Association of neurocognitive functioning with sleep stage dissociation and REM sleep instability in medicated patients with schizophrenia

Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep

STUDY OBJECTIVES

To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

METHODS

Power spectra from PSGs of 8716 participants, including from the MrOS Sleep Study and the Sleep Heart Health Study, were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models.

RESULTS

Survival analyses, adjusted for known covariates, identified multiple EEG frequency bands across all sleep stages predicting all-cause mortality. For EEG, we found an all-cause mortality hazard ratio (HR) of 0.90 (CI: 95% 0.85 to 0.96) for 12-15 Hz in N2, 0.86 (CI: 95% 0.82 to 0.91) for 0.75-1.5 Hz in N3, and 0.87 (CI: 95% 0.83 to 0.92) for 14.75-33.5 Hz in rapid-eye-movement sleep. For EOG, we found several low-frequency effects including an all-cause mortality HR of 1.19 (CI: 95% 1.11 to 1.28) for 0.25 Hz in N3, 1.11 (CI: 95% 1.03 to 1.21) for 0.75 Hz in N1, and 1.11 (CI: 95% 1.03 to 1.20) for 1.25-1.75 Hz in wake. The gain in the concordance index (C-index) for all-cause mortality is minimal, with only a 0.24% increase: The best single mortality predictor was EEG N3 (0-0.5 Hz) with a C-index of 77.78% compared to 77.54% for confounders alone.

CONCLUSIONS

Spectral power features, possibly reflecting abnormal sleep microstructure, are associated with mortality risk. These findings add to a growing literature suggesting that sleep contains incipient predictors of health and mortality.

Associations between Sleep, Depression, and Cognitive Performance in Adolescence

The relevance of cognitive performance during adolescence requires further studies that analyze potential associated factors. This study aimed to analyze inductive reasoning, reading comprehension, and mathematical thinking (problem-solving and number and calculation) in relation to sleep and depression in 244 students aged 12-17 years (47.6% boys and 52.4% girls). Daytime sleepiness, sleep quality, dysthymia, and euthymia (state and trait) were assessed by self-reported questionnaires. Moreover, correlations between these variables and cognitive performance, and differences depending on sociodemographic variables (sex, age, or academic year) were analyzed using non-parametric tests. Robust regression models were also conducted to evaluate the predictive role of significant variables on cognitive performance. The results showed significant bidirectional relationships between sleep- and depression-related variables, and between the latter ones and cognitive performance. Depression-trait was more related to cognitive performance than depression-state, and euthymia more than dysthymia, but neither daytime sleepiness nor sleep quality significantly correlated with it. As for sociodemographic variables, girls reported worse sleep and more depressive symptoms than boys did, and younger students reported better sleep but performed worse than the older ones. Although these findings should be further explored in forthcoming studies adding other promising variables, they highlight the importance of promoting euthymia to improve cognitive performance in adolescents.

Quantification of REM sleep without atonia: A review of study methods and meta-analysis of their performance for the diagnosis of RBD

The present review focuses on REM sleep without atonia (RSWA) scoring methods. In consideration of the numerous papers published in the last decade, that used different methods for the quantification of RSWA, their systematic revision is an emerging need. We made a search using the PubMed, Embase, Scopus and Web of Science Databases, from 2010 until December 2021, combining the search term "RSWA" with "scoring methods", "IRBD", "alfasyn disease", and "neurodegenerative disease", and with each of the specific sleep disorders, diagnosed according to current criteria, with the identification of the references of interest for the topic. Furthermore, a Meta-analysis of the diagnostic performance of RSWA scoring methods, in terms of sensitivity and specificity, was carried out. The comparison of the hierarchical summary receiver-operating characteristic curves obtained for visual methods and that obtained for the automated REM sleep atonia index (RAI), shows substantially similar prediction areas indicating a comparable performance. This systematic review and meta-analysis support the validity of a series of visual methods and of the automated RAI in the quantification of RSWA with the purpose to guide clinicians in the interpretation of their results and their correct and efficient use within the diagnostic work-up for REM sleep behavior disorder.