Why REM sleep? Clues beyond the laboratory in a more challenging world

Insights from the 2nd China intelligent sleep staging competition

STUDY OBJECTIVES

Artificial intelligence (AI) is quickly advancing in the field of sleep medicine, which bodes well for the potential of actual clinical use. In this study, an analysis of the 2nd China Intelligent Sleep Staging Competition was conducted to gain insights into the general level and constraints of AI-assisted sleep staging in China.

METHODS

The outcomes of 10 teams from the children's track and 13 teams from the adult track were investigated in this study. The analysis included overall performance, differences between five different sleep stages, variations across subjects, and performance during stage transitions.

RESULTS

The adult track's accuracy peaked at 80.46%, while the children's track's accuracy peaked at 88.96%. On average, accuracy rates stood at 71.43% for children and 68.40% for adults. All results were produced within a mere 5-min timeframe. The N1 stage was prone to misclassification as W, N2, and R stages. In the adult track, significant differences were apparent among subjects (p < 0.05), whereas in the children's track, such differences were not observed. Nonetheless, both tracks experienced a performance decline during stage transitions.

CONCLUSIONS

The computational speed of AI is remarkably fast, simultaneously holding the potential to surpass the accuracy of physicians. Improving the machine learning model's classification of the N1 stage and transitional periods between stages, along with bolstering its robustness to individual subject variations, is imperative for maximizing its ability in assisting clinical scoring.

The unified theory of sleep: Eukaryotes endosymbiotic relationship with mitochondria and REM the push-back response for awakening

The Unified Theory suggests that sleep is a process that developed in eukaryotic animals from a relationship with an endosymbiotic bacterium. Over evolutionary time the bacterium evolved into the modern mitochondrion that continues to exert an effect on sleep patterns, e.g. the bacterium Wolbachia establishes an endosymbiotic relationship with Drosophila and many other species of insects and is able to change the host's behaviour by making it sleep. The hypothesis is supported by other host-parasite relationships, e.g., Trypanosoma brucei which causes day-time sleepiness and night-time insomnia in humans and cattle. For eukaryotes such as Monocercomonoids that don't contain mitochondria we find no evidence of them sleeping. Mitochondria produce the neurotransmitter gamma aminobutyric acid (GABA), and ornithine a precursor of the neurotransmitter GABA, together with substances such as 3,4dihydroxy phenylalanine (DOPA) a precursor for the neurotransmitter dopamine: These substances have been shown to affect the sleep/wake cycles in animals such as Drosophilia and Hydra. Eukaryote animals have traded the very positive side of having mitochondria providing aerobic respiration for them with the negative side of having to sleep. NREM (Quiet sleep) is the process endosymbionts have imposed upon their host eukaryotes and REM (Active sleep) is the push-back adaptation of eukaryotes with brains, returning to wakefulness.

The Ponto-Geniculo-Occipital (PGO) Waves in Dreaming: An Overview

Rapid eye movement (REM) sleep is the main sleep correlate of dreaming. Ponto-geniculo-occipital (PGO) waves are a signature of REM sleep. They represent the physiological mechanism of REM sleep that specifically limits the processing of external information. PGO waves look just like a message sent from the pons to the lateral geniculate nucleus of the visual thalamus, the occipital cortex, and other areas of the brain. The dedicated visual pathway of PGO waves can be interpreted by the brain as visual information, leading to the visual hallucinosis of dreams. PGO waves are considered to be both a reflection of REM sleep brain activity and causal to dreams due to their stimulation of the cortex. In this review, we summarize the role of PGO waves in potential neural circuits of two major theories, i.e., (1) dreams are generated by the activation of neural activity in the brainstem; (2) PGO waves signaling to the cortex. In addition, the potential physiological functions during REM sleep dreams, such as memory consolidation, unlearning, and brain development and plasticity and mood regulation, are discussed. It is hoped that our review will support and encourage research into the phenomenon of human PGO waves and their possible functions in dreaming.

Discriminating rapid eye movement sleep from wakefulness by analyzing high frequencies from single-channel EEG recordings in mice

Rapid eye movement sleep (REMS) is characterized by the appearance of fast, desynchronized rhythms in the cortical electroencephalogram (EEG), similar to wakefulness. The low electromyogram (EMG) amplitude during REMS distinguishes it from wakefulness; therefore, recording EMG signal seems to be imperative for discriminating between the two states. The present study evaluated the high frequency components of the EEG signal from mice (80-500 Hz) to support REMS detection during sleep scoring without an EMG signal and found a strong positive correlation between waking and the average power of 80-120 Hz, 120-200 Hz, 200-350 Hz and 350-500 Hz. A highly negative correlation was observed with REMS. Furthermore, our machine learning approach demonstrated that simple EEG time-series features are enough to discriminate REMS from wakefulness with sensitivity of roughly 98 percent and specificity of around 92 percent. Interestingly, assessing only the higher frequency bands (200-350 Hz as well as 350-500 Hz) gives significantly greater predictive power than assessing only the lower end of the EEG frequency spectrum. This paper proposes an approach that can detect subtle changes in REMS reliably, and future unsupervised sleep-scoring approaches could greatly benefit from it.

