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Current Neurology and Neuroscience... Feb 2014Sleep benefits memory consolidation. Previous theoretical accounts have proposed a differential role of slow-wave sleep (SWS), rapid-eye-movement (REM) sleep, and stage... (Review)
Review
Sleep benefits memory consolidation. Previous theoretical accounts have proposed a differential role of slow-wave sleep (SWS), rapid-eye-movement (REM) sleep, and stage N2 sleep for different types of memories. For example the dual process hypothesis proposes that SWS is beneficial for declarative memories, whereas REM sleep is important for consolidation of non-declarative, procedural and emotional memories. In fact, numerous recent studies do provide further support for the crucial role of SWS (or non-REM sleep) in declarative memory consolidation. However, recent evidence for the benefit of REM sleep for non-declarative memories is rather scarce. In contrast, several recent studies have related consolidation of procedural memories (and some also emotional memories) to SWS (or non-REM sleep)-dependent consolidation processes. We will review this recent evidence, and propose future research questions to advance our understanding of the role of different sleep stages for memory consolidation.
Topics: Animals; Humans; Memory; Sleep Stages; Sleep, REM
PubMed: 24395522
DOI: 10.1007/s11910-013-0430-8 -
Neuropsychopharmacology : Official... Jan 2020The development of alcohol use disorder (AUD) involves binge or heavy drinking to high levels of intoxication that leads to compulsive intake, the loss of control in... (Review)
Review
The development of alcohol use disorder (AUD) involves binge or heavy drinking to high levels of intoxication that leads to compulsive intake, the loss of control in limiting intake, and a negative emotional state when alcohol is removed. This cascade of events occurs over an extended period within a three-stage cycle: binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation. These three heuristic stages map onto the dysregulation of functional domains of incentive salience/habits, negative emotional states, and executive function, mediated by the basal ganglia, extended amygdala, and frontal cortex, respectively. Sleep disturbances, alterations of sleep architecture, and the development of insomnia are ubiquitous in AUD and also map onto the three stages of the addiction cycle. During the binge/intoxication stage, alcohol intoxication leads to a faster sleep onset, but sleep quality is poor relative to nights when no alcohol is consumed. The reduction of sleep onset latency and increase in wakefulness later in the night may be related to the acute effects of alcohol on GABAergic systems that are associated with sleep regulation and the effects on brain incentive salience systems, such as dopamine. During the withdrawal/negative affect stage, there is a decrease in slow-wave sleep and some limited recovery in REM sleep when individuals with AUD stop drinking. Limited recovery of sleep disturbances is seen in AUD within the first 30 days of abstinence. The effects of withdrawal on sleep may be related to the loss of alcohol as a positive allosteric modulator of GABA receptors, a decrease in dopamine function, and the overactivation of stress neuromodulators, including hypocretin/orexin, norepinephrine, corticotropin-releasing factor, and cytokines. During the preoccupation/anticipation stage, individuals with AUD who are abstinent long-term present persistent sleep disturbances, including a longer latency to fall asleep, more time awake during the night, a decrease in slow-wave sleep, decreases in delta electroencephalogram power and evoked delta activity, and an increase in REM sleep. Glutamatergic system dysregulation that is observed in AUD is a likely substrate for some of these persistent sleep disturbances. Sleep pathology contributes to AUD pathology, and vice versa, possibly as a feed-forward drive to an unrecognized allostatic load that drives the addiction process.
Topics: Alcoholism; Allostasis; Animals; Behavior, Addictive; Brain Chemistry; Emotions; Ethanol; Humans; Sleep Stages; Sleep Wake Disorders
PubMed: 31234199
DOI: 10.1038/s41386-019-0446-0 -
Sensors (Basel, Switzerland) Aug 2022The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura...
The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00-08:00 h). Participants were fitted with all six wearable devices-and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen's kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen's kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address-in some way, we could consider that the whole world is becoming a sleep laboratory.
Topics: Adult; Heart Rate; Humans; Polysomnography; Sleep; Sleep Stages; Wearable Electronic Devices
PubMed: 36016077
DOI: 10.3390/s22166317 -
Sleep May 2021Consumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible...
STUDY OBJECTIVES
Consumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible use as alternatives to standard actigraphy. We, therefore, tested the performance of many of the latest consumer sleep-tracking devices, alongside actigraphy, versus the gold-standard sleep assessment technique, polysomnography (PSG).
METHODS
In total, 34 healthy young adults (22 women; 28.1 ± 3.9 years, mean ± SD) were tested on three consecutive nights (including a disrupted sleep condition) in a sleep laboratory with PSG, along with actigraphy (Philips Respironics Actiwatch 2) and a subset of consumer sleep-tracking devices. Altogether, four wearable (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3) and three nonwearable (EarlySense Live, ResMed S+, SleepScore Max) devices were tested. Sleep/wake summary and epoch-by-epoch agreement measures were compared with PSG.
