Distributed EEG phase-resetting dynamics predict depressive symptom severity in untreated adults

Abstract Objective neurometric markers that track depressive symptom severity across the continuum remain limited, particularly in untreated populations. Although electroencephalography offers promise, most prior studies are inconsistent, restricted to case–control discrimination or difficult to interpret mechanistically. These limitations raise the possibility that symptom-related information is not confined to focal electroencephalography features but instead distributed across cortical networks. Here we show that self-reported depressive symptom severity is associated with large-scale phase-resetting dynamics and can be characterized by an integrated index derived from phase-resetting rate modulation (PRRM), a measure of source-locked, time-lagged modulation of phase-resetting rate from source to target electrodes. In a resting-state cross-sectional cohort of 276 untreated adults (139 males and 137 females; aged 18–59 years) spanning a broad symptom range, including 116 with moderate-to-severe symptoms, an integrated index aggregating multiple individually weak but collectively informative PRRM-based features in the upper-alpha and low-gamma bands showed cross-validated predictability of symptom scores ( r > 0.6) and achieved approximately 80% accuracy in discriminating moderate-to-severe from low-symptom individuals. These findings provide evidence for a physiologically interpretable neurometric signature and suggest that the PRRM framework may support future objective assessment and longitudinal monitoring of depressive symptom severity in untreated populations, although independent validation in clinically characterized cohorts is needed before clinical application.