Agrypnia excitata: a clinical biomarker of acute relapse in anti-NMDAr encephalitis.

Sleep disorders have been described in anti-NMDAr encephalitis including insomnia, hypersomnia, narcolepsy, and sleep-disordered breathing. A patient presented with typical features of anti-NMDAr encephalitis associated with a right ovarian teratoma. After two months of clinical improvement with immunotherapy, the patient deteriorated. A 24-hour video EEG-polysomnography revealed a severe sleep quantity deficit, a total destruction of sleep architecture consisting of short clusters of N1 and rapid eye movement sleep stages, associated with motor and autonomic hyperactivity. These features were consistent with agrypnia excitata and were associated with disease reactivation due to a left ovarian teratoma. A new course of immunotherapy and surgery improved clinical symptoms and normalized sleep patterns. Agrypnia excitata, the most severe form of status dissociatus, was a sleep biomarker of disease relapse in this patient. Polysomnographic studies in the acute phase of anti-NMDAr encephalitis are lacking and are needed to better understand the evolution of sleep patterns.

Mortality Risk Assessment Using Deep Learning-Based Frequency Analysis of EEG and EOG 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 8,716 participants, included from the MrOS Sleep Study and the Sleep Heart Health Study (SHHS), 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 (CI95% 0.85-0.96) for 12-15 Hz in N2, 0.86 (CI95% 0.82-0.91) for 0.75-1.5 Hz in N3, and 0.87 (CI95% 0.83-0.92) for 14.75-33.5 Hz in REM sleep. For EOG, we found several low-frequency effects including an all-cause mortality HR of 1.19 (CI95% 1.11-1.28) for 0.25 Hz in N3, 1.11 (CI95% 1.03-1.21) for 0.75 Hz in N1, and 1.11 (CI95% 1.03-1.20) 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 C-index of 77.78% compared to 77.54% for confounders alone. CONCLUSION: 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.

Cerebral blood flow in sleep: A systematic review and meta-analysis.

Sleep plays an essential role in physiology, allowing the brain and body to restore itself. Despite its critical role, our understanding of the underlying processes in the sleeping human brain is still limited. Sleep comprises several distinct stages with varying depths and temporal compositions. Cerebral blood flow (CBF), which delivers essential nutrients and oxygen to the brain, varies across brain regions throughout these sleep stages, reflecting changes in neuronal function and regulation. This systematic review and meta-analysis assesses global and regional CBF across sleep stages. We included, appraised, and summarized all 38 published sleep studies on CBF in healthy humans that were not or only slightly (<24 h) sleep deprived. Our main findings are that CBF varies with sleep stage and depth, being generally lowest in NREM sleep and highest in REM sleep. These changes appear to stem from sleep stage-specific regional brain activities that serve particular functions, such as alterations in consciousness and emotional processing.

Sleep defined by arousal threshold reveals decreases in corticocortical functional correlations independently from the conventional sleep stages.

Sleep research and sleep medicine have benefited from the use of polysomnography but have also suffered from an overreliance on the conventional, polysomnography-defined sleep stages. For example, reports of sleep-specific brain activity patterns have, with few exceptions, been constrained by assessing brain function as it relates to the conventional sleep stages. This limits the variety of sleep states and underlying activity patterns that one can discover. If undiscovered brain activity patterns exist during sleep, then removing the constraint of a stage-specific analysis may uncover them. The current study used all-night functional magnetic resonance imaging sleep data and defined sleep behaviorally with auditory arousal threshold (AAT) to begin to search for new brain states. It was hypothesized that, during sleep compared to wakefulness, corticocortical functional correlations would decrease. Functional correlation values calculated in a window immediately before the determination of AAT were entered into a linear mixed effects model, allowing multiple arousals across the night per subject into the analysis. The hypothesis was supported using both correlation matrices of brain networks and single seed-region analyses showing whole-brain maps. This represents a novel approach to studying the neuroanatomical correlates of sleep with high spatial resolution by defining sleep in a way that was independent from the conventional sleep stages. This work provides initial evidence to justify searching for sleep stages that are more neuroanatomically localized and unrelated to the conventional sleep stages.

The association between sleep and Alzheimer's disease: a systematic review.