The Effect of Night Float Rotation on Resident Sleep, Activity, and Well-Being

BACKGROUND

Night float call systems are becoming increasingly common at training programs with the goal of reducing fatigue related to sleep deprivation and sleep disturbance. Previous studies have shown that trainees obtain less sleep during the night float rotation and have decreased sleep efficiency for several days after the rotation. The impact on physical and emotional well-being has not been documented.

METHODS

Twenty-seven anesthesia residents were enrolled in a study using wearable sleep and activity trackers and National Institutes of Health Patient-Reported Outcome Measurement Information System (NIH PROMIS) surveys for sleep disturbance, fatigue, and positive affect to record data the week before ("baseline"), during ("night float"), and 1 week after ("recovery") their night float rotation. Each subject's data during the night float week and recovery week were compared to his or her own baseline week data using a paired, nonparametric analysis. The primary outcome variable was the change in average daily sleep hours during the night float week compared to the baseline week. Average daily rapid eye movement (REM) sleep, daily steps, and NIH PROMIS scores comparing night float and recovery weeks to baseline week were prespecified secondary outcomes. NIH PROMIS scores range from 0 to 100 with 50 as the national mean and more of the construct having a higher score.

RESULTS

There was no difference in average daily sleep hours between the night float and the baseline weeks (6.7 [5.9-7.8] vs 6.7 [5.5-7.7] hours, median [interquartile range]; P = .20). Residents had less REM sleep during the night float compared to the baseline weeks (1.1 [0.7-1.5] vs 1.4 [1.1-1.9] hours, P = .002). NIH PROMIS fatigue scores were higher during the night float than the baseline week (58.8 [54.6-65.1] vs 48.6 [46.0-55.1], P = .0004) and did not return to baseline during the recovery week (51.0 [48.6-58.8], P = .029 compared to baseline). Sleep disturbance was not different among the weeks. Positive affect was reduced after night float compared to baseline (39.6 [35.0-43.5] vs 44.8 [40.1-49.6], P = .0009), but returned to baseline during the recovery week (43.6 [39.6-48.2], P = .38).

CONCLUSIONS

The residents slept the same number of total hours during their night float week but had less REM sleep, were more fatigued, and had less positive affect. All of these resolved to baseline except fatigue, that was still greater than the baseline week. This methodology appears to robustly capture psychophysiological data that might be useful for quality initiatives.

Functional roles of REM sleep

Rapid eye movement (REM) sleep is an enigmatic and intriguing sleep state. REM sleep differs from non-REM sleep by its characteristic brain activity and from wakefulness by a reduced anti-gravity muscle tone. In addition to these key traits, diverse physiological phenomena appear across the whole body during REM sleep. However, it remains unclear whether these phenomena are the causes or the consequences of REM sleep. Experimental approaches using humans and animal models have gradually revealed the functional roles of REM sleep. Extensive efforts have been made to interpret the characteristic brain activity in the context of memory functions. Numerous physical and psychological functions of REM sleep have also been proposed. Moreover, REM sleep has been implicated in aspects of brain development. Here, we review the variety of functional roles of REM sleep, mainly as revealed by animal models. In addition, we discuss controversies regarding the functional roles of REM sleep.

A novel approach to automatic sleep stage classification using forehead electrophysiological signals

Background

Sleep stage scoring is very important for the effective diagnosis and intervention of sleep disorders. However, the current automatic sleep staging methods generally have the problems of poor model generalization ability and non-portable acquisition equipment.

Method

In this paper, we propose a novel automatic sleep scoring system based on forehead electrophysiological signals that is more effective and convenient than other systems. We extract 3 channel signals from the forehead, named forehead electroencephalogram 1 (Fh1), forehead electroencephalogram 2 (Fh2), and forehead center electrooculogram (Fhz). Spectral features, statistical features, and entropy features are extracted using the discrete wavelet transform (DWT) method. Light gradient boosting machine (LGB), random forest (RF), and support vector machine (SVM) are employed to classify four stages: awake, light sleep (LS), deep sleep (DS), and rapid eye movement (REM).

Result

The performance of the proposed method is validated using databases of the Sleep-EDFX and our Own-data, which include polysomnograms (PSGs) and forehead signals of 28 subjects. The overall classification accuracy of using the combination of Fh1, Fh2, and Fhz can reach up to 90.25% accuracy with a kappa coefficient of 0.857.

Conclusions

The proposed method could provide state-of-art multichannel sleep stage scoring performance with higher portability. This will facilitate the application of long-term monitoring of sleep quality in the future.

Is paradoxical sleep setting up innate and acquired complex sensorimotor and adaptive behaviours?: A proposed function based on literature review

We summarize here the progress in identifying the neuronal network as well as the function of paradoxical sleep and the gaps of knowledge that should be filled in priority. The core system generating paradoxical sleep localized in the brainstem is now well identified, and the next step is to clarify the role of the forebrain in particular that of the hypothalamus including the melanin-concentrating hormone neurons and of the basolateral amygdala. We discuss these two options, and also the discovery that cortical activation during paradoxical sleep is restricted to a few limbic cortices activated by the lateral supramammillary nucleus and the claustrum. Such activation nicely supports the findings recently obtained showing that neuronal reactivation occurs during paradoxical sleep in these structures, and induces both memory consolidation of important memory and forgetting of less relevant ones. The question that still remains to be answered is whether paradoxical sleep is playing more crucial roles in processing emotional and procedural than other types of memories. One attractive hypothesis is that paradoxical sleep is responsible for erasing negative emotional memories, and that this function is not properly functioning in depressed patients. On the other hand, the presence of a muscle atonia during paradoxical sleep is in favour of a role in procedural memory as new types of motor behaviours can be tried without harm during the state. In a way, it also fits with the proposed role of paradoxical sleep in setting up the sensorimotor system during development.

