Sleep Architecture Differences and Predictive Markers of Sleep Perception Impairment in Depression (Preprint)

BACKGROUND Sleep perception impairment (SPI), or paradoxical insomnia, characterized by subjective-objective discrepancies in sleep, is common among patients with depression. Its neurophysiological mechanisms remain unclear. OBJECTIVE This study investigated associations between polysomnography (PSG)-derived sleep macro- and micro-architecture features and SPI in depressed patients. METHODS We enrolled 63 adults (aged 18–65) with DSM-V major depressive disorder. SPI was defined as a ≥60-minute discrepancy in total sleep time or ≥15% difference in sleep efficiency between subjective reports and objective PSG, identifying SPI (n=26) and non-SPI (n=37) groups. All underwent overnight PSG and completed clinical assessments including the Pittsburgh Sleep Quality Index, Hamilton Depression Rating Scale, and neurocognitive testing. We analyzed sleep macro-architecture (e.g., sleep efficiency, latency) and EEG micro-architecture (band power, spectral ratios, interhemispheric symmetry), including sleep temporal entropy (STE), reflecting fragmentation of sleep-stage transitions. Logistic and linear regression models assessed predictors of SPI, adjusting for demographics, clinical, and sleep-related covariates. RESULTS Despite comparable objective sleep duration, SPI patients significantly underestimated their sleep, reported poorer subjective sleep quality, exhibited lower EEG total power (median 9.7 vs 12.5 kμV²; p=0.003), decreased interhemispheric EEG symmetry (0.50 vs 0.51; p=0.02), and elevated high-frequency relative to slow-wave EEG activity. Higher EEG total power (OR=0.35 per 1000 μV² increase) and greater EEG symmetry (OR=0.47 per 0.01 increase) independently predicted reduced odds of SPI in adjusted models. CONCLUSIONS EEG-derived biomarkers (spectral power, symmetry, entropy) may differentiate sleep perception phenotypes in depression, offering potential targets for tailored clinical interventions.