It is estimated that 45% of individuals with cognitive impairment experience sleep disturbances prior to the onset of cognitive symptoms. Assessing sleeping problems and enhancing sleep quality are critical first steps to reduce the risk of cognitive impairment. Objective: To review existing literature based on predefined eligibility criteria to understand the connection between sleep disturbance and Alzheimer's disease. Methods: A thorough and systematic evaluation of numerous studies was carried out to assess one or more of the following epidemiological factors: (1) sleep disorders, (2) cognitive impairment, and (3) risk estimates for cognitive impairment due to sleep. Results: Studies suggest that individuals who experience memory loss may encounter sleep disturbances before noticing other symptoms. Numerous sleep disorders, such as excessive and inadequate sleep duration, poor sleep quality, circadian rhythm abnormalities, insomnia, and obstructive sleep apnea were found to increase the risk of cognitive dysfunction and dementia. Additionally, lower sleep quality and shorter sleep duration have been linked to higher cerebral-ß-amyloid levels. Objective evidence for the development of cognitive impairment is provided by the architecture of sleep stages. Patients experiencing sleep problems may benefit from specific types of sleep medicine as a preventative measure against cognitive decline. Conclusion: Sleep disorders can have adverse effects on cognitive health. The duration and quality of sleep are fundamental factors for maintaining a healthy brain as we age. Proper sleep can aid prevent cognitive impairment, particularly Alzheimer's disease and dementia.

Acredita-se que 45% dos indivíduos com comprometimento cognitivo experimentem distúrbios do sono antes do início dos sintomas cognitivos. Avaliar problemas de sono e melhorar a qualidade do sono são passos críticos iniciais para reduzir o risco de comprometimento cognitivo. Objetivo: Revisar a literatura existente com base em critérios de elegibilidade predefinidos para entender a conexão entre distúrbios do sono e a Doença de Alzheimer. Métodos: Uma avaliação completa e sistemática de vários estudos foi realizada para avaliar um ou mais dos seguintes fatores epidemiológicos: (1) distúrbios do sono, (2) comprometimento cognitivo e (3) estimativas de risco de comprometimento cognitivo decorrente do sono. Resultados: Os estudos sugerem que indivíduos que experimentam perda de memória podem enfrentar distúrbios do sono antes de notarem outros sintomas. Foi constatado que vários distúrbios do sono, como duração excessiva e inadequada do sono, má qualidade do sono, anormalidades no ritmo circadiano, insônia e apneia obstrutiva do sono, podem aumentar o risco de disfunção cognitiva e demência. Além disso, menor qualidade do sono e duração mais curta do sono têm sido associadas a níveis mais altos de ß-amiloide cerebral. Evidências objetivas para o desenvolvimento de comprometimento cognitivo são fornecidas pela arquitetura dos estágios do sono. Pacientes que experimentam problemas de sono podem se beneficiar de tipos específicos de medicamentos para o sono como medida preventiva contra o declínio cognitivo. Conclusão: Os distúrbios do sono podem ter efeitos adversos na saúde cognitiva. A duração e a qualidade do sono são fatores fundamentais para manter um cérebro saudável à medida que envelhecemos. Um sono adequado pode ajudar a prevenir o comprometimento cognitivo, especialmente a Doença de Alzheimer e a demência.

Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep.

Introduction: Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough. Methods: This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, ß, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. Results and discussion: The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.

Associations of Subjective Sleep Quality with Wearable Device-Derived Resting Heart Rate During REM Sleep and Non-REM Sleep in a Cohort of Japanese Office Workers.

