Sleep Stages Research Articles

From Infancy to Childhood: A Comprehensive Review of Event- and Task-Related Brain Oscillations.

Brain development from infancy through childhood involves complex structural and functional changes influenced by both internal and external factors. This review provides a comprehensive analysis of event and task-related brain oscillations, focusing on developmental changes across different frequency bands, including delta, theta, alpha, beta, and gamma. Electroencephalography (EEG) studies highlight that these oscillations serve as functional building blocks for sensory and cognitive processes, with significant variations observed across different developmental stages. Delta oscillations, primarily associated with deep sleep and early cognitive demands, gradually diminish as children age. Theta rhythms, crucial for attention and memory, display a distinct pattern in early childhood, evolving with cognitive maturation. Alpha oscillations, reflecting thalamocortical interactions and cognitive performance, increase in complexity with age. Beta rhythms, linked to active thinking and problem-solving, show developmental differences in motor and cognitive tasks. Gamma oscillations, associated with higher cognitive functions, exhibit notable changes in response to sensory stimuli and cognitive tasks. This review underscores the importance of understanding oscillatory dynamics to elucidate brain development and its implications for sensory and cognitive processing in childhood. The findings provide a foundation for future research on developmental neuroscience and potential clinical applications.

What Radio Waves Tell Us about Sleep!

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=880) demonstrate that the model captures the sleep hypnogram (with an accuracy of 80.5% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.89), and measures the patient's Apnea-Hypopnea Index (ICC=0.90; 95% CI = [0.88, 0.91]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

The Association of Depression with Obstructive Sleep Apnea in Patients with Cystic and Non-Cystic Fibrosis Bronchiectasis.

Obstructive sleep apnea (OSA) and cystic fibrosis (CF) are chronic conditions that profoundly impact quality of life. OSA, characterized by repeated episodes of upper airway collapse, can exacerbate CF symptoms due to nocturnal airway obstruction. Recent studies highlight the prevalence of OSA in CF patients, especially in adults, and its detrimental effects on health and quality of life. From April 2019 to December 2021, we conducted a study with 104 bronchiectasis patients at Marmara University Pendik Training and Research Hospital. After exclusions, 70 participants (35 CF and 35 non-CF) were included. Sleep parameters were assessed with polysomnography, and depressive mood was evaluated using the Zung Self-Rating Depression Scale (SDS). Daytime sleepiness was measured using the Epworth Sleepiness Scale (ESS). The statistical analyses included t-tests, chi-square tests, and logistic regression. Among the CF patients, depressive mood was significantly associated with female sex (OR: 4.28, 95% CI: 1.27-12.04) and anemia (OR: 7.87, 95% CI: 1.50-41.27). Higher ESS scores indicated greater daytime sleepiness in the depressive groups (p = 0.051). Depressive CF patients also had a significantly longer disease duration and more frequent annual exacerbations. No significant differences were found in total sleep time, sleep efficiency, or sleep stages between the depressive and non-depressive groups. A lower forced vital capacity (FVC) was observed in the depressive CF patients, although not significantly. Depression is prevalent among adult CF patients with OSA, with significant associations with female sex and anemia. These findings underscore the need for integrated care addressing both physical and mental health aspects, including interventions for respiratory symptoms, anemia management, and sleep quality enhancement to improve overall quality of life.

Acoustically evoked K-complexes together with sleep spindles boost verbal declarative memory consolidation in healthy adults

Over the past decade, phase-targeted auditory stimulation (PTAS), a neuromodulation approach which presents auditory stimuli locked to the ongoing phase of slow waves during sleep, has shown potential to enhance specific aspects of sleep functions. However, the complexity of PTAS responses complicates the establishment of causality between specific electroencephalographic events and observed benefits. Here, we used down-PTAS during sleep to specifically evoke the early, K-complex (KC)-like response following PTAS without leading to a sustained increase in slow-wave activity throughout the stimulation window. Over the course of two nights, one with down-PTAS, the other without, high-density electroencephalography (hd-EEG) was recorded from 14 young healthy adults. The early response exhibited striking similarities to evoked KCs and was associated with improved verbal memory consolidation via stimulus-evoked spindle events nested into the up-phase of ongoing 1 Hz waves in a central region. These findings suggest that the early, KC-like response is sufficient to boost memory, potentially by orchestrating aspects of the hippocampal-neocortical dialogue.

