site Munich, Munich, Germany., Keeser D; Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany.; German Center for Mental Health (DZPG), Partner Site Munich, Munich, Germany.; NeuroImaging Core Unit Munich (NICUM), LMU University Hospital, LMU Munich, Munich, Germany.; Munich Center for Neurosciences, LMU Munich, Munich, Germany., Khuntia A; Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany.; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany., Schiltz K; Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany., Falkai P; Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.; Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany.; German Center for Mental Health (DZPG), Partner Site Munich, Munich, Germany., Pogarell O; Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany., Koutsouleris N; Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.; Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany.; German Center for Mental Health (DZPG), Partner Site Munich, Munich, Germany.; Munich Center for Neurosciences, LMU Munich, Munich, Germany.; Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.
Jazyk:	angličtina
Zdroj:	Schizophrenia bulletin [Schizophr Bull] 2024 Sep 09. Date of Electronic Publication: 2024 Sep 09.
DOI:	10.1093/schbul/sbae150
Abstrakt:	Background: 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. Hypothesis: Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD). Study Design: From 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 Results: The 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). Conclusions: 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. (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu