Cross-sectional study reveals altered EEG microstates in depression converted to bipolar disorder, indicating resting-state brain dynamics may assist early differential diagnosis.
Key Points
To determine whether resting-state EEG microstate dynamics can differentiate patients with bipolar disorder who initially presented with major depressive disorder from those with unipolar depression.
Enrolled 53 drug-naïve patients with major depressive disorder and 17 patients who converted to bipolar disorder.
Assessed clinical scores for bipolarity, depression, anxiety, impulsivity, and cognition alongside resting-state EEG microstate analysis across the 1–30 Hz band.
Trained machine-learning algorithms on microstate duration, occurrence, coverage, and transition probabilities to classify patient groups.
Microstate B demonstrated significantly greater duration, occurrence, and coverage in the converted bipolar disorder group compared to the unipolar depression group.
Transition probabilities from microstates A, C, and D into microstate B were elevated in converted bipolar disorder, while delta-band transitions from D to A correlated with impulsivity.
Machine-learning classification using microstate parameters differentiated the groups with 75.71% accuracy (95% CI 65.71%–85.71%; p < 0.001), 70.59% sensitivity (95% CI 47.06%–88.24%; p = 0.027), 77.36% specificity (95% CI 66.04%–88.68%; p < 0.001), and an ROC-AUC of 0.766 (95% CI 0.644–0.875; p < 0.001).