The subthalamic corticotropin-releasing hormone neurons mediate adaptive REM-sleep responses to threat

When an animal faces a threatening situation while asleep, rapid arousal is the essential prerequisite for an adequate response. Here, we find that predator stimuli induce immediate arousal from REM sleep compared with NREM sleep. Using in vivo neural activity recording and cell-type-specific manipulations, we identify neurons in the medial subthalamic nucleus (mSTN) expressing corticotropin-releasing hormone (CRH) that mediate arousal and defensive responses to acute predator threats received through multiple sensory modalities across REM sleep and wakefulness. We observe involvement of the same neurons in the normal regulation of REM sleep and the adaptive increase in REM sleep induced by sustained predator stress. Projections to the lateral globus pallidus (LGP) are the effector pathway for the threat-coping responses and REM-sleep expression. Together, our findings suggest adaptive REM-sleep responses could be protective against threats and uncover a critical component of the neural circuitry at their basis.

REM sleep-dependent short-term and long-term hourglass processes in the ultradian organization and recovery of REM sleep in the rat

STUDY OBJECTIVES

To evaluate the contribution of long-term and short-term REM sleep homeostatic processes to REM sleep recovery and the ultradian organization of the sleep wake cycle.

METHODS

Fifteen rats were sleep recorded under a 12:12 LD cycle. Animals were subjected during the rest phase to two protocols (2T2I or 2R2I) performed separately in non-consecutive experimental days. 2T2I consisted of 2 h of total sleep deprivation (TSD) followed immediately by 2 h of intermittent REM sleep deprivation (IRD). 2R2I consisted of 2 h of selective REM sleep deprivation (RSD) followed by 2 h of IRD. IRD was composed of four cycles of 20-min RSD intervals alternating with 10 min of sleep permission windows.

RESULTS

REM sleep debt that accumulated during deprivation (9.0 and 10.8 min for RSD and TSD, respectively) was fully compensated regardless of cumulated NREM sleep or wakefulness during deprivation. Protocol 2T2I exhibited a delayed REM sleep rebound with respect to 2R2I due to a reduction of REM sleep transitions related to enhanced NREM sleep delta-EEG activity, without affecting REM sleep consolidation. Within IRD permission windows there was a transient and duration-dependent diminution of REM sleep transitions.

CONCLUSIONS

REM sleep recovery in the rat seems to depend on a long-term hourglass process activated by REM sleep absence. Both REM sleep transition probability and REM sleep episode consolidation depend on the long-term REM sleep hourglass. REM sleep activates a short-term REM sleep refractory period that modulates the ultradian organization of sleep states.

Total Sleep Deprivation and Recovery Sleep Affect the Diurnal Variation of Agility Performance: The Gender Differences

ABSTRACT

Romdhani, M, Hammouda, O, Smari, K, Chaabouni, Y, Mahdouani, K, Driss, T, and Souissi, N. Total sleep deprivation and recovery sleep affect the diurnal variation of agility performance: The gender differences. J Strength Cond Res 35(1): 132-140, 2021-This study aimed to investigate the effects of time-of-day, 24 and 36 hours of total sleep deprivation (TSD), and recovery sleep (RS) on repeated-agility performances. Twenty-two physical education students (11 male and 11 female students) completed 5 repeated modified agility T-test (RMAT) sessions (i.e., 2 after normal sleep night [NSN] [at 07:00 and 17:00 hours], 2 after TSD [at 07:00 hours, i.e., 24-hour TSD and at 17:00 hours, i.e., 36-hour TSD], and 1 after RS at 17:00 hours). The RMAT index decreased from the morning to the afternoon after NSN (p < 0.05, d = 1.05; p < 0.01, d = 0.73) and after TSD (p < 0.001, d = 0.92; d = 1.08), respectively, for total time (TT) and peak time (PT). This finding indicates a diurnal variation in repeated agility, which persisted after TSD. However, the diurnal increase in PT was less marked in the female group after NSN (2.98 vs. 6.24%). Moreover, TT and PT increased, respectively, after 24-hour TSD (p < 0.001; d = 0.84, d = 0.87) and 36-hour TSD (p < 0.001, d = 1.12; p < 0.01, d = 0.65). Female subjects' PT was less affected by 24-hour TSD (1.76 vs. 6.81%) compared with male subjects' PT. After 36-hour TSD, the amount of decrease was not different between groups, which increased the diurnal amplitude of PT only for male subjects. Total sleep deprivation suppressed the diurnal increase of PT and increased the diurnal amplitude of oral temperature only in women. Nevertheless, RS normalized the sleep-loss-induced performance disruption. Conclusively, sleep loss and RS differently affect repeated-agility performance of men and women during the day. Sleep extension postdeprivation could have potent restorative effect on repeated-agility performances, and female subjects could extract greater benefits.

