Showing papers on "Slow-wave sleep published in 2019"

Are sleep patterns influenced by race/ethnicity - a marker of relative advantage or disadvantage? Evidence to date.

Abstract: Sleep is a fundamental necessity of life. However, sleep health and sleep disorders are not equitably distributed across racial/ethnic groups. In fact, growing research consistently demonstrates that racial/ethnic minorities are more likely to experience, for instance, shorter sleep durations, less deep sleep, inconsistent sleep timing, and lower sleep continuity in comparison to Whites. However, racial/ethnic disparities in reports of sleepiness and sleep complaints are inconsistent. Racial/ethnic groups have significant heterogeneity, yet within-group analyses are limited. Among the few published within-group analyses, there are differences in sleep between non-US-born and US-born racial/ethnic groups, but the group with the more favorable sleep profile is consistent for non-US-born Latinos compared to US-born Latinos and Whites but unclear for other racial/ethnic minority groups. These sleep health disparities are a significant public health problem that should garner support for more observational, experimental, intervention, and policy/implementation research. In this review, we 1) summarize current evidence related to racial/ethnic disparities in sleep health and within-group differences, focusing on the sleep of the following racial/ethnic minority categories that are defined by the United States Office of Management and Budget as: American Indian/Alaska Native, Asian, African American/Black, Hispanic/Latino, and Native Hawaiian/Pacific Islander; 2) discuss measurement challenges related to investigating sleep health disparities; 3) discuss potential contributors to sleep health disparities; 4) present promising interventions to address sleep health disparities; and 5) discuss future research directions on intersectionality and sleep health.

Bidirectional prefrontal-hippocampal dynamics organize information transfer during sleep in humans.

Abstract: How are memories transferred from short-term to long-term storage? Systems-level memory consolidation is thought to be dependent on the coordinated interplay of cortical slow waves, thalamo-cortical sleep spindles and hippocampal ripple oscillations. However, it is currently unclear how the selective interaction of these cardinal sleep oscillations is organized to support information reactivation and transfer. Here, using human intracranial recordings, we demonstrate that the prefrontal cortex plays a key role in organizing the ripple-mediated information transfer during non-rapid eye movement (NREM) sleep. We reveal a temporally precise form of coupling between prefrontal slow-wave and spindle oscillations, which actively dictates the hippocampal-neocortical dialogue and information transfer. Our results suggest a model of the human sleeping brain in which rapid bidirectional interactions, triggered by the prefrontal cortex, mediate hippocampal activation to optimally time subsequent information transfer to the neocortex during NREM sleep.

Changes in EEG multiscale entropy and power-law frequency scaling during the human sleep cycle.

Abstract: We explored changes in multiscale brain signal complexity and power-law scaling exponents of electroencephalogram (EEG) frequency spectra across several distinct global states of consciousness induced in the natural physiological context of the human sleep cycle. We specifically aimed to link EEG complexity to a statistically unified representation of the neural power spectrum. Further, by utilizing surrogate-based tests of nonlinearity we also examined whether any of the sleep stage-dependent changes in entropy were separable from the linear stochastic effects contained in the power spectrum. Our results indicate that changes of brain signal entropy throughout the sleep cycle are strongly time-scale dependent. Slow wave sleep was characterized by reduced entropy at short time scales and increased entropy at long time scales. Temporal signal complexity (at short time scales) and the slope of EEG power spectra appear, to a large extent, to capture a common phenomenon of neuronal noise, putatively reflecting cortical balance between excitation and inhibition. Nonlinear dynamical properties of brain signals accounted for a smaller portion of entropy changes, especially in stage 2 sleep.

Sleep Disturbance as a Potential Modifiable Risk Factor for Alzheimer’s Disease

Abstract: Sleep disturbance is a common symptom in patients with various neurodegenerative diseases, including Alzheimer's disease (AD), and it can manifest in the early stages of the disease. Impaired sleep in patients with AD has been attributed to AD pathology that affects brain regions regulating the sleep⁻wake or circadian rhythm. However, recent epidemiological and experimental studies have demonstrated an association between impaired sleep and an increased risk of AD. These studies have led to the idea of a bidirectional relationship between AD and impaired sleep; in addition to the conventional concept that impaired sleep is a consequence of AD pathology, various evidence strongly suggests that impaired sleep is a risk factor for the initiation and progression of AD. Despite this recent progress, much remains to be elucidated in order to establish the benefit of therapeutic interventions against impaired sleep to prevent or alleviate the disease course of AD. In this review, we provide an overview of previous studies that have linked AD and sleep. We then highlight the studies that have tested the causal relationship between impaired sleep and AD and will discuss the molecular and cellular mechanisms underlying this link. We also propose future works that will aid the development of a novel disease-modifying therapy and prevention of AD via targeting impaired sleep through non-pharmacological and pharmacological interventions.