RESULTS
Most devices (Fatigue Science Readiband, Fitbit Alta HR, EarlySense Live, ResMed S+, SleepScore Max) performed as well as or better than actigraphy on sleep/wake performance measures, while the Garmin devices performed worse. Overall, epoch-by-epoch sensitivity was high (all ≥0.93), specificity was low-to-medium (0.18-0.54), sleep stage comparisons were mixed, and devices tended to perform worse on nights with poorer/disrupted sleep.
CONCLUSIONS
Consumer sleep-tracking devices exhibited high performance in detecting sleep, and most performed equivalent to (or better than) actigraphy in detecting wake. Device sleep stage assessments were inconsistent. Findings indicate that many newer sleep-tracking devices demonstrate promising performance for tracking sleep and wake. Devices should be tested in different populations and settings to further examine their wider validity and utility.
Topics: Actigraphy; Adult; Female; Humans; Polysomnography; Reproducibility of Results; Sleep; Sleep Stages; Young Adult
PubMed: 33378539
DOI: 10.1093/sleep/zsaa291 -
Psychological Bulletin Mar 2020Targeted memory reactivation (TMR) is a methodology employed to manipulate memory processing during sleep. TMR studies have great potential to advance understanding of... (Meta-Analysis)
Meta-Analysis
Targeted memory reactivation (TMR) is a methodology employed to manipulate memory processing during sleep. TMR studies have great potential to advance understanding of sleep-based memory consolidation and corresponding neural mechanisms. Research making use of TMR has developed rapidly, with over 70 articles published in the last decade, yet no quantitative analysis exists to evaluate the overall effects. Here we present the first meta-analysis of sleep TMR, compiled from 91 experiments with 212 effect sizes (N = 2,004). Based on multilevel modeling, overall sleep TMR was highly effective (Hedges' g = 0.29, 95% CI [0.21, 0.38]), with a significant effect for two stages of non-rapid-eye-movement (NREM) sleep (Stage NREM 2: Hedges' g = 0.32, 95% CI [0.04, 0.60]; and slow-wave sleep: Hedges' g = 0.27, 95% CI [0.20, 0.35]). In contrast, TMR was not effective during REM sleep nor during wakefulness in the present analyses. Several analysis strategies were used to address the potential relevance of publication bias. Additional analyses showed that TMR improved memory across multiple domains, including declarative memory and skill acquisition. Given that TMR can reinforce many types of memory, it could be useful for various educational and clinical applications. Overall, the present meta-analysis provides substantial support for the notion that TMR can influence memory storage during NREM sleep, and that this method can be useful for understanding neurocognitive mechanisms of memory consolidation. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Topics: Humans; Memory Consolidation; Reinforcement, Psychology; Sleep Stages
PubMed: 32027149
DOI: 10.1037/bul0000223 -
JMIR MHealth and UHealth Nov 2023Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited...
BACKGROUND
Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography.
OBJECTIVE
This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment.
METHODS
The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography.
RESULTS
The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal.
CONCLUSIONS
Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.
Topics: Humans; Female; Male; Prospective Studies; Sleep; Polysomnography; Sleep Stages; Fitness Trackers
PubMed: 37917155
DOI: 10.2196/50983 -
Neurobiology of Learning and Memory Apr 2019Alternations of up and down can be seen across many different levels during sleep. Neural firing-rates, synaptic markers, molecular pathways, and gene expression all... (Review)
Review
Alternations of up and down can be seen across many different levels during sleep. Neural firing-rates, synaptic markers, molecular pathways, and gene expression all show differential up and down regulation across brain areas and sleep stages. And also the hallmarks of sleep - sleep stage specific oscillations - are characterized themselves by up and down as seen within the slow oscillation or theta cycles. In this review, we summarize the up and down of sleep covering molecules to electrophysiology and present different theories how this up and down could be regulated by the up and down of sleep oscillations. Further, we propose a tentative theory how this differential up and down could contribute to various outcomes of sleep related memory consolidation: enhancement of hippocampal representations of very novel memories and cortical consolidation of memories congruent with previous knowledge-networks.
Topics: Animals; Brain Waves; Gene Expression; Humans; Memory Consolidation; Signal Transduction; Sleep Stages
PubMed: 29544727
DOI: 10.1016/j.nlm.2018.03.013 -
Journal of Sleep Research Feb 2022Sleep stage scoring can lead to important inter-expert variability. Although likely, whether this issue is amplified in older populations, which show alterations of...