An automatic EEG-based sleep staging system with introducing NAoSP and NAoGP as new metrics for sleep staging systems

Different biological signals are recorded in sleep labs during sleep for the diagnosis and treatment of human sleep problems. Classification of sleep stages with electroencephalography (EEG) is preferred to other biological signals due to its advantages such as providing clinical information, cost-effectiveness, comfort, and ease of use. The evaluation of EEG signals taken during sleep by clinicians is a tiring, time-consuming, and error-prone method. Therefore, it is clinically mandatory to determine sleep stages by using software-supported systems. Like all classification problems, the accuracy rate is used to compare the performance of studies in this domain, but this metric can be accurate when the number of observations is equal in classes. However, since there is not an equal number of observations in sleep stages, this metric is insufficient in the evaluation of such systems. For this purpose, in recent years, Cohen's kappa coefficient and even the sensitivity of NREM1 have been used for comparing the performance of these systems. Still, none of them examine the system from all dimensions. Therefore, in this study, two new metrics based on the polygon area metric, called the normalized area of sensitivity polygon and normalized area of the general polygon, are proposed for the performance evaluation of sleep staging systems. In addition, a new sleep staging system is introduced using the applications offered by the MATLAB program. The existing systems discussed in the literature were examined with the proposed metrics, and the best systems were compared with the proposed sleep staging system. According to the results, the proposed system excels in comparison with the most advanced machine learning methods. The single-channel method introduced based on the proposed metrics can be used for robust and reliable sleep stage classification from all dimensions required for real-time applications.

Cannabinoids, Endocannabinoids and Sleep

Sleep is a vital function of the nervous system that contributes to brain and bodily homeostasis, energy levels, cognitive ability, and other key functions of a variety of organisms. Dysfunctional sleep induces neural problems and is a key part of almost all human psychiatric disorders including substance abuse disorders. The hypnotic effects of cannabis have long been known and there is increasing use of phytocannabinoids and other formulations as sleep aids. Thus, it is crucial to gain a better understanding of the neurobiological basis of cannabis drug effects on sleep, as well as the role of the endogenous cannabinoid system in sleep physiology. In this review article, we summarize the current state of knowledge concerning sleep-related endogenous cannabinoid function derived from research on humans and rodent models. We also review information on acute and chronic cannabinoid drug effects on sleep in these organisms, and molecular mechanisms that may contribute to these effects. We point out the potential benefits of acute cannabinoids for sleep improvement, but also the potential sleep-disruptive effects of withdrawal following chronic cannabinoid drug use. Prescriptions for future research in this burgeoning field are also provided.

Sleep environments and sleep physiology: A review

Sleep loss impairs task performance and post-physical activity recovery, cognitive performance and mood, heightens fatigue and decreases vigour; poor sleep quality impairs decision-making, the speed and accuracy of task performance, and post-exercise recovery. Sleep time and quality are affected by age, psychological and physiological conditions, culture and environmental factors. Skin temperature, rapid temperature change and sweating during sleep can significantly reduce sleep quality. Hence, the thermal properties of bedding and sleepwear, both in steady-state and transient ambient temperature conditions, are logically important factors. Research to date on sleeping thermal microclimates and their effect on sleep quality is scarce. This present review covers the fundamental elements of human sleep, highlighting physically active people, such as athletes, and the influence of sleepwear and bedding on sleep thermal microclimates, as well as the research methods that have been and could be used in this field. This review identifies opportunity for future research direction and approaches to understanding thermal environments that may support better human sleep.

Impacts of artificial light at night on sleep: A review and prospectus

Natural cycles of light and darkness govern the timing of most aspects of animal behavior and physiology. Artificial light at night (ALAN)-a recent and pervasive form of pollution-can mask natural photoperiodic cues and interfere with biological rhythms. One such rhythm vulnerable to perturbation is the sleep-wake cycle. ALAN may greatly influence sleep in humans and wildlife, particularly in animals that sleep predominantly at night. There has been some recent evidence for impacts of ALAN on sleep, but critical questions remain. Some of these can be addressed by adopting approaches already entrenched in sleep research. In this paper, we review the current evidence for impacts of ALAN on sleep, highlight gaps in our understanding, and suggest opportunities for future research.

Analysis of Multichannel EEG Patterns During Human Sleep: A Novel Approach

Classic visual sleep stage scoring is based on electroencephalogram (EEG) frequency band analysis of 30 s epochs and is commonly performed by highly trained medical sleep specialists using additional information from submental EMG and eye movements electrooculogram (EOG). In this study, we provide the proof-of-principle in 40 subjects that sleep stages can be consistently differentiated solely on the basis of spatial 3-channel EEG patterns based on root-mean-square (RMS) amplitudes. The polysomnographic 3-channel EEG data are pre-processed by RMS averaging over intervals of 30 s leading to spatial cortical activity patterns represented by 3-dimensional vectors. These patterns are visualized using multidimensional scaling (MDS), allowing a comparison of the spatial cortical activity patterns with the conventional visual sleep scoring system according to the American Academy of Sleep Medicine (AASM). Spatial cortical activity patterns based on RMS amplitudes naturally divide into different clusters that correspond to visually scored sleep stages. Furthermore, these clusters are reproducible between different subjects. Especially the cluster associated with the REM sleep stage seems to be very different from the one associated with the wake state. This study provides a proof-of-principle that it is possible to separate sleep stages solely by analyzing spatially distributed EEG RMS amplitudes reflecting cortical activity and without classical EEG feature extractions like power spectrum analysis.

