Showing papers on "Slow-wave sleep published in 2018"

Sleep disorders in the elderly: a growing challenge

Abstract: In contrast to newborns, who spend 16-20 h in sleep each day, adults need only about sleep daily. However, many elderly may struggle to obtain those 8 h in one block. In addition to changes in sleep duration, sleep patterns change as age progresses. Like the physical changes that occur during old age, an alteration in sleep pattern is also a part of the normal ageing process. As people age, they tend to have a harder time falling asleep and more trouble staying asleep. Older people spend more time in the lighter stages of sleep than in deep sleep. As the circadian mechanism in older people becomes less efficient, their sleep schedule is shifted forward. Even when they manage to obtain 7 or 8 h sleep, they wake up early, as they have gone to sleep quite early. The prevalence of sleep disorders is higher among older adults. Loud snoring, which is more common in the elderly, can be a symptom of obstructive sleep apnoea, which puts a person at risk for cardiovascular diseases, headaches, memory loss, and depression. Restless legs syndrome and periodic limb movement disorder that disrupt sleep are more prevalent in older persons. Other common medical problems of old age such as hypertension diabetes mellitus, renal failure, respiratory diseases such as asthma, immune disorders, gastroesophageal reflux disease, physical disability, dementia, pain, depression, and anxiety are all associated with sleep disturbances.

Quantitative phosphoproteomic analysis of the molecular substrates of sleep need.

Abstract: Sleep and wake have global effects on brain physiology, from molecular changes1–4 and neuronal activities to synaptic plasticity3–7. Sleep–wake homeostasis is maintained by the generation of a sleep need that accumulates during waking and dissipates during sleep8–11. Here we investigate the molecular basis of sleep need using quantitative phosphoproteomic analysis of the sleep-deprived and Sleepy mouse models of increased sleep need. Sleep deprivation induces cumulative phosphorylation of the brain proteome, which dissipates during sleep. Sleepy mice, owing to a gain-of-function mutation in the Sik3 gene 12 , have a constitutively high sleep need despite increased sleep amount. The brain proteome of these mice exhibits hyperphosphorylation, similar to that seen in the brain of sleep-deprived mice. Comparison of the two models identifies 80 mostly synaptic sleep-need-index phosphoproteins (SNIPPs), in which phosphorylation states closely parallel changes of sleep need. SLEEPY, the mutant SIK3 protein, preferentially associates with and phosphorylates SNIPPs. Inhibition of SIK3 activity reduces phosphorylation of SNIPPs and slow wave activity during non-rapid-eye-movement sleep, the best known measurable index of sleep need, in both Sleepy mice and sleep-deprived wild-type mice. Our results suggest that phosphorylation of SNIPPs accumulates and dissipates in relation to sleep need, and therefore SNIPP phosphorylation is a molecular signature of sleep need. Whereas waking encodes memories by potentiating synapses, sleep consolidates memories and restores synaptic homeostasis by globally downscaling excitatory synapses4–6. Thus, the phosphorylation–dephosphorylation cycle of SNIPPs may represent a major regulatory mechanism that underlies both synaptic homeostasis and sleep–wake homeostasis.

Rapid Eye Movement Sleep, Sleep Continuity and Slow Wave Sleep as Predictors of Cognition, Mood, and Subjective Sleep Quality in Healthy Men and Women, Aged 20-84 Years.

Abstract: Sleep and its sub-states are assumed to be important for brain function across the lifespan but which aspects of sleep associate with various aspects of cognition, mood and self-reported sleep quality has not yet been established in detail. Sleep was quantified by polysomnography, quantitative Electroencephalogram (EEG) analysis and self-report in 206 healthy men and women, aged 20–84 years, without sleep complaints. Waking brain function was quantified by five assessments scheduled across the day covering objectively assessed performance across cognitive domains including sustained attention and arousal, decision and response time, motor and sequence control, working memory, and executive function as well as self-reports of alertness, mood and affect. Controlled for age and sex, self-reported sleep quality was negatively associated with number of awakenings and positively associated with the duration of Rapid Eye Movement (REM) sleep, but no significant associations with Slow Wave Sleep (SWS) measures were observed. Controlling only for age showed that associations between objective and subjective sleep quality were much stronger in women than in men. Analysis of 51 performance measures demonstrated that, after controlling for age and sex, fewer awakenings and more REM sleep were associated significantly with better performance on the Goal Neglect task, which is a test of executive function. Factor analysis of the individual performance measures identified four latent variables labeled Mood/Arousal, Response Time, Accuracy, and Visual Perceptual Sensitivity. Whereas Mood/Arousal improved with age, Response Times became slower, while Accuracy and Visual perceptual sensitivity showed little change with age. After controlling for sex and age, nominally significant association between sleep and factor scores were observed such that Response Times were faster with more SWS, and Accuracy was reduced where individuals woke more often or had less REM sleep. These data identify a positive contribution of SWS to processing speed and in particular highlight the importance of sleep continuity and REM sleep for subjective sleep quality and performance accuracy across the adult lifespan. These findings warrant further investigation of the contribution of sleep continuity and REM sleep to brain function.