Background: Associations between subjective sleep quality and stage-specific heart rate (HR) may have important clinical relevance when aiming to optimize sleep and overall health. The majority of previously studies have been performed during short periods under laboratory-based conditions. The aim of this study was to investigate the associations of subjective sleep quality with heart rate during REM sleep (HR REMS) and non-REM sleep (HR NREMS) using a wearable device (Fitbit Versa). Methods: This is a secondary analysis of data from the intervention group of a randomized controlled trial (RCT) performed between December 3, 2018, and March 2, 2019, in Tokyo, Japan. The intervention group consisted of 179 Japanese office workers with metabolic syndrome (MetS), Pre-MetS or a high risk of developing MetS. HR was collected with a wearable device and sleep quality was assessed with a mobile application where participants answered The St. Mary's Hospital Sleep Questionnaire. Both HR and sleep quality was collected daily for a period of 90 days. Associations of between-individual and within-individual sleep quality with HR REMS and HR NREMS were analyzed with multi-level model regression in 3 multivariate models. Results: The cohort consisted of 92.6% men (n=151) with a mean age (± standard deviation) of 44.1 (±7.5) years. A non-significant inverse between-individual association was observed for sleep quality with HR REMS (HR REMS -0.18; 95% CI -0.61, 0.24) and HR NREMS (HR NREMS -0.23; 95% CI -0.66, 0.21), in the final multivariable adjusted models; a statistically significant inverse within-individual association was observed for sleep quality with HR REMS (HR REMS -0.21 95% CI -0.27, -0.15) and HR NREMS (HR NREMS -0.21 95% CI -0.27, -0.14) after final adjustments for covariates. Conclusion: The present study shows a statistically significant within-individual association of subjective sleep quality with HR REMS and HR NREMS. These findings emphasize the importance of considering sleep quality on the individual level. The results may contribute to early detection and prevention of diseases associated with sleep quality which may have important implications on public health given the high prevalence of sleep disturbances in the population.

Sleep-related bruxism, microarousals and oxyhaemoglobin desaturations in sleep stages: A cross-sectional study in a large apnoeic population.

BACKGROUND: Sleep-related bruxism (SB) is the habit of grinding or clenching the teeth during sleep, mediated by the non-peripheral central nervous system. PURPOSE: The objectives of this cross-sectional study were to evaluate associations between SB, microarousals and oxyhaemoglobin desaturations and to compare the frequency of SB and microarousals in sleep stages, in an apnoeic population. METHODS: Two hundred and forty individuals composed the sample, who underwent a single full-night polysomnography. Self-reports and clinical inspections were not considered for assessing SB. The polysomnographic assessment of SB was performed using electrodes placed on masseter muscles and chin. SB was defined as more than two events of rhythmic masticatory muscle activity per hour of sleep. Microarousals were considered when there were abrupt changes in electroencephalogram frequencies, without complete awakening, lasting from 3 to 15 s. Oxyhaemoglobin desaturations were defined as significant drops (≥3%) in basal oxygen saturations. With these data, SB, microarousals and oxyhaemoglobin desaturations were evaluated and submitted to statistical analysis. RESULTS: Statistically significant differences were observed between bruxers and non-bruxers when comparing the rates of microarousals (p < .001) and oxyhaemoglobin desaturations (p = .038). There was a higher number of SB and microarousals in NREM (non-rapid eye movement) two sleep stage (p < 0.001). Bruxers had a greater risk of higher numbers of microarousals (OR = 1.023; p = .003), which did not occur for oxyhaemoglobin desaturations (OR = 0.998; p = .741). CONCLUSIONS: A higher number of microarousals presents relationship with SB; associations between SB and oxyhaemoglobin desaturations remained inconclusive; higher frequency of SB and microarousals was observed in NREM 2 sleep stage.

Influence of Sleep Stage on the Determination of Positional Dependency in Patients With Obstructive Sleep Apnea.

OBJECTIVES: The supine sleep position and the rapid eye movement (REM) stage are widely recognized to exacerbate the severity of obstructive sleep apnea (OSA). Position-dependent OSA is generally characterized by an apnea-hypopnea index (AHI) that is at least twice as high in the supine position compared to other sleep positions. However, this condition can be misdiagnosed if a particular sleep stage-REM or non-REM (NREM)-predominates in a specific position. We explored the impact of the sleep stage on positional dependency in OSA. METHODS: Polysomnographic data were retrospectively analyzed from 111 patients with OSA aged 18 years or older, all of whom had an AHI exceeding five events per hour and slept in both supine and non-supine positions for at least 5% of the total sleep time. The overall ratio of non-supine AHI to supine AHI (NS/S-AHI ratio) was compared between total, REM, and NREM sleep. Additionally, a weighted NS/S-AHI ratio, reflecting the proportion of time spent in each sleep stage, was calculated and compared to the original ratio. RESULTS: The mean NS/S-AHI ratio was consistent between the entire sleep period and the specific sleep stages. However, the NS/S-AHI ratios for individual patients displayed poor agreement between total sleep and the specific stages. Additionally, the weighted NS/S-AHI ratio displayed poor agreement with the original NS/S-AHI ratio, primarily due to discrepancies in patients with mild to moderate OSA. CONCLUSION: The weighted NS/S-AHI ratio may help precisely assess positional dependency.

