Sleep Stages Research Articles

Efficacy of hyperbaric oxygen therapy as an adjunct therapy in the treatment of sleep disorders among patients with Parkinson’s disease: a meta-analysis

ObjectiveTo systematically evaluate the efficacy of hyperbaric oxygen therapy (HBOT) as an adjunct therapy for treating sleep disorders in patients with Parkinson’s disease (PD).MethodsWe conducted comprehensive searches in eight databases from inception through September 2023, including PubMed, Cochrane Library, Embase, Web of Science, SinoMed, China National Knowledge Infrastructure (CNKI), China Science and Technology Periodical Database (VIP), and Wanfang Database. The objective was to identify randomized controlled trials (RCTs) evaluating HBOT’s effectiveness in alleviating sleep disorder symptoms in PD patients as an adjunct therapy. Literature screening and data extraction were independently executed by the authors. Meta-analyses were performed using Review Manager 5.3 software, and publication bias and sensitivity analyses were assessed using Stata 17.0 software.ResultsSeven RCTs involving 461 participants were included. The findings revealed that the addition of HBOT significantly enhanced sleep efficiency (MD = 15.26, 95% CI [10.89, 19.63], p < 0.00001), increased time in bed (MD = 69.65, 95% CI [43.01, 96.30], p < 0.00001), total sleep time (MD = 75.87, 95% CI [25.42, 126.31], p = 0.003), slow-wave sleep (SWS) time (MD = 6.14, 95% CI [3.95, 8.34], p < 0.00001), and rapid eye movement sleep (REM) time (MD = 4.07, 95% CI [2.05, 6.08], p < 0.0001), and reduced awakening frequency (MD = −11.55, 95% CI [−15.42, −7.68], p < 0.00001) and sleep latency (MD = −6.60, 95% CI [−9.43, −3.89], p < 0.00001). Additionally, significant improvements were observed in the Pittsburgh Sleep Quality Index (PSQI) (MD = −2.52, 95% CI [−2.85, −2.18], p < 0.00001), Epworth Sleepiness Scale (ESS) (MD = −2.90, 95% CI [−3.34, −2.47], p < 0.00001), Unified Parkinson’s Disease Rating Scale Part III (UPDRS III) (MD = −1.32, 95% CI [−2.16, −0.47], p = 0.002), and Hoehn and Yahr grading (H-Y grading) (MD = −0.15, 95% CI [−0.28, −0.01], p = 0.03).ConclusionThe current meta-analysis supports the efficacy of HBOT as an adjunct therapy in managing sleep disorders in PD patients. It is recommended for PD patients experiencing sleep disturbances.Systematic review registration:https://www.crd.york.ac.uk/, identifier: CRD42023462201.

Sleep stages and brain oscillations: An in-depth analysis using quantitative and mathematical models

Sleep stages and brain oscillations: An in-depth analysis using quantitative and mathematical models

Dexmedetomidine Promotes NREM Sleep by Depressing Oxytocin Neurons in the Paraventricular Nucleus in Mice.

Dexmedetomidine (DEX) is a highly selective α2-adrenoceptor agonist with sedative effects on sleep homeostasis. Oxytocin-expressing (OXT) neurons in the paraventricular nucleus (PVN) of the hypothalamus (PVNOXT) regulate sexual reproduction, drinking, sleep-wakefulness, and other instinctive behaviors. To investigate the effect of DEX on the activity and signal transmission of PVNOXT in regulating the sleep-wakefulness cycle. Here, we employed OXT-cre mice to selectively target and express the designer receptors exclusively activated by designer drugs (DREADD)-based chemogenetic tool hM3D(Gq) in PVNOXT neurons. Combining chemogenetic methods with electroencephalogram (EEG) /electromyogram (EMG) recordings, we found that cannula injection of DEX in PVN significantly increased the duration of non-rapid eye movement (NREM) sleep in mice. Furthermore, the chemogenetic activation of PVNOXT neurons using i.p. injection of clozapine N-oxide (CNO) after cannula injection of DEX to PVN led to a substantial increase in wakefulness. Electrophysiological results showed that DEX decreased the frequency of action potential (AP) and the spontaneous excitatory postsynaptic current (sEPSC) of PVNOXT neurons through α2-adrenoceptors. Therefore, these results identify that DEX promotes sleep and maintains sleep homeostasis by inhibiting PVNOXT neurons through the α2-adrenoceptor.