Magnesium-L-threonate improves sleep quality and daytime functioning in adults with self-reported sleep problems: A randomized controlled trial

Objective/BackgroundSleep problems challenge overall wellbeing. Magnesium has been implicated to benefit sleep, although the clinical evidences varied based on the magnesium source used. Magnesium L-threonate (MgT) is a promising intervention due to its brain bioavailability and effects on cognition, memory and mood. We investigated MgT supplementation on sleep quality and daily function. Patients/methodsEighty 35–55-year-olds with self-assessed sleep problems participated in a randomized, double-blind, placebo-controlled, parallel-arm study, taking 1 g/day of MgT or placebo for 21 days. Sleep and daily behaviors were measured subjectively using standardized questionnaires including the Insomnia Severity Index, Leeds Sleep Evaluation Questionnaire, and Restorative Sleep Questionnaire, and objectively using an Oura ring. The Profile of Mood States questionnaire and a daily diary were used to evaluate mood, energy and productivity, and record any safety concerns. ResultsThe MgT group maintained good sleep quality and daytime functioning, while placebo declined. From objective Oura ring measurements, MgT significantly (p < 0.05) improved vs placebo deep sleep score, REM sleep score, light sleep time, and activity and readiness parameters activity score, activity daily movement score, readiness score, readiness activity balance, and readiness sleep balance. From subjective questionnaires, MgT significantly (p < 0.05) improved vs placebo behavior upon awakening, energy and daytime productivity, grouchiness, mood and mental alertness. MgT was safe and well tolerated. ConclusionsThis showed MgT improved sleep quality, especially deep/REM sleep stages, improved mood, energy, alertness, and daily activity and productivity. These are consistent with how MgT works in neuron cells and animal models, suggesting broader positive impacts on overall brain health.

A hippocampal circuit mechanism to balance memory reactivation during sleep.

Memory consolidation involves the synchronous reactivation of hippocampal cells active during recent experience in sleep sharp-wave ripples (SWRs). How this increase in firing rates and synchrony after learning is counterbalanced to preserve network stability is not understood. We discovered a network event generated by an intrahippocampal circuit formed by a subset of CA2 pyramidal cells to cholecystokinin-expressing (CCK+) basket cells, which fire a barrage of action potentials ("BARR") during non-rapid eye movement sleep. CA1 neurons and assemblies that increased their activity during learning were reactivated during SWRs but inhibited during BARRs. The initial increase in reactivation during SWRs returned to baseline through sleep. This trend was abolished by silencing CCK+ basket cells during BARRs, resulting in higher synchrony of CA1 assemblies and impaired memory consolidation.

High-Frequency Imaging Reveals Synchronised Delta- and Theta-Band Ca2+ Oscillations in the Astrocytic Soma In Vivo.

One of the major breakthroughs of neurobiology was the identification of distinct ranges of oscillatory activity in the neuronal network that were found to be responsible for specific biological functions, both physiological and pathological in nature. Astrocytes, physically coupled by gap junctions and possessing the ability to simultaneously modulate the functions of a large number of surrounding synapses, are perfectly positioned to introduce synchronised oscillatory activity into the neural network. However, astrocytic somatic calcium signalling has not been investigated to date in the frequency ranges of common neuronal oscillations, since astrocytes are generally considered to be slow responders in terms of Ca2+ signalling. Using high-frequency two-photon imaging, we reveal fast Ca2+ oscillations in the soma of astrocytes in the delta (0.5-4 Hz) and theta (4-8 Hz) frequency bands in vivo in the rat cortex under ketamine-xylazine anaesthesia, which is known to induce permanent slow-wave sleep. The high-frequency astrocytic Ca2+ signals were not observed under fentanyl anaesthesia, excluding the possibility that the signals were introduced by motion artefacts. We also demonstrate that these fast astrocytic Ca2+ signals, previously considered to be exclusive to neurons, are present in a large number of astrocytes and are phase synchronised at the astrocytic network level. We foresee that the disclosure of these high-frequency astrocytic signals may help with understanding the appearance of synchronised oscillatory signals and may open up new avenues of treatment for neurological conditions characterised by altered neuronal oscillations.