Gating and the Need for Sleep: Dissociable Effects of Adenosine A1 and A2A Receptors.

Abstract: Roughly one-third of the human lifetime is spent in sleep, yet the reason for sleep remains unclear. Understanding the physiologic function of sleep is crucial toward establishing optimal health. Several proposed concepts address different aspects of sleep physiology, including humoral and circuit-based theories of sleep-wake regulation, the homeostatic two-process model of sleep regulation, the theory of sleep as a state of adaptive inactivity, and observations that arousal state and sleep homeostasis can be dissociated in pathologic disorders. Currently, there is no model that places the regulation of arousal and sleep homeostasis in a unified conceptual framework. Adenosine is well known as a somnogenic substance that affects normal sleep-wake patterns through several mechanisms in various brain locations via A1 or A2A receptors (A1Rs or A2ARs). Many cells and processes appear to play a role in modulating the extracellular concentration of adenosine at neuronal A1R or A2AR sites. Emerging evidence suggests that A1Rs and A2ARs have different roles in the regulation of sleep. In this review, we propose a model in which A2ARs allow the brain to sleep, i.e., these receptors provide sleep gating, whereas A1Rs modulate the function of sleep, i.e., these receptors are essential for the expression and resolution of sleep need. In this model, sleep is considered a brain state established in the absence of arousing inputs.

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors

Abstract: Background: It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. Objective: This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. Methods: A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night’s sleep in participants’ homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed–rank test was performed to investigate the effect of user-specific factors. Results: Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics. Conclusions: Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Association between diet quality and sleep apnea in the Multi-Ethnic Study of Atherosclerosis.

Abstract: Rationale Although short sleep duration has been linked to unhealthy dietary patterns, little is known about the association of obstructive sleep apnea (OSA), a disorder characterized by sleep fragmentation and diet.

Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population.

Abstract: Objective: Recent research suggests that sleep disorders or changes in sleep stages or EEG waveform precede over time the onset of the clinical signs of pathological cognitive impairment (e.g., Alzheimer's disease). The aim of this study was to identify biomarkers based on EEG power values and spindle characteristics during sleep that occur in the early stages of mild cognitive impairment (MCI) in older adults. Methods: This study was a case-control cross-sectional study with 1-year follow-up of cases. Patients with isolated subjective cognitive complaints (SCC) or MCI were recruited in the Bordeaux Memory Clinic (MEMENTO cohort). Cognitively normal controls were recruited. All participants were recorded with two successive polysomnography 1 year apart. Delta, theta, and sigma absolute spectral power and spindle characteristics (frequency, density, and amplitude) were analyzed from purified EEG during NREM and REM sleep periods during the entire second night. Results: Twenty-nine patients (8 males, age = 71 ± 7 years) and 29 controls were recruited at T0. Logistic regression analyses demonstrated that age-related cognitive impairment were associated with a reduced delta power (odds ratio (OR) 0.072, P < 0.05), theta power (OR 0.018, P < 0.01), sigma power (OR 0.033, P < 0.05), and spindle maximal amplitude (OR 0.002, P < 0.05) during NREM sleep. Variables were adjusted on age, gender, body mass index, educational level, and medication use. Seventeen patients were evaluated at 1-year follow-up. Correlations showed that changes in self-reported sleep complaints, sleep consolidation, and spindle characteristics (spectral power, maximal amplitude, duration, and frequency) were associated with cognitive impairment (P < 0.05). Conclusion: A reduction in slow-wave, theta and sigma activities, and a modification in spindle characteristics during NREM sleep are associated very early with a greater risk of the occurrence of cognitive impairment. Poor sleep consolidation, lower amplitude, and faster frequency of spindles may be early sleep biomarkers of worsening cognitive decline in older adults.

Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: A comparison against polysomnography

Abstract: Measuring sleep duration and early onset rapid eye movement sleep (REMS) is critical in the assessment of suspected central disorders of hypersomnolence (CDH). Current multi-sensor activity trackers that integrate accelerometry and heart rate are purported to accurately quantify sleep time and REMS; however, their utility in suspected CDH has not been established. This investigation aimed to determine the ability of a current, multi-sensor tracker, Fitbit Alta HR (FBA-HR), to quantify and classify sleep in patients with suspected CDH relative to polysomnography (PSG). Forty-nine patients (46 female; mean age, 30.3 ± 9.84 years) underwent ad libitum PSG with concurrent use of the FBA-HR. FBA-HR sleep variable quantification was assessed using Bland-Altman analysis. FBA-HR all sleep (AS), light sleep (LS; PSG N1 + N2), deep sleep (DS; PSG N3) and REMS classification was evaluated using epoch-by-epoch comparisons. FBA-HR-detected sleep-onset rapid eye movement periods (SOREMPs) were compared against PSG SOMREMPs. FBA-HR displayed significant overestimation of total sleep time (11.6 min), sleep efficiency (1.98%) and duration of deep sleep (18.2 min). FBA-HR sensitivity and specificity were as follows: AS, 0.96, 0.58; LS, 0.73, 0.72;DS, 0.67, 0.92; REMS, 0.74, 0.93. The device failed to detect any nocturnal SOREMPs. Device performance did not differ appreciably among diagnostic subgroups. These results suggest FBA-HR cannot replace EEG-based measurements of sleep and wake in the diagnostic assessment of suspected CDH, and that improvements in device performance are required prior to adoption in clinical or research settings.

Sleepers track informative speech in a multitalker environment.

Abstract: Sleep is a vital need, forcing us to spend a large portion of our life unable to interact with the external world. Current models interpret such extreme vulnerability as the price to pay for optimal learning. Sleep would limit external interferences on memory consolidation1-3 and allow neural systems to reset through synaptic downscaling4. Yet, the sleeping brain continues generating neural responses to external events5,6, revealing the preservation of cognitive processes ranging from the recognition of familiar stimuli to the formation of new memory representations7-15. Why would sleepers continue processing external events and yet remain unresponsive? Here we hypothesized that sleepers enter a 'standby mode' in which they continue tracking relevant signals, finely balancing the need to stay inward for memory consolidation with the ability to rapidly awake when necessary. Using electroencephalography to reconstruct competing streams in a multitalker environment16, we demonstrate that the sleeping brain amplifies meaningful speech compared to irrelevant signals. However, the amplification of relevant stimuli was transient and vanished during deep sleep. The effect of sleep depth could be traced back to specific oscillations, with K-complexes promoting relevant information in light sleep, whereas slow waves actively suppress relevant signals in deep sleep. Thus, the selection of relevant stimuli continues to operate during sleep but is strongly modulated by specific brain rhythms.

Slow-wave sleep and motor progression in Parkinson disease.

Abstract: Growing evidence from Alzheimer disease supports a potentially beneficial role of slow-wave sleep in neurodegeneration. However, the importance of slow-wave sleep in Parkinson disease is unknown. In 129 patients with Parkinson disease, we retrospectively tested whether sleep slow waves, objectively quantified with polysomnography, relate to longitudinal changes in Unified Parkinson's Disease Rating Scale motor scores. We found that higher accumulated power of sleep slow waves was associated with slower motor progression, particularly of axial motor symptoms, over a mean time of 4.6 ± 2.3 years. This preliminary finding suggests that deeper sleep relates to slower motor progression in Parkinson disease. Ann Neurol 2019;85:765-770.

Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model.

Abstract: The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. During slow oscillations, spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process. It is characterized by both a specific unsupervised enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This hierarchical organization of post-sleep internal representations favours higher performances in retrieval and classification tasks. The mechanism is based on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations. Indeed, when learned patterns are replayed during sleep, cortico-thalamo-cortical connections favour the activation of other neurons coding for similar thalamic inputs, promoting their association. Such mechanism hints at possible applications to artificial learning systems.

