Deep Sleep Stages Research Articles

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) & 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.

Relationship between Sports Participation and Academic Discipline in Middle School Students in Egypt

Purpose: The aim of the study was to analyze the relationship between sports participation and academic discipline in middle school students in Egypt. 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: Self-determination theory (SDT), social cognitive theory (SCT) & achievement goal theory (AGT) may be used to anchor future studies on the relationship between sports participation and academic discipline in middle school students in Egypt. Schools should implement structured sports programs that emphasize not only physical skills but also the development of discipline, teamwork, and time management. Schools should adopt policies that promote the integration of sports and academics, ensuring that sports participation is not viewed as a distraction but as a complementary activity that enhances academic discipline.

Sleep apnea and glymphatic dysfunction as a mediator of executive dysfunction and neurodegenerative risk in cancer related cognitive impairment (CRCI).

12091 Background: Cancer survivors often experience cognitive changes, formally known as Cancer-Related Cognitive Impairment (CRCI), during or after treatment. CRCI commonly consists of executive dysfunction, which includes impairments in short-term memory, sustained attention/inhibitory control, cognitive flexibility, processing speed, and top-down processing. CRCI may be impacted by comorbidities and can increase neurodegenerative disease risk. Sleep disorders and disruptions (SDs) impair executive function in the general population. SDs may contribute to CRCI by impeding progression to deep sleep stages associated with enhanced glymphatic flow (GF) and clearance of inflammatory byproducts, leading to ventriculomegaly (VM- enlarged ventricles). VM is associated with cognitive impairment in other disorders, such as normal pressure hydrocephalus. Using data from the University of Cincinnati Cancer Cognitive Registry, we investigated relationships between the incidence and type of SD in CRCI, SDs and executive dysfunction, and evidence of altered GF in SD as measured by Evans Indices (EIs). SDs are known to be prevalent in CRCI patients. We hypothesize that evidence of impaired GF as measured by EIs will be present and associated with SDs and executive dysfunction. Identifying modifiable contributors to CRCI and their mechanisms of action are critical steps towards increasing quality of life in survivors. Methods: 174 patients referred for CRCI between 10/2021-10/2023 underwent assessments for CRCI and sleep hygiene with a cognitive battery, subjective questionnaires, and nocturnal polysomnography. Retrospective data gathered from Epic was entered into a REDCap registry. MRIs were analyzed in McKesson to calculate EIs. Results: Our data demonstrated the presence of SDs in 84% of patients and obstructive sleep apnea in 45%. Measures of apnea severity (apnea-hypopnea index-AHI) and GF (EIs) inversely correlated with executive function scores. This implicates sleep-related GF as a potential mediator of CRCI. Conclusions: CRCI and accompanying increased neurodegenerative risk may be the result of restricted cerebral spinal fluid exchange due to SDs in conjunction with systemic inflammation from cancer and its treatment. Sleep assessments should be routine in CRCI evaluations. Longitudinal assessments will help determine the relationship of SDs and their treatment to the course of CRCI and risk of neurodegenerative disease.

Associations between bedroom environment and sleep quality when sleeping less or more than 6h: A cross sectional study during summer

A cross-sectional study was conducted in 50 bedrooms in Shanghai, each for one week during summer, to investigate the effects of the bedroom environment on sleep quality. Sleep quality was recorded with a wrist-worn sleep tracker and assessed with a questionnaire. The measurements from 168 person-nights were analysed after excluding unreliable data. The overnight means of bedroom temperature, relative humidity, CO2 and PM2.5 concentrations, which were continuously measured were between 21 to 30 °C, 43 to 86 %, 375 to 2756 ppm, and <1 to 43 μg/m3, respectively. Sleep duration in 36.3 % of the nights was less than 6 h, which is too short according to the recommendation of the American Sleep Foundation. The nights were then classified into two categories, with sleep duration above or below 6 h. The results show that the sleep quality in the short-sleep category was more sensitive to the bedroom environment. In the longer-sleep duration category, light sleep decreased with higher humidity and increased with higher PM2.5 concentration. In the short-sleep duration category, all the main sleep quality parameters including sleep efficiency and the duration and percentage of deep and rapid eye movement sleep stage decreased, and time spent awake increased with increased bedroom temperature, humidity, CO2 and PM2.5 concentration. The occupants who used air conditioning had a lower indoor temperature and humidity at night; opening windows decreased the indoor CO2 concentration but increased the PM2.5 concentration. These results suggest the need for alternative solutions to natural ventilation during summer in dwellings in Shanghai.