Sleep stage scoring can lead to important inter-expert variability. Although likely, whether this issue is amplified in older populations, which show alterations of sleep electrophysiology, has not been thoroughly assessed. Algorithms for automatic sleep stage scoring may appear ideal to eliminate inter-expert variability. Yet, variability between human experts and algorithm sleep stage scoring in healthy older individuals has not been investigated. Here, we aimed to compare stage scoring of older individuals and hypothesized that variability, whether between experts or considering the algorithm, would be higher than usually reported in the literature. Twenty cognitively normal and healthy late midlife individuals' (61 ± 5 years; 10 women) night-time sleep recordings were scored by two experts from different research centres and one algorithm. We computed agreements for the entire night (percentage and Cohen's κ) and each sleep stage. Whole-night pairwise agreements were relatively low and ranged from 67% to 78% (κ, 0.54-0.67). Sensitivity across pairs of scorers proved lowest for stages N1 (8.2%-63.4%) and N3 (44.8%-99.3%). Significant differences between experts and/or algorithm were found for total sleep time, sleep efficiency, time spent in N1/N2/N3 and wake after sleep onset (p ≤ 0.005), but not for sleep onset latency, rapid eye movement (REM) and slow-wave sleep (SWS) duration (N2 + N3). Our results confirm high inter-expert variability in healthy aging. Consensus appears good for REM and SWS, considered as a whole. It seems more difficult for N3, potentially because human raters adapt their interpretation according to overall changes in sleep characteristics. Although the algorithm does not substantially reduce variability, it would favour time-efficient standardization.
Topics: Aged; Electroencephalography; Female; Humans; Polysomnography; Reproducibility of Results; Sleep; Sleep Stages
PubMed: 34169604
DOI: 10.1111/jsr.13424 -
Sensors (Basel, Switzerland) Mar 2023Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be...
Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simultaneous identification of a sleep stage and a sleep disorder. This paper investigates the automatic recognition of sleep stages and disorders from multimodal sensory data (EEG, ECG, and EMG). We propose a new distributed multimodal and multilabel decision-making system (MML-DMS). It comprises several interconnected classifier modules, including deep convolutional neural networks (CNNs) and shallow perceptron neural networks (NNs). Each module works with a different data modality and data label. The flow of information between the MML-DMS modules provides the final identification of the sleep stage and sleep disorder. We show that the fused multilabel and multimodal method improves the diagnostic performance compared to single-label and single-modality approaches. We tested the proposed MML-DMS on the PhysioNet CAP Sleep Database, with VGG16 CNN structures, achieving an average classification accuracy of 94.34% and F1 score of 0.92 for sleep stage detection (six stages) and an average classification accuracy of 99.09% and F1 score of 0.99 for sleep disorder detection (eight disorders). A comparison with related studies indicates that the proposed approach significantly improves upon the existing state-of-the-art approaches.
Topics: Humans; Deep Learning; Electroencephalography; Sleep; Sleep Stages; Sleep Wake Disorders
PubMed: 37050528
DOI: 10.3390/s23073468 -
Frontiers of Neurology and Neuroscience 2021Since its description in the 19th century, narcolepsy type 1 (NT1) has been considered as a model sleep disorder, and after the discovery of rapid eye movement (REM)... (Review)
Review
Since its description in the 19th century, narcolepsy type 1 (NT1) has been considered as a model sleep disorder, and after the discovery of rapid eye movement (REM) sleep onset in the disorder, a gateway to understanding REM sleep. The discovery that NT1 is caused by hypocretin/orexin deficiency, together with neurochemical studies of this system, has helped to establish how this neuropeptide regulates the organization of sleep and wake in humans. Current analyses suggest that the main functions of the hypocretin/orexin system are (1) maintenance of wakefulness in the face of moderate sleep deprivation; (2) passive wake promotion, especially in the evening, driven by the circadian clock; (3) inhibition of REM sleep, with possible differential modulating effects on various subcomponents of the sleep-stage, explaining REM sleep dissociation events in NT1. Narcolepsy is also associated with an inability to consolidate sleep, a more complex phenotype that may result from secondary changes or be central to the role of hypocretin in coordinating the activity of other sleep- and wake-promoting systems. Novel technologies, such as the use of deep learning analysis of electroencephalographic signals, is revealing a complex pattern of sleep abnormalities in human narcolepsy that can be used diagnostically. The availability of novel devices measuring sleep 24 h per day also holds promise to provide new insights into how brain electrical activity and muscle tone are regulated by hypocretin.
Topics: Humans; Narcolepsy; Orexins; Sleep Stages
PubMed: 34052809
DOI: 10.1159/000514959