Brain Microdialysate Monoamines in Relation to Circadian Rhythms, Sleep, and Sleep Deprivation – a Systematic Review, Network Meta-analysis, and New Primary Data

Abstract: Disruption of the monoaminergic system, e.g. by sleep deprivation (SD), seems to promote certain diseases. Assessment of monoamine levels over the circadian cycle, during different sleep stages and during SD is instrumental to understand the molecular dynamics during and after SD. To provide a complete overview of all available evidence, we performed a systematic review. A comprehensive search was performed for microdialysis and certain monoamines (dopamine, serotonin, noradrenaline, adrenaline), certain monoamine metabolites (3,4-dihydroxyphenylacetic acid (DOPAC), 5-hydroxyindoleacetic acid (5-HIAA)) and a precursor (5-hydroxytryptophan (5-HTP)) in PubMed and EMBASE. After screening of the search results by two independent reviewers, 94 publications were included. All results were tabulated and described qualitatively. Network-meta analyses (NMAs) were performed to compare noradrenaline and serotonin concentrations between sleep stages. We further present experimental monoamine data from the medial prefrontal cortical (mPFC). Monoamine levels varied with brain region and circadian cycle. During sleep, monoamine levels generally decreased compared to wake. These qualitative observations were supported by the NMAs: noradrenaline and serotonin levels decreased from wakefulness to slow wave sleep and decreased further during Rapid Eye Movement sleep. In contrast, monoamine levels generally increased during SD, and sometimes remained high even during subsequent recovery. Decreases during or after SD were only reported for serotonin. In our experiment, SD did not affect any of the mPFC monoamine levels. Concluding, monoamine levels vary over the light-dark cycle and between sleep stages. SD modifies the patterns, with effects sometimes lasting beyond the SD period.

Sleep Physiology, Circadian Rhythms, Waking Performance and the Development of Sleep-Wake Therapeutics.

Abstract: Disturbances of the sleep-wake cycle are highly prevalent and diverse. The aetiology of some sleep disorders, such as circadian rhythm sleep-wake disorders, is understood at the conceptual level of the circadian and homeostatic regulation of sleep and in part at a mechanistic level. Other disorders such as insomnia are more difficult to relate to sleep regulatory mechanisms or sleep physiology. To further our understanding of sleep-wake disorders and the potential of novel therapeutics, we discuss recent findings on the neurobiology of sleep regulation and circadian rhythmicity and its relation with the subjective experience of sleep and the quality of wakefulness. Sleep continuity and to some extent REM sleep emerge as determinants of subjective sleep quality and waking performance. The effects of insufficient sleep primarily concern subjective and objective sleepiness as well as vigilant attention, whereas performance on higher cognitive functions appears to be better preserved albeit at the cost of increased effort. We discuss age-related, sex and other trait-like differences in sleep physiology and sleep need and compare the effects of existing pharmacological and non-pharmacological sleep- and wake-promoting treatments. Successful non-pharmacological approaches such as sleep restriction for insomnia and light and melatonin treatment for circadian rhythm sleep disorders target processes such as sleep homeostasis or circadian rhythmicity. Most pharmacological treatments of sleep disorders target specific signalling pathways with no well-established role in either sleep homeostasis or circadian rhythmicity. Pharmacological sleep therapeutics induce changes in sleep structure and the sleep EEG which are specific to the mechanism of action of the drug. Sleep- and wake-promoting therapeutics often induce residual effects on waking performance and sleep, respectively. The need for novel therapeutic approaches continues not at least because of the societal demand to sleep and be awake out of synchrony with the natural light-dark cycle, the high prevalence of sleep-wake disturbances in mental health disorders and in neurodegeneration. Novel approaches, which will provide a more comprehensive description of sleep and allow for large-scale sleep and circadian physiology studies in the home environment, hold promise for continued improvement of therapeutics for disturbances of sleep, circadian rhythms and waking performance.

Cholinergic modulation of hippocampal calcium activity across the sleep-wake cycle.