"Are We in Sync with Each Other?" Exploring the Effects of Cosleeping on Heterosexual Couples' Sleep Using Simultaneous Polysomnography: A Pilot Study

The present study aimed to explore dynamic and interactive aspects of cosleep in heterosexual couples. The sample consisted of eight young healthy adults who belonged to four heterosexual couples with a good relationship quality and a history of cosleeping. All individuals underwent simultaneous polysomnography in a sleep laboratory for four nights in which they slept individually and with their partner. Also, a sleep protocol of subjective sleep measures was completed. Statistical analyses included cross recurrence quantification analysis to assess synchronization during sleep. Cosleeping was associated with better subjective sleep quality, increased total sleep time, sleep efficiency, total slow wave sleep, and REM sleep. Sleep stages were more synchronized during cosleep independent of awakenings. Cardiorespiratory measures remained unchanged. The results indicate that young healthy couples in good relationships benefit from cosleeping on a subjective and objective level. Combining simultaneous polysomnography and cross recurrence quantification analysis is a promising method to study dynamic and interactive aspects of cosleep possibly leading to deeper understanding of the role of sleep for sociality, the nature of REM sleep, and the partner as a social zeitgeber. Moreover, clinical implications may arise from these findings.

The Visual Scoring of Sleep in Infants 0 to 2 Months of Age

In March 2014, the American Academy of Sleep Medicine (AASM) Board of Directors requested the Scoring Manual Editorial Board develop rules, terminology, and technical specifications for scoring sleep/wake states in full-term infants from birth to 2 mo of age, cognizant of the 1971 Anders, Emde, and Parmelee Manual for Scoring Sleep in Newborns. On July 1, 2015, the AASM published rules for scoring sleep in infants, ages 0-2 mo. This evidence-based review summarizes the background information provided to the Scoring Manual Editorial Board to write these rules. The Anders Manual only provided criteria for coding physiological and behavioral state characteristics in polysomnograms (PSG) of infants, leaving specific sleep scoring criteria to the individual investigator. Other infant scoring criteria have been published, none widely accepted or used. The AASM Scoring Manual infant scoring criteria incorporate modern concepts, digital PSG recording techniques, practicalities, and compromises. Important tenets are: (1) sleep/wake should be scored in 30-sec epochs as either wakefulness (W), rapid eye movement, REM (R), nonrapid eye movement, NREM (N) and transitional (T) sleep; (2) an electroencephalographic (EEG) montage that permits adequate display of young infant EEG is: F3-M2, F4-M1, C3-M2, C4-M1, O1-M2, O2-M1; additionally, recording C3-Cz, Cz-C4 help detect early and asynchronous sleep spindles; (3) sleep onsets are more often R sleep until 2-3 mo postterm; (4) drowsiness is best characterized by visual observation (supplemented by later video review); (5) wide open eyes is the most crucial determinant of W; (6) regularity (or irregularity) of respiration is the single most useful PSG characteristic for scoring sleep stages at this age; (7) trace alternant (TA) is the only relatively distinctive EEG pattern, characteristic of N sleep, and usually disappears by 1 mo postterm replaced by high voltage slow (HVS); (8) sleep spindles first appear 44-48 w conceptional age (CA) and when present prompt scoring N; (9) score EEG activity in an epoch as "continuous" or "discontinuous" for inter-scorer reliability; (10) score R if four or more of the following conditions are present, including irregular respiration and rapid eye movement(s): (a) low chin EMG (for the majority of the epoch); (b) eyes closed with at least one rapid eye movement (concurrent with low chin tone); (c) irregular respiration; (d) mouthing, sucking, twitches, or brief head movements; and (e) EEG exhibits a continuous pattern without sleep spindles; (11) because rapid eye movements may not be seen on every page, epochs following an epoch of definite R in the absence of rapid eye movements may be scored if the EEG is continuous without TA or sleep spindles, chin muscle tone low for the majority of the epoch; and there is no intervening arousal; (12) Score N if four or more of the following conditions are present, including regular respiration, for the majority of the epoch: (a) eyes are closed with no eye movements; (b) chin EMG tone present; (c) regular respiration; and (d) EEG patterns of either TA, HVS, or sleep spindles are present; and (13) score T sleep if an epoch contains two or more discordant PSG state characteristics (either three NREM and two REM characteristics or two NREM and three REM characteristics). These criteria for ages 0-2 mo represent far more than baby steps. Like all the other AASM Manual rules and specifications none are fixed in stone, all open for debate, discussion and revision with the fundamental goal to provide standards for comparison of methods and results.

COMMENTARY

A commentary on this article appears in this issue on page 291.

