Showing papers on "Slow-wave sleep published in 2022"
TL;DR: How animal experiments aimed at exploring the oscillators driving the circadian sleep–wake rhythm led to the recognition of gradients of sleep states within the daily sleep period provided the basis for the first version of the two‐process model.
Abstract: The two‐process model serves as a major conceptual framework in sleep science. Although dating back more than four decades, it has not lost its relevance for research today. Retracing its origins, I describe how animal experiments aimed at exploring the oscillators driving the circadian sleep–wake rhythm led to the recognition of gradients of sleep states within the daily sleep period. Advances in signal analysis revealed that the level of slow‐wave activity in non‐rapid eye movement sleep electroencephalogram is high at the beginning of the 12‐light period and then declines. After sleep deprivation, the level of slow‐wave activity is enhanced. By scheduling recovery sleep to the animal's activity period, the conflict between the sleep–wake‐dependent and the circadian influence resulted in a two‐stage recovery pattern. These experiments provided the basis for the first version of the two‐process model. Sleep deprivation experiments in humans showed that the decline of slow‐wave activity during sleep is exponential. The two‐process model posits that a sleep–wake‐dependent homeostatic process (Process S) interacts with a process controlled by the circadian pacemaker (Process C). At present, homeostatic and circadian facets of sleep regulation are being investigated at the synaptic level as well as in the transcriptome and proteome domains. The notion of sleep has been extended from a global phenomenon to local representations, while the master circadian pacemaker has been supplemented by multiple peripheral oscillators. The original interpretation that the emergence of sleep may be viewed as an escape from the rigid control imposed by the circadian pacemaker is still upheld.
26 citations
TL;DR: The first-night effect (FNE) affects the accuracy of polysomnography (PSG) findings as discussed by the authors , but the levels of FNE in different ages are unclear.
22 citations
TL;DR: A systematic review and meta-analysis of reported polysomnography differences between AD patients and healthy controls was conducted in this paper , which revealed significant reductions in total sleep time, sleep efficiency, and percentage of slow-wave sleep (SWS) and rapid eye movement (REM) sleep, and increases in sleep latency, wake time after sleep onset, number of awakenings, and REM latency in AD compared to controls.
Abstract: Polysomnography (PSG) studies of sleep changes in Alzheimer's disease (AD) have reported but not fully established the relationship between sleep disturbances and AD. To better detail this relationship, we conducted a systematic review and meta-analysis of reported PSG differences between AD patients and healthy controls. An electronic literature search was conducted in EMBASE, MEDLINE, All EBM databases, CINAHL, and PsycINFO inception to Mar 2021. Twenty-eight studies were identified for systematic review, 24 of which were used for meta-analysis. Meta-analyses revealed significant reductions in total sleep time, sleep efficiency, and percentage of slow-wave sleep (SWS) and rapid eye movement (REM) sleep, and increases in sleep latency, wake time after sleep onset, number of awakenings, and REM latency in AD compared to controls. Importantly, both decreased SWS and REM were significantly associated with the severity of cognitive impairment in AD patients. Alterations in electroencephalogram (EEG) frequency components and sleep spindles were also observed in AD, although the supporting evidence for these changes was limited. Sleep in AD is compromised with increased measures of wake and decreased TST, SWS, and REM sleep relative to controls. AD-related reductions in SWS and REM sleep correlate with the degree of cognitive impairment. Alterations in sleep EEG frequency components such as sleep spindles may be possible biomarkers with relevance for diagnosing AD although their sensitivity and specificity remain to be clearly delineated. AD-related sleep changes are potential targets for early therapeutic intervention aimed at improving sleep and slowing cognitive decline.
19 citations
TL;DR: In this paper , the authors present a fully remote, randomized, cross-over trial in healthy adults aged 62-78 years (clinicaltrials.gov: NCT03420677).
Abstract: Auditory stimulation has emerged as a promising tool to enhance non-invasively sleep slow waves, deep sleep brain oscillations that are tightly linked to sleep restoration and are diminished with age. While auditory stimulation showed a beneficial effect in lab-based studies, it remains unclear whether this stimulation approach could translate to real-life settings.We present a fully remote, randomized, cross-over trial in healthy adults aged 62-78 years (clinicaltrials.gov: NCT03420677). We assessed slow wave activity as the primary outcome and sleep architecture and daily functions, e.g., vigilance and mood as secondary outcomes, after a two-week mobile auditory slow wave stimulation period and a two-week Sham period, interleaved with a two-week washout period. Participants were randomized in terms of which intervention condition will take place first using a blocked design to guarantee balance. Participants and experimenters performing the assessments were blinded to the condition.Out of 33 enrolled and screened participants, we report data of 16 participants that received identical intervention. We demonstrate a robust and significant enhancement of slow wave activity on the group-level based on two different auditory stimulation approaches with minor effects on sleep architecture and daily functions. We further highlight the existence of pronounced inter- and intra-individual differences in the slow wave response to auditory stimulation and establish predictions thereof.While slow wave enhancement in healthy older adults is possible in fully remote settings, pronounced inter-individual differences in the response to auditory stimulation exist. Novel personalization solutions are needed to address these differences and our findings will guide future designs to effectively deliver auditory sleep stimulations using wearable technology.
10 citations
TL;DR: In this paper , the authors used longitudinal electro-encephalographic (EEG) recordings to define changes in sleep from weaning to young adulthood in an ASD mouse model and showed that Shank3∆C male mice sleep less overall throughout their lifespan, have increased rapid eye movement (REM) sleep early in life despite significantly reduced non-rapid eye movement sleep, and have abnormal responses to increased sleep pressure that emerge during a specific developmental period.