Abstract: Calcium is a critical second messenger in neurons that contributes to learning and memory, but how the coordination of action potentials of neuronal ensembles with the hippocampal local field potential (LFP) is reflected in dynamic calcium activity remains unclear. Here, we recorded hippocampal calcium activity with endoscopic imaging of the genetically encoded fluorophore GCaMP6 with concomitant LFP in freely behaving mice. Dynamic calcium activity was greater in exploratory behavior and REM sleep than in quiet wakefulness and slow wave sleep, behavioral states that differ with respect to theta and septal cholinergic activity, and modulated at sharp wave ripples (SWRs). Chemogenetic activation of septal cholinergic neurons expressing the excitatory hM3Dq DREADD increased calcium activity and reduced SWRs. Furthermore, inhibition of muscarinic acetylcholine receptors (mAChRs) reduced calcium activity while increasing SWRs. These results demonstrate that hippocampal dynamic calcium activity depends on behavioral and theta state as well as endogenous mAChR activation.

Relationships between resting heart rate, heart rate variability and sleep characteristics among female collegiate cross-country athletes.

Abstract: Even though sleep has been shown to be influenced by athletes’ training status, the association with resting heart rate and heart rate variability remains unclear. The purpose of this study was to compare the changes in and relationships between resting heart rate, heart rate variability and sleep characteristics across a female collegiate cross-country season. Ten NCAA Division I collegiate female cross-country athletes (mean ± SD; age, 19 ± 1 year; height, 167.6 ± 7.6 cm; body mass, 57.7 ± 10.2 kg; VO2max, 53.3 ± 5.9 ml kg-1 min-1) participated in this study. Resting heart rate, heart rate variability and the percentage of time in slow wave sleep were captured using a wrist-worn multisensor sleep device throughout the 2016 competitive cross-country season (12 weeks). Linear mixed-effects models and magnitude-based inferences were used to assess differences between each week. Pearson product moment correlations were used to investigate relationships between variables. Resting heart rate at the end of the season, specifically during weeks 10–12 (mean ± SE; week 10, 48 ± 2; week 11, 48 ± 3; week 12, 48 ± 3), showed a practically meaningful increase compared to the beginning of the season, weeks 2–4 (week 2, 44 ± 2; week 3, 45 ± 2; week 4, 44 ± 2). Higher resting heart rate (r = 0.55) and lower heart rate variability (r = -0.62) were largely associated with an increase in percentage of time spent in slow wave sleep. These data suggest that when physiological state was impaired, meaning the physiological restorative demand was higher, the percentage of time in slow wave sleep was increased to ensure recovery. Thus, it is important to implement sleep hygiene strategies to promote adequate slow wave sleep when the body needs physiological restoration.

A Multi-Class Automatic Sleep Staging Method Based on Long Short-Term Memory Network Using Single-Lead Electrocardiogram Signals

Abstract: To overcome the disadvantage of clinical manual sleep staging, a convenient, economical, and efficient multi-class automatic sleep staging method is proposed based on long short-term memory network (LSTM) using single-lead electrocardiogram signals. From electrocardiogram signals, heart rate variability and respiratory signals were calculated, and, then, totally 25 features were extracted. Four different classifiers, including the two-class classifier to distinguish between wake and sleep, the three-class classifier to distinguish wake, non-rapid eye movement sleep, and rapid eye movement, the four-class classifier to distinguish wake, light sleep, slow wave sleep, and rapid eye movement, and the five-class classifier to distinguish wake, sleep stage N1, sleep stage N2, sleep stage N3, and rapid eye movement, were constructed using the LSTM. The single-lead electrocardiogram data from 238 patients with full sleep stages during sleep were used for the training set and the data from other 60 patients were regarded as a validation set. The rest of 75 patients have left aside for testing set. The accuracy of two-class, three-class, four-class, and five-class sleep staging was 89.84%, 84.07%, 77.76%, and 71.16% and the Cohen’s kappa statistic $k$ was 0.52, 0.58, 0.55, and 0.52, respectively, which realized the moderate agreement with clinical analysis. When expanding the dataset to extra 1068 patients with missing sleep stages, the accuracy has no obvious reduction but the Cohen’s kappa statistic $k$ dropped to 0.51, 0.52, 0.48, and 0.43, respectively. The proposed method, in this paper, is promising for low-cost, efficient, and convenient sleep staging in home care monitoring.

