Internal structure of sleep cycles in a healthy population

Towards an objective measurement of sleep quality in non-human animals: using the horse as a model species for the creation of sleep quality indices

Sleep disturbance is observed across species, resulting in neurocognitive dysfunction, poor impulse control and poor regulation of negative emotion. Understanding animal sleep disturbance is thus important to understand how environmental factors influence animal sleep and day-to-day welfare. Self-reporting tools for sleep disturbance commonly used in human research to determine sleep quality cannot be transferred to non-verbal animal species research. Human research has, however, successfully used frequency of awakenings to create an objective measurement of sleep quality. The aim of this study was to use a novel sleep-quality scoring system for a non-human mammalian species. Five separate sleep quality indices calculations were developed, using frequency of awakenings, total sleep time and total time spent in different sleep states. These indices were applied to a pre-existing data set of equine sleep behaviour taken from a study investigating the effects of environmental change (lighting and bedding) on the duration of time in different sleep states. Significant treatment effects for index scores both differed and aligned with the original sleep quantity results, thus sleep quality may be a useful alternative measurement of sleep disturbance that could be used to investigate impactful (emotional, cognitive) effects on the animal.

Phenotypic Interindividual Differences in the Dynamic Structure of Sleep in Healthy Young Adults

Introduction

Evaluating the dynamic structure of sleep may yield new insights into the mechanisms underlying human sleep physiology.

Methods

We analyzed data from a 12-day, 11-night, strictly controlled laboratory study with an adaptation night, 3 iterations of a baseline night followed by a recovery night after 36 h of total sleep deprivation, and a final recovery night. All sleep opportunities were 12 h in duration (22:00-10:00) and recorded with polysomnography (PSG). The PSG records were scored for the sleep stages: rapid eye movement (REM) sleep; non-REM (NREM) stage 1 sleep (S1), stage 2 sleep (S2), and slow wave sleep (SWS); and wake (W). Phenotypic interindividual differences were assessed using indices of dynamