Lee Poh Foong, Kan Donica Pei Xin, Croarkin Paul, Phang Cheng Kar, Doruk Deniz
Mechatronics and BioMedical Engineering Department, Lee Kong Chien Faculty of Engineering & Science, University Tunku Abdul Rahman, Malaysia.
Mechatronics and BioMedical Engineering Department, Lee Kong Chien Faculty of Engineering & Science, University Tunku Abdul Rahman, Malaysia.
J Clin Neurosci. 2018 Jan;47:315-322. doi: 10.1016/j.jocn.2017.09.030. Epub 2017 Oct 21.
There is an unmet need for practical and reliable biomarkers for mood disorders in young adults. Identifying the brain activity associated with the early signs of depressive disorders could have important diagnostic and therapeutic implications. In this study we sought to investigate the EEG characteristics in young adults with newly identified depressive symptoms.
Based on the initial screening, a total of 100 participants (n = 50 euthymic, n = 50 depressive) underwent 32-channel EEG acquisition. Simple logistic regression and C-statistic were used to explore if EEG power could be used to discriminate between the groups. The strongest EEG predictors of mood using multivariate logistic regression models.
Simple logistic regression analysis with subsequent C-statistics revealed that only high-alpha and beta power originating from the left central cortex (C3) have a reliable discriminative value (ROC curve >0.7 (70%)) for differentiating the depressive group from the euthymic group. Multivariate regression analysis showed that the single most significant predictor of group (depressive vs. euthymic) is the high-alpha power over C3 (p = 0.03).
The present findings suggest that EEG is a useful tool in the identification of neurophysiological correlates of depressive symptoms in young adults with no previous psychiatric history.
Our results could guide future studies investigating the early neurophysiological changes and surrogate outcomes in depression.
对于年轻成年人情绪障碍，亟需实用且可靠的生物标志物。识别与抑郁症早期症状相关的大脑活动可能具有重要的诊断和治疗意义。在本研究中，我们旨在调查新发现有抑郁症状的年轻成年人的脑电图特征。
基于初步筛查，共有100名参与者（n = 50名心境正常者，n = 50名抑郁者）接受了32通道脑电图采集。使用简单逻辑回归和C统计量来探讨脑电图功率是否可用于区分两组。使用多变量逻辑回归模型确定情绪的最强脑电图预测指标。
随后进行C统计量的简单逻辑回归分析显示，只有源自左中央皮层（C3）的高α波和β波功率对于区分抑郁组和心境正常组具有可靠的判别价值（ROC曲线>0.7（70%））。多变量回归分析表明，区分两组（抑郁组与心境正常组）的最显著单一预测指标是C3区域的高α波功率（p = 0.03）。
目前的研究结果表明，脑电图是识别无既往精神病史的年轻成年人抑郁症状神经生理相关性的有用工具。
我们的结果可为未来研究抑郁症早期神经生理变化和替代结局提供指导。