Sleep in Huntington’s disease: a systematic review and meta-analysis of polysomongraphic findings

Abstract: Study objectives Disturbed overnight sleep is a prominent feature of advanced stage Huntington's disease (HD). Several polysomnography (PSG) studies have reported significant changes of sleep in HD patients, but the findings are not unequivocal. To date, no meta-analysis has investigated the PSG changes in HD patients. The present study meta-analyzed results from studies examining the PSG changes in HD patients compared with controls. Methods A literature search performed in MEDLINE, EMBASE, All EBM databases, PsycINFO, and CINAHL databases identified seven studies involving 152 HD patients and 144 controls which were included in our meta-analysis. Results Pooled results indicated decreased sleep efficiency, percentage of slow wave sleep and rapid eye movement sleep, and increased percentage of N1 sleep, wake time after sleep onset, and rapid eye movement sleep latency in HD patients compared with controls. We found high heterogeneity in the effect sizes and no indication of systematic publication biases across studies. Meta-regression analyses showed that some of the heterogeneity was explained by age, body mass index (BMI), CAG repeat length, and disease severity of HD patients. Conclusions Our study showed that polysomnographic abnormalities are present in HD. Our findings also underscore the need for a comprehensive PSG assessment of sleep changes in patients with HD. Furthermore, the effects of age, BMI and CAG repeat length on sleep changes should be carefully considered and closely monitored in the management of HD.

Acute Social Defeat Stress Increases Sleep in Mice

Abstract: Social conflict is a major source of stress in humans. Animals also experience social conflicts and cope with them by stress responses that facilitate arousal and activate sympathetic and neuroendocrine systems. The effect of acute social defeat (SoD) stress on the sleep/wake behavior of mice has been reported in several models based on a resident-intruder paradigm. However, the post-SoD stress sleep/wake effects vary between the studies and the contribution of specific effects in response to SoD or non-specific effects of the SoD procedure (e.g., sleep deprivation) is not well established. In this study, we established a mouse model of acute SoD stress based on strong aggressive mouse behavior toward unfamiliar intruders. In our model, we prevented severe attacks of resident mice on submissive intruder mice to minimize behavioral variations during SoD. In response to SoD, slow-wave sleep (SWS) strongly increased during 9 h. Although some sleep changes after SoD stress can be attributed to non-specific effects of the SoD procedure, most of the SWS increase is likely a specific response to SoD. Slow-wave activity was only enhanced for a short period after SoD and dissipated long before the SWS returned to baseline. Moreover, SoD evoked a strong corticosterone response that may indicate a high stress level in the intruder mice after SoD. Our SoD model may be useful for studying the mechanisms and functions of sleep in response to social stress.

Data-Driven Analysis of EEG Reveals Concomitant Superficial Sleep During Deep Sleep in Insomnia Disorder

Abstract: Study Objectives: The subjective suffering of people with Insomnia Disorder (ID) is insufficiently accounted for by traditional sleep classification, which presumes a strict sequential occurrence of global brain states. Recent studies challenged this presumption by showing concurrent sleep- and wake-type neuronal activity. We hypothesized enhanced co-occurrence of diverging EEG vigilance signatures during sleep in ID. Methods: Electroencephalography (EEG) in 55 cases with ID and 64 controls without sleep complaints was subjected to a Latent Dirichlet Allocation topic model describing each 30 s epoch as a mixture of six vigilance states called Topics (T), ranked from N3-related T1 and T2 to wakefulness-related T6. For each stable epoch we determined topic dominance (the probability of the most likely topic), topic co-occurrence (the probability of the remaining topics), and epoch-to-epoch transition probabilities. Results: In stable epochs where the N1-related T4 was dominant, T4 was more dominant in ID than in controls, and patients showed an almost doubled co-occurrence of T4 during epochs where the N3-related T1 was dominant. Furthermore, patients had a higher probability of switching from T1- to T4-dominated epochs, at the cost of switching to N3-related T2-dominated epochs, and a higher probability of switching from N2-related T3- to wakefulness-related T6-dominated epochs. Conclusion: Even during their deepest sleep, the EEG of people with ID express more N1-related vigilance signatures than good sleepers do. People with ID are moreover more likely to switch from deep to light sleep and from N2 sleep to wakefulness. The findings suggest that hyperarousal never rests in ID.