Sleep Cycles and Health: Role of Sleep Stages, Circadian Rhythms, and Lifestyle Factors on Optimizing Physical Performance and Mental Well-Being – a literature review

Sleep Cycles and Health: Role of Sleep Stages, Circadian Rhythms, and Lifestyle Factors on Optimizing Physical Performance and Mental Well-Being – a literature review

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data

BackgroundWide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. New methodA hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. ResultsSleep states were classified with an accuracy of 84 % and Cohen’s κ of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. Comparison with existing methodOn a held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ of 0.65 corresponding to the human EEG/EMG-based scoring. ConclusionsThe CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep research.

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis.

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

Chinese formula Guben-Jiannao Ye alleviates the dysfunction of circadian and sleep rhythms in APP/PS1 mice implicated in activation of the PI3K/AKT/mTOR signaling pathway

Ethnopharmacological relevanceThe Chinese formula Guben-Jiannao Ye (GBJNY) formula has a long history of usage in traditional Chinese medicine (TCM) for the treatment of learning and memory disorders as well as senile insomnia. This formulation is derived from Sun Simiao's five tonic pills. Furthermore, modern pharmacological investigations have revealed its ability to improve cognitive impairment and ameliorate sleep-wake circadian rhythm disorders. However, the precise mechanism underlying its efficacy remains elusive. Aim of the studyThe current research explored the modulatory effects and possible mechanisms of GBJNY in circadian rhythm sleep-wake disorders and cognitive dysfunction in Alzheimer's disease using transcriptome sequencing and experimental validation. Materials and methodsThe LC-MS/MS tandem technology was utilized to qualitatively discern the active components present in GBJNY. The APP/PS1 mice received continuous treatment with GBJNY or Melatonin for 3 months. The learning and memory abilities of mice were assessed utilizing the Morris water maze (MWM) test, while sleep changes were studied utilizing the electroencephalogram (EEG) and electromyogram (EMG). Concurrently, mice's hippocampus clock gene rhythmicity was investigated. Subsequently, we employed HE staining, Golgi staining, and immunofluorescence to observe GBJNY's impact on synaptic damage and neuronal loss. We performed high-throughput sequencing to analyze the mRNA expression profiles of mice, aiming to identify differentially expressed genes (DEGs). Subsequently, we conducted GO and KEGG enrichment analyses to explore associated signaling pathways. Furthermore, we evaluated the expression levels of proteins involved in the PI3K/AKT/mTOR pathway and Aβ deposition in the hippocampus of mice. Through this comprehensive approach, we sought to elucidate and validate the potential mechanisms of action of GBJNY in APP/PS1 mice. ResultsResults showed 216 DEGs. Following this, we conducted GO enrichment and KEGG pathway analyses to delve deeper into the distinctions and fundamental functions of the mRNA target genes. The enrichment analysis underscored the prominence of the PI3K/Akt/mTOR signaling pathway as the most pivotal among them. Through in vivo experiments, it was further demonstrated that the administration of GBJNY enhanced memory and learning capacities in APP/PS1 mice. Additionally, GBJNY treatment resulted in alterations in the sleep-wake circadian rhythm, characterized by reduced wakefulness and an increase in non-rapid eye movement (NREM) sleep. Moreover, alterations in the peak expression of Per1, Per2, Clock, Cry1, Cry2, and Bmal1 mRNA were noted in the hippocampus of treated mice. Particularly noteworthy were the observed reductions in amyloid-beta (Aβ) deposition within the hippocampus, improvements in neuronal synaptic integrity, and upregulation of mTOR, Akt, and PI3K protein expression in the hippocampal region. These findings underscore the critical involvement of the PI3K/Akt/mTOR signaling pathway in mitigating disturbances in sleep-wake circadian rhythms. ConclusionsGBJNY enhanced the cognitive performance of APP/PS1 mice and altered clock gene expression patterns, alleviating sleep-wake circadian rhythm disruptions. The fundamental mechanism appears to be linked to the PI3K/Akt/mTOR pathway regulation, offering a foundation for potential clinical applications.

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 Na+/K+-ATPase generically enables deterministic bursting in class I neurons by shearing the spike-onset bifurcation structure.

