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.

中文翻译：

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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：监督机器学习调查


背景脑电图 （EEG） 是一种无创、经济高效且强大的工具，可直接测量体内神经元质量活动，具有很高的时间分辨率。结合最先进的机器学习 （ML） 技术，脑电图记录可能会产生严重精神障碍的计算机生物标志物。假设 病理和生理衰老过程影响精神分裂症 （SCZ） 和重度抑郁症 （MDD） 的电生理特征。研究设计 从由健康对照个体 （HC，N = 245） 和患有 SCZ （N = 250） 或 MDD （N = 240） 的住院患者组成的单中心队列 （N = 735,51.6% 男性） 中，我们获得了静息态 19 通道脑电图记录。使用重复的嵌套交叉验证，训练支持向量机模型以 （1） 对 SCZ 或 MDD 患者和 HC 个体进行分类，以及 （2） 预测 HC 个体的年龄。将年龄模型应用于患者组，以计算电生理年龄差距估计 （EphysAGE） 作为预测年龄与实际年龄之间的差异。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 分类模型稳健地区分 SCZ 与 HC （平衡准确率，BAC = 72.7%，P < .001），MDD 与 HC （BAC = 67.0%，P < .001） 和 SCZ 与 MDD 个体 （BAC = 63.2%，P < .001）。值得注意的是，中心 α （8-11 Hz） 功率降低是 SCZ 和 MDD 最一致的预测特征。较高的 EphysAGE 与 HC 和 MDD 中被误分类为 SCZ 的可能性增加相关 （ρHC = 0.23，P < .001;ρMDD = 0.17，P = .01）。结论 ML 模型可以提取 MDD 和 SCZ 的电生理特征，以供潜在的临床使用。 然而，衰老过程对诊断可分离性的影响要求及时应用此类模型，可能在早期识别环境中。