3 research outputs found
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ASSOCIATIONS OF NIGHT AND DAY SLEEP WITH COGNITIVE PERFORMANCE IN SHIFT WORKERS: PRELIMINARY RESULTS
Emerging evidence suggests that night shift workers are at higher risk of severe cognitive impairment, mainly because of the repeated disruption of the sleep–wake schedule and the resulting poor sleep (e.g., less deep sleep or shorter sleep time). However, questions remain about how daytime and nighttime sleep characteristics correlate with cognitive performance in shift workers. The purpose of this study is to examine the association of objective sleep quality, measured on workdays and days off, with cognitive performance, determined by the psychomotor vigilance task (PVT), in shift workers compared to day workers. We included eight night shift and ten day shift workers from an ongoing pilot study in this analysis. Participants undergo multiple home sleep studies with electroencephalograms on their workdays or days off. Sleep quality measures include total sleep time (TST), percentage of deep sleep stage in TST, and wake time after sleep onset (WASO). Night shift workers showed longer TST and shorter WASO during their nighttime sleep on days off compared to day shift workers. In night shift workers, longer deep sleep on days off was significantly correlated with faster mean reaction time to a visual stimulus (better cognitive performance) on the PVT (r = -.944, p = .016), after controlling for age. However, longer deep sleep during their daytime sleep was associated with slower reaction time (r = .756, p = .049). Further studies are necessary to elucidate differential impacts of daytime and nighttime sleep on the risk of future cognitive impairment in night shift workers
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Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders
•We proposed a sleep EEG-based brain age prediction model using convolutional neural networks.•A higher BAI is associated with cortical thinning in various functional areas.•A higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and θ waves for sleep apnea vs. higher power in β and σ for insomnia).•This result suggested that sleep EEG-BAI may reflect not only neural electroactivity responding to the same night sleep quality/depth but also neuroelectrophysiological changes in relation to chronic neural loss and altered brain connectivity.•Suggested EEG-based BAI can be used to phenotype sleep disorders as well as screen for sleep abnormalities that potentially harm brain health.
Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in β and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy
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Predicting brain age based on sleep EEG and DenseNet
We proposed a sleep EEG-based brain age prediction model which showed higher accuracy than previous models. Six-channel EEG data were acquired for 6 hours sleep. We then converted the EEG data into 2D scalograms, which were subsequently inputted to DenseNet used to predict brain age. We then evaluated the association between brain aging acceleration and sleep disorders such as insomnia and OSA.The correlation between chronological age and expected brain age through the proposed brain age prediction model was 80% and the mean absolute error was 5.4 years. The proposed model revealed brain age increases in relation to the severity of sleep disorders.In this study, we demonstrate that the brain age estimated using the proposed model can be a biomarker that reflects changes in sleep and brain health due to various sleep disorders.Clinical Relevance-Proposed brain age index can be a single index that reflects the association of various sleep disorders and serve as a tool to diagnose individuals with sleep disorders