EEG Microstate Dynamics Consistently Predict Depressive Symptoms in Healthy Young Adults

Early detection of depressive symptoms is crucial for reducing their impact on social and cognitive functioning and can be effectively supported by non-invasive, cost-effective biomarkers derived from brain electrical activity. Previous research has identified altered temporal and transition patterns of EEG microstates in clinical populations diagnosed with major depressive disorder (MDD) as well as in healthy individuals exhibiting elevated depressive symptoms. In this study, we aimed to replicate recent EEG microstate findings in young, generally healthy adults reporting high (N = 38) versus low (N = 38) levels of depressive symptoms, while also examining the long-term temporal memory of microstate sequences.Microstate analysis was performed on 5-minute resting-state EEG recordings obtained with eyes closed. EEG data were categorized into five microstate classes, and significant parameter differences were observed between groups. Participants with high depressive symptoms exhibited decreased occurrence of microstate A, reduced coverage of microstates A and D, and diminished bidirectional transition probabilities between microstates A and D. Conversely, increased values were found for the Hurst exponent and bidirectional transition probabilities between microstates B and C, between microstates C and E, and from microstate B to E. Linear regression analysis demonstrated that these microstate parameters significantly predicted depressive symptom scores (R² = 0.389).Our results underscore the potential of resting-state EEG microstate temporal and sequence parameters as biomarkers for the early identification of depressive symptoms in generally healthy young adults.