Sleep Stages Research Articles

Sleep Characteristics of Patients Admitted to Intensive Care Units after Major Abdominal Surgery

Purpose : The aim of this study was to investigate sleep parameters and architecture in patients admitted to the intensive care unit (ICU) on the day of abdominal surgery. Methods : A total of ten patients who underwent laparotomy under general anesthesia at the hepatopancreatobiliary surgery department of a general hospital in Busan, South Korea participated in this observational study. We measured total sleep time (TST), sleep efficiency (SE), wake after sleep onset (WASO), and sleep stages on their first night in the ICU using the WatchPAT300. Furthermore, we used nonparametric statistics to examine differences in postoperative sleep based on sedative administration. Results : The Median (IQR) TST for the participants was 399.50(263.80) minutes; the SE was 75.1 (30.4)%; the WASO rate was 27.8(30.6)%; and the WASO frequency was 5.50(8.50) times. Patients’ rapid eye movement (REM) sleep time was 114.31(87.88) minutes, with 55.8(11.9)% of N1, 18.2(5.3)% of N2, and 24.8 (11.7)% of N3 sleep stages. The sedation group had a longer TST (Z=-2.619, <i>p</i> =.008), higher SE (Z=-2.611, <i>p</i> =.008), lower WASO rate (Z=-2.611, <i>p</i> =.008), smaller number of WASOs (Z=-2.627, <i>p</i> =.008), and longer REM sleep (Z=-2.617, <i>p</i> =.008) compared to the non-sedation group. Conclusion : We observed a high rate of light sleep and awakenings during sleep in patients admitted to the ICU after surgery. Moreover, non-sedated patients lacked either deep or REM sleep. Sleep-improving interventions are needed for non-sedated surgical patients who are admitted to the ICU.

An Intelligent Framework for Sleep Stage Scoring by 1D-Convolutional Neural Network with Bi-LSTM Layer Using EEG signal

An Intelligent Framework for Sleep Stage Scoring by 1D-Convolutional Neural Network with Bi-LSTM Layer Using EEG signal

Does radiofrequency radiation impact sleep? A double-blind, randomised, placebo-controlled, crossover pilot study

The most common source of Radiofrequency Electromagnetic Field (RF-EMF) exposures during sleep includes digital devices, yet there are no studies investigating the impact of multi-night exposure to electromagnetic fields emitted from a baby monitor on sleep under real-world conditions in healthy adults. Given the rise in the number of people reporting to be sensitive to manmade electromagnetic fields, the ubiquitous use of Wi-Fi enabled digital devices and the lack of real-world data, we investigated the effect of 2.45 GHz radiofrequency exposure during sleep on subjective sleep quality, and objective sleep measures, heart rate variability and actigraphy in healthy adults. This pilot study was a 4-week randomised, double-blind, crossover trial of 12 healthy adults. After a one-week run-in period, participants were randomised to exposure from either an active or inactive (sham) baby monitor for 7 nights and then crossed over to the alternate intervention after a one-week washout period. Subjective and objective assessments of sleep included the Pittsburgh Insomnia Rating Scale (PIRS-20), electroencephalography (EEG), actigraphy and heart rate variability (HRV) derived from electrocardiogram. Sleep quality was reduced significantly (p < 0.05) and clinically meaningful during RF-EMF exposure compared to sham-exposure as indicated by the PIRS-20 scores. Furthermore, at higher frequencies (gamma, beta and theta bands), EEG power density significantly increased during the Non-Rapid Eye Movement sleep (p < 0.05). No statistically significant differences in HRV or actigraphy were detected. Our findings suggest that exposure to a 2.45 GHz radiofrequency device (baby monitor) may impact sleep in some people under real-world conditions however further large-scale real-world investigations with specified dosimetry are required to confirm these findings.

Nasal Continuous Positive Airway Pressure Inhibits Gastroesophageal Refluxes in the Preterm Lamb.