Automatic Wake and Deep-Sleep Stage Classification Based on Wigner-Ville Distribution Using a Single Electroencephalogram Signal.

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner-Ville Distribution (WVD) for time-frequency analysis to determine EEG energy per second in specific frequency bands (δ, θ, α, and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10-20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future.

Polysomnographic Characteristics of Sleep Architecture in Children With Obstructive Sleep Apnea.

The conventional measure of sleep fragmentation is via polysomnographic evaluation of sleep architecture. Adults with OSA have disruption in their sleep cycles and spend less time in deep sleep stages. However, there is no available evidence to suggest that this is also true for children and published results have been inconclusive. To determine polysomnographic characteristics of sleep architecture in children with OSA and investigate effects relative to OSA severity. Overnight polysomnograms (PSG) of children referred for suspected OSA were reviewed. Subjects were classified by apnea hypopnea index (AHI). PSG parameters of sleep architecture were recorded and analyzed according to OSA severity. Two hundred and eleven children were studied (median age of 7.0 years, range 4-10 years) Stage N1 sleep was longer while stage N2 sleep and REM sleep was reduced in the OSA group when compared to those without OSA (6.10 vs 2.9, P < .001; 42.0 vs 49.7, P < .001; 14.0 vs 15.9, P = .05). The arousal index was also higher in the OSA group (12.9 vs 8.2, P < .001). There was a reduction in sleep efficiency and total sleep time and an increase in wake after sleep onset noted in the OSA group (83.90 vs 89.40, P = .003; 368.50 vs 387.25, P = .001; 40.1 ± 35.59 vs 28.66 ± 24.14, P = .007; 29.00 vs 20.50; P = .011). No significant difference was found in N3 sleep stage (33.60 vs 30.60, P = .14). We found evidence that children with OSA have a disturbance in their sleep architecture. The changes indicate greater sleep fragmentation and more time spent in lighter stages of sleep. Future research is needed and should focus on more effective methods to measure alterations in sleep architecture.

Sleep-stage-dependent alterations in cerebral oxygen metabolism quantified by magnetic resonance.

A key function of sleep is to provide a regular period of reduced brain metabolism, which is critical for maintenance of healthy brain function. The purpose of this work was to quantify the sleep-stage-dependent changes in brain energetics in terms of cerebral metabolic rate of oxygen (CMRO2 ) as a function of sleep stage using quantitative magnetic resonance imaging (MRI) with concurrent electroencephalography (EEG) during sleep in the scanner. Twenty-two young and older subjects with regular sleep hygiene and Pittsburgh Sleep Quality Index (PSQI) in the normal range were recruited for the study. Cerebral blood flow (CBF) and venous oxygen saturation (SvO2 ) were obtained simultaneously at 3 Tesla field strength and 2.7-s temporal resolution during an 80-min time series using OxFlow, an in-house developed imaging sequence. The method yields whole-brain CMRO2 in absolute physiologic units via Fick's Principle. Nineteen subjects yielded evaluable data free of subject motion artifacts. Among these subjects, 10 achieved slow-wave (N3) sleep, 16 achieved N2 sleep, and 19 achieved N1 sleep while undergoing the MRI protocol during scanning. Mean CMRO2 was 98 ± 7(μmol min-1 )/100 g awake, declining progressively toward deepest sleep stage: 94 ± 10.8 (N1), 91 ± 11.4 (N2), and 76 ± 9.0 μmol min-1 /100 g (N3), with each level differing significantly from the wake state. The technology described is able to quantify cerebral oxygen metabolism in absolute physiologic units along with non-REM sleep stage, indicating brain oxygen consumption to be closely associated with depth of sleep, with deeper sleep stages exhibiting progressively lower CMRO2 levels.