Abstract: Sleep problems are prevalent in autism spectrum disorder (ASD), can be observed before diagnosis, and are associated with increased restricted and repetitive behaviors. Therefore, sleep abnormalities may be a core feature of the disorder, but the developmental trajectory remains unknown. Animal models provide a unique opportunity to understand sleep ontogenesis in ASD. Previously we showed that adult mice with a truncation in the high-confidence ASD gene Shank3 (Shank3∆C ) recapitulate the clinical sleep phenotype. In this study we used longitudinal electro-encephalographic (EEG) recordings to define, for the first time, changes in sleep from weaning to young adulthood in an ASD mouse model. We show that Shank3∆C male mice sleep less overall throughout their lifespan, have increased rapid eye movement (REM) sleep early in life despite significantly reduced non-rapid eye movement (NREM) sleep, and have abnormal responses to increased sleep pressure that emerge during a specific developmental period. We demonstrate that the ability to fall asleep quickly in response to sleep loss develops normally between 24 and 30 days in mice. However, mutants are unable to reduce sleep latency after periods of prolonged waking and maintain the same response to sleep loss regardless of age. This phenomenon seems independent of homeostatic NREM sleep slow-wave dynamics. Overall, our study recapitulates both preclinical models and clinical studies showing that reduced sleep is consistently associated with ASD and suggests that problems falling asleep may reflect abnormal development of sleep and arousal mechanisms.
9 citations
TL;DR: Results show that sleep spindles are associated with memory consolidation only when N is followed by R sleep, that is in physiologically ordered N-R naps, thus providing support to the sequential hypothesis in humans.
Abstract: Sleep is known to benefit memory consolidation, but little is known about the contribution of sleep stages within the sleep cycle. The sequential hypothesis proposes that memories are first replayed during non-rapid-eye-movement (NREM or N) sleep and then integrated into existing networks during rapid-eye-movement (REM or R) sleep, two successive critical steps for memory consolidation. However, it lacks experimental evidence as N always precedes R sleep in physiological conditions. We tested this sequential hypothesis in patients with central hypersomnolence disorder, including patients with narcolepsy who present the unique, anti-physiological peculiarity of frequently falling asleep in R sleep before entering N sleep. Patients performed a visual perceptual learning task before and after daytime naps stopped after one sleep cycle, starting in N or R sleep and followed by the other stage (i.e. N-R vs. R-N sleep sequence). We compared over-nap changes in performance, reflecting memory consolidation, depending on the sleep sequence during the nap. Thirty-six patients who slept for a total of 67 naps were included in the analysis. Results show that sleep spindles are associated with memory consolidation only when N is followed by R sleep, that is in physiologically ordered N-R naps, thus providing support to the sequential hypothesis in humans. In addition, we found a negative effect of rapid-eye-movements in R sleep on perceptual consolidation, highlighting the complex role of sleep stages in the balance to remember and to forget.
7 citations
TL;DR: In this article , the first four non-rapid eye movement (NREM) periods of night sleep records of 251 healthy human subjects (4-69 years) were used to reveal the flattening of spectral slopes and decrease in several measures of the spectral intercepts during consecutive sleep cycles.
Abstract: Abstract Unfolding the overnight dynamics in human sleep features plays a pivotal role in understanding sleep regulation. Studies revealed the complex reorganization of the frequency composition of sleep electroencephalogram (EEG) during the course of sleep, however the scale-free and the oscillatory measures remained undistinguished and improperly characterized before. By focusing on the first four non-rapid eye movement (NREM) periods of night sleep records of 251 healthy human subjects (4–69 years), here we reveal the flattening of spectral slopes and decrease in several measures of the spectral intercepts during consecutive sleep cycles. Slopes and intercepts are significant predictors of slow wave activity (SWA), the gold standard measure of sleep intensity. The overnight increase in spectral peak sizes (amplitudes relative to scale-free spectra) in the broad sigma range is paralleled by a U-shaped time course of peak frequencies in frontopolar regions. Although, the set of spectral indices analyzed herein reproduce known age- and sex-effects, the interindividual variability in spectral slope steepness is lower as compared to the variability in SWA. Findings indicate that distinct scale-free and oscillatory measures of sleep EEG could provide composite measures of sleep dynamics with low redundancy, potentially affording new insights into sleep regulatory processes in future studies.
7 citations
TL;DR: In this paper , a nonlinear version of Granger causality was used to describe changes in directed network activity between the somatosensory cortex and rostral reticular thalamic nucleus (rRTN) and caudal reticular reticular nucleus (cRTN), the higher order posterior (PO)- and anterior-thalamic nuclei (ATN) as assessed in local field potential recordings acquired during passive wakefulness (PW), light slow-wave sleep (LSWS) in freely behaving rats.