Gastroesophageal refluxes (GERs) are universal in newborns and may induce deleterious consequences, especially in preemies. We have previously shown that nasal continuous positive airway pressure (nCPAP) inhibits GERs in full-term lambs, complementing similar results in adult humans. The effect of high-flow nasal cannula (HFNC) on GERs, however, remains unknown. This study aimed to assess the effects of nCPAP and HFNC on GERs in our preterm lamb model. Eleven preterm lambs born 14 days before full-term (~ 34 weeks of human gestation) were studied. Esophageal pH-impedancemetry and polysomnography recordings were performed simultaneously for six hours under three randomly ordered conditions: nCPAP 6 cmH2O, HFNC 7 L/min and no respiratory support (control). The indexes (/hour) of GERs and air-containing swallows were analyzed during each condition. The states of alertness and cardiorespiratory events were also analyzed during the identified GERs. nCPAP significantly decreased the GER index compared to control and HFNC during both quiet wakefulness and non-rapid eye movement sleep, whereas HFNC did not alter the GER index. In addition, nCPAP significantly increased the air-containing swallow index compared to control and HFNC. No significant differences were observed between the tested conditions for GER-related cardiorespiratory events, which were nevertheless rarely observed. Similar to full-term lambs, nCPAP strongly inhibited GERs in the preterm lamb, despite an increase in air-containing swallows. In contrast, HFNC did not impact GERs. Our results are clinically relevant when discussing the use of CPAP in preemies with GERs.

Single-Channel Sleep EEG Classification Method Based on LSTM and Hidden Markov Model

Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term memory and a hidden Markov model (LSTM-HMM). First, the single-channel EEG is decomposed using wavelet transform (WT), and multi-domain features are extracted from the component signals to characterize the EEG characteristics fully. Considering the temporal nature of sleep stage changes, this paper uses a multi-step time series as the input for the model. After that, the multi-step time series features are input into the LSTM. Finally, the HMM improves the classification results, and the final prediction results are obtained. Results: A complete experiment was conducted on the Sleep-EDFx dataset. The results show that the proposed method can extract deep information from EEG and make full use of the sleep stage transition rule. The proposed method shows the best performance in single-channel sleep EEG classification; the accuracy, macro average F1 score, and kappa are 82.71%, 0.75, and 0.76, respectively. Conclusions: The proposed method can realize single-channel sleep EEG classification and provide a reference for other EEG classifications.

Sex differences in sleep deficits in mice with an autism-linked Shank3 mutation

BackgroundInsomnia is more prevalent in individuals with Autism Spectrum Disorder (ASD), can worsen core-symptoms and reduces quality of life of both individuals and caregivers. Although ASD is four times more prevalent in males than females, less is known about sex specific sleep differences in autistic individuals. Recent ASD studies suggest that sleep problems may be more severe in females, which aligns with the sex bias seen in insomnia for the general population. We have previously shown that male mice with a mutation in the high confidence ASD gene Shank3, Shank3∆C, recapitulate most aspects of the ASD insomnia phenotype. The objective of the present study was to leverage the Shank3∆C model to investigate sex-specific effects in sleep using polysomnography.MethodsAdult male and female Shank3∆C and wildtype (WT) littermates were first recorded for 24 h of baseline recordings. Subsequently, they were sleep deprived (SD) for five hours via gentle handling and allowed 19 h of recovery sleep to characterize the homeostatic response to SD. Vigilance states (rapid eye movement (REM) sleep, non-rapid eye movement (NREM) sleep and wake) were assigned by manual inspection using SleepSign. Data processing, statistical analysis and visualization were conducted using MATLAB.ResultsSex and genotype effects were found during baseline sleep and after SD. At baseline, male Shank3∆C mice sleep less during the dark period (active phase) while female Shank3∆C mice sleep less during the light period (rest phase) and sleep more during the dark period. Both male and female Shank3∆C mice show reduced spectral power in NREM sleep. We detect a significant effect of sex and genotype in sleep onset latency and homeostatic sleep pressure (sleepiness). In addition, while male Shank3∆C mice fail to increase sleep time following SD as seen in WT, female Shank3∆C mice decrease sleep time.ConclusionsOverall, our study demonstrates sex differences in sleep architecture and homeostatic response to SD in adult Shank3∆C mice. Thus, our study demonstrates an interaction between sex and genotype in Shank3∆C mice and supports the use of the Shank3∆C model to better understand mechanisms contributing to the sex differences in insomnia in ASD in clinical populations.