Sleep Disorders Associated With Alzheimer's Disease: A Perspective.

Abstract: Sleep disturbances, as well as sleep-wake rhythm disturbances, are typical symptoms of Alzheimer's disease (AD) that may precede the other clinical signs of this neurodegenerative disease. Here, we describe clinical features of sleep disorders in AD and the relation between sleep disorders and both cognitive impairment and poor prognosis of the disease. There are difficulties of the diagnosis of sleep disorders based on sleep questionnaires, polysomnography or actigraphy in the AD patients. Typical disturbances of the neurophysiological sleep architecture in the course of the AD include deep sleep and paradoxical sleep deprivation. Among sleep disorders occurring in patients with AD, the most frequent disorders are sleep breathing disorders and restless legs syndrome. Sleep disorders may influence circadian fluctuations of the concentrations of amyloid-β in the interstitial brain fluid and in the cerebrovascular fluid related to the glymphatic brain system and production of the amyloid-β. There is accumulating evidence suggesting that disordered sleep contributes to cognitive decline and the development of AD pathology. In this mini-review, we highlight and discuss the association between sleep disorders and AD.

Regulation of cortical activity and arousal by the matrix cells of the ventromedial thalamic nucleus

Abstract: The "non-specific" ventromedial thalamic nucleus (VM) has long been considered a candidate for mediating cortical arousal due to its diffuse, superficial projections, but direct evidence was lacking. Here, we show in mice that the activity of VM calbindin1-positive matrix cells is high in wake and REM sleep and low in NREM sleep, and increases before cortical activity at the sleep-to-wake transition. Optogenetic stimulation of VM cells rapidly awoke all mice from NREM sleep and consistently caused EEG activation during slow wave anesthesia, while arousal did not occur from REM sleep. Conversely, chemogenetic inhibition of VM decreased wake duration. Optogenetic activation of the "specific" ventral posteromedial nucleus (VPM) did not cause arousal from either NREM or REM sleep. Thus, matrix cells in VM produce arousal and broad cortical activation during NREM sleep and slow wave anesthesia in a way that accounts for the effects classically attributed to "non-specific" thalamic nuclei.

Performance of an Ambulatory Dry-EEG Device for Auditory Closed-Loop Stimulation of Sleep Slow Oscillations in the Home Environment

Abstract: Recent research has shown that auditory closed-loop stimulation can enhance sleep slow oscillations (SO) to improve N3 sleep quality and cognition. Previous studies have been conducted in lab environments. The present study aimed to validate and assess the performance of a novel ambulatory wireless dry-EEG device (WDD), for auditory closed-loop stimulation of SO during N3 sleep at home. The performance of the WDD to detect N3 sleep automatically and to send auditory closed-loop stimulation on SO were tested on 20 young healthy subjects who slept with both the WDD and a miniaturized polysomnography (part 1) in both stimulated and sham nights within a double blind, randomized and crossover design. The effects of auditory closed-loop stimulation on delta power increase were assessed after one and 10 nights of stimulation on an observational pilot study in the home environment including 90 middle-aged subjects (part 2).The first part, aimed at assessing the quality of the WDD as compared to a polysomnograph, showed that the sensitivity and specificity to automatically detect N3 sleep in real-time were 0.70 and 0.90, respectively. The stimulation accuracy of the SO ascending-phase targeting was 45 ± 52°. The second part of the study, conducted in the home environment, showed that the stimulation protocol induced an increase of 43.9% of delta power in the 4 s window following the first stimulation (including evoked potentials and SO entrainment effect). The increase of SO response to auditory stimulation remained at the same level after 10 consecutive nights. The WDD shows good performances to automatically detect in real-time N3 sleep and to send auditory closed-loop stimulation on SO accurately. These stimulation increased the SO amplitude during N3 sleep without any adaptation effect after 10 consecutive nights. This tool provides new perspectives to figure out novel sleep EEG biomarkers in longitudinal studies and can be interesting to conduct broad studies on the effects of auditory stimulation during sleep.