Abstract: Introduction: The thalamus, a heterogeneous brain structure, is involved in the generation of sleep-related thalamo-cortical oscillations. Higher order nuclei might possess a distinct function compared with first-order nuclei in brain communication. Here it is investigated whether this distinction can also be found during the process of falling asleep and deepening of slow-wave sleep. Methods: A nonlinear version of Granger causality was used to describe changes in directed network activity between the somatosensory cortex and rostral reticular thalamic nucleus (rRTN) and caudal reticular thalamic nucleus (cRTN), the higher order posterior (PO)- and anterior-thalamic nuclei (ATN), and the first-order ventral posteromedial thalamic nucleus (VPM) as assessed in local field potential recordings acquired during passive wakefulness (PW), light slow-wave sleep (LSWS), and deep slow-wave sleep (DSWS) in freely behaving rats. Surrogate statistics was used to assess significance. Results: Decreases in cortico-thalamo-cortical couplings were found. In contrast, multiple increases in intrathalamic couplings were observed. In particular, the rRTN increased its inhibition on the ATN from PW to LSWS, and this was further strengthened from LSWS to DSWS. The cRTN increased its coupling to VPM and PO from PW to LSWS, but the coupling from cRTN to VPM weakened at the transition from LSWS to DSWS, while its coupling to PO strengthened. Furthermore, intra-RTN coupling from PW to LSWS was differently changed compared with the change from LSWS to DSWS. Discussion: It can be inferred that higher order (ATN and PO) and first-order nuclei (VPM) are differentially inhibited during DSWS, which might be relevant for a proper functioning of sleep-related processes. Impact statement The functionally heterogeneous thalamus is affected by the different sleep/wake states. Changes in directed functional coupling between the thalamus and cortex and between functional different thalamic nuclei during the process of falling asleep and deepening to slow-wave sleep were investigated. It was revealed that the rostral and caudal subparts of the reticular thalamic nucleus, constituting the major source of intrathalamic inhibition, decouple from each other and show different coupling profiles with other thalamic nuclei. Specifically, higher order nuclei were found to be more inhibited than first-order nuclei during deep slow-wave sleep. These differences might be relevant for a proper coordination of sleep-related processes such as housekeeping, forgetting of irrelevant information, and consolidation of episodic memory.
7 citations
TL;DR: The FNE is characterized by less efficient, more fragmented, shallower sleep that tends to affect especially certain brain regions, and the magnitude and specificity of these effects should be considered when designing sleep studies aiming to compare across night effects.
Abstract: Difficulty sleeping in a novel environment is a common phenomenon that is often described as the first night effect (FNE). Previous works have found FNE on sleep architecture and sleep power spectra parameters, especially during non-rapid eye movement (NREM) sleep. However, the impact of FNE on sleep parameters, including local differences in electroencephalographic (EEG) activity across nights, has not been systematically assessed. Here, we performed high-density EEG sleep recordings on 27 healthy individuals on two nights and examined differences in sleep architecture, NREM (stages 2 and 3) EEG power spectra, and NREM power topography across nights. We found higher wakefulness after sleep onset (WASO), reduced sleep efficiency, and less deep NREM sleep (stage 3), along with increased high-frequency NREM EEG power during the first night of sleep, corresponding to small to medium effect sizes (Cohen’s d ≤ 0.5). Furthermore, study individuals showed significantly lower slow-wave activity in right frontal/prefrontal regions as well as higher sigma and beta activities in medial and left frontal/prefrontal areas, yielding medium to large effect sizes (Cohen’s d ≥ 0.5). Altogether, these findings suggest the FNE is characterized by less efficient, more fragmented, shallower sleep that tends to affect especially certain brain regions. The magnitude and specificity of these effects should be considered when designing sleep studies aiming to compare across night effects.
6 citations
TL;DR: In this paper , the authors evaluated the benefits of applying an improved sleep detection and staging algorithm on minimally processed multi-sensor wearable data collected from older generation hardware, including accelerometer, infra-red photoplethysmography and temperature sensors.
Abstract: To evaluate the benefits of applying an improved sleep detection and staging algorithm on minimally processed multi-sensor wearable data collected from older generation hardware.58 healthy, East Asian adults aged 23-69 years (M = 37.10, SD = 13.03, 32 males), each underwent 3 nights of PSG at home, wearing 2nd Generation Oura Rings equipped with additional memory to store raw data from accelerometer, infra-red photoplethysmography and temperature sensors. 2-stage and 4-stage sleep classifications using a new machine-learning algorithm (Gen3) trained on a diverse and independent dataset were compared to the existing consumer algorithm (Gen2) for whole-night and epoch-by-epoch metrics.Gen 3 outperformed its predecessor with a mean (SD) accuracy of 92.6% (0.04), sensitivity of 94.9% (0.03), and specificity of 78.5% (0.11); corresponding to a 3%, 2.8% and 6.2% improvement from Gen2 across the three nights, with Cohen's d values >0.39, t values >2.69, and p values <0.01. Notably, Gen 3 showed robust performance comparable to PSG in its assessment of sleep latency, light sleep, rapid eye movement (REM), and wake after sleep onset (WASO) duration. Participants <40 years of age benefited more from the upgrade with less measurement bias for total sleep time (TST), WASO, light sleep and sleep efficiency compared to those ≥40 years. Males showed greater improvements on TST and REM sleep measurement bias compared to females, while females benefitted more for deep sleep measures compared to males.These results affirm the benefits of applying machine learning and a diverse training dataset to improve sleep measurement of a consumer wearable device. Importantly, collecting raw data with appropriate hardware allows for future advancements in algorithm development or sleep physiology to be retrospectively applied to enhance the value of longitudinal sleep studies.
6 citations
TL;DR: These findings provide mechanistic insight into the potential of auditory slow wave enhancement to modulate cardiovascular restorative conditions during sleep, and future studies need to investigate whether the potentially increased restorative capacity through slow wave enhancements translates into a more rested cardiovascular system on the subsequent day.