Automatic calculation of the parasympathetic, sympathetic and Baevsky stress indexes provides a more comprehensive assessment of cardiac autonomic modulation

Abstract Background Cardiac autonomic modulation (CAM) is clinically evaluated by measuring the Heart Rate Variability (HRV) parameters, related to sympathetic and parasympathetic efferences to the heart, with linear (L), nonlinear (NL), and time-varying (TV) mathematical methods. Recently, automatic calculation of the parasympathetic (PNSi), sympathetic (SNSi), and Baevsky’s stress (BSTRi) indexes [1,2], integrating some L and NL parameters, has become available for more synthetic assessment of CAM. This study aimed to evaluate in healthy subjects (HS) the normal range of PNSi, SNSi, and BSTRi and their physiological variations during daily activity (ACT) and Non-REM sleep (NREM), taking also into account aging-related changes. Methods 24-hour (H24) Holter ECG recordings of 87 HS, divided into three (Age1, Age2 and Age3) groups (16-34, 35-52, and 53-84 years old, respectively), were retrospectively analyzed with the Kubios software (version 4.0), which automatically calculates the PNSi, SNSi, and BSTRi [1], and time domain (TD), frequency domain (FD), NL and TV HRV parameters. HRV was calculated from H24 and short-term (2 minutes) intervals, selected during ACT and NREM. All HRV calculations were done after "detrending" with the "smooth priors" function (lambda=500). Statistical analysis was performed with SPSS 21. Results Average values of the three indexes and several independent L and NL parameters are summarized in Table 1. Average BSTRi values of each age group were not different between ACT and NREM, whereas significant (p<0.05) increments between Age3 and Age1 (during ACT) and between Age3 and both Age1 and 2 (during NREM and H24) were found. Average SNSi values were slightly above the normality range [1] during ACT, or normal (during NREM and H24). Average PNSi values were within the suggested normality range [1] with minimal aging-related variations. Significant aging-related decrement of the selected TD and FD parameters was found in all analysed conditions. Among NL parameters, the correlation dimension (D2) values showed significant aging-related decrement (H24 and NREM), while H24 and ACT sample entropy (SampEn) was lower in Age1 and Age2, compared with Age3 (p<0.05). An example (H24 analysis of Age3 data) of significant (p<0.01) correlation among the three indexes and the selected independent linear and non-linear parameters is shown in Figure 1. Conclusions In this study, a significant aging-related increment of BSTRi was found in all investigated conditions. PNSi and SNSi were always within the suggested normality range [1]. In spite of the significant changes of linear parameters consistent with the well-known aging-related decrement of HRV, the higher SampEn (and BSTRi) found in Age3 HS suggests that an "healthy elderly", is characterized by a more complex adaptation of CAM, likely mediated also by the observed moderate (healthy?) increment of physiological stress. However further investigation on a broader population is warranted.Table 1Figure 1

Unexpected macrophage-mediated myocardial cGas-STING activation in a novel mouse model of stress-related sleep disturbance

