Published online Jan 19, 2024. doi: 10.5498/wjp.v14.i1.128
Peer-review started: September 29, 2023
First decision: November 2, 2023
Revised: November 9, 2023
Accepted: December 22, 2023
Article in press: December 22, 2023
Published online: January 19, 2024
Depression is a chronic and debilitating disease that is characterized by depressed mood, diminished interests, and cognitive deficits manifested as low self-esteem, sleep disturbance, weight loss, and even disability. Electroencephalography (EEG), commonly used to study electrophysiological processes in the cerebral cortex, is capable of describing local and global neuronal activity in the brain neural networks. Therefore, there is a need to conduct a more comprehensive study of EEG microstates in patients with depression.
The results were calibrated through statistical methods, attempting to find more realistic and reliable characterizations of EEG microstates. In addition, we also analyzed the correlation between EEG microstate characteristics and cognitive scales, which has rarely been studied before. Third, we correlated EEG microstate parameters with the Hamilton Depression Scale (HAMD) to figure out possible relationships between depression severity and EEG microstates.
This study was to investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions. Our study demonstrated that, EEG microstate, especially C and D, is a possible biomarker in depression. In addition, we found that patients with depression had a more frequent transition from microstate C to B, which may be related to more negative rumination and visual processing. In future clinical practice, healthcare professionals can combine with clinical examination to assess and diagnose depression comprehensively from multiple angles and dimensions.
Demographic and clinical characteristics, as well as data from the repeatable battery for the assessment of neuropsychological status (RBANS; Chinese version) and EEG, were collected from a sample of 24 patients diagnosed with depression (DEP) and 32 healthy controls (CON). Participants were seated comfortably in a reclining chair and instructed to close their eyes and maintain a relaxed and quiet state for a duration of 3 min. Microstate analysis was conducted utilizing the EEGLAB microstate plugin and the atomize and agglomerate hierarchical clustering algorithm was used to compute four optimal microstate topographies.
Our study found that years of education and HAMD score showed significant differences in the two groups (education: t = 2.056, P = 0.045; HAMD score: W = 83, P < 0.001). Compared with the controls, the duration, occurrence, and contribution of microstate C were significantly higher (duration F = 6.02, P = 0.049; Occurrence F = 6.19, P = 0.049; contribution F = 10.82, P = 0.011) while the duration, occurrence, and contribution of microstate D were significantly lower (duration F = 19.18, P < 0.001; Occurrence F = 5.79, P = 0.050; Contribution F = 9.41, P = 0.013) in depressed patients. Additionally, a positive correlation was observed between visuospatial/constructional scores and the transition probability of microstate class C to B (r = 0.405, P = 0.049).
We examined the temporal dynamics of resting-state EEG microstates in patients with depression and healthy controls. EEG microstate analyzed the possible changes in neurons in the brain of patients with depression from the perspective of sub-second brain dynamics and was a possible biomarker (especially microstate C and D) in depression. Furthermore, the more frequent transition from microstate C to B, which may be related to more negative rumination and visual pro-cessing.
In the future, more studies with larger numbers of patients with depression and normal controls should be conducted to assess more accurately the relationship between depressive disorders and electroencephalography EEG microstates. Furthermore, we will further perform a longitudinal interventional cohort study on therapy in the DEP group to find any possible associations between EEG microstates and prognosis through regular follow-up. Finally, future studies could combine EEG data with resting-state fMRI data from patients with depression to study brain neural network changes through both temporal and spatial dimensions in an integrated manner.