Effects of Aging on Cortical Neural Dynamics and Local Sleep Homeostasis in Mice

Abstract: Healthy aging is associated with marked effects on sleep, including its daily amount and architecture, as well as the specific EEG oscillations. Neither the neurophysiological underpinnings nor the biological significance of these changes are understood, and crucially the question remains whether aging is associated with reduced sleep need or a diminished capacity to generate sufficient sleep. Here we tested the hypothesis that aging may affect local cortical networks, disrupting the capacity to generate and sustain sleep oscillations, and with it the local homeostatic response to sleep loss. We performed chronic recordings of cortical neural activity and local field potentials from the motor cortex in young and older male C57BL/6J mice, during spontaneous waking and sleep, as well as during sleep after sleep deprivation. In older animals, we observed an increase in the incidence of non-rapid eye movement sleep local field potential slow waves and their associated neuronal silent (OFF) periods, whereas the overall pattern of state-dependent cortical neuronal firing was generally similar between ages. Furthermore, we observed that the response to sleep deprivation at the level of local cortical network activity was not affected by aging. Our data thus suggest that the local cortical neural dynamics and local sleep homeostatic mechanisms, at least in the motor cortex, are not impaired during healthy senescence in mice. This indicates that powerful protective or compensatory mechanisms may exist to maintain neuronal function stable across the life span, counteracting global changes in sleep amount and architecture.SIGNIFICANCE STATEMENT The biological significance of age-dependent changes in sleep is unknown but may reflect either a diminished sleep need or a reduced capacity to generate deep sleep stages. As aging has been linked to profound disruptions in cortical sleep oscillations and because sleep need is reflected in specific patterns of cortical activity, we performed chronic electrophysiological recordings of cortical neural activity during waking, sleep, and after sleep deprivation from young and older mice. We found that all main hallmarks of cortical activity during spontaneous sleep and recovery sleep after sleep deprivation were largely intact in older mice, suggesting that the well-described age-related changes in global sleep are unlikely to arise from a disruption of local network dynamics within the neocortex.

Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram

Abstract: OBJECTIVE This study classifies sleep stages from a single lead electrocardiogram (ECG) using beat detection, cardiorespiratory coupling in the time-frequency domain and a deep convolutional neural network (CNN). APPROACH An ECG-derived respiration (EDR) signal and synchronous beat-to-beat heart rate variability (HRV) time series were derived from the ECG using previously described robust algorithms. A measure of cardiorespiratory coupling (CRC) was extracted by calculating the coherence and cross-spectrogram of the EDR and HRV signal in 5 min windows. A CNN was then trained to classify the sleep stages (wake, rapid-eye-movement (REM) sleep, non-REM (NREM) light sleep and NREM deep sleep) from the corresponding CRC spectrograms. A support vector machine was then used to combine the output of CNN with the other features derived from the ECG, including phase-rectified signal averaging (PRSA), sample entropy, as well as standard spectral and temporal HRV measures. The MIT-BIH Polysomnographic Database (SLPDB), the PhysioNet/Computing in Cardiology Challenge 2018 database (CinC2018) and the Sleep Heart Health Study (SHHS) database, all expert-annotated for sleep stages, were used to train and validate the algorithm. MAIN RESULTS Ten-fold cross validation results showed that the proposed algorithm achieved an accuracy (Acc) of 75.4% and a Cohen's kappa coefficient of [Formula: see text] = 0.54 on the out of sample validation data in the classification of Wake, REM, NREM light and deep sleep in SLPDB. This rose to Acc = 81.6% and [Formula: see text] = 0.63 for the classification of Wake, REM sleep and NREM sleep and Acc = 85.1% and [Formula: see text] = 0.68 for the classification of NREM sleep versus REM/wakefulness in SLPDB. SIGNIFICANCE The proposed ECG-based sleep stage classification approach that represents the highest reported results on non-electroencephalographic data and uses datasets over ten times larger than those in previous studies. By using a state-of-the-art QRS detector and deep learning model, the system does not require human annotation and can therefore be scaled for mass analysis.

Slow-Wave Sleep Is Associated With Incident Hypertension: The Sleep Heart Health Study.