Abstract Background Delayed bedtime following stress disorder is prevalent in waves of pandemics and modern life. Considered to be a specific and important contributor to cardiovascular health, stress-related sleep disturbance has an unmet need in steady preclinical models. We previously found exciting corticotropin-releasing hormone (CRH) neurons in the paraventricular nucleus of the hypothalamus (PVH) area of mice could induce 3-hour-long wakefulness. Purpose This study aimed to induce reproducible phenotypes of stress-related sleep disturbance in mice and explore the potential impact on cardiac function. Methods The chemogenetic method of designer receptors exclusively activated by designer drugs (DREADD) system was adopted to mimic stress-related sleep disturbance. We transfected PVH CRH neurons with rAAV-hSyn-DIO-hM3Dq-mCherry and rAAV-Crh-CRE. Prolonged CRH neuron activation was induced by daily intraperitoneal injection of clozapine N-oxide (CNO, 3mg/kg) at 9 am. Bulk RNA-sequencing and bioinformatics analysis were conducted for mechanistic exploration. Results 2-week repeated chemogenetic activation of PVH CRH neurons induced a 5-fold corticosterone release, consistent with increased daily 3-hour wakefulness and corresponding decreases in both rapid eye movement (REM) as well as non-REM sleep. Over 30% of chronic CRH activation mice displayed difficulties in maintaining balance and experienced premature mortality. Mice subjected to prolonged CRH activation showed impaired left ventricular ejection fraction (67.9% versus 48.2%, p=0.0011), and a dilated appearance demonstrated by histological staining. Intriguingly, the number of circulating monocytes increased. Then, we performed bulk RNA-sequencing of heart and bone marrow from CRH-activated and control mice. Differential gene expression and gene set enrichment analysis (GSEA) indicated marked activation of interferon-beta-related pathways in both tissues. Cytosolic DNA-sensing pathway and related key effector genes (cGAS, Cxcl10, CCL5) were found up-regulated in the heart, while mitochondrial oxidative phosphorylation was suppressed. We adopted the CIBERSORT tool to estimate immune infiltration in heart tissues and characterized M1 macrophage as the main pro-inflammatory cell. Further, we screened a set of secreted factors and mediators, which might play active roles in inflamed hearts. Conclusion Taken together, we report a failing heart in a mouse model of stress-related sleep disturbance. The neuro-immune axis involvement and molecular mechanisms merit in-depth explorations.

Synthetic assessment of cardiac autonomic modulation and Baevsky stress index in patients with synucleinopathies

Abstract Background The Baevsky Stress Index (BSTRi), calculated from Heart Rate Variability (HRV) data, has been proposed for quantitative assessment of physiological stress, with higher scores indicating higher levels of physical and/or psychological stress [1]. Cardiac autonomic modulation can be synthetically quantified by calculating the parasympathetic (PNSi) and the sympathetic (SNSi) indexes. The values of the BSTRi, SNSi, and PNSi in patients with synucleinopathies of different prognoses have not been reported yet. Purpose To quantify, in patients with Parkinson's disease (PD) and Multisystem Atrophy (MSA), the BSTRi, its correlation with the PNSi and the SNSi indexes, and their potential value for a synthetic assessment of cardiac autonomic modulation and early differential diagnosis between PD and MSA. Methods Holter ECG data of 66 patients with Parkinsonian syndromes were retrospectively analyzed after 10 years of follow-up. Out of them, 34 patients with a certain diagnosis of PD (17) and MSA (17) were selected for this study. HRV was analyzed with the Kubios software (version 4.0), providing automatic calculation of the BSTRi, PNSi, and SNSi [2], as well as of time domain (TD), frequency domain (FD), nonlinear (NL), and time-varying (TV) HRV parameters. HRV was calculated from selected short-term intervals (of 5 minutes), during daily activity (ACT5’), and non-REM sleep (NREM5’), and from 24-hours Holter recordings. Patients’ HRV findings were compared with those of 51 age-matched healthy subjects (HS). Results An example of individual automatic graphic summary is shown in Figure 1. Both short-term and 24-hour BSTRi and SNSi values were significantly (p < 0.05) higher in PD and MSA compared to those of HS, in all investigated conditions. Short-term PNSi was significantly higher in HS than in PD in all conditions, but only during NREM5’ in MSA patients (Table 1). No significant difference was found between the indexes of PD and MSA patients. As in HS (R=92), BSTRi was positively correlated with the SNSi in MSA (average R= 0.83 and 0.89 during ACT5’ and NREM5’, respectively), but less evident in PD (R=0.67). The inverse correlation between PNSi and SNSi (R=0.75 in HS) was lost in both MSA and PD. Conclusions Higher values of BSTRi in PD and MSA compared to HS suggest that patients with synucleinopathies do experience higher levels of psychophysiological stress, which may affect their quality of life negatively. However, since no significant difference was found in this study between BSTRi values of PD and MSA patients, BSTRi seems at present unreliable for early differentiation between the two diseases. Telematic assessment of BSTRi, SNSi, and PNSi could be useful for remote monitoring of PD and MSA patients’ psychophysiological stress, to provide timely intervention and adaptive treatment.Fig.1 Example of graphic summary (ACT5')