Abstract: Sleep modulation techniques to elucidate the functional role of sleep brain oscillations in brain and body functions have gained large interest. Slow waves, the hallmark feature of deep non-rapid eye movement sleep, do potentially drive restorative effects on brain and cardiovascular functions. Auditory stimulation to modulate slow waves is a promising tool, however, directly comparing different auditory stimulation approaches within a night and analyzing induced dynamic brain and cardiovascular effects are yet missing. Here, we tested various auditory stimulation approaches in a windowed, 10 s ON (stimulations) followed by 10 s OFF (no stimulations), within-night stimulation design and compared them to a SHAM control condition. We report the results of three studies and a total of 51 included stimulation nights. We found a large and global increase in slow wave activity (SWA) in the stimulation window compared to SHAM. Furthermore, slow wave dynamics were most pronouncedly increased at the start of the stimulation and declined across the stimulation window. Beyond the changes in brain oscillations, we observed, for some conditions, a significant increase in the mean interval between two heartbeats within a stimulation window, indicating a slowing of the heart rate, and increased heart rate variability derived parasympathetic activity. Those cardiovascular changes were positively correlated with the change in SWA and thus, our findings provide mechanistic insight into the potential of auditory slow wave enhancement to modulate cardiovascular restorative conditions during sleep. However, future studies need to investigate whether the potentially increased restorative capacity through slow wave enhancements translates into a more rested cardiovascular system on the subsequent day.
TL;DR: In this paper , the authors used a between-subject design in which healthy young adults underwent relaxation-based progressive muscle relaxation (PMR) training or listened to Mozart music (control) prior to a 90min nap opportunity.
Abstract: Sleep is critical for health, cognition, and restorative processes, and yet, many experience chronic sleep restriction. Sleep interventions have been designed to enhance overnight sleep quality and physiology. Components of these interventions, like relaxation‐based progressive muscle relaxation (PMR), have been studied in isolation and have shown direct effects on sleep architecture, including increasing time in restorative, slow‐wave sleep (SWS). These relaxation methods have been understudied in naps, which are effective fatigue countermeasures that reduce deleterious effects of chronic sleep restriction. We hypothesised that PMR should boost SWS in a nap, as compared to an active control. We used a between‐subject design in which healthy young adults underwent PMR training or listened to Mozart music (control) prior to a 90‐min nap opportunity. We assessed changes in the amount and lateralisation of SWS, as evidence suggests left hemispheric lateralisation may be a proxy for recuperative sleep needs, and changes to state‐dependent anxiety and fatigue before and after the nap to assess intervention success. We found PMR participants spent ~10 min more in SWS, equivalent to 125% more time, than the control group, and concomitantly, significantly less time in rapid eye movement sleep. PMR participants also had greater right lateralised slow‐wave activity and delta activity compared to the control suggesting a more well‐rested brain profile during sleep. Further, pre‐sleep anxiety levels predicted nap architecture in the intervention group, suggesting benefits may be impacted by anxiety. The feasibility and accessibility of PMR prior to a nap make this an interesting research avenue to pursue with strong translational application.
TL;DR: It is suggested that a high proportion of deep SWS, usually observed after sleep onset, is a necessary condition for Hspa1 upregulation during subsequent REMS, and can inform the understanding of the molecular mechanisms integrating SWS and REMS and key biological function(s) of sleep.
Abstract: The molecular mechanisms of sleep cycle integration at the beginning and the end of the inactive period are not clear. Sleep cycles with a predominance of deep slow-wave sleep (SWS) seem to be associated with accelerated protein synthesis in the brain. The inducible Hsp70 chaperone corrects protein conformational changes and has protective properties. This research explores (1) whether the Hspa1 gene encoding Hsp70 protein activates during the daily rapid-eye-movement sleep (REMS) maximum, and (2) whether a lower daily deep SWS maximum affects the Hspa1 expression level during the subsequent REMS. Combining polysomnography in male Wistar rats, RT-qPCR, and Western blotting, we reveal a three-fold Hspa1 upregulation in the nucleus reticularis pontis oralis, which regulates REMS. Hspa1 expression increases during the daily REMS maximum, 5–7 h after the natural peak of deep SWS. Using short-term selective REMS deprivation, we demonstrate that REMS rebound after deprivation exceeds the natural daily maximum, but it is not accompanied by Hspa1 upregulation. The results suggest that a high proportion of deep SWS, usually observed after sleep onset, is a necessary condition for Hspa1 upregulation during subsequent REMS. The data obtained can inform the understanding of the molecular mechanisms integrating SWS and REMS and key biological function(s) of sleep.
TL;DR: This article investigated whether the coupling of spindles and slow waves (SW) is associated with early amyloid-β (Aβ) brain burden, a hallmark of AD neuropathology, and cognitive change over 2 years in 100 healthy individuals in late-midlife (50-70 years; 68 women).
Abstract: Sleep alteration is a hallmark of ageing and emerges as a risk factor for Alzheimer's disease (AD). While the fine-tuned coalescence of sleep microstructure elements may influence age-related cognitive trajectories, its association with AD processes is not fully established. Here, we investigated whether the coupling of spindles and slow waves (SW) is associated with early amyloid-β (Aβ) brain burden, a hallmark of AD neuropathology, and cognitive change over 2 years in 100 healthy individuals in late-midlife (50-70 years; 68 women). We found that, in contrast to other sleep metrics, earlier occurrence of spindles on slow-depolarisation SW is associated with higher medial prefrontal cortex Aβ burden (p=0.014, r²β*=0.06) and is predictive of greater longitudinal memory decline in a large subsample (p=0.032, r²β*=0.07, N=66). These findings unravel early links between sleep, AD-related processes, and cognition and suggest that altered coupling of sleep microstructure elements, key to its mnesic function, contributes to poorer brain and cognitive trajectories in ageing.