Slow brain rhythms, for example during slow-wave sleep or pathological conditions like seizures and spreading depolarization, can be accompanied by oscillations in extracellular potassium concentration. Such slow brain rhythms typically have a lower frequency than tonic action-potential firing. They are assumed to arise from network-level mechanisms, involving synaptic interactions and delays, or from intrinsically bursting neurons. Neuronal burst generation is commonly attributed to ion channels with slow kinetics. Here, we explore an alternative mechanism generically available to all neurons with class I excitability. It is based on the interplay of fast-spiking voltage dynamics with a one-dimensional slow dynamics of the extracellular potassium concentration, mediated by the activity of the Na+/K+-ATPase. We use bifurcation analysis of the complete system as well as the slow-fast method to reveal that this coupling suffices to generate a hysteresis loop organized around a bistable region that emerges from a saddle-node loop bifurcation-a common feature of class I excitable neurons. Depending on the strength of the Na+/K+-ATPase, bursts are generated from pump-induced shearing the bifurcation structure, spiking is tonic, or cells are silenced via depolarization block. We suggest that transitions between these dynamics can result from disturbances in extracellular potassium regulation, such as glial malfunction or hypoxia affecting the Na+/K+-ATPase activity. The identified minimal mechanistic model outlining the sodium-potassium pump's generic contribution to burst dynamics can, therefore, contribute to a better mechanistic understanding of pathologies such as epilepsy syndromes and, potentially, inform therapeutic strategies.

Deep CNN for EEG in Sleep Staging

Deep CNN for EEG in Sleep Staging

Effects and mechanisms of electroacupuncture on fear extinction and sleep phase in single prolonged stress mice

To observe the effects of electroacupuncture (EA) on fear extinction and sleep phase in single prolonged stress (SPS) mice, and explore its mechanism in view of the expression of relevant synaptic proteins. Thirty-two C57BL/6J male mice were randomly divided into a control group, a model group, an EA group and a paroxetine (PRX) group, with 8 mice in each one. Modified SPS method was used to establish PTSD model in the model group, the EA group and the PRX group. Seven days after modeling completion, in the EA group, the intervention was delivered at "Baihui" (GV 20) and bilateral "Zusanli" (ST 36), with disperse-dense wave, 3 Hz/15 Hz in frequency and 1 mA in current intensity, for 30 min. In the PRX group, paroxetine solution (2.5 g/L) was administered intragastrically (10 mg/kg). The intervention was given once daily and for consecutive 10 days in the above two groups. The fear conditioning task and the elevated plus-maze test were adopted to evaluate the fear extinction and anxiety of the mice in each group. Using Medusa electroencephalogram (EEG) and electromyography (EMG) recording system from rats and mice, the sleep phase was determined in the mice. With Western blot method adopted, the protein expression of the postsynaptic density protein 95 (PSD95), activity-regulated cytoskeleton-associated protein (ARC), brain-derived neurotrophic factor (BDNF), N-methyl-D-aspartic acid receptor 2A (GluN2A), N-methyl-D-aspartic acid receptor 2B (GluN2B) and alpha-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor 1 (GluA1) in the hippocampus was detected in the mice. Compared with the control group, the freezing time for the fear re-exposure in 3 min to 15 min and the fear extinction in 0 min to 3 min were prolonged (P<0.05), the fear extinction index decreased (P<0.05), and the open arm time (OT) of the elevated plus-maze was shortened (P<0.05) in the model group. When compared with the model group, in the EA group and the PRX group, the freezing time for the fear re-exposure in 3 min to 6 min and 12 min to 15 min, as well as the fear extinction in 0 min to 3 min was shortened (P<0.05), the fear extinction index increased (P<0.05); the OT in elevated plus-maze was longer in the mice of the EA group (P<0.05). The period of wake (Wake) was prolonged (P<0.05), the non-rapid eye movement period (NREM) and the total sleep time (Sleep) were reduced in the model group (P<0.05) in comparison with the control group. Compared with the model group, the Wake was declined (P<0.05), and the NREM and Sleep increased in the EA group and the PRX group (P<0.05). When compared with the control group, the protein expression of PSD95, ARC, BDNF, GluN2A and GluA1 in the hippocampus decreased (P<0.05), and that of GluN2B increased (P<0.05) in the model group. In the EA group and the PRX group, the protein expression of PSD95, ARC, BDNF, GluN2A and GluA1 in the hippocampus was elevated (P<0.05), and that of GluN2B reduced (P<0.05) when compared with the model group. Electroacupuncture at "Baihui" (GV 29) and "Zusanli" (ST 36) can ameliorate anxiety-like behavior, fear extinction disorder and abnormal sleep phase in SPS mice, which may be related to the regulation of synaptic transmission and synaptic plasticity expression in the hippocampus.