Assessment of Sleep Stages in Unconscious Patients with Acute Severe Traumatic Brain Injury

Abstract Background The existence of a sleep cycle in deeply unconscious patients with severe traumatic brain injury (TBI) remains unknown. Evaluating the sleep patterns of these patients may provide insights into their consciousness and help predict functional recovery. Aims and Objectives This prospective observational study aimed to assess the usefulness of monitoring the sleep cycle in the prognostication of unconscious TBI patients. Materials and Methods A purposive sampling technique was employed to include 39 patients with severe TBI (Glasgow Coma Scale [GCS] ≤ 8). The average GCS score at admission was (5.76 ± 1.65). Sleep was monitored using an actigraphy smartwatch for 4 consecutive days, (3.15 ± 2.49) days postbrain injury. Sleep cycle monitoring tool and unconscious patient outcome monitoring tool were employed. An outcome assessment was done based on the GCS score. Results Severe TBI patients exhibit intact sleep cycle (i.e., deep sleep, light sleep, and rapid eye movement sleep) despite being deeply unconscious, even in the acute stage. Total sleep duration was found to be significantly higher at a mean of 19.97 hours as compared to the mean of 8 hours as per the smartwatch reference value for normal individuals. Patients with improved sleep cycles had a higher likelihood of GCS improvement (p < 0.05). Conclusion This study is the first of its kind to demonstrate that the sleep cycle is a reliable prognostic factor for the recovery of consciousness in the acute phase of severe TBI among unconscious patients, with improvement in the sleep cycle mirroring neurological improvement.

Habitual rapid eye movementsleep predicts changes in test-anxiety levels weeks in advance.

Previous research has linked rapid eye movement sleep to emotional processing, particularly stress. Lab studies indicate that rapid eye movement sleep deprivation and fragmentation heighten emotional reactivity and stress response. This relationship extends to natural settings, where poor-quality sleep among college students correlates with increased academic stress and lower academic performance. However, there is a lack of research into how specific sleep stages, like rapid eye movement, affect real-life stress development. This study investigated whether habitual rapid eye movement sleep in college students can predict the future development of real-life stress symptoms associated with final exams. Fifty-two participants (mean age = 19 years, 62% females) monitored their sleep for a week during the academic semester using a mobile electroencephalogram device, and then completed self-evaluations measuring test anxiety and other relevant factors. They completed the same evaluations again just prior to final exams. We found that rapid eye movement sleep was the most dominant factor predicting changes in participants' test anxiety. However, contrasting with our predictions, habitual rapid eye movement sleep was associated with an increase rather than decrease in anxiety. We discuss these results in terms of the rapid eye movement recalibration hypothesis, which suggests rapid eye movement sleep modulates activity in stress-encoding areas in the brain, leading to both decreased sensitivity and increased selectivity of stress responses.

Sleep consolidation potentiates sensorimotor adaptation.

Contrary to its well-established role in declarative learning, the impact of sleep on motor memory consolidation remains a subject of debate. Current literature suggests that while motor skill learning benefits from sleep, consolidation of sensorimotor adaptation (SMA) depends solely on the passage of time. This has led to the proposal that SMA may be an exception to other types of memories. Here, we addressed this ongoing controversy in humans through three comprehensive experiments using the visuomotor adaptation paradigm (N=290, 150 females). In Experiment 1, we investigated the impact of sleep on memory retention when the temporal gap between training and sleep was not controlled. In line with the previous literature, we found that memory consolidates with the passage of time. In Experiment 2, we used an anterograde interference protocol to determine the time window during which SMA memory is most fragile and, thus, potentially most sensitive to sleep intervention. Our results show that memory is most vulnerable during the initial hour post-training. Building on this insight, in Experiment 3 we investigated the impact of sleep when it coincided with the critical first hour of memory stabilization. This manipulation unveiled a benefit of sleep (30% memory enhancement) alongside an increase in spindle density and spindle-SO coupling during NREM sleep, two well-established neural markers of sleep consolidation. Our findings reconcile seemingly conflicting perspectives on the active role of sleep in motor learning and point to common mechanisms at the basis of memory formation.Significance statement While there is compelling evidence that sleep improves declarative memory, its role in the consolidation of motor memories remains a long-standing debate. For example, it is currently established that sensorimotor adaptation (SMA) consolidates with the passage of time, irrespective of sleep. This has led to the proposal that SMA may be an exception to other types of memories. Our findings indicate that SMA memories may indeed consolidate with both the passage of time and sleep, depending on the proximity between training and bedtime. Our work sheds light on this controversy and points to the existence of common mechanisms supporting consolidation across memory domains. Furthermore, it may impact rehabilitation programs, expediting motor injury recovery by aligning training sessions with sleep.