TL;DR: In this paper , a 12-year-old drug-naïve and otherwise healthy child with a long-lasting history of sleepwalking was found to have high-density (hd)-EEG (256 vertex-referenced geodesic system) coupled with standard video-polysomnography (v-PSG).
TL;DR: An automated sleep stage classifier using modern machine learning methods with cardiorespiratory time series as input signals with a Cohen’s kappa score of 0.80 for distinguishing Wakefulness, NREM and REM is presented.
TL;DR: In this paper , the effect of pre-sleep psychosocial stress on sleep and arousal in a 90-min daytime nap, in 33 healthy female participants compared to an anticipated within-subject relaxation task, was examined.
Abstract: Abstract The anticipation of a future stressor can increase worry and cognitive arousal and has a detrimental effect on sleep. Similarly, experiencing a stressful event directly before sleep increases physiological and cognitive arousal and impairs subsequent sleep. However, the effects of post- vs. pre-sleep stress on sleep and their temporal dynamics have never been directly compared. Here, we examined the effect of an anticipated psychosocial stressor on sleep and arousal in a 90-min daytime nap, in 33 healthy female participants compared to an anticipated within-subject relaxation task. We compared the results to an additional group (n = 34) performing the same tasks directly before sleep. Anticipating stress after sleep reduced slow-wave activity/beta power ratio, slow-wave sleep, sleep spindles, and slow-wave parameters, in particular during late sleep, without a concomitant increase in physiological arousal. In contrast, pre-sleep psychosocial stress deteriorated the same parameters during early sleep with a concomitant increase in physiological arousal. Our results show that presleep cognitions directly affect sleep in temporal proximity to the stressor. While physiological arousal mediates the effects of presleep stress on early sleep, we suggest that effects during late sleep originate from a repeated reactivation of mental concepts associated with the stressful event during sleep.
TL;DR: In this paper , the cerebral cortex is spontaneously active during sleep, and it is unclear how this global cortical activity is spatiotemporally organized, and whether such activity not only reflects sleep states but also contributes to sleep state switching.
Abstract: The cerebral cortex is spontaneously active during sleep, yet it is unclear how this global cortical activity is spatiotemporally organized, and whether such activity not only reflects sleep states but also contributes to sleep state switching. Here we report that cortex-wide calcium imaging in mice revealed distinct sleep stage-dependent spatiotemporal patterns of global cortical activity, and modulation of such patterns could regulate sleep state switching. In particular, elevated activation in the occipital cortical regions (including the retrosplenial cortex and visual areas) became dominant during rapid-eye-movement (REM) sleep. Furthermore, such pontogeniculooccipital (PGO) wave-like activity was associated with transitions to REM sleep, and optogenetic inhibition of occipital activity strongly promoted deep sleep by suppressing the NREM-to-REM transition. Thus, whereas subcortical networks are critical for initiating and maintaining sleep and wakefulness states, distinct global cortical activity also plays an active role in controlling sleep states.
TL;DR: The results show that presleep cognitions directly affect sleep in temporal proximity to the stressor, and suggest that effects during late sleep originate from a repeated reactivation of mental concepts associated with the stressful event during sleep.
Abstract: Abstract The anticipation of a future stressor can increase worry and cognitive arousal and has a detrimental effect on sleep. Similarly, experiencing a stressful event directly before sleep increases physiological and cognitive arousal and impairs subsequent sleep. However, the effects of post- vs. pre-sleep stress on sleep and their temporal dynamics have never been directly compared. Here, we examined the effect of an anticipated psychosocial stressor on sleep and arousal in a 90-min daytime nap, in 33 healthy female participants compared to an anticipated within-subject relaxation task. We compared the results to an additional group (n = 34) performing the same tasks directly before sleep. Anticipating stress after sleep reduced slow-wave activity/beta power ratio, slow-wave sleep, sleep spindles, and slow-wave parameters, in particular during late sleep, without a concomitant increase in physiological arousal. In contrast, pre-sleep psychosocial stress deteriorated the same parameters during early sleep with a concomitant increase in physiological arousal. Our results show that presleep cognitions directly affect sleep in temporal proximity to the stressor. While physiological arousal mediates the effects of presleep stress on early sleep, we suggest that effects during late sleep originate from a repeated reactivation of mental concepts associated with the stressful event during sleep.
TL;DR: In this paper , the authors investigated sleep power topography in all traditional frequency bands, in all sleep stages and across sleep cycles using high-density EEG (HD-EEG) and found that patients with ADHD consistently displayed a widespread increase in low-frequency activity (between 3 and 10 Hz) during NREM sleep, but not during REM sleep and wake before sleep onset.
Abstract: Objective: Recent years saw an increasing interest towards sleep microstructure abnormalities in attention-deficit/hyperactivity disorder (ADHD). However, the existing literature on sleep electroencephalographic (EEG) power in ADHD is still controversial, often based on single electrode recordings, and mainly focused on slow wave activity (SWA) during NREM sleep. This study aimed to systematically investigate sleep power topography in all traditional frequency bands, in all sleep stages and across sleep cycles using high-density EEG (HD-EEG). Method: Thirty drug-naïve children with ADHD (10.5 ± 2.1 years, 21 male) and 23 typically developing (TD) control participants (mean age: 10.2 ± 1.6 years, 13 male) were included in the current analysis. Signal power topography was computed in classical frequency bands during sleep, contrasted between groups and sleep cycles, and correlated with measures of ADHD severity, cognitive functioning and estimated total sleep time. Results: Compared to TD subjects, patients with ADHD consistently displayed a widespread increase in low-frequency activity (between 3 and 10 Hz) during NREM sleep, but not during REM sleep and wake before sleep onset. Such a difference involved a wide centro-posterior cluster of channels in the upper SWA range, in Theta, and low-Alpha. Between-group difference was maximal in sleep stage N3 in the first sleep cycle, and positively correlated with average total sleep time. Conclusions: These results support the concept that children with ADHD, compared to TD peers, have a higher sleep pressure and altered sleep homeostasis, which possibly interfere with (and delay) cortical maturation.