Decreased Delta/Beta ratio index as the sleep state-independent electrophysiological signature of sleep state misperception in Insomnia disorder: A focus on the sleep onset and the whole night

PurposeSleep State Misperception (SSM) is described as the tendency of Insomnia Disorder (ID) patients to overestimate Sleep Latency (SL) and underestimate Total Sleep Time (TST). Literature exploring topographical components in ID with SSM is scarce and does not allow us to fully understand the potential mechanisms underlying this phenomenon. This study aims to evaluate the existence of sleep EEG topography alterations in ID patients associated with SSM compared to Healthy Controls (HC), focusing on two distinct periods: the Sleep Onset (SO) and the whole night. MethodsTwenty ID patients (mean age: 43.5 ± 12.7; 7 M/13F) and 18 HCs (mean age: 41.6 ± 11.9; 8 M/10F) underwent a night of Polysomnography (PSG) and completed sleep diaries the following morning upon awakening. Two SSM indices, referring to the misperception of SL (SLm) and TST (TSTm), were calculated by comparing objective and subjective sleep indices extracted by PSG and sleep diary. According to these indices, the entire sample was split into 4 sub-groups: ID +SLm vs HC –SLm; ID +TSTm vs HC –TSTm. ResultsConsidering the SO, the two-way mixed-design ANOVA showed a significant main effect of Groups pointing to a decreased delta/beta ratio in the whole scalp topography. Moreover, we found a significant interaction effect for the sigma and beta bands. Post Hoc tests showed higher sigma and beta power in anterior and temporo-parietal sites during the SO period in IDs +SLm compared to HC –SLm.Considering the whole night, the unpaired t-test revealed in IDs +TSTm significantly lower delta power during NREM, and lower delta/beta ratio index during NREM and REM sleep compared to HCs –TSTm.Finally, we found diffuse significant negative correlations between SSM indices and the delta/beta ratio during SO, NREM, and REM sleep. ConclusionThe main finding of the present study suggests that higher SL overestimation and TST underestimation are both phenomena related to diffuse cortical hyperarousal interpreted as a sleep state-independent electrophysiological correlate of the SSM, both during the SO and the whole night.

The First-Night Effect of sleep occurs over non-consecutive nights in unfamiliar and familiar environments.

The first night in an unfamiliar environment is marked by reduced sleep quality and changes in sleep architecture. This so-called First-Night Effect (FNE) is well established for two consecutive nights and lays the foundation for including an adaptation night in sleep research to counteract FNEs. However, adaptation nights rarely happen immediately before experimental nights, which raises the question of how sleep adapts over non-consecutive nights. Furthermore, it is yet unclear, how environmental familiarity and hemispheric asymmetry of slow-wave sleep (SWS) contribute to the explanation of FNEs. To address this gap, 45 healthy participants spent two weekly separated nights in the sleep laboratory. In a separate study, we investigated the influence of environmental familiarity on 30 participants who spent two non-consecutive nights in the sleep laboratory and two nights at home. Sleep was recorded by polysomnography. Results of both studies show that FNEs also occur in non-consecutive nights, particularly affecting wake after sleep onset, sleep onset latency, and total sleep time. Sleep disturbances in the first night happen in both familiar and unfamiliar environments. The degree of asymmetric SWS was not correlated with the FNE but rather tended to vary over the course of several nights. Our findings suggest that non-consecutive adaptation nights are effective in controlling for FNEs, justifying the current practice in basic sleep research. Further research should focus on trait- and fluctuating state-like components explaining interhemispheric asymmetries.