An automated sleep staging tool based on simple statistical features of mice electroencephalography (EEG) and electromyography (EMG) data.

Electroencephalogram (EEG) and electromyogram (EMG) are fundamental tools in sleep research. However, investigations into the statistical properties of rodent EEG/EMG signals in the sleep-wake cycle have been limited. The lack of standard criteria in defining sleep stages forces researchers to rely on human expertise to inspect EEG/EMG. The recent increasing demand for analysing large-scale and long-term data has been overwhelming the capabilities of human experts. In this study, we explored the statistical features of EEG signals in the sleep-wake cycle. We found that the normalized EEG power density profile changes its lower and higher frequency powers to a comparable degree in the opposite direction, pivoting around 20-30 Hz between the NREM sleep and the active brain state. We also found that REM sleep has a normalized EEG power density profile that overlaps with wakefulness and a characteristic reduction in the EMG signal. Based on these observations, we proposed three simple statistical features that could span a 3D space. Each sleep-wake stage formed a separate cluster close to a normal distribution in the 3D space. Notably, the suggested features are a natural extension of the conventional definition, making it useful for experts to intuitively interpret the EEG/EMG signal alterations caused by genetic mutations or experimental treatments. In addition, we developed an unsupervised automatic staging algorithm based on these features. The developed algorithm is a valuable tool for expediting the quantitative evaluation of EEG/EMG signals so that researchers can utilize the recent high-throughput genetic or pharmacological methods for sleep research.

Network signatures define consciousness state during focal seizures.

Epilepsy is a common neurological disorder affecting 1% of the global population. Loss of consciousness in focal impaired awareness seizures (FIASs) and focal-to-bilateral tonic-clonic seizures (FBTCSs) can be devastating, but the mechanisms are not well understood. Although ictal activity and interictal connectivity changes have been noted, the network states of focal aware seizures (FASs), FIASs, and FBTCSs have not been thoroughly evaluated with network measures ictally. We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types. We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures. This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy.

Perioperative Staff’s Experiences of Premedication for Children

PurposeProviding perioperative care for children who express anxiety or react with negative anxiety-associated consequences can be a challenge. The use of premedication is established as an important intervention for young children before surgery, yet research into care providers’ experiences of premedication is limited. The aim of this study was to explore perioperative staff’s experiences of premedication for preschool-age children. DesignA descriptive inductive qualitative study was performed based on focus group discussions. MethodsA purposive sample of a team from the operating department with experience in anesthetizing and caring for children in the perioperative period was interviewed in small focus groups: five preoperative and postoperative care nurses, five nurse anesthetists, and five anesthesiologists. The transcribed text was categorized using qualitative content analysis. FindingsThe content analysis revealed three themes: a matter of time, do not wake the sleeping bear, and on responsive tiptoes. ConclusionsCare providers must adapt their work to the child’s emotional state of mind and needs, allowing time for the child to trust and accept the premedication and for the premedication to reach its peak effect. Premedication provides light sleep preoperatively, which requires careful treatment of the child to avoid emotional reactions, and the postoperative path is most peaceful when the premedication supports a long duration of sedation. Our findings highlight the need for safety precautions and a permissive and flexible organization with the goal of achieving a smooth and safe journey for the child in the perioperative path.

Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet.

The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achieve optimal classification performance due to a paucity of raw data. To leverage the data characteristics and enhance the classification accuracy, we propose and evaluate a novel dual-link deep neural network model, 'DoubleLinkSleepCLNet'. The DoubleLinkSleepCLNet model performs feature extraction and efficient classification on both the raw EEG and the EEG processed with the Hilbert transform. It leverages the frequency domain and time domain feature modules, resulting in superior performance compared to other models. The DoubleLinkSleepCLNet model, using the 2 Raw/2 Hilbert data modes, achieved the highest classification performance with an accuracy of 88.47%. The average accuracy of the EEG was improved by approximately 4.08% after the application of the Hilbert transform. Additionally, Convolutional Neural Network (CNN) demonstrated superior performance in processing phase information, whereas Long Short-Term Memory (LSTM) excelled in handling time series data. The application of the Hilbert transform to EEG data, followed by processing it with a convolutional neural network, enhances the accuracy of the model. These findings introduce novel concepts for accelerating sleep stage prediction research, suggesting potential applications of these methods to other EEG analyses.

Characterisation of sleep apneas and respiratory circuitry in mice lacking CDKL5.