TL;DR: It seems that spike-wave epilepsy per se promotes micro-arousals during NREM sleep, however, subjects with a higher number of micro-Arousals at the age of 5 months were characterized by a reduction of SWDs between 5 and 7 months of age.
Abstract: The current study was done in Wistar Albino Glaxo Rijswijk (WAG/Rij) rats, which are genetically prone to develop spontaneous spike-wave discharges (SWDs) and are widely used as a genetic model of absence epilepsy. Here, we examined functional links between sleep and spike-wave epilepsy in aging WAG/Rij rats using advanced techniques of EEG analysis. SWDs, periods of NREM sleep and micro-arousals were automatically detected in three-channel epidural EEG recorded in freely moving WAG/Rij rats consequently at the age 5, 7 and 9 months. We characterized the developmental profile of spike-wave epilepsy in drug-naïve WAG/Rij rats and defined three epi-phenotypes—severe, mild and minor epilepsy. Age-related changes of SWDs were associated with changes in NREM sleep. Several signs of NREM sleep fragmentation were defined in epileptic WAG/Rij rats. It seems that spike-wave epilepsy per se promotes micro-arousals during NREM sleep. However, subjects with a higher number of micro-arousals (and NREM sleep episodes) at the age of 5 months were characterized by a reduction of SWDs between 5 and 7 months of age.
TL;DR: In this article , the performance of a new generation of consumer activity trackers (Fitbit Charge 4TM: FBC) to measure sleep variables and sleep stage classifications in patients with chronic insomnia was compared to polysomnography (PSG) and a widely used actigraph (Actiwatch Spectrum Pro: AWS).
Abstract: Our research aims to assess the performance of a new generation of consumer activity trackers (Fitbit Charge 4TM: FBC) to measure sleep variables and sleep stage classifications in patients with chronic insomnia, compared to polysomnography (PSG) and a widely used actigraph (Actiwatch Spectrum Pro: AWS). We recruited 37 participants, all diagnosed with chronic insomnia disorder, for one night of sleep monitoring in a sleep laboratory using PSG, AWS, and FBC. Epoch-by-epoch analysis along with Bland–Altman plots was used to evaluate FBC and AWS against PSG for sleep-wake detection and sleep variables: total sleep time (TST), sleep efficiency (SE), waking after sleep onset (WASO), and sleep onset latency (SOL). FBC sleep stage classification of light sleep (LS), deep sleep (DS), and rapid eye movement (REM) was also compared to that of PSG. When compared with PSG, FBC notably underestimated DS (-41.4, p < 0.0001) and SE (-4.9%, p = 0.0016), while remarkably overestimating LS (37.7, p = 0.0012). However, the TST, WASO, and SOL assessed by FBC presented no significant difference from that assessed by PSG. Compared with PSG, AWS and FBC showed great accuracy (86.9% vs. 86.5%) and sensitivity (detecting sleep; 92.6% vs. 89.9%), but comparatively poor specificity (detecting wake; 35.7% vs. 62.2%). Both devices showed better accuracy in assessing sleep than wakefulness, with the same sensitivity but statistically different specificity. FBC supplied equivalent parameters estimation as AWS in detecting sleep variables except for SE. This research shows that FBC cannot replace PSG thoroughly in the quantification of sleep variables and classification of sleep stages in Chinese patients with chronic insomnia; however, the user-friendly and low-cost wearables do show some comparable functions. Whether FBC can serve as a substitute for actigraphy and PSG in patients with chronic insomnia needs further investigation.
TL;DR: In this paper , a simple cluster synchronization model was proposed to explain the emergence of slow wave sleep in healthy people without sleep disorders, which can unify brain wave dynamics and their corresponding physiologic changes.
Abstract: Spontaneous synchronization over large networks is ubiquitous in nature, ranging from inanimate to biological systems. In the human brain, neuronal synchronization and de-synchronization occur during sleep, with the greatest degree of neuronal synchronization during slow wave sleep (SWS). The current sleep classification schema is based on electroencephalography and provides common criteria for clinicians and researchers to describe stages of non-rapid eye movement (NREM) sleep as well as rapid eye movement (REM) sleep. These sleep stage classifications have been based on convenient heuristic criteria, with little consideration of the accompanying normal physiological changes across those same sleep stages. To begin to resolve those inconsistencies, first focusing only on NREM sleep, we propose a simple cluster synchronization model to explain the emergence of SWS in healthy people without sleep disorders. We apply the empirical mode decomposition (EMD) analysis to quantify slow wave activity in electroencephalograms, and provide quantitative evidence to support our model. Based on this synchronization model, NREM sleep can be classified as SWS and non-SWS, such that NREM sleep can be considered as an intrinsically bistable process. Finally, we develop an automated algorithm for SWS classification. We show that this new approach can unify brain wave dynamics and their corresponding physiologic changes.