Abstract: We sought to quantify the association between slow-wave (stage N3) sleep and hypertension in a large cohort of middle-aged men and women. Data from 1850 participants free of baseline hypertension from the Sleep Heart Health Study were analyzed. The primary exposure was percentage of N3 sleep on baseline in-home polysomnography and the primary outcome was incident hypertension, defined as systolic blood pressure ≥ 140 mm Hg, diastolic blood pressure ≥ 90 mm Hg, and/or use of any blood pressure lowering medications at follow-up. Multivariable logistic regression models were adjusted for study site, age, sex, race, waist circumference, tobacco use, alcohol use, apnea-hypopnea index, nocturnal oxygen desaturation, sleep duration, sleep efficiency, and arousal index. Mean age was 59.4 ± 10.1 years and 55.5% were female. The mean baseline systolic and diastolic blood pressure was 118.8 and 70.6 mm Hg, respectively. Approximately 30% of the sample developed hypertension during a mean follow-up of 5.3 years. In the multi-adjusted model, participants in quartiles 1 (<9.8%) and 2 (9.8%-17.7%) of N3 sleep had significantly greater odds of incident hypertension compared with those in quartile 3 (17.7%-25.2%) (OR 1.69, 95% CI 1.21-2.36, p = .002 and OR 1.45, 95% CI 1.04-2.00, p = .03, respectively). No significant effect modification by sex on the N3-hypertension association was observed. In conclusion, compared with intermediate levels of N3 sleep (overlapping the "normal" adult range), lower levels of percent N3 sleep are associated with increased odds of incident hypertension in both men and women, independent of potential confounders, including indices of sleep apnea and sleep fragmentation.

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent.

Abstract: Objective: Measurements of heart rate variability (HRV) during sleep have become increasingly popular as sleep could provide an optimal state for HRV assessments. While sleep stages have been reported to affect HRV, the effect of sleep stages on the variance of HRV parameters were hardly investigated. We aimed to assess the variance of HRV parameters during the different sleep stages. Further, we tested the accuracy of an algorithm using HRV to identify a 5-min segment within an episode of slow wave sleep (SWS, deep sleep). Methods: Polysomnographic (PSG) sleep recordings of 3 nights of 15 healthy young males were analyzed. Sleep was scored according to conventional criteria. HRV parameters of consecutive 5-min segments were analyzed within the different sleep stages. The total variance of HRV parameters was partitioned into between-subjects variance, between-nights variance, and between-segments variance and compared between the different sleep stages. Intra-class correlation coefficients of all HRV parameters were calculated for all sleep stages. To identify an SWS segment based on HRV, Pearson correlation coefficients of consecutive R-R intervals (rRR) of moving 5-min windows (20-s steps). The linear trend was removed from the rRR time series and the first segment with rRR values 0.1 units below the mean rRR for at least 10 min was identified. A 5-min segment was placed in the middle of such an identified segment and the corresponding sleep stage was used to assess the accuracy of the algorithm. Results: Good reproducibility within and across nights was found for heart rate in all sleep stages and for high frequency (HF) power in SWS. Reproducibility of low frequency (LF) power and of LF/HF was poor in all sleep stages. Of all the 5-min segments selected based on HRV data, 87% were accurately located within SWS. Conclusions: SWS, a stable state that, in contrast to waking, is unaffected by internal and external factors, is a reproducible state that allows reliable determination of heart rate, and HF power, and can satisfactorily be detected based on R-R intervals, without the need of full PSG. Sleep may not be an optimal condition to assess LF power and LF/HF power ratio.

Odor-evoked category reactivation in human ventromedial prefrontal cortex during sleep promotes memory consolidation.

Abstract: Slow-wave sleep is an optimal opportunity for memory consolidation: when encoding occurs in the presence of a sensory cue, delivery of that cue during sleep enhances retrieval of associated memories. Recent studies suggest that cues might promote consolidation by inducing neural reinstatement of cue-associated content during sleep, but direct evidence for such mechanisms is scant, and the relevant brain areas supporting these processes are poorly understood. Here, we address these gaps by combining a novel olfactory cueing paradigm with an object-location memory task and simultaneous EEG-fMRI recording in human subjects. Using pattern analysis of fMRI ensemble activity, we find that presentation of odor cues during sleep promotes reactivation of category-level information in ventromedial prefrontal cortex that significantly correlates with post-sleep memory performance. In identifying the potential mechanisms by which odor cues selectively modulate memory in the sleeping brain, these findings bring unique insights into elucidating how and what we remember.