Construction and validation of clinical prediction model of tongue base collapse under drug-induced sleep endoscopy in OSA patients

Objective: To analyze the correlation between drug-induced sleep endoscopy (DISE), results, polysomnography (PSG) indicators, and clinical parameters in patients with obstructive sleep apnea (OSA), and to establish and validate a predictive model for tongue base plane obstruction. Methods: This retrospective study analyzed 117 OSA patients diagnosed via PSG and treated at the Department of Otolaryngology-Head and Neck Surgery, Nanfang Hospital, Southern Medical University, between October 2014 and March 2022. The cohort comprised of 114 males and 3 females, with an age range of 20 to 54 years (mean age 38.1±8.4 years). Data on DISE results, PSG results, and clinical indicators were collected for all 117 patients. Logistic regression analysis was performed to identify relevant indicators, and a predictive model for tongue base plane obstruction was constructed and internally validated using the R programming language. Results: Univariate logistic regression analysis identified four independent risk factors for predicting tongue root plane obstruction: tonsil grading, N2, N3, and rapid eye movement sleep(REM) stage [OR:0.412(0.260~0.652),1.045(1.012~1.079),0.943(0.903~0.984),0.961(0.925~0.998),P <0.05]. Multivariate logistic regression analysis confirmed tonsil grading and N3 sleep stage (12.48±12.22%) as significant predictors. A nomogram model incorporating these factors demonstrated good predictive performance, with an area under curve(AUC) of 0.82 (95%CI: 0.548-1.000), an optimal cutoff of 0.519, a specificity of 80.0%, and a sensitivity of 86.7%. Internal validation of the model in the validation cohort yielded an AUC of 0.751 (95%CI: 0.625-0.876). Conclusions: Tongue base plane obstruction observed during DISE in OSA patients is associated with tonsil grading and N3 sleep stage duration. The predictive model developed for tongue base plane obstruction based on DISE demonstrates good efficacy, as evidenced by its internal validation.

Automated remote sleep monitoring needs uncertainty quantification.

Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage classification, but trust and reliability issues have hampered its adoption in clinical applications. Estimating uncertainty is a crucial factor in enhancing reliability by identifying regions of heightened and diminished confidence. In this study, we used an uncertainty-centred machine learning pipeline, U-PASS, to automate sleep staging in a challenging real-world dataset of single-channel electroencephalography and accelerometry collected with a wearable device from an elderly population. We were able to effectively limit the uncertainty of our machine learning model and to reliably inform clinical experts of which predictions were uncertain to improve the machine learning model's reliability. This increased the five-stage sleep-scoring accuracy of a state-of-the-art machine learning model from 63.9% to 71.2% on our dataset. Remarkably, the machine learning approach outperformed the human expert in interpreting these wearable data. Manual review by sleep specialists, without specific training for sleep staging on wearable electroencephalography, proved ineffective. The clinical utility of this automated remote monitoring system was also demonstrated, establishing a strong correlation between the predicted sleep parameters and the reference polysomnography parameters, and reproducing known correlations with the apnea-hypopnea index. In essence, this work presents a promising avenue to revolutionize remote patient care through the power of machine learning by the use of an automated data-processing pipeline enhanced with uncertainty estimation.

Effect of Physical Exercise on Sleep Quality in College Athletes in Malaysia

Purpose: The aim of the study was to analyze the effect of physical exercise on sleep quality in college athletes in Malaysia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Research on college athletes in Malaysia reveals that regular physical exercise significantly improves sleep quality by increasing sleep duration and efficiency. Exercise reduces sleep latency, helping athletes fall asleep faster, and enhances deep sleep stages crucial for recovery. Additionally, physical activity aids in managing stress and anxiety, which are linked to poor sleep. However, the impact of exercise can vary depending on its intensity and timing. Unique Contribution to Theory, Practice and Policy: The theory of exercise and sleep interactions, the cognitive behavioral theory of insomnia (CBT-I) &amp; the circadian rhythm theory may be used to anchor future studies on the effect of physical exercise on sleep quality in college athletes in Malaysia. Athletic trainers and coaches should design personalized exercise regimens that include a mix of aerobic, resistance, and flexibility exercises tailored to individual athletes' needs. Sports organizations and educational institutions should establish policies that recommend optimal timing for workouts. Research indicates that morning sessions are less disruptive to sleep than evening sessions, and such guidelines can help in scheduling training sessions to promote better sleep quality.