Isolated theta waves originating from the midline thalamus trigger memory reactivation during NREM sleep in mice

During non-rapid eye movement (NREM) sleep, neural ensembles in the entorhinal-hippocampal circuit responsible for encoding recent memories undergo reactivation to facilitate the process of memory consolidation. This reactivation is widely acknowledged as pivotal for the formation of stable memory and its impairment is closely associated with memory dysfunction. To date, the neural mechanisms driving the reactivation of neural ensembles during NREM sleep remain poorly understood. Here, we show that the neural ensembles in the medial entorhinal cortex (MEC) that encode spatial experiences exhibit reactivation during NREM sleep. Notably, this reactivation consistently coincides with isolated theta waves. In addition, we found that the nucleus reuniens (RE) in the midline thalamus exhibits typical theta waves during NREM sleep, which are highly synchronized with those occurring in the MEC in male mice. Closed-loop optogenetic inhibition of the RE-MEC pathway specifically suppressed these isolated theta waves, resulting in impaired reactivation and compromised memory consolidation following a spatial memory task in male mice. The findings suggest that theta waves originating from the ventral midline thalamus play a role in initiating memory reactivation and consolidation during sleep.

Spectral Dynamics Prior to Motor Events Differ Between NREM Sleep Parasomnias and Healthy Sleepers.

The umbrella term "Disorders of Arousal" (DoA), encompassing sleepwalking, confusional arousals, and sleep terrors, refers to parasomnias manifesting during non-rapid eye movement (NREM) sleep, commonly thought to arise from an aberrant arousal process. While previous studies have detailed EEG changes linked to DoA episodes, it remains uncertain how these alterations differ from a physiological arousal process. This study directly compared brain activity between DoA episodes and arousals associated with physiological movements (motor arousal) in individuals with DoA and healthy sleepers. Fifty-three adult patients with DoA (25 males, 32.2±15.5years) and 33 control subjects (14 males, 31.4±11.4years) underwent one or more home-EEG recordings. A semiparametric regression model was employed to elucidate the complex relationship between EEG activity across channels, within and across different groups, including motor arousals in DoA (n=169), parasomnia episodes in DoA (n=361), and motor arousals in healthy sleepers (n=137). Parasomnia episodes and motor arousals in both groups were preceded by a diffuse increase in slow-wave activity (SWA) and beta power, and a widespread decrease in sigma power. However, motor arousals in DoA displayed lower beta and central sigma than in healthy sleepers. Within DoA patients, episodes were preceded by lower beta, frontal sigma, and higher SWA than motor arousals. Our findings suggest that the arousal process is altered in DOA patients, and that specific EEG patterns are required for DOA episodes to emerge. These insights will help guide future research into the underlying circuits and objective markers of DOA.

Study While You Sleep: Using Targeted Memory Reactivation as an Independent Research Project for Undergraduates.

