背景:脑电图(EEG)是一种无创,成本效益高,和强大的工具,它以高时间分辨率直接测量体内神经元质量活动。结合最先进的机器学习(ML)技术,脑电图记录可能会产生严重精神障碍的计算机生物标志物。
目的: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。
方法:来自一个由健康对照个体(HC,N=245)和患有SCZ(N=250)或MDD(N=240)的住院患者,我们获得了静息状态19通道脑电图记录。使用重复的嵌套交叉验证,对支持向量机模型进行了训练,以(1)对SCZ或MDD患者和HC个体进行分类,(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)作为预测年龄和实际年龄之间的差异。EphysAGE之间的联系,诊断,然后进一步探索药物。
结果:分类模型稳健地将SCZ与HC区分开(平衡精度,BAC=72.7%,P<.001),来自HC的MDD(BAC=67.0%,P<.001),和来自MDD个体的SCZ(BAC=63.2%,P<.001)。值得注意的是,中央α(8-11Hz)功率下降是SCZ和MDD最一致的预测特征。较高的EphysAGE与HC和MDD中误分类为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,用于潜在的临床应用。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,可能在早期识别设置中。
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.
OBJECTIVE: Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD).
METHODS: 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.
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.