Mapping Slow Waves by EEG Topography and Source Localization: Effects of Sleep Deprivation.

Abstract: Slow waves are a salient feature of the electroencephalogram (EEG) during non-rapid eye movement (non-REM) sleep. The aim of this study was to assess the topography of EEG power and the activation of brain structures during slow wave sleep under normal conditions and after sleep deprivation. Sleep EEG recordings during baseline and recovery sleep after 40 h of sustained wakefulness were analyzed (eight healthy young men, 27 channel EEG). Power maps were computed for the first non-REM sleep episode (where sleep pressure is highest) in baseline and recovery sleep, at frequencies between 0.5 and 2 Hz. Power maps had a frontal predominance at all frequencies between 0.5 and 2 Hz. An additional occipital focus of activity was observed below 1 Hz. Power maps ≤ 1 Hz were not affected by sleep deprivation, whereas an increase in power was observed in the maps ≥ 1.25 Hz. Based on the response to sleep deprivation, low-delta (0.5-1 Hz) and mid-delta activity (1.25-2 Hz) were dissociated. Electrical sources within the cortex of low- and mid-delta activity were estimated using eLORETA. Source localization revealed a predominantly frontal distribution of activity for low-delta and mid-delta activity. Sleep deprivation resulted in an increase in source strength only for mid-delta activity, mainly in parietal and frontal regions. Low-delta activity dominated in occipital and temporal regions and mid-delta activity in limbic and frontal regions independent of the level of sleep pressure. Both, power maps and electrical sources exhibited trait-like aspects.

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

Abstract: The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.

Closed-Loop Targeted Memory Reactivation during Sleep Improves Spatial Navigation.

Abstract: Sounds associated with newly learned information that are replayed during non-rapid eye movement (NREM) sleep can improve recall in simple tasks. The mechanism for this improvement is presumed to be reactivation of the newly learned memory during sleep when consolidation takes place. We have developed an EEG-based closed-loop system to precisely deliver sensory stimulation at the time of down-state to up-state transitions during NREM sleep. Here, we demonstrate that applying this technology to participants performing a realistic navigation task in virtual reality results in a significant improvement in navigation efficiency after sleep that is accompanied by increases in the spectral power especially in the fast (12-15 Hz) sleep spindle band. Our results show promise for the application of sleep-based interventions to drive improvement in real-world tasks.

Incorporation of recent waking-life experiences in dreams correlates with frontal theta activity in REM sleep

Abstract: Rapid eye movement (REM) sleep and its main oscillatory feature, frontal theta, have been related to the processing of recent emotional memories. As memories constitute much of the source material for our dreams, we explored the link between REM frontal theta and the memory sources of dreaming, so as to elucidate the brain activities behind the formation of dream content. Twenty participants were woken for dream reports in REM and slow wave sleep (SWS) while monitored using electroencephalography. Eighteen participants reported at least one REM dream and 14 at least one SWS dream, and they, and independent judges, subsequently compared their dream reports with log records of their previous daily experiences. The number of references to recent waking-life experiences in REM dreams was positively correlated with frontal theta activity in the REM sleep period. No such correlation was observed for older memories, nor for SWS dreams. The emotional intensity of recent waking-life experiences incorporated into dreams was higher than the emotional intensity of experiences that were not incorporated. These results suggest that the formation of wakefulness-related dream content is associated with REM theta activity, and accords with theories that dreaming reflects emotional memory processing taking place in REM sleep.

Slow-Wave Sleep Estimation for Healthy Subjects and OSA Patients Using R–R Intervals

Abstract: We developed an automatic slow-wave sleep (SWS) detection algorithm that can be applied to groups of healthy subjects and patients with obstructive sleep apnea (OSA). This algorithm detected SWS based on autonomic activations derived from the heart rate variations of a single sensor. An autonomic stability, which is an SWS characteristic, was evaluated and quantified using R–R intervals from an electrocardiogram (ECG). The thresholds and the heuristic rule to determine SWS were designed based on the physiological backgrounds for sleep process and distribution across the night. The automatic algorithm was evaluated based on a fivefold cross validation using data from 21 healthy subjects and 24 patients with OSA. An epoch-by-epoch (30 s) analysis showed that the overall Cohen's kappa, accuracy, sensitivity, and specificity of our method were 0.56, 89.97%, 68.71%, and 93.75%, respectively. SWS-related information, including SWS duration (min) and percentage (%), were also calculated. A significant correlation in these parameters was found between automatic and polysomnography scorings. Compared with similar methods, the proposed algorithm convincingly discriminated SWS from non-SWS. The simple method using only R–R intervals has the potential to be utilized in mobile and wearable devices that can easily measure this information. Moreover, when combined with other sleep staging methods, the proposed method is expected to be applicable to long-term sleep monitoring at home and ambulatory environments.