Associations between longitudinal changes in sleep stages and risk of cognitive decline in older men.

STUDY OBJECTIVES: To investigate the relationships between longitudinal changes in sleep stages and the risk of cognitive decline in older men. METHODS: This study included 978 community-dwelling older men who participated in the first (2003-2005) and second (2009-2012) sleep ancillary study visits of the Osteoporotic Fractures in Men Study. We examined the longitudinal changes in sleep stages at the initial and follow-up visits, and the association with concurrent clinically relevant cognitive decline during the 6.5-year follow-up. RESULTS: Men with low to moderate (quartile 2, Q2) and moderate increase (Q3) in N1 sleep percentage had a reduced risk of cognitive decline on the modified mini-mental state examination compared to those with a substantial increase (Q4) in N1 sleep percentage. Additionally, men who experienced a low to moderate (Q2) increase in N1 sleep percentage had a lower risk of cognitive decline on the Trails B compared with men in the reference group (Q4). Furthermore, men with the most pronounced reduction (Q1) in N2 sleep percentage had a significantly higher risk of cognitive decline on the Trails B compared to those in the reference group (Q4). No significant association was found between changes in N3 and rapid eye movement sleep and the risk of cognitive decline. CONCLUSIONS: Our results suggested that a relatively lower increase in N1 sleep showed a reduced risk of cognitive decline. However, a pronounced decrease in N2 sleep was associated with concurrent cognitive decline. These findings may help identify older men at risk of clinically relevant cognitive decline.

Sleep stages classification by fusing the time-related synchronization analysis and brain activations.

Sleep staging plays an important role in the diagnosis and treatment of clinical sleep disorders. The sleep staging standard defines every 30 seconds as a sleep period, which may mean that there exist similar brain activity patterns during the same sleep period. Thus, in this work, we propose a novel time-related synchronization analysis framework named time-related multimodal sleep scoring model (TRMSC) to explore the potential time-related patterns of sleeping. In the proposed TRMSC, the time-related synchronization analysis is first conducted on the single channel electrophysiological signal, i.e., Electroencephalogram (EEG) and Electrooculogram (EOG), to explore the time-related patterns, and the spectral activation features are also extracted by spectrum analysis to obtain the multimodal features. With the extracted multimodal features, the feature fusion and selection strategy is utilized to obtain the optimal feature set and achieve robust sleep staging. To verify the effectiveness of the proposed TRMSC, sleep staging experiments were conducted on the Sleep-EDF dataset, and the experimental results indicate that the proposed TRMSC has achieved better performance than other existing strategies, which proves that the time-related synchronization features can make up for the shortcomings of traditional spectrum-based strategies and achieve a higher classification accuracy. The proposed TRMSC model may be helpful for portable sleep analyzers and provide a new analytical method for clinical sleeping research.

Sleep staging algorithm based on smartwatch sensors for healthy and sleep apnea populations.

OBJECTIVE: Sleep stages can provide valuable insights into an individual's sleep quality. By leveraging movement and heart rate data collected by modern smartwatches, it is possible to enable the sleep staging feature and enhance users' understanding about their sleep and health conditions. METHOD: In this paper, we present and validate a recurrent neural network based model with 23 input features extracted from accelerometer and photoplethysmography sensors data for both healthy and sleep apnea populations. We designed a lightweight and fast solution to enable the prediction of sleep stages for each 30-s epoch. This solution was developed using a large dataset of 1522 night recordings collected from a highly heterogeneous population and different versions of Samsung smartwatch. RESULTS: In the classification of four sleep stages (wake, light, deep, and rapid eye movements sleep), the proposed solution achieved 71.6 % of balanced accuracy and a Cohen's kappa of 0.56 in a test set with 586 recordings. CONCLUSION: The results presented in this paper validate our proposal as a competitive wearable solution for sleep staging. Additionally, the use of a large and diverse data set contributes to the robustness of our solution, and corroborates the validation of algorithm's performance. Some additional analysis performed for healthy and sleep apnea population demonstrated that algorithm's performance has low correlation with demographic variables.