Convolutional Neural Networks for Deep Sleep Detection Based on Data Augmentation

Sleep is a necessary process that individuals undergo daily for physical recovery, and the proportion of deep sleep in the sleep stages is a critical aspect of the recovery process. Convolutional Neural Networks (CNNs) have shown remarkable success in automatically identifying deep sleep stages through the analysis of electroencephalogram (EEG) signals. This article introduces three data augmentation techniques, including time shifting, amplitude scaling and noise addition, to enhance the diversity and features of the data. These techniques aim to enable machine learning models to extract features from various aspects of sleep EEG data, thus improving the model’s accuracy. Three deep learning models are introduced, namely DeepConvNet, ShallowConvNet and EEGNet, for the identification of deep sleep. To evaluate the proposed methods, the Sleep-EDF public dataset was utilized. Experimental results demonstrate that the enhanced dataset formed by applying the three data augmentation techniques achieved higher accuracy in all deep learning models compared to the original dataset. This highlights the feasibility and effectiveness of these methods in deep sleep detection.

The Reciprocal Relationship between Sleep and Epilepsy.

The relationship between sleep and epilepsy is bidirectional. Certain epilepsy syndromes predominantly or exclusively manifest during sleep, with seizures frequently originating from non-rapid eye movement (NREM) sleep. Interictal epileptiform discharges observed on electroencephalograms are most likely to be activated during the deep NREM sleep stage known as N3. Conversely, epileptiform discharges, anti-seizure medications (ASMs), as well as other anti-seizure therapies can exert detrimental effects on sleep architecture. Moreover, the co-occurrence of sleep disorders has the potential to exacerbate seizure control. Understating the relationship between sleep and epilepsy is crucial for healthcare providers. Addressing and managing sleep-related problems in individuals with epilepsy can potentially contribute to improved seizure control and overall well-being. At the same time, improving seizure control can improve sleep quality and quantity, thus further improving the health of individuals with epilepsy.

Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex.

Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (μECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which is a signature of non-equilibrium dynamics, captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret's cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different cortical areas and frequency bands in this process, showing a predominance of the parietal cortex and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.

Dynamic Sleep Architecture Revealed: Sleep Auditory Evoked Potentials from N100 to P900

Background: The research landscape in sleep is currently shifting toward minute changes in brain functioning during different stages. This fine approach necessitates a dynamic assessment as in the case of electrophysiology. The present study aimed to investigate brain responsiveness during different stages and times of nocturnal sleep using auditory evoked potentials (AEPs). Materials and Methods: Sleep recordings of 18 volunteer subjects were analyzed. During sleep recordings, auditory stimuli were presented in the ear channels. Sleep stages were scored according to the American Academy of Sleep Medicine system. AEPs were analyzed in nonrapid eye movement sleep stages. Alterations of N100, P200, N300, P450, N550, and P900 waveforms were investigated in the first and second halves of sleep duration. Results: N100 amplitude was higher in light sleep, both in the first and the second halves of the night (sleep duration). The amplitude of P900 was higher in the deep sleep stage in both halves. Significant amplitude reductions were found in N100, P200, P450, and P900 in the second half of the sleep. Conclusions: The changes found suggest that they are related to the dynamics of sensory processing to maintain functional sleep. The findings of the present study emphasize that in the course of sleep, both the proportions of sleep stages and the microarchitecture within the stages alter between the first and second halves of sleep.

ASSOCIATIONS OF NIGHT AND DAY SLEEP WITH COGNITIVE PERFORMANCE IN SHIFT WORKERS: PRELIMINARY RESULTS

Abstract 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.

Differential Heart Rate Variability Profiles During Sleep in Older Adults with Remitted Early‐onset versus Late‐onset Depression