CDKL5 deficiency disorder is a rare genetic disease caused by mutations in the CDKL5 gene. Central apneas during wakefulness have been reported in patients with CDKL5 deficiency disorder. Studies on CDKL5-knockout mice, a CDKL5 deficiency disorder model, reported sleep apneas, but it is still unclear whether these events are central (central sleep apnea) or obstructive (obstructive sleep apnea) and may be related to alterations of brain circuits that modulate breathing rhythm. This study aimed to discriminate central sleep apnea and obstructive sleep apnea in CDKL5-knockout mice, and explore changes in the somatostatin neurons expressing high levels of neurokinin-1 receptors within the preBötzinger complex. Ten adult male wild-type and 12 CDKL5-knockout mice underwent electrode implantation for sleep stage discrimination and diaphragmatic activity recording, and were studied using whole-body plethysmography for 7 hr during the light (resting) period. Sleep apneas were categorised as central sleep apnea or obstructive sleep apnea based on the recorded signals. The number of somatostatin neurons in the preBötzinger complex and their neurokinin-1 receptors expression were assessed through immunohistochemistry in a sub-group of animals. CDKL5-knockout mice exhibited a higher apnea occurrence rate and a greater prevalence of obstructive sleep apnea during rapid eye movement sleep, compared with wild-type, whereas no significant difference was observed for central sleep apnea. Moreover, CDKL5-knockout mice showed a reduced number of somatostatin neurons in the preBötzinger complex, and these neurons expressed a lower level of neurokinin-1 receptors compared with wild-type controls. These findings underscore the pivotal role of CDKL5 in regulating normal breathing, suggesting its potential involvement in shaping preBötzinger complex neural circuitry and controlling respiratory muscles during sleep.

Poor sleep quality and its associated factors among HIV/ADIS patients living in sub-Saharan African countries: a systematic review and meta-analysis

Good-quality sleep is defined by its ability to minimize disturbances, provide adequate duration, and maintain a balanced progression through sleep stages. Sleep disturbance is a common complaint in people living with HIV/AIDS. Despite the influence of sleep disturbance on treatment adherence, quality of life, work productivity, risk of chronic illness. Studies have reported sleep disturbances among HIV/AIDS patients in sub-Saharan African countries (SSA), yielding varied results at the country level. Therefore, conducting a systematic review and meta-analysis is essential. This systematic review and meta-analysis aimed to evaluate the prevalence of poor sleep quality and identify associated factors among HIV/AIDS patients in sub-Saharan African countries. We systematically searched across various databases, including PubMed, African Journals Online, Scopus, Cochrane Library, HINARI, and Science Direct. Additionally, we conducted searches using Google and Google Scholar search engines. Microsoft Excel was used for data extraction, and the data were analysed using STAT version 17.0. We assessed heterogeneity using Cochran’s Q test and I2 test and checked for small study effects using funnel plot symmetry and Egger’s test. Pooled prevalence and associated factors were estimated using a random-effects model at a 95% confidence interval (CI) and significance level of p < 0.05. To identify factors associated with poor sleep quality among individuals living with HIV/AIDS, odds ratios (ORs) and their corresponding 95% CI were calculated. This analysis combined data from 15 separate studies involving a total sample size of 5176 participants. The pooled prevalence of poor sleep quality among HIV/AIDS patients in SSA countries was 49.32% (95% CI 41.32–56.8%). Factors significantly associated with poor sleep quality included depression (OR 2.78; 95% CI 1.21–6.40) and CD4 count < 200 cells/mm3 (AOR 3.15; 95% CI 2.41–4.15). In this study the prevalence of poor sleep quality among HIV/AIDS patients in SSA was higher and differs across the countries, ranging from 21.7 to 73.7%. The findings underscore the urgent necessity for programs aimed at improving sleep quality, particularly in addressing factors such as participant income and depression that are linked to poor sleep quality in HIV/AIDS patients.Systematic review registration: PROSPERO CRD42024517229.

Efferent pathways from the suprachiasmatic nucleus to the horizontal limbs of diagonal band promote NREM sleep during the dark phase in mice

The regulation of circadian rhythms and the sleep–wake states involves in multiple neural circuits. The suprachiasmatic nucleus (SCN) is a circadian pacemaker that controls the rhythmic oscillation of mammalian behaviors. The basal forebrain (BF) is a critical brain region of sleep–wake regulation, which is the downstream of the SCN. Retrograde tracing of cholera toxin subunit B showed a direct projection from the SCN to the horizontal limbs of diagonal band (HDB), a subregion of the BF. However, the underlying function of the SCN–HDB pathway remains poorly understood. Herein, activation of this pathway significantly increased non–rapid eye movement (NREM) sleep during the dark phase by using optogenetic recordings. Moreover, activation of this pathway significantly induced NREM sleep during the dark phase for first 4 h by using chemogenetic methods. Taken together, these findings reveal that the SCN–HDB pathway participates in NREM sleep regulation and provides direct evidence of a novel SCN-related pathway involved in sleep–wake states regulation.