The clinical characteristic of catathrenia: a new look at an old issue-a systematic review of existing literature.

STUDY OBJECTIVES: The International Classification of Sleep Disorders categorized catathrenia as a respiratory disorder, but there are doubts whether episodes appear during rapid eye movement (REM) sleep or the non-rapid eye movement (NREM), their duration, and symptoms. The main objectives were to identify the most common features and relations of catathrenia. METHODS: PubMed, Embase, and Web of Science were searched according to the PRISMA 2020 guidelines. The Joanna Briggs Institute and the ROBINS-I tools were chosen to assess the risk of bias. RESULTS: A total of 288 records were identified, 31 articles were included. The majority of the studies had a moderate risk of bias. 49.57% of episodes occurred during the NREM sleep, while 46% took place during REM. In 60.34% females, catathrenia was more common in the NREM, while in 59.26% of males was in REM sleep (p < 0.05). Females and obese individuals were found to have shorter episodes (p < 0.05). Age was inversely correlated with minimal episodes duration (r = - 0.34). The continuous positive airway pressure (CPAP) therapy was inversely correlated with the maximal episode duration (r = - 0.48). CONCLUSIONS: Catathrenia occurs with similar frequency in both genders. The most frequent symptoms embraced groaning, awareness of disturbing bedpartners, and daytime somnolence-not confirmed by the Epworth Sleepiness Scale. The episodes occur more frequently in NREM than in REM sleep. Catathrenia may be considered as a sex-specific condition. The effects of CPAP treatment leading to shortening episodes duration, which may indicate the respiratory origin of catathrenia.

Wavelet Entropy Analysis of Electroencephalogram Signals During Wake and Different Sleep Stages in Patients with Insomnia Disorder.

Objective: To investigate the changes in the wavelet entropy during wake and different sleep stages in patients with insomnia disorder. Methods: Sixteen patients with insomnia disorder and sixteen normal controls were enrolled. They underwent scale assessment and two consecutive nights of polysomnography (PSG). Wavelet entropy analysis of electroencephalogram (EEG) signals recorded from all participants in the two groups was performed. The changes in the integral wavelet entropy (En) and individual-scale wavelet entropy (En(a)) during wake and different sleep stages in the two groups were observed, and the differences between the two groups were compared. Results: The insomnia disorder group exhibited lower En during the wake stage, and higher En during the N3 stage compared with the normal control group (all P < 0.001). In terms of En(a), patients with insomnia disorder exhibited lower En(a) in the ß and α frequency bands during the wake stage compared with normal controls (ß band, P < 0.01; α band, P < 0.001), whereas they showed higher En(a) in the ß and α frequency bands during the N3 stage than normal controls (ß band, P < 0.001; α band, P < 0.001). Conclusion: Wavelet entropy can reflect the changes in the complexity of EEG signals during wake and different sleep stages in patients with insomnia disorder, which provides a new method and insights about understanding of pathophysiological mechanisms of insomnia disorder. Wavelet entropy provides an objective indicator for assessing sleep quality.

Performance Evaluation of a New Sport Watch in Sleep Tracking: A Comparison against Overnight Polysomnography in Young Adults.

Introduction: This study aimed to validate the ability of a prototype sport watch (Polar Electro Oy, FI) to recognize wake and sleep states in two trials with and without an interval training session (IT) 6 h prior to bedtime. Methods: Thirty-six participants completed this study. Participants performed a maximal aerobic test and three polysomnography (PSG) assessments. The first night served as a device familiarization night and to screen for sleep apnea. The second and third in-home PSG assessments were counterbalanced with/without IT. Accuracy and agreement in detecting sleep stages were calculated between PSG and the prototype. Results: Accuracy for the different sleep stages (REM, N1 and N2, N3, and awake) as a true positive for the nights without exercise was 84 ± 5%, 64 ± 6%, 81 ± 6%, and 91 ± 6%, respectively, and for the nights with exercise was 83 ± 7%, 63 ± 8%, 80 ± 7%, and 92 ± 6%, respectively. The agreement for the sleep night without exercise was 60.1 ± 8.1%, k = 0.39 ± 0.1, and with exercise was 59.2 ± 9.8%, k = 0.36 ± 0.1. No significant differences were observed between nights or between the sexes. Conclusion: The prototype showed better or similar accuracy and agreement to wrist-worn consumer products on the market for the detection of sleep stages with healthy adults. However, further investigations will need to be conducted with other populations.

