Background Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory–II scores). Methods Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal. Results Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band (r = 0.839; P < .0001, root mean square error score of 7.69). Conclusion We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.

中文翻译：

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基于功能连通性和脑电信号复杂性的抑郁严重程度评分预测

背景 抑郁症是最常见的精神障碍之一，也是导致功能障碍的主要原因。本研究旨在说明大脑活动的功能连接性和复杂性是否可以预测抑郁症的严重程度（贝克抑郁量表-II 评分）。方法记录60例抑郁症患者静息状态、闭眼脑电图数据。相位同步测量用于估计 delta (1-4 Hz)、theta (4-8 Hz)、alpha (8-13 Hz) 和 beta (13-30 Hz) 中所有 EEG 通道对之间的功能连接) 频段。为了量化功能连接的局部价值，测量了 2 个图论指标、度和聚类系数 (CC)。此外，Lempel-Ziv 复杂度（LZC）和模糊熵（FuzzyEn）被用来衡量脑电信号的复杂度。结果 通过相关性分析，在 alpha 波段中，图表指标与抑郁严重程度之间存在显着的负相关关系。这种关联对 alpha 和 delta 波段中的复杂性度量非常积极。此外，线性回归模型代表了基于 alpha 波段的 EEG 特征的抑郁严重程度预测的实质性表现（r = 0.839；P < .0001，均方根误差得分为 7.69）。结论 我们发现抑郁症患者的大脑活动与抑郁症的严重程度有关。异常的大脑活动反映了抑郁症严重程度的增加。提出的回归模型提供了定量的抑郁症严重程度预测，