Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression

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.

Keywords: depression; microstate; omega complexity; resting-state EEG; visual processing.

Figure 1

Analysis procedures for resting-state data sets. (A) Resting-state data pre-processing; (B) microstates analysis; (C) omega complexity analysis; (D) subclinical depression prediction.

Figure 2

The four microstate classes significant transitions. *: p < 0.05, **: p < 0.01.

Figure 3

The mean regional omega complexities of seven EEG frequency bands (i.e., delta, theta, alpha-1, alpha-2, beta-1, beta-2, gamma). *: p < 0.05, **: p < 0.01.

Figure 4

Receiver operating characteristic curves for spatial (microstate), temporal (omega complexity), and their combination using the SVM model based on Gaussian kernel function (left) and linear kernel function (right).