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<p>Sleep perception impairment (SPI) characterized by subjective-objective discrepancies in sleep, is common among patients with depression. Its neurophysiological mechanisms remain unclear. This study investigated associations between polysomnography (PSG)-derived sleep macro- and micro-architecture features and SPI in depressed patients. We enrolled 63 adults (aged 18–65) with DSM-V major depressive disorder. The participants were divided into two groups: the SPI (<i>n</i> = 26) and non-SPI (<i>n</i> = 37). All underwent overnight PSG and completed clinical assessments. We analyzed sleep macro-architecture and EEG micro-architecture, 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. Despite comparable objective sleep duration, SPI patients significantly underestimated their sleep duration based on their post-PSG subjective sleep time estimates, reported poorer subjective sleep quality, exhibited lower EEG total power (median 9.7 vs. 12.5 kµV²; <i>p</i> = 0.003), decreased interhemispheric EEG symmetry (0.50 vs. 0.51; <i>p</i> = 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. EEG-derived biomarkers (spectral power, symmetry, entropy) may differentiate sleep perception phenotypes in depression, offering potential targets for tailored clinical interventions.</p>

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Sleep architecture differences and predictive markers of sleep perception impairment in depression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Hongjuan Li,
  • Jiong Chen,
  • Meng Qi,
  • Leilei Wang,
  • Shuangjiang Zhou,
  • Jingxu Chen,
  • Song Gao,
  • Yue Leng,
  • Shuping Tan

摘要

Sleep perception impairment (SPI) characterized by subjective-objective discrepancies in sleep, is common among patients with depression. Its neurophysiological mechanisms remain unclear. This study investigated associations between polysomnography (PSG)-derived sleep macro- and micro-architecture features and SPI in depressed patients. We enrolled 63 adults (aged 18–65) with DSM-V major depressive disorder. The participants were divided into two groups: the SPI (n = 26) and non-SPI (n = 37). All underwent overnight PSG and completed clinical assessments. We analyzed sleep macro-architecture and EEG micro-architecture, 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. Despite comparable objective sleep duration, SPI patients significantly underestimated their sleep duration based on their post-PSG subjective sleep time estimates, 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. EEG-derived biomarkers (spectral power, symmetry, entropy) may differentiate sleep perception phenotypes in depression, offering potential targets for tailored clinical interventions.