Alpha anteriorization and theta posteriorization during deep sleep.

Brain states (wake, sleep, general anesthesia, etc.) are profoundly associated with the spatiotemporal dynamics of brain oscillations. Previous studies showed that the EEG alpha power shifted from the occipital cortex to the frontal cortex (alpha anteriorization) after being induced into a state of general anesthesia via propofol. The sleep research literature suggests that slow waves and sleep spindles are generated locally and propagated gradually to different brain regions. Since sleep and general anesthesia are conceptualized under the same framework of consciousness, the present study examines whether alpha anteriorization similarly occurs during sleep and how the EEG power in other frequency bands changes during different sleep stages. The results from the analysis of three polysomnography datasets of 234 participants show consistent alpha anteriorization during the sleep stages N2 and N3, beta anteriorization during stage REM, and theta posteriorization during stages N2 and N3. Although it is known that the neural circuits responsible for sleep are not exactly the same for general anesthesia, the findings of alpha anteriorization in this study suggest that, at macro level, the circuits for alpha oscillations are organized in the similar cortical areas. The spatial shifts of EEG power in different frequency bands during sleep may offer meaningful neurophysiological markers for the level of consciousness.

Simulated quick returns in a laboratory context and effects on sleep and pre-sleep arousal between shifts: a crossover controlled trial.

This trial presents a laboratory model investigating the effect of quick returns (QRs, <11 h time off between shifts) on sleep and pre-sleep arousal. Using a crossover design, 63 participants worked a simulated QR condition (8 h time off between consecutive evening- and day shifts) and a day-day (DD) condition (16 h time off between consecutive day shifts). Participants slept at home and sleep was measured using a sleep diary and sleep radar. Compared to the DD condition, the QR condition reduced subjective and objective total sleep time by approximately one hour (both p < .001), reduced time in light- (p < .001), deep- (p = .004), rapid eye movement (REM, p < .001), percentage of REM sleep (p = .023), and subjective sleep quality (p < .001). Remaining sleep parameters and subjective pre-sleep arousal showed no differences between conditions. Results corroborate previous field studies, validating the QR model and indicating causal effects of short rest between shifts on common sleep parameters and sleep architecture.

This trial proposes a laboratory model to investigate the consequences of quick returns (QRs, <11h time off between shifts) on subjective/objective sleep and pre-sleep arousal. QRs reduced total sleep time, light-, deep-, REM sleep, whereas pre-sleep arousal was unaffected. Results emphasise the importance of ensuring sufficient rest time between shifts.Abbreviation: QR: Quick return; DD: Day-day; NREM: Non-rapid eye movement; REM: Rapid eye movement; PSG: Polysomnography; TIB: Time in bed; SOL: Sleep onset latency; WASO: Wake after sleep onset; TST: Total sleep time; EMA: Early morning awakening; PSAS: Pre-Sleep Arousal Scale; MEQ: Morning-Evening Questionnaire; LMM: Linear mixed model; EMM: Estimated marginal mean; SD: Standard deviation; SE: Standard error; d: Cohens' d; h: hours; m: minutes.

Sleep alterations as a function of 88 health indicators.