Opposite Impact of REM Sleep on Neurobehavioral Functioning in Children with Common Psychiatric Disorders Compared to Typically Developing Children

Rapid eye movement (REM) sleep has been shown to be related to many adaptive cognitive and behavioral functions. However, its precise functions are still elusive, particularly in developmental psychiatric disorders. The present study aims at investigating associations between polysomnographic (PSG) REM sleep measurements and neurobehavioral functions in children with common developmental psychiatric conditions compared to typically developing children (TDC). Twenty-four children with attention-deficit/hyperactivity disorder (ADHD), 21 with Tourette syndrome/tic disorder (TD), 21 with ADHD/TD comorbidity, and 22 TDC, matched for age and gender, underwent a two-night PSG, and their psychopathological scores and intelligence quotient (IQ) were assessed. Major PSG findings showed more REM sleep and shorter REM latency in the children with psychiatric disorders than in the TDC. Multiple regression analyses revealed that in groups with developmental psychopathology, REM sleep proportion correlated positively with scores of inattention and negatively with performance IQ. In contrast, in the group of TDC, REM sleep proportion correlated negatively with scores of inattention and positively with performance IQ. Whilst shorter REM latency was associated with greater inattention scores in children with psychopathology, no such an association existed in the group of TDC. Altogether, these results indicate an opposite impact of REM sleep on neurobehavioral functioning, related to presence or absence of developmental psychiatric disorders. Our findings suggest that during development, REM sleep functions may interact dissimilarly with different pathways of brain maturation.

A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features

BACKGROUND

Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time-consuming and error-prone. Therefore, there is a dire need of an automated sleep staging scheme.

NEW METHOD

In this work, we decompose sleep-EEG signal segments using tunable-Q factor wavelet transform (TQWT). Various spectral features are then computed from TQWT sub-bands. The performance of spectral features in the TQWT domain has been determined by intuitive and graphical analyses, statistical validation, and Fisher criteria. Random forest is used to perform classification. Optimal choices and the effects of TQWT and random forest parameters have been determined and expounded.

RESULTS

Experimental outcomes manifest the efficacy of our feature generation scheme in terms of p-values of ANOVA analysis and Fisher criteria. The proposed scheme yields 90.38%, 91.50%, 92.11%, 94.80%, 97.50% for 6-stage to 2-stage classification of sleep states on the benchmark Sleep-EDF data-set. In addition, its performance on DREAMS Subjects Data-set is also promising.

COMPARISON WITH EXISTING METHODS

The performance of the proposed method is significantly better than the existing ones in terms of accuracy and Cohen's kappa coefficient. Additionally, the proposed scheme gives high detection accuracy for sleep stages non-REM 1 and REM.

CONCLUSIONS

Spectral features in the TQWT domain can discriminate sleep-EEG signals corresponding to various sleep states efficaciously. The proposed scheme will alleviate the burden of the physicians, speed-up sleep disorder diagnosis, and expedite sleep research.

Sleep as spatiotemporal integration of biological processes that evolved to periodically reinforce neurodynamic and metabolic homeostasis: The 2m3d paradigm of sleep

Sleep continues to perplex scientists and researchers. Despite decades of sleep research, we still lack a clear understanding of the biological functions and evolution of sleep. In this review, we will examine sleep from a functional and phylogenetic perspective and describe some important conceptual gaps in understanding sleep. Classical theories of the biology and evolution of sleep emphasize sensory activation, energy balance, and metabolic homeostasis. Advances in electrophysiology, functional neuroimaging, and neuroplasticity allow us to view sleep within the framework of neural dynamics. With this paradigm shift, we have come to realize the importance of neurodynamic homeostasis in shaping the biology of sleep. Evidently, animals sleep to achieve neurodynamic and metabolic homeostasis. We are not aware of any framework for understanding sleep where neurodynamic, metabolic, homeostatic, chronophasic, and afferent variables are all taken into account. This motivated us to propose the two-mode three-drive (2m3d) paradigm of sleep. In the 2m3d paradigm, local neurodynamic/metabolic (N/M) processes switch between two modes-m0 and m1-in response to three drives-afferent, chronophasic, and homeostatic. The spatiotemporal integration of local m0/m1 operations gives rise to the global states of sleep and wakefulness. As a framework of evolution, the 2m3d paradigm allows us to view sleep as a robust adaptive strategy that evolved so animals can periodically reinforce neurodynamic and metabolic homeostasis while remaining sensitive to their internal and external environment.

Neuroscience: A Distributed Neural Network Controls REM Sleep

How does the brain control dreams? New science shows that a small node of cells in the medulla - the most primitive part of the brain - may function to control REM sleep, the brain state that underlies dreaming.

Dream to Predict? REM Dreaming as Prospective Coding

The dream as prediction seems inherently improbable. The bizarre occurrences in dreams never characterize everyday life. Dreams do not come true! But assuming that bizarreness negates expectations may rest on a misunderstanding of how the predictive brain works. In evolutionary terms, the ability to rapidly predict what sensory input implies-through expectations derived from discerning patterns in associated past experiences-would have enhanced fitness and survival. For example, food and water are essential for survival, associating past experiences (to identify location patterns) predicts where they can be found. Similarly, prediction may enable predator identification from what would have been only a fleeting and ambiguous stimulus-without prior expectations. To confront the many challenges associated with natural settings, visual perception is vital for humans (and most mammals) and often responses must be rapid. Predictive coding during wake may, therefore, be based on unconscious imagery so that visual perception is maintained and appropriate motor actions triggered quickly. Speed may also dictate the form of the imagery. Bizarreness, during REM dreaming, may result from a prospective code fusing phenomena with the same meaning-within a particular context. For example, if the context is possible predation, from the perspective of the prey two different predators can both mean the same (i.e., immediate danger) and require the same response (e.g., flight). Prospective coding may also prune redundancy from memories, to focus the image on the contextually-relevant elements only, thus, rendering the non-relevant phenomena indeterminate-another aspect of bizarreness. In sum, this paper offers an evolutionary take on REM dreaming as a form of prospective coding which identifies a probabilistic pattern in past events. This pattern is portrayed in an unconscious, associative, sensorimotor image which may support cognition in wake through being mobilized as a predictive code. A particular dream illustrates.