AbstractBackgroundSleep is a physiological state involving a dynamic autonomic profile that is ideal for detecting subtle changes in autonomic nervous system functioning. The current study sought to determine if heart rate variability (HRV) during different periods of sleep is impaired in older adults with early‐onset (EOD), late‐onset (LOD), and no history of lifetime depression (NOD).MethodA total of 188 older adults aged 50‐90 years were recruited from the Healthy Brain Ageing clinic at the Brain and Mind Centre, University of Sydney. This samples comprised 49 participants with EOD, 37 with LOD, and 102 with NOD. Each participant underwent medical, psychiatric and neuropsychological assessments, as well as overnight polysomnography.ResultFindings show that for HRV during wake‐after‐sleep‐onset (WASO), only older adults with remitted EOD have significantly lower SDNN and Shannon Entropy, two HRV indices of interest, in comparison to the NOD group, suggesting that those with EOD have diminished overall autonomic function and cardiac complexity respectively. By contrast, older adults with remitted LOD have significantly lower parasympathetic activities during deeper sleep stages (i.e. N2 and N3), as evidenced by a reduction in high‐frequency HRV relative to the EOD group. Overall, our findings suggest that the EOD and LOD phenotypes have differential heart rate profiles during sleep.ConclusionA history of depression, in particular whether this history is early‐ or late‐ onset, is associated with the integrity of autonomic functioning during both wakefulness and sleep in older adults. HRV may have potential as a biomarker of depression and could complement other biomarkers to enhance diagnostic accuracy and prognostic monitoring. As older adults with early‐life depression seem to experience more pronounced autonomic dysfunction, this further emphasizes the need for early and effective interventions, as well as proactive preventative strategies for those with remitted major depressive disorder.

Differential diagnosis of chorea (guidelines of the German Neurological Society)

IntroductionChoreiform movement disorders are characterized by involuntary, rapid, irregular, and unpredictable movements of the limbs, face, neck, and trunk. These movements often initially go unnoticed by the affected individuals and may blend together with seemingly intended, random motions. Choreiform movements can occur both at rest and during voluntary movements. They typically increase in intensity with stress and physical activity and essentially cease during deep sleep stages. In particularly in advanced stages of Huntington disease (HD), choreiform hyperkinesia occurs alongside with dystonic postures of the limbs or trunk before they typically decrease in intensity.Summary or definition of the topicThe differential diagnosis of HD can be complex. Here, the authors aim to provide guidance for the diagnostic process. This guidance was prepared for the German Neurological Society (DGN) for German-speaking countries.RecommendationsHereditary (inherited) and non-hereditary (non-inherited) forms of chorea can be distinguished. Therefore, the family history is crucial. However, even in conditions with autosomal-dominant transmission such as HD, unremarkable family histories do not necessarily rule out a hereditary form (e.g., in cases of early deceased or unknown parents, uncertainties in familial relationships, as well as in offspring of parents with CAG repeats in the expandable range (27–35 CAG repeats) which may display expansions into the pathogenic range).ConclusionsThe differential diagnosis of chorea can be challenging. This guidance prepared for the German Neurological Society (DGN) reflects the state of the art as of 2023.

Effects of vagus nerve stimulation on the quality of sleep and sleep apnea in patients with drug-resistant epilepsy: A systematic review.

The objective was to systematically evaluate the current evidence surrounding the effect of vagus nerve stimulation (VNS) on quality of sleep and obstructive sleep apnea (OSA) among patients with epilepsy. A literature search was conducted using the Embase and MEDLINE databases. Studies were included if they involved patients with drug-resistant epilepsy treated with VNS and used validated tools to report on quality of sleep or sleep apnea. The literature search yielded 112 citations related to VNS and sleep quality, and 82 citations related to sleep apnea. Twelve articles were included in the review, of which five measured quality of sleep among patients who underwent VNS, six studies measured sleep apnea, and one study measured both outcomes. Studies measuring quality of sleep used different methods, including sleep quality questionnaires and the percentage of sleep in each cycle. Studies also varied in patient populations, the use of control groups, and whether multiple measurements were taken for each patient. Some studies found improved sleep quality after VNS, whereas others found reductions in deep sleep stages. Additionally, mixed results in sleep quality were found when comparing patients with epilepsy who received VNS treatment versus patients with epilepsy who did not receive VNS treatment. Variables such as VNS intensity and age could potentially confound quality of sleep. Studies measuring sleep apnea consistently found increased proportions of patients diagnosed with OSA or increased sleep index scores after VNS implantation. Overall, the effect of VNS on quality of sleep remains unclear, as studies were very heterogeneous, although the effect on sleep apnea has consistently shown an increase in sleep apnea severity indices after VNS implantation. Future studies with consistent measures and discussions of confounding are required to determine the effect of VNS on quality of sleep, and the effect of VNS parameters should be further explored among patients who develop sleep apnea.

Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders

Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespiratory and body movement data. Overnight polysomnography (PSG) data from 123 participants (age: 19–82 years) with suspected sleep disorders were analyzed. Multivariate time series data, including heart rate, respiratory rate, cardiorespiratory coupling, and body movement frequency, were input into a bidirectional long short-term memory (biLSTM) network model to train and predict five-class sleep stages. The trained model's performance was evaluated using balanced accuracy, Cohen's κ coefficient, and F1 scores on an epoch-per-epoch basis and compared with the ground truth using the leave-one-out cross-validation scheme. The model achieved an accuracy of 71.2 ± 5.8%, Cohen's κ of 0.425 ± 0.115, and an F1 score of 0.650 ± 0.083 across all sleep stages, and all metrics were negatively correlated with the apnea–hypopnea index, as well as age, but positively correlated with sleep efficiency. Moreover, the model performance varied for each sleep stage, with the highest F1 score observed for N2 and the lowest for N3. Regression and Bland–Altman analyses between sleep parameters of interest derived from deep learning and PSG showed substantial correlations (r = 0.33–0.60) with low bias. The findings demonstrate the efficacy of the biLSTM deep learning model in accurately classifying sleep stages and in estimating sleep parameters for sleep structure analysis using a reduced set of physiological parameters. The current model without using EEG information may expand the application of unobtrusive in-home monitoring to clinically assess the prevalence of sleep disorders outside of a sleep laboratory.

Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms

EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to different complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research. We determined the quantities entropy rate, excess entropy, Lempel–Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and non-REM sleep stages and used first-order Markov surrogate data to determine which time scales contributed to the different complexity measures. We demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We confirmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amigó et al., Neural Comput 16:717–736, https://doi.org/10.1162/089976604322860677, 2004). Surrogate data analyses prove that entropy-based quantities and LZC focus on short-range temporal correlations, whereas Hurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity. Microstate jump sequences, where duplicate states have been removed, show higher randomness, lower statistical complexity, and no long-range correlations. Regarding the practical use of these methods, we suggest that LZC can be used as an efficient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear fit. We conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored in other areas of brain research.

The maturation of aperiodic EEG activity across development reveals a progressive differentiation of wakefulness from sleep

During development, the brain undergoes radical structural and functional changes following a posterior-to-anterior gradient, associated with profound changes of cortical electrical activity during both wakefulness and sleep. However, a systematic assessment of the developmental effects on aperiodic EEG activity maturation across vigilance states is lacking, particularly regarding its topographical aspects. Here, in a population of 160 healthy infants, children and teenagers (from 2 to 17 years, 10 subjects for each year), we investigated the development of aperiodic EEG activity in wakefulness and sleep. Specifically, we parameterized the shape of the aperiodic background of the EEG Power Spectral Density (PSD) by means of the spectral exponent and offset; the exponent reflects the rate of exponential decay of power over increasing frequencies and the offset reflects an estimate of the y-intercept of the PSD.We found that sleep and development caused the EEG-PSD to rotate over opposite directions: during wakefulness the PSD showed a flatter decay and reduced offset over development, while during sleep it showed a steeper decay and a higher offset as sleep becomes deeper. During deep sleep (N2, N3) only the spectral offset decreased over age, indexing a broad-band voltage reduction. As a result, the difference between values in deep sleep and those in both light sleep (N1) and wakefulness increased with age, suggesting a progressive differentiation of wakefulness from sleep EEG activity, most prominent over the frontal regions, the latest to complete maturation. Notably, the broad-band spectral exponent values during deep sleep stages were entirely separated from wakefulness values, consistently across developmental ages and in line with previous findings in adults. Concerning topographical development, the location showing the steepest PSD decay and largest offset shifted from posterior to anterior regions with age. This shift, particularly evident during deep sleep, paralleled the migration of sleep slow wave activity and was consistent with neuroanatomical and cognitive development.Overall, aperiodic EEG activity distinguishes wakefulness from sleep regardless of age; while, during development, it reveals a postero-anterior topographical maturation and a progressive differentiation of wakefulness from sleep. Our study could help to interpret changes due to pathological conditions and may elucidate the neurophysiological processes underlying the development of wakefulness and sleep.