Melanin-concentrating hormone-expressing neurons adjust slow-wave sleep dynamics to catalyze paradoxical (REM) sleep.

Abstract: Study Objectives Experimental studies over the last 15 years established a role in sleep of the tuberal hypothalamic neurons that express melanin-concentrating hormone (MCH). Controversies still remain regarding their actual contribution to both slow-wave sleep (SWS) and paradoxical sleep (PS also known as REM sleep) or PS alone. Methods To address this point, we compared effects of chemogenetic activation and inhibition of MCH neurons on SWS and PS amounts and EEG rhythmic activities in transgenic Pmch-cre mice. Results In agreement with recently reported optogenetic data, the activation of MCH neurons invariably facilitates PS onset and maintenance. Our chemogenetic experiments further disclose that the ultradian rhythm of SWS is also notably related to the activity of MCH neurons. We observed that the mean duration of SWS episodes is significantly extended when MCH neurons are inhibited. Conversely, when they were excited, SWS bouts were drastically shortened and depicted substantial changes in δ rhythmic activities in electroencephalographic recording likely reflecting a deeper SWS. Conclusions According to these original findings, we propose that when MCH neurons are physiologically recruited, SWS depth is increased and the extinction of SWS episodes is accelerated, two joint physiological processes strengthening the probability for natural SWS to PS transition and likely facilitating PS onset.

Odor cueing during slow-wave sleep benefits memory independently of low cholinergic tone.

Abstract: Sleep-dependent memory consolidation depends on the concerted reactivation of memories in the hippocampo-neocortical system. The communication of reactivated information from the hippocampus to the neocortex is assumed to be enabled by low levels of acetylcholine, particularly during slow-wave sleep (SWS). Recent studies suggest that the reactivation of memories does not only occur spontaneously but can also be externally triggered by re-presenting learning-associated cues during sleep. Here we investigated whether the beneficial effect of cued memory reactivation during sleep depends on similar mechanisms as spontaneous reactivation, and specifically on low cholinergic tone. In two experimental nights, healthy volunteers learned a visuo-spatial memory task in the presence of an odor before going to sleep for 40 min. In one night, subjects were presented with the odor again during SWS, whereas in the other night they received an odorless vehicle. In half of the subjects, the availability of acetylcholine during sleep was increased by administering the acetylcholine-esterase inhibitor physostigmine. Contrary to our hypothesis, increased cholinergic tone during sleep did not abolish the beneficial effect of odor cueing: memory performance was better after odor cueing compared to odorless vehicle, independent of physostigmine or placebo administration. This finding challenges the assumption that odor-cued and spontaneous memory reactivation rely on the same neuropharmacological mechanisms.

Sleep Architecture in Patients With Primary Snoring and Obstructive Sleep Apnea.

Abstract: Introduction This study aimed to investigate sleep architecture in patients with primary snoring and obstructive sleep apnea. Methods In this study, we analyzed polysomnographic data of 391 clients who referred to Sleep Disorders Research Center (SDRS). These people were classified into three groups based on their Apnea-Hypopnea Index (AHI) and snoring; control, Primary Snoring (PS), and Obstructive Sleep Apnea (OSA) group. Sleep architecture variables were then assessed in all groups. Results The results of this study indicated a decrease in deep sleep or Slow Waves Sleep (SWS) and increase in light sleep or stage 1 of non-REM sleep (N1) in OSA patients compared with the control and PS groups. After controlling the effects of confounding factors, i.e. age and Body Mass Index (BMI) (which was performed through multiple regression analysis) significant differences were observed among the three groups with regard to N1. However, with regard to SWS, after controlling confounding variables (age and BMI), no significant difference was found among the groups. Conclusion The results indicated that OSA, regardless of age and BMI, may increase light (N1) sleep possibly via a decline in blood oxygen saturation (SpO2 ). Such increase in N1 may be responsible for brain arousal. In addition, by controlling confounding factors (age and BMI), OSA did not affect SWS in OSA patients. However, further research is necessary to determine sleep architecture in more detail in the patients with OSA.