Newly acquired information is stabilized into long-term memory through the process of consolidation. Memories are not static; rather, they are constantly updated via reactivation, and this reactivation occurs preferentially during Slow-Wave Sleep (SWS, also referred to as N3 in humans). Here we present a scalable neuroscience research investigation of memory reactivation using low-cost electroencephalogram (EEG) recording hardware and open-source software, for students and educators across the K-12 and higher education spectrum. The investigation uses a method called targeted memory reactivation (TMR), whereby auditory cues that were previously associated with learning are re-presented during sleep, triggering the recall of stored memories and (through this) strengthening these memories. We demonstrated the efficacy of this technique on seven healthy human subjects (ages 19-35). The subjects learned to play a spatial memory game on an app where they associated pictures (e.g., a clock) with locations on a grid while they listened to picture-appropriate sounds (e.g., "tic-toc"); next, they took a nap while undergoing EEG recordings. During SWS, half of the sounds from the game were replayed by the app, while half were substituted with non-learned sounds. Subjects then played the memory game again after waking. Results showed that spatial recall was improved more for cued than uncued memories, demonstrating the benefits of memory replay during sleep and suggesting that one may intervene in this process to boost recall of specific memories. This research investigation takes advantage of the importance of sleep for memory consolidation and demonstrates improved memory performance by cueing sounds during SWS.

Threshold-dependent association between non-rapid eye movement obstructive sleep apnea and interictal epileptiform discharges: A hospital study.

Obstructive sleep apnea frequently coexists with epilepsy, potentially influencing its pathophysiology. However, the effect of obstructive sleep apnea severity on interictal epileptiform discharges is not well understood. To explore this, we studied 108 Asian patients with epilepsy who underwent single-night polysomnography. We utilized generalized linear models, adjusting for age, sex, epilepsy type (focal versus generalized), antiepileptic medication use and disease duration, to analyse the relationship between obstructive sleep apnea severity, as measured by the apnea-hypopnea index, and interictal epileptiform discharge frequency during non-rapid eye movement and rapid eye movement sleep. Our analysis revealed that severe obstructive sleep apnea (apnea-hypopnea index ≥ 30) was associated with a higher frequency of interictal epileptiform discharges during non-rapid eye movement sleep (p = 0.04), but no such association was observed during rapid eye movement sleep. Additionally, the frequency of interictal epileptiform discharges in non-rapid eye movement sleep was positively correlated with the wake time between sleep onset and offset (p = 0.03). Further studies are warranted to validate our findings across diverse ethnicities, and over multiple nights of sleep and interictal epileptiform discharge recordings.

Coupling of Slow Oscillations in the Prefrontal and Motor Cortex Predicts Onset of Spindle Trains and Persistent Memory Reactivations.

Sleep is known to drive the consolidation of motor memories. During nonrapid eye movement (NREM) sleep, the close temporal proximity between slow oscillations (SOs) and spindles ("nesting" of SO-spindles) is known to be essential for consolidation, likely because it is closely associated with the reactivation of awake task activity. Interestingly, recent work has found that spindles can occur in temporal clusters or "trains." However, it remains unclear how spindle trains are related to the nesting phenomenon. Here, we hypothesized that spindle trains are more likely when SOs co-occur in the prefrontal and motor cortex. We conducted simultaneous neural recordings in the medial prefrontal cortex (mPFC) and primary motor cortex (M1) of male rats training on the reach-to-grasp motor task. We found that intracortically recorded M1 spindles are organized into distinct temporal clusters. Notably, the occurrence of temporally precise SOs between mPFC and M1 was a strong predictor of spindle trains. Moreover, reactivation of awake task patterns is much more persistent during spindle trains in comparison with that during isolated spindles. Together, our work suggests that the precise coupling of SOs across mPFC and M1 may be a potential driver of spindle trains and persistent reactivation of motor memory during NREM sleep.