TL;DR: Multiple features of sleep–wake state distribution and electrographic patterns associated with behavioral states in marmosets closely match human states, although marmoset have shorter sleep cycles, demonstrates that marmosETS represent an excellent model to study origin of human electrographical rhythms and brain states.
Abstract: Abstract Study Objectives We evaluated common marmosets as a perspective animal model to study human sleep and wake states. Methods Using wireless neurologger recordings, we performed longitudinal multichannel local field potential (LFP) cortical, hippocampal, neck muscle, and video recordings in three freely behaving marmosets. The brain states were formally identified using self-organizing maps. Results Marmosets were generally awake during the day with occasional 1–2 naps, and they slept during the night. Major electrographic patterns fall in five clearly distinguished categories: wakefulness, drowsiness, light and deep NREM sleep, and REM. Marmosets typically had 14–16 sleep cycles per night, with either gradually increasing or relatively low, but stable delta power within the cycle. Overall, the delta power decreased throughout the night sleep. Marmosets demonstrated prominent high amplitude somatosensory mu-rhythm (10–15 Hz), accompanied with neocortical ripples, and alternated with occipital alpha rhythm (10–15 Hz). NREM sleep was characterized with the presence of high amplitude slow waves, sleep spindles and ripples in neocortex, and sharp-wave-ripple complexes in CA1. Light and deep stages differed in levels of delta and sigma power and muscle tone. REM sleep was defined with low muscle tone and activated LFP with predominant beta-activity and rare spindle-like or mu-like events. Conclusions Multiple features of sleep–wake state distribution and electrographic patterns associated with behavioral states in marmosets closely match human states, although marmoset have shorter sleep cycles. This demonstrates that marmosets represent an excellent model to study origin of human electrographical rhythms and brain states.
TL;DR: Flatter slopes of aperiodic EEG power with increased variability may reflect unstable, noisy neural activity due to increased excitation-to-inhibition balance, representing a new disease-relevant feature of sleep in MDD.
Abstract: Background. In major depressive disorder (MDD), patients often express subjective sleep complaints while polysomnographic studies report only subtle alterations in neural oscillations. We hypothesize that the study of aperiodic electroencephalographic (EEG) dynamics, a marker of excitation-to-inhibition balance, may bring new insights into our understanding of sleep abnormalities in MDD. Specifically, we investigate aperiodic neural activity during sleep and its relationships with the time of sleep, depression severity, and responsivity to antidepressant treatment. Methods. Polysomnography was recorded in 38 MDD patients (in unmedicated and 7d medicated states) and 38 age-matched healthy controls (N1=76). Aperiodic EEG activity was evaluated using the Irregularly Resampled Auto-Spectral Analysis with slopes' means and intra-individual variability as outcome measures. Depression severity was assessed with the Hamilton Depression Rating Scale. We replicated the analysis using two independently collected datasets of medicated patients and controls (N2=60, N3=80). Results. Unmedicated patients showed flatter aperiodic slopes compared to controls during N2 (p-value=0.002) and steeper slopes compared to their later medicated state (p-values<0.02) during all sleep stages. Within unmedicated patients, slopes were flatter during late compared to early N2 sleep (p-value=0.006). Late N2 slopes further correlated with depression severity after 7d of antidepressant treatment (r=-0.34, p-value=0.04). Variability of slopes was increased in both unmedicated (p-values<0.03) and medicated states (p-values <0.006) of patients' N2, N3, and REM sleep compared to controls. Conclusion. Flatter slopes of aperiodic EEG power with increased variability may reflect unstable, noisy neural activity due to increased excitation-to-inhibition balance, representing a new disease-relevant feature of sleep in MDD.
TL;DR: The data indicate that sleep restriction does not cause elevated levels of circulating glucose during nighttime sleep when slow-wave sleep is maintained, and it will be important to determine whether increased insulin is required to maintain circulating glucose at a normal level when sleep is restricted.
Abstract: The aim of this laboratory-based study was to examine the effect of sleep restriction on glucose regulation during nighttime sleep. Healthy males were randomly assigned to one of two conditions: 9 h in bed (n = 23, age = 24.0 year) or 5 h in bed (n = 18, age = 21.9 year). Participants had a baseline night with 9 h in bed (23:00–08:00 h), then seven nights of 9 h (23:00–08:00 h) or 5 h (03:00–08:00 h) in bed. Participants were mostly seated during the daytime but had three bouts of treadmill walking (4 km·h−1 for 10 min) at ~14:40 h, ~17:40 h, and ~20:40 h each day. On the baseline night and night seven, glucose concentration in interstitial fluid was assessed by using continuous glucose monitors, and sleep was assessed by using polysomnography. On night seven, compared to the 9 h group, the 5 h group obtained less total sleep (292 min vs. 465 min) and less REM sleep (81 min vs. 118 min), but their slow-wave sleep did not differ (119 min vs. 120 min), and their glucose concentration during sleep did not differ (5.1 mmol·L−1 vs. 5.1 mmol·L−1). These data indicate that sleep restriction does not cause elevated levels of circulating glucose during nighttime sleep when slow-wave sleep is maintained. In the future, it will be important to determine whether increased insulin is required to maintain circulating glucose at a normal level when sleep is restricted.
30 Aug 2022
TL;DR: In this paper , the authors investigated the relationship between NREM sleep alterations and internalizing behaviors in children with ASD vs. typical developing controls (TD) and found evidence of this relationship in ASD.
