EEG microstate biomarkers for major depressive disorder: A comparative analysis using two independent datasets.

BACKGROUND

Major Depressive Disorder (MDD) is characterized by impaired mood and cognitive functioning. Exploring EEG microstate biomarkers is crucial for enhancing clinical diagnosis.

METHODS

Four EEG microstate features-duration, occurrence, coverage, and transition probability between different microstates were extracted from two independent resting-state EEG datasets. Statistical analysis was conducted to examine differences in microstates between individuals with MDD and healthy controls. Pearson's correlation coefficients were used to assess the relationship between clinical scale scores and microstate parameters.

RESULTS

In both datasets, significant temporal dynamic patterns were observed in the MDD group. Specifically, the coverage of microstate B was significantly reduced, while the probability of transition between microstate B and C was significantly increased. However, the transitions between microstate B and D, as well as between microstates A and microstate B, showed differing patterns between the two datasets. Among these significant findings, only the occurrence of microstate B maintained statistical significance after FDR correction. Additionally, changes in PHQ-9 scores demonstrated significant negative correlations with both the occurrence of microstate B and the transition probability from microstate B to D.

LIMITATIONS: The sample size is small, and the risk of false positives in the statistical analysis results still requires special attention. Since clinical depression scores were obtained from only a single dataset, their consistency across different datasets still needs to be studied using expanded datasets.

CONCLUSION

EEG signals in MDD exhibit specific temporal dynamic patterns that are closely linked to alterations in brain functional networks. Microstate B parameters could serve as reliable biomarkers for the clinical diagnosis of MDD. These findings provide novel insights into the abnormal EEG dynamics of MDD and contribute to a deeper understanding of the neuropathological mechanisms underlying depression.