0845 Individuals with Persistent Low Back Pain Have Less Time in Deep Sleep Stage than Asymptomatic Controls

Abstract Introduction Sleep consists of stages of light sleep, deep sleep, and rapid eye movement (REM), which have different characteristics. These stages are thought to be associated with different physiological and psychological processes. Pain is an unpleasant sensory and emotional experience generated by neural activity that potentially impacts several networks in the brain. There is limited information regarding the potential association between objectively determined sleep characteristics and persistent low back pain (pLBP). The purpose of this study was to examine whether sleep characteristics differ between individuals with pLBP and asymptomatic controls. Methods Individuals in a current episode of back pain with symptoms impacting function and persisting greater than 3 months (pLBP group, n=20) and asymptomatic individuals (control group, n=16) participated in the study. They wore a home sleep test device (Zmachine® Insight+, General Sleep Corporation) for three nights. Results The pLBP and control groups had similar age (24.8 ± 5.8 and 25.4 ± 4.3 years, respectively; mean ± SD), height, weight, and waist and hip circumferences. The two groups also had similar total sleep time (6.0 ± 1.2 and 6.6 ± 1.0 hours, for pLBP and control groups, respectively), sleep efficiency (82.8 ± 8.8% and 84.9 ± 5.7%), and wake time after sleep onset (35.8 ±27.4 and 31.5 ±18.1 minutes). The duration in REM (1.5 ± 0.5 and 1.5 ± 0.6 hours) and light sleep (3.1 ± 0.7 and 3.4 ± 0.9 hours) stages were also similar. However, the pLBP group had less deep sleep time (1.4 ± 0.4 hours) than the control group (1.7 ± 0.3 hours) (p = 0.029). Conclusion Individuals with pLBP had less deep sleep time than asymptomatic controls, although other objectively determined sleep characteristics appear to be similar. These results suggest an association between the deep sleep stage and persistent low back pain that requires further investigation. Support (if any) National Institutes of Health R01HD095959 and University of South Carolina ASPIRE 80004373.

0074 Investigating the Impacts of Pharmacological Slow-Wave Sleep Enhancement on Cognition in an Alzheimer's Disease Model

Abstract Introduction Alzheimer’s disease (AD) is a debilitating and irreversible neurodegenerative disease associated with major disruptions to sleep. Recent works point to a strong bidirectional relationship between loss of slow-wave sleep (SWS), the deepest sleep stage, and the pathological and cognitive features of AD. Here, we investigate the impacts of chronic pharmacological SWS enhancement with gaboxadol, a delta GABAA receptor agonist, on cognitive performance the APP/PS1 mouse model of AD. Currently, we are assessing the impacts of this intervention on Abeta plaque burden. Methods Using electroencephalography (EEG), we first assessed if gaboxadol is capable of stimulating SWS sleep in 6-month-old AD mice. Next, 6-month-old control (CTRL) or AD mice received gaboxadol 5x/week for 4 weeks at the start of the light cycle. Groups underwent subsequent testing in the open field (OF), Y-maze, and contextual fear conditioning (CFC) assays (n = 8-9 mice per group). Data were analyzed using two- or three-way ANOVAs, with repeated measures when appropriate. Results Our EEG studies demonstrate that gaboxadol (6 mg/kg) significantly enhances SWS/NREM from baseline (p &amp;lt; 0.0308). Gaboxadol did not impact gross locomotor behavior assessed by distance travelled in the in the OF. However, rearing was decreased in AD mice compared to CTRL mice (p &amp;lt; 0.0128). In the Y-maze, gaboxadol significantly increased time spent in the novel arm in CTRL (p &amp;lt; 0.0386) but not AD mice. We did not find significant effects of gaboxadol on memory recall in the CFC assay. Conclusion Chronic pharmacological stimulation of SWS using gaboxadol does not show robust cognition-enhancing effects in APP/PS1 mice, but may be cognitively enhancing in CTRL mice. It is possible that longer treatment duration is necessary to rescue cognitive deficits in AD mice. Support (if any) NIA: 5R21AG064774-02 T32 GM7367-46