Retraction Note: A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification

Retraction Note: A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification

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

Sleep-related bruxism (SB) is the habit of grinding or clenching the teeth during sleep, mediated by the non-peripheral central nervous system. 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. 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. 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). 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.

Ageing-related modification of sleep and breathing in orexin-knockout narcoleptic mice.

Narcolepsy type-1 (NT1) is a lifelong sleep disease, characterised by impairment of the orexinergic system, with a typical onset during adolescence and young adulthood. Since the wake-sleep cycle physiologically changes with ageing, this study aims to compare sleep patterns between orexin-knockout (KO) and wild type (WT) control mice at different ages. Four groups of age-matched female KO and WT mice (16 weeks of age: 8 KO-YO and 9 WT-YO mice; 87 weeks of age: 13 KO-OLD and 12 WT-OLD mice) were implanted with electrodes for discriminating wakefulness, rapid-eye-movement sleep (REMS), and non-REMS (NREMS). Mice were recorded for 48 h in their home cages and for 7 more hours into a plethysmographic chamber to characterise their sleep-breathing pattern. Regardless of orexin deficiency, OLD mice spent less time awake and had fragmentation of this behavioural state showing more bouts of shorter length than YO mice. OLD mice also had more NREMS bouts and less frequent NREMS apneas than YO mice. Regardless of age, KO mice showed cataplexy-like episodes and shorter REMS latency than WT controls and had a faster breathing rate and an increased minute ventilation during REMS. KO mice also had more wakefulness, NREMS and REMS bouts, and a shorter mean length of wakefulness bouts than WT controls. Our experiment indicated that the lack of orexins as well as ageing importantly modulate the sleep and breathing phenotype in mice. The narcoleptic phenotype caused by orexin deficiency in female mice was substantially preserved with ageing.

Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns

Sleep staging is a crucial tool for diagnosing and monitoring sleep disorders, but the standard clinical approach using polysomnography (PSG) in a sleep lab is time-consuming, expensive, uncomfortable, and limited to a single night. Advancements in sensor technology have enabled home sleep monitoring, but existing devices still lack sufficient accuracy to inform clinical decisions. To address this challenge, we propose a deep learning architecture that combines a convolutional neural network and bidirectional long short-term memory to accurately classify sleep stages. By supplementing photoplethysmography (PPG) signals with respiratory sensor inputs, we demonstrated significant improvements in prediction accuracy and Cohen's kappa (k) for 2- (92.7 %; k = 0.768), 3- (80.2 %; k = 0.714), 4- (76.8 %, k = 0.550), and 5-stage (76.7 %, k = 0.616) sleep classification using raw data. This relatively translatable approach, with a less intensive AI model and leveraging only a few, inexpensive sensors, shows promise in accurately staging sleep. This has potential for diagnosing and managing sleep disorders in a more accessible and practical manner, possibly even at home.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain.

Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program.

Poor sleep health is associated with increased all-cause mortality and incidence of many chronic conditions. Previous studies have relied on cross-sectional and self-reported survey data or polysomnograms, which have limitations with respect to data granularity, sample size and longitudinal information. Here, using objectively measured, longitudinal sleep data from commercial wearable devices linked to electronic health record data from the All of Us Research Program, we show that sleep patterns, including sleep stages, duration and regularity, are associated with chronic disease incidence. Of the 6,785 participants included in this study, 71% were female, 84% self-identified as white and 71% had a college degree; the median age was 50.2 years (interquartile range = 35.7, 61.5) and the median sleep monitoring period was 4.5 years (2.5, 6.5). We found that rapid eye movement sleep and deep sleep were inversely associated with the odds of incident atrial fibrillation and that increased sleep irregularity was associated with increased odds of incident obesity, hyperlipidemia, hypertension, major depressive disorder and generalized anxiety disorder. Moreover, J-shaped associations were observed between average daily sleep duration and hypertension, major depressive disorder and generalized anxiety disorder. These findings show that sleep stages, duration and regularity are all important factors associated with chronic disease development and may inform evidence-based recommendations on healthy sleeping habits.

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers.

Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI). This study aimed to use data collected from fitness trackers to predict MCI status. In this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status. Our machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time. Our results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies.