Abstract：
Synchronization of the dynamic processes in structural networks connect the brain across a wide range of temporal and spatial scales, creating a dynamic and complex functional network. Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data. Few studies have focused on potential brain spatiotemporal dynamics in the early stages of depression to use as an early screening feature for depression. Thus, this study aimed to explore large-scale brain network dynamics of individuals both with and without subclinical depression, from the perspective of temporal and spatial dimensions and to input them as features into a machine learning framework for the automatic diagnosis of early-stage depression. To achieve this, spatio-temporal dynamics of rest-state EEG signals in female college students (n = 40) with and without (n = 38) subclinical depression were analyzed using EEG microstate and omega complexity analysis. Then, based on differential features of EEGs between the two groups, a support vector machine was utilized to compare performances of spatio-temporal features and single features in the classification of early depression. Microstate results showed that the occurrence rate of microstate class B was significantly higher in the group with subclinical depression when compared with the group without. Moreover, the duration and contribution of microstate class C in the subclinical group were both significantly lower than in the group without subclinical depression. Omega complexity results showed that the global omega complexity of β-2 and γ band was significantly lower for the subclinical depression group compared with the other group (p < 0.05). In addition, the anterior and posterior regional omega complexities were lower for the subclinical depression group compared to the comparison group in α-1, β-2 and γ bands. It was found that AUC of 81% for the differential indicators of EEG microstates and omega complexity was deemed better than a single index for predicting subclinical depression. Thus, since temporal and spatial complexity of EEG signals were manifestly altered in female college students with subclinical depression, it is possible that this characteristic could be adopted as an early auxiliary diagnostic indicator of depression.
摘要：
结构网络中动态过程的同步将大脑连接在广泛的时间和空间尺度上，创建一个动态和复杂的功能网络。微状态和ω复杂度是两种无参考脑电图（EEG）度量，可以表示EEG数据的时间和空间复杂性。很少有研究关注抑郁症早期的潜在大脑时空动力学，以用作抑郁症的早期筛查特征。因此，这项研究旨在探索有和没有亚临床抑郁症的个体的大规模脑网络动力学，从时间和空间维度的角度出发，并将它们作为特征输入到机器学习框架中，以自动诊断早期抑郁症。为了实现这一点，使用EEG微状态和omega复杂性分析，分析了有和没有（n=38）亚临床抑郁症的女大学生（n=40）的休息状态EEG信号的时空动力学。然后，基于两组之间脑电图的差异特征，利用支持向量机来比较时空特征和单一特征在早期抑郁症分类中的表现。微状态结果显示，亚临床抑郁症组的微状态B类发生率明显高于无抑郁症组。此外，亚临床组中微状态C类的持续时间和贡献均显著低于无亚临床抑郁症组.Omega复杂性结果显示，亚临床抑郁症组的β-2和γ带的整体Omega复杂性明显低于其他组（p<0.05）。此外,与对照组相比，亚临床抑郁症组的α-1，β-2和γ带的前后区域omega复杂性较低。发现脑电图微状态和omega复杂性的差异指标的AUC为81％，被认为比预测亚临床抑郁症的单个指标更好。因此，由于在患有亚临床抑郁症的女大学生中，EEG信号的时间和空间复杂性明显改变，这一特征有可能被用作抑郁症的早期辅助诊断指标。