Not only … but also: REM sleep creates and NREM Stage 2 instantiates landmark junctions in cortical memory networks

This article argues both rapid eye movement (REM) and non-rapid eye movement (NREM) sleep contribute to overnight episodic memory processes but their roles differ. Episodic memory may have evolved from memory for spatial navigation in animals and humans. Equally, mnemonic navigation in world and mental space may rely on fundamentally equivalent processes. Consequently, the basic spatial network characteristics of pathways which meet at omnidirectional nodes or junctions may be conserved in episodic brain networks. A pathway is formally identified with the unidirectional, sequential phases of an episodic memory. In contrast, the function of omnidirectional junctions is not well understood. In evolutionary terms, both animals and early humans undertook tours to a series of landmark junctions, to take advantage of resources (food, water and shelter), whilst trying to avoid predators. Such tours required memory for emotionally significant landmark resource-place-danger associations and the spatial relationships amongst these landmarks. In consequence, these tours may have driven the evolution of both spatial and episodic memory. The environment is dynamic. Resource-place associations are liable to shift and new resource-rich landmarks may be discovered, these changes may require re-wiring in neural networks. To realise these changes, REM may perform an associative, emotional encoding function between memory networks, engendering an omnidirectional landmark junction which is instantiated in the cortex during NREM Stage 2. In sum, REM may preplay associated elements of past episodes (rather than replay individual episodes), to engender an unconscious representation which can be used by the animal on approach to a landmark junction in wake.

Human REM sleep: influence on feeding behaviour, with clinical implications

Rapid eye movement (REM) sleep shares many underlying mechanisms with wakefulness, to a much greater extent than does non-REM, especially those relating to feeding behaviours, appetite, curiosity, exploratory (locomotor) activities, as well as aspects of emotions, particularly 'fear extinction'. REM is most evident in infancy, thereafter declining in what seems to be a dispensable manner that largely reciprocates increasing wakefulness. However, human adults retain more REM than do other mammals, where for us it is most abundant during our usual final REM period (fREMP) of the night, nearing wakefulness. The case is made that our REM is unusual, and that (i) fREMP retains this 'dispensability', acting as a proxy for wakefulness, able to be forfeited (without REM rebound) and substituted by physical activity (locomotion) when pressures of wakefulness increase; (ii) REM's atonia (inhibited motor output) may be a proxy for this locomotion; (iii) our nocturnal sleep typically develops into a physiological fast, especially during fREMP, which is also an appetite suppressant; (iv) REM may have 'anti-obesity' properties, and that the loss of fREMP may well enhance appetite and contribute to weight gain ('overeating') in habitually short sleepers; (v) as we also select foods for their hedonic (emotional) values, REM may be integral to developing food preferences and dislikes; and (vii) REM seems to have wider influences in regulating energy balance in terms of exercise 'substitution' and energy (body heat) retention. Avenues for further research are proposed, linking REM with feeding behaviours, including eating disorders, and effects of REM-suppressant medications.

A new function of rapid eye movement sleep: improvement of muscular efficiency

Previously I demonstrated that the slow wave sleep (SWS) functioned to adjust the emotional balance disrupted by emotional memories randomly accumulated during waking, while the rapid eye movement (REM) sleep played the opposite role. Many experimental results have unambiguously shown that various emotional memories are processed during REM sleep. In this article, it is attempted to combine this confirmed function of REM sleep with the atonic state unique to REM sleep, and to integrate a new theory suggesting that improvement of muscular efficiency be a new function of REM sleep. This new function of REM sleep is more advantageous than the function of REM sleep in emotional memories and disinhibited drives to account for the phylogenetic variations of REM sleep, especially the absence of REM sleep in dolphins and short duration of REM sleep in birds in contrary to that in humans and rodents, the absence of penile erections in REM sleep in armadillo, as well as the higher voltage in EEG during REM sleep in platypus and ostrich. Besides, this new function of REM sleep is also advantageous to explain the association of REM sleep with the atonic episodes in SWS, the absence of drastic menopausal change in duration of REM sleep, and the effects of ambient temperature on the duration of REM sleep. These comparative and experimental evidences support the improvement of muscular efficiency as a new and major function of REM sleep.

A low computational cost algorithm for REM sleep detection using single channel EEG

The push towards low-power and wearable sleep systems requires using minimum number of recording channels to enhance battery life, keep processing load small and be more comfortable for the user. Since most sleep stages can be identified using EEG traces, enormous power savings could be achieved by using a single channel of EEG. However, detection of REM sleep from one channel EEG is challenging due to its electroencephalographic similarities with N1 and Wake stages. In this paper we investigate a novel feature in sleep EEG that demonstrates high discriminatory ability for detecting REM phases. We then use this feature, that is based on spectral edge frequency (SEF) in the 8–16 Hz frequency band, together with the absolute power and the relative power of the signal, to develop a simple REM detection algorithm. We evaluate the performance of this proposed algorithm with overnight single channel EEG recordings of 5 training and 15 independent test subjects. Our algorithm achieved sensitivity of 83%, specificity of 89% and selectivity of 61% on a test database consisting of 2221 REM epochs. It also achieved sensitivity and selectivity of 81 and 75% on PhysioNet Sleep-EDF database consisting of 8 subjects. These results demonstrate that SEF can be a useful feature for automatic detection of REM stages of sleep from a single channel EEG.