Retrospective validation of automatic sleep analysis with grey areas model for human-in-the-loop scoring approach.

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.e., in human-in-the-loop applications. To address these challenges, we propose an automatic sleep staging model (aSAGA) that effectively utilises both electroencephalography and electro-oculography channels while incorporating transparency of uncertainty in the decision-making process. We validated the model through extensive retrospective testing using a range of datasets, including open-access, clinical, and research-driven sources. Our channel-wise ensemble model, trained on both electroencephalography and electro-oculography signals, demonstrated robustness and the ability to generalise across various types of sleep recordings, including novel self-applied home polysomnography. Additionally, we compared model uncertainty with human uncertainty in sleep staging and studied various uncertainty mapping metrics to identify ambiguous regions, or "grey areas", that may require manual re-evaluation. The validation of this grey area concept revealed its potential to enhance sleep staging accuracy and to highlight regions in the recordings where sleep experts may struggle to reach a consensus. In conclusion, this study provides a technical basis and understanding of automatic sleep staging uncertainty. Our approach has the potential to improve the integration of automatic sleep staging into clinical practice; however, further studies are needed to test the model prospectively in real-world clinical settings and human-in-the-loop scoring applications.

Sleep disturbances in anorexia nervosa.

Sleep is crucial for physical and psychological health, and disturbances are closely linked to psychiatric disorders, making their management essential for improving treatment outcomes and preventing relapse. Although sleep disturbances are implicated in psychopathology of eating disorders, its role in anorexia nervosa (AN) remains unclear. This review aimed to characterise sleep in AN. A systematic search was conducted in four scientific databases, including papers from inception to 25 May 2024. A machine learning algorithm (ASReview) was utilised to screen titles and abstracts for eligibility. Sleep quantity, architecture, and quality were investigated. Meta-analyses were conducted to investigate the difference between patients with AN and healthy controls (HC) in total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), sleep efficiency, Sleep Stage 1, 2, slow wave sleep, rapid eye movement (REM) sleep and REM latency. Certainty of evidence was assessed using the GRADE approach. Out of 67 potentially eligible papers, 31 were included in this review, with 15 in the meta-analyses. Statistically significant average mean differences were found for TST (-32.1min [95% CI: -50.9, -13.4]), WASO (19.0min [95% CI: -2.4, 40.3]), and sleep efficiency (-4.4% [95% CI: -7.9, -0.9]). Additionally, Sleep stage 1 was significantly increased (2.4% [95%-CI: 0.05, 4.7]), while REM sleep was reduced (-2.1% [95%-CI: -4.2, -0.02]). Subgroup analysis showed that TST and WASO did not improve significantly after weight restoration. Sleep in patients with AN is impaired, with lower TST and sleep efficiency, higher WASO, more time in stage 1 sleep, and less in REM. Weight restoration alone may not improve sleep. While more research is needed, substantial accompanying sleep disturbances in AN justifies addressing these in current treatment practice, also because of the chronic character of AN and importance of sleep for long term (mental) health.

Pharmacological inhibition of histamine N-methyltransferase extends wakefulness and suppresses cataplexy in a mouse model of narcolepsy.

Histamine, a neurotransmitter, plays a predominant role in maintaining wakefulness. Further, our previous studies showed that histamine N-methyltransferase (HNMT), a histamine-metabolising enzyme, is important for regulating brain histamine concentration. However, the effects of pharmacological HNMT inhibition on mouse behaviour, including the sleep-wake cycle and cataplexy, in a mouse model of narcolepsy have not yet been investigated. In the present study, we investigated the effects of metoprine, an HNMT inhibitor with high blood-brain barrier permeability, in wild-type (WT) and orexin-deficient (OxKO) narcoleptic mice. Metoprine increased brain histamine concentration in a time- and dose-dependent manner without affecting peripheral histamine concentrations. Behavioural tests showed that metoprine increased locomotor activity in both novel and familiar environments, but did not alter anxiety-like behaviour. Sleep analysis showed that metoprine increased wakefulness and decreased non-rapid eye movement (NREM) sleep through the activation of the histamine H1 receptor (H1R) in WT mice. In contrast, the reduction of rapid eye movement (REM) sleep by metoprine occurred independent of H1R. In OxKO mice, metoprine was found to prolong wakefulness and robustly suppress cataplexy. In addition, metoprine has a greater therapeutic effect on cataplexy than pitolisant, which induces histamine release in the brain, and has been approved for patients with narcolepsy. These data demonstrate that HNMT inhibition has a strong effect on wakefulness, demonstrating therapeutic potential against cataplexy in narcolepsy.

