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

IF 5.3 1区 医学 Q1 PSYCHIATRY Schizophrenia Bulletin Pub Date : 2024-09-09 DOI:10.1093/schbul/sbae150
Elif Sarisik, David Popovic, Daniel Keeser, Adyasha Khuntia, Kolja Schiltz, Peter Falkai, Oliver Pogarell, Nikolaos Koutsouleris
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Abstract

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.

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基于脑电图的精神分裂症、抑郁症和异常衰老特征:监督机器学习研究》。
背景:脑电图(EEG)是一种无创、经济、可靠的工具,可直接测量具有高时间分辨率的体内神经元质量活动。结合最先进的机器学习(ML)技术,脑电图记录有可能产生严重精神障碍的硅学生物标志物:假设:病理和生理衰老过程会影响精神分裂症(SCZ)和重度抑郁障碍(MDD)的电生理特征:研究设计:我们从健康对照组(HC,245人)和SCZ(250人)或MDD(240人)住院患者组成的单中心队列(735人,51.6%为男性)中获取了静息态19通道脑电图记录。通过重复嵌套交叉验证,我们训练了支持向量机模型来(1)对 SCZ 或 MDD 患者和 HC 患者进行分类,以及(2)预测 HC 患者的年龄。年龄模型应用于患者群体,以计算电生理年龄差距估计值(EphysAGE),即预测年龄与实际年龄之差。然后进一步探讨了 EphysAGE、诊断和药物治疗之间的联系:研究结果:分类模型可将 SCZ 与 HC 区分开来(平衡准确率,BAC = 72.7%,P 结论:ML 模型可提取电生理数据,并将其用于诊断:ML 模型可以提取 MDD 和 SCZ 的电生理特征,具有潜在的临床应用价值。然而,老化过程对诊断可分性的影响要求及时应用此类模型,可能是在早期识别环境中。
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来源期刊
Schizophrenia Bulletin
Schizophrenia Bulletin 医学-精神病学
CiteScore
11.40
自引率
6.10%
发文量
163
审稿时长
4-8 weeks
期刊介绍: Schizophrenia Bulletin seeks to review recent developments and empirically based hypotheses regarding the etiology and treatment of schizophrenia. We view the field as broad and deep, and will publish new knowledge ranging from the molecular basis to social and cultural factors. We will give new emphasis to translational reports which simultaneously highlight basic neurobiological mechanisms and clinical manifestations. Some of the Bulletin content is invited as special features or manuscripts organized as a theme by special guest editors. Most pages of the Bulletin are devoted to unsolicited manuscripts of high quality that report original data or where we can provide a special venue for a major study or workshop report. Supplement issues are sometimes provided for manuscripts reporting from a recent conference.
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