BACKGROUND: Alterations in sleep have been described in multiple health conditions and as a function of several medication effects. However, evidence generally stems from small univariate studies. Here, we apply a large-sample, data-driven approach to investigate patterns between in sleep macrostructure, quantitative sleep EEG, and health. METHODS: We use data from the MrOS Sleep Study, containing polysomnography and health data from a large sample (N = 3086) of elderly American men to establish associations between sleep macrostructure, the spectral composition of the electroencephalogram, 38 medical disorders, 2 health behaviors, and the use of 48 medications. RESULTS: Of sleep macrostructure variables, increased REM latency and reduced REM duration were the most common findings across health indicators, along with increased sleep latency and reduced sleep efficiency. We found that the majority of health indicators were not associated with objective EEG power spectral density (PSD) alterations. Associations with the rest were highly stereotypical, with two principal components accounting for 85-95% of the PSD-health association. PC1 consists of a decrease of slow and an increase of fast PSD components, mainly in NREM. This pattern was most strongly associated with depression/SSRI medication use and age-related disorders. PC2 consists of changes in mid-frequency activity. Increased mid-frequency activity was associated with benzodiazepine use, while decreases were associated with cardiovascular problems and associated medications, in line with a recently proposed hypothesis of immune-mediated circadian demodulation in these disorders. Specific increases in sleep spindle frequency activity were associated with taking benzodiazepines and zolpidem. Sensitivity analyses supported the presence of both disorder and medication effects. CONCLUSIONS: Sleep alterations are present in various health conditions.

Tripolar concentric ring electrodes for capturing localised electroencephalography signals during sleep.

By design, tripolar concentric ring electrodes (TCRE) provide more focal brain activity signals than conventional electroencephalography (EEG) electrodes placed further apart. This study compared spectral characteristics and rates of data loss to noisy epochs with TCRE versus conventional EEG signals recorded during sleep. A total of 20 healthy sleepers (12 females; mean [standard deviation] age 27.8 [9.6] years) underwent a 9-h sleep study. Participants were set up for polysomnography recording with TCRE to assess brain activity from 18 sites and conventional electrodes for EEG, eyes, and muscle movement. A fast Fourier transform using multitaper-based estimation was applied in 5-s epochs to scored sleep. Odds ratios with Bonferroni-adjusted 95% confidence intervals were calculated to determine the proportional differences in the number of noisy epochs between electrode types. Relative power was compared in frequency bands throughout sleep. Linear mixed models showed significant main effects of signal type (p < 0.001) and sleep stage (p < 0.001) on relative spectral power in each power band, with lower relative spectral power across all stages in TCRE versus EEG in alpha, beta, sigma, and theta activity, and greater delta power in all stages. Scalp topography plots showed distinct beta activation in the right parietal lobe with TCRE versus EEG. EEG showed higher rates of noisy epochs compared to TCRE (1.3% versus 0.8%, p < 0.001). TCRE signals showed marked differences in brain activity compared to EEG, consistent with more focal measurements and region-specific differences during sleep. TCRE may be useful for evaluating regional differences in brain activity with reduced muscle artefact compared to conventional EEG.

Fundamentals of sleep regulation: Model and benchmark values for fractal and oscillatory neurodynamics.

Homeostatic, circadian and ultradian mechanisms play crucial roles in the regulation of sleep. Evidence suggests that ratios of low-to-high frequency power in the electroencephalogram (EEG) spectrum indicate the instantaneous level of sleep pressure, influenced by factors such as individual sleep-wake history, current sleep stage, age-related differences and brain topography characteristics. These effects are well captured and reflected in the spectral exponent, a composite measure of the constant low-to-high frequency ratio in the periodogram, which is scale-free and exhibits lower interindividual variability compared to slow wave activity, potentially serving as a suitable standardization and reference measure. Here we propose an index of sleep homeostasis based on the spectral exponent, reflecting the level of membrane hyperpolarization and/or network bistability in the central nervous system in humans. In addition, we advance the idea that the U-shaped overnight deceleration of oscillatory slow and fast sleep spindle frequencies marks the biological night, providing somnologists with an EEG-index of circadian sleep regulation. Evidence supporting this assertion comes from studies based on sleep replacement, forced desynchrony protocols and high-resolution analyses of sleep spindles. Finally, ultradian sleep regulatory mechanisms are indicated by the recurrent, abrupt shifts in dominant oscillatory frequencies, with spindle ranges signifying non-rapid eye movement and non-spindle oscillations - rapid eye movement phases of the sleep cycles. Reconsidering the indicators of fundamental sleep regulatory processes in the framework of the new Fractal and Oscillatory Adjustment Model (FOAM) offers an appealing opportunity to bridge the gap between the two-process model of sleep regulation and clinical somnology.