EEG microstate dynamics reveal progressive sensorimotor network dysfunction across levels of anxiety severity in drug-naïve major depressive disorder.

Anxiety symptoms are highly prevalent in major depressive disorder (MDD) and significantly influence clinical outcomes, yet neurophysiological mechanisms underlying anxiety severity remain poorly understood. To characterize neurophysiological alterations associated with different levels of anxiety severity in drug-naïve MDD, we used resting-state electroencephalogram (EEG) microstate analysis. Resting-state EEG was recorded from 113 MDD patients with varying anxiety levels (DDA) and 60 healthy controls (HC). Patients were stratified into low (DDLA), moderate (DDMA), and severe (DDSA) groups using the Hamilton Anxiety Rating Scale. K-means clustering identified seven microstate classes (A-G). Microstate B duration was reduced in all patient groups compared with HC, indicating visual network dysfunction common to depression. Microstate G-corresponding to sensorimotor and interoceptive networks-showed robust progressive alterations, with duration, occurrence, and coverage increasing from DDLA to DDMA and DDSA. Non-random transition pairs were reduced in patients compared with HC (22 pairs) but increased progressively with anxiety severity (DDLA: 10, DDMA: 16, DDSA: 18 pairs). The degree of anxiety was positively correlated with significantly enhanced transitions between microstates D, F, and G (executive control, anterior default mode network, and sensorimotor networks). Microstate G parameters and anxiety severity were found to be positively correlated in the DDA patient. Microstate G may index anxiety severity at the neurophysiological level in depressed patients with anxiety, and progressive D-F-G network disruption reflects cumulative dysregulation of large-scale brain networks associated with co-occurring anxiety. These findings may have implications for biomarker development and targeted interventions.