Korean Minjok Leadership Academy, Hoengseong-gun, Gangwon-do, Republic of Korea.
Brain Behav. 2023 Aug;13(8):e3173. doi: 10.1002/brb3.3173. Epub 2023 Jul 21.
Depression is a common mental disorder that impacts millions of people across the world. However, its diagnosis is difficult due to the dependence on subjective testing. Although quantitative electroencephalography (EEG) has been investigated as a promising diagnostic tool for depression, the associated results have proven contradictory. The current study determines whether the alpha/beta (ABR), alpha/theta (ATR), and theta/beta (TBR) ratios can serve as biological markers of depression.
We used open-access EEG data from OpenNeuro to investigate power ratios in the resting state of 46 patients with depression and 75 healthy controls. Spectral data were extracted by fast Fourier transform at the theta band (4-8 Hz), alpha band (8-13 Hz), and beta band (13-32 Hz). Neural network, logistic regression, and receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracies of each suggested index. Additionally, the cutoff point, sensitivity, specificity, positive predictive value, and negative predictive value at the maximized Youden index were compared for each variable.
Decreased anterior frontal, frontal, central, parietal, occipital, and temporal ABR and decreased central and parietal TBR were observed in the depression group. The area under the curve of the ROC curves further revealed that these ratios could all effectively differentiate depression. In particular, the central, frontal, and parietal ABR exhibited high discrimination scores. Multiple logistic regression analysis demonstrated that the Beck Depression Inventory and Spielberger Trait Anxiety Inventory scores, as well as the probability of depression, increased with a decrease in the central ABR. Moreover, neural network analysis revealed that the global ABR was the most effective index for diagnosing depression among the three global EEG power ratios.
The central, frontal, and parietal ABR represent potential biomarkers to differentiate patients with depression from healthy controls.
抑郁症是一种常见的精神障碍,影响着全球数百万人。然而,由于对主观测试的依赖,其诊断较为困难。尽管定量脑电图(EEG)已被研究作为抑郁症的一种有前途的诊断工具,但相关结果却相互矛盾。本研究旨在确定α/β(ABR)、α/θ(ATR)和θ/β(TBR)比值是否可以作为抑郁症的生物标志物。
我们使用 OpenNeuro 的公开 EEG 数据,研究了 46 名抑郁症患者和 75 名健康对照者静息状态下的功率比。通过快速傅里叶变换在θ带(4-8 Hz)、α带(8-13 Hz)和β带(13-32 Hz)提取频谱数据。使用神经网络、逻辑回归和接收者操作特征(ROC)曲线评估每个建议指标的诊断准确性。此外,还比较了每个变量在最大 Youden 指数时的截断点、敏感度、特异性、阳性预测值和阴性预测值。
抑郁症组前额、额、中央、顶、枕和颞部 ABR 降低,中央和顶 TBR 降低。ROC 曲线的曲线下面积进一步表明,这些比值均能有效区分抑郁症。特别是中央、额和顶 ABR 具有较高的判别评分。多变量逻辑回归分析表明,贝克抑郁量表和斯皮尔伯格特质焦虑量表评分以及抑郁概率随着中央 ABR 的降低而增加。此外,神经网络分析表明,在三个全局 EEG 功率比中,全局 ABR 是诊断抑郁症最有效的指标。
中央、额和顶 ABR 代表区分抑郁症患者与健康对照者的潜在生物标志物。