Abstract: Insomnia and daytime behavioral problems are common issues in pediatric autism spectrum disorder (ASD), yet specific underlying relationships with NonRapid Eye Movement sleep (NREM) and Rapid Eye Movement (REM) sleep architecture are understudied. We hypothesize that REM sleep alterations (REM%, REM EEG power) are associated with more internalizing behaviors and NREM sleep deficits (N3%; slow wave activity (SWA) 0.5-3 Hz EEG power) are associated with increased externalizing behaviors in children with ASD vs. typical developing controls (TD).In an age- and gender-matched pediatric cohort of n = 23 ASD and n = 20 TD participants, we collected macro/micro sleep architecture with overnight home polysomnogram and daytime behavior scores with Child Behavior Checklist (CBCL) scores.Controlling for non-verbal IQ and medication use, ASD and TD children have similar REM and NREM sleep architecture. Only ASD children show positive relationships between REM%, REM theta power and REM beta power with internalizing scores. Only TD participants showed an inverse relationship between NREM SWA and externalizing scores.REM sleep measures reflect concerning internalizing behaviours in ASD and could serve as a biomarker for mood disorders in this population. While improving deep sleep may help externalizing behaviours in TD, we do not find evidence of this relationship in ASD.
TL;DR: In this paper , the authors investigated aperiodic neural activity during sleep and its relationship with the sleep architecture, depression severity, and responsivity to antidepressant treatment in major depressive disorder (MDD).
Abstract: In major depressive disorder (MDD), patients often express subjective sleep complaints while polysomnographic studies report only subtle alterations of the electroencephalographic (EEG) signal. We hypothesize that differentiating the signal into its oscillatory and aperiodic components may bring new insights into our understanding of sleep abnormalities in MDD. Specifically, we investigate aperiodic neural activity during sleep and its relationships with the sleep architecture, depression severity, and responsivity to antidepressant treatment. Polysomnography was recorded in 38 MDD patients (in unmedicated and 7-day medicated states) and 38 age-matched healthy controls (n=76). Aperiodic power component was calculated using the Irregularly Resampled Auto-Spectral Analysis. Depression severity was assessed with the Hamilton Depression Rating Scale. We replicated the analysis using two independently collected datasets of medicated patients and controls (n=60 and n=80). Unmedicated patients showed flatter aperiodic slopes compared to controls during non-REM 2 sleep (p=0.009). Medicated patients showed flatter aperiodic slopes compared to their later medicated state (p-values<0.001) and controls during all sleep stages (p-values<0.03). In medicated patients, flatter aperiodic slopes during non-REM sleep were linked to the higher proportion of non-REM 1, lower proportion of REM, delayed onset of non-REM-3 and REM, and shorter total sleep time. Flatter slopes of aperiodic EEG power may reflect noisier neural activity due to increased excitation-to-inhibition balance, representing a new disease-relevant feature of sleep in MDD.
TL;DR: Functional gradients in cortical activity during REM sleep are mapped using mesoscale imaging in mice and local SW patterns occurring mainly in somatomotor and auditory cortical regions are observed, with minimum presence within the default mode network.
Abstract: Sleep consists of two basic stages: non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. NREM sleep is characterized by slow high-amplitude cortical EEG signals, while REM sleep is characterized desynchronized cortical rhythms. While, until recently, it has been widely believed that cortical activity during REM sleep is globally desynchronized, recent electrophysiological studies showed slow waves (SW) in some cortical areas during REM sleep. Electrophysiological techniques, however, have been unable to resolve the regional structure of these activities, due to relatively sparse sampling. We mapped functional gradients in cortical activity during REM sleep using mesoscale imaging in mice, and observed local SW patterns occurring mainly in somatomotor and auditory cortical regions, with minimum presence within the default mode network. The role of the cholinergic system in local desynchronization during REM sleep was also explored by calcium imaging of cholinergic terminal activity within the mouse cortex. Terminal activity was weaker in regions exhibiting SW activity more frequently during REM sleep. We also analyzed Allen Mouse Brain Connectivity dataset and found that these regions have weaker cholinergic projections from the basal forebrain.
TL;DR: Both aged and AD mice show SWS enhancement following CNO injection, characterized by a shorter SWS latency, increased SWS amount and consolidation, and enhanced slow wave activity, compared with vehicle injection.
Abstract: Abstract Aging and Alzheimer’s disease (AD) are both associated with reduced quantity and quality of the deepest stage of sleep, called slow-wave-sleep (SWS). Slow-wave-sleep deficits have been shown to worsen AD symptoms and prevent healthy aging. However, the mechanism remains poorly understood due to the lack of animal models in which SWS can be specifically manipulated. Notably, a mouse model of SWS enhancement has been recently developed in adult mice. As a prelude to studies assessing the impact of SWS enhancement on aging and neurodegeneration, we first asked whether SWS can be enhanced in animal models of aging and AD. The chemogenetic receptor hM3Dq was conditionally expressed in GABAergic neurons of the parafacial zone of aged mice and AD (APP/PS1) mouse model. Sleep–wake phenotypes were analyzed in baseline condition and following clozapine-N-oxide (CNO) and vehicle injections. Both aged and AD mice display deficits in sleep quality, characterized by decreased slow wave activity. Both aged and AD mice show SWS enhancement following CNO injection, characterized by a shorter SWS latency, increased SWS amount and consolidation, and enhanced slow wave activity, compared with vehicle injection. Importantly, the SWS enhancement phenotypes in aged and APP/PS1 model mice are comparable to those seen in adult and littermate wild-type mice, respectively. These mouse models will allow investigation of the role of SWS in aging and AD, using, for the first time, gain-of SWS experiments.