NREM and REM Sleep: Complementary Roles in Recovery after Wakefulness

The overall function of sleep is hypothesized to provide "recovery" after preceding waking activities, thereby ensuring optimal functioning during subsequent wakefulness. However, the functional significance of the temporal dynamics of sleep, manifested in the slow homeostatic process and the alternation between non-rapid eye movement (NREM) and REM sleep remains unclear. We propose that NREM and REM sleep have distinct and complementary contributions to the overall function of sleep. Specifically, we suggest that cortical slow oscillations, occurring within specific functionally interconnected neuronal networks during NREM sleep, enable information processing, synaptic plasticity, and prophylactic cellular maintenance ("recovery process"). In turn, periodic excursions into an activated brain state-REM sleep-appear to be ideally placed to perform "selection" of brain networks, which have benefited from the process of "recovery," based on their offline performance. Such two-stage modus operandi of the sleep process would ensure that its functions are fulfilled according to the current need and in the shortest time possible. Our hypothesis accounts for the overall architecture of normal sleep and opens up new perspectives for understanding pathological conditions associated with abnormal sleep patterns.

Selective REM-Sleep Deprivation Does Not Diminish Emotional Memory Consolidation in Young Healthy Subjects

Sleep enhances memory consolidation and it has been hypothesized that rapid eye movement (REM) sleep in particular facilitates the consolidation of emotional memory. The aim of this study was to investigate this hypothesis using selective REM-sleep deprivation. We used a recognition memory task in which participants were shown negative and neutral pictures. Participants (N = 29 healthy medical students) were separated into two groups (undisturbed sleep and selective REM-sleep deprived). Both groups also worked on the memory task in a wake condition. Recognition accuracy was significantly better for negative than for neutral stimuli and better after the sleep than the wake condition. There was, however, no difference in the recognition accuracy (neutral and emotional) between the groups. In summary, our data suggest that REM-sleep deprivation was successful and that the resulting reduction of REM-sleep had no influence on memory consolidation whatsoever.

Exercise benefits for the aging brain depend on the accompanying cognitive load: insights from sleep electroencephalogram

Although exercise clearly offsets aging effects on the body, its benefits for the aging brain are likely to depend on the extent that physical activity (especially locomotion) facilitates multisensory encounters, curiosity, and interactions with novel environments; this is especially true for exploratory activity, which occupies much of wakefulness for most mammals in the wild. Cognition is inseparable from physical activity, with both interlinked to promote neuroplasticity and more successful brain aging. In these respects and for humans, exercising in a static, featureless, artificially lit indoor setting contrasts with exploratory outdoor walking within a novel environment during daylight. However, little is known about the comparative benefits for the aging brain of longer-term daily regimens of this latter nature including the role of sleep, to the extent that sleep enhances neuroplasticity as shown in short-term laboratory studies. More discerning analyses of sleep electroencephalogram (EEG) slow-wave activity especially 0.5-2-Hz activity would provide greater insights into use-dependent recovery processes during longer-term tracking of these regimens and complement slower changing waking neuropsychologic and resting functional magnetic resonance imaging (fMRI) measures, including those of the brain's default mode network. Although the limited research only points to ephemeral small sleep EEG effects of pure exercise, more enduring effects seem apparent when physical activity incorporates cognitive challenges. In terms of "use it or lose it," curiosity-driven "getting out and about," encountering, interacting with, and enjoying novel situations may well provide the brain with its real exercise, further reflected in changes to the dynamics of sleep.

Linking melanism to brain development: expression of a melanism-related gene in barn owl feather follicles covaries with sleep ontogeny

Background

Intra-specific variation in melanocyte pigmentation, common in the animal kingdom, has caught the eye of naturalists and biologists for centuries. In vertebrates, dark, eumelanin pigmentation is often genetically determined and associated with various behavioral and physiological traits, suggesting that the genes involved in melanism have far reaching pleiotropic effects. The mechanisms linking these traits remain poorly understood, and the potential involvement of developmental processes occurring in the brain early in life has not been investigated. We examined the ontogeny of rapid eye movement (REM) sleep, a state involved in brain development, in a wild population of barn owls (Tyto alba) exhibiting inter-individual variation in melanism and covarying traits. In addition to sleep, we measured melanistic feather spots and the expression of a gene in the feather follicles implicated in melanism (PCSK2).

Results

As in mammals, REM sleep declined with age across a period of brain development in owlets. In addition, inter-individual variation in REM sleep around this developmental trajectory was predicted by variation in PCSK2 expression in the feather follicles, with individuals expressing higher levels exhibiting a more precocial pattern characterized by less REM sleep. Finally, PCSK2 expression was positively correlated with feather spotting.

Conclusions

We demonstrate that the pace of brain development, as reflected in age-related changes in REM sleep, covaries with the peripheral activation of the melanocortin system. Given its role in brain development, variation in nestling REM sleep may lead to variation in adult brain organization, and thereby contribute to the behavioral and physiological differences observed between adults expressing different degrees of melanism.