The evaluation of a novel single-lead biopotential device for home sleep testing.

This paper reports on the clinical evaluation of the sleep staging performance of a novel single-lead biopotential device. 133 patients suspected of obstructive sleep apnea were included in a multi-site cohort. All patients underwent polysomnography and received the study device, a single-lead biopotential measurement device attached to the forehead. Clinical endpoint parameters were selected to evaluate the device's ability to determine sleep stages. Finally, the device's performance was compared to the clinical study results of comparable devices. Concurrent PSG and study device data were successfully acquired for 106 of the 133 included patients. The results of this study demonstrated significant similarity in overall sleep staging performance (5-stage Cohen's Kappa of 0.70) to the best-performing reduced-lead biopotential device to which it was compared (5-stage Cohen's Kappa of 0.73). Contrary to the comparator devices, the study device reported a higher Cohen's Kappa for REM (0.78) compared to N3 (0.61), which can be explained by its particular measuring electrode placement (diagonally across the lateral cross-section of the eye). This placement was optimized to ensure the polarity of rapid eye movements could be adequately captured, enhancing the capacity to discriminate between N3 and REM sleep when using only a single-lead setup. The results of this study demonstrate the feasibility of incorporating a single-lead biopotential extension in a reduced-channel home sleep apnea testing setup. Such incorporation could narrow the gap in the functionality of reduced-channel home sleep testing and in-lab polysomnography without compromising the patient's ease of use and comfort.

Nuclear parcellation of pontine catecholaminergic and cholinergic neurons in gray parrots and pied crow brains.

Employing immunohistochemical procedures with antibodies raised against tyrosine hydroxylase (TH) and choline acetyltransferase we identified and mapped the locus coeruleus complex (LoC) and the pontine laterodorsal tegmental (LDT) and pedunculopontine tegmental (PPN) cholinergic nuclei in the brains of a Congo gray parrot, a timneh gray parrot, and a pied crow. The LoC and LDT/PPN are centrally involved in the regulation and generation of different sleep states, and as all birds studied to date show both REM and non-REM sleep states, like mammals, we investigated whether these noradrenergic and cholinergic nuclei in the avian pons shared anatomical features with those in the mammalian pons. The LoC was parcellated into 3 distinct nuclei, including the locus coeruleus (A6), subcoeruleus (A7), and the fifth arcuate nucleus (A5), while distinct LDT and PPN nuclei were revealed. Several similarities that allow the assumption of homology of these nuclei between birds and mammals were revealed, including their location relative to each other and other structures within the pontine region, as well as a specific degree of topographical overlap of the noradrenergic and cholinergic neurons. Despite this, some differences were noted that may be of interest in understanding the differences in sleep between birds and mammals. Further anatomical and physiological studies are needed to determine whether these pontine nuclei in birds play the same role as in mammals, as while the homology is apparent, the functional analogy needs to be revealed.

Enhanced Sleep Stage Classification Using EEG and EOG: A Novel Approach for Feature Selection with Deep Learning and Gaussian Noise Data Augmentation

Enhanced Sleep Stage Classification Using EEG and EOG: A Novel Approach for Feature Selection with Deep Learning and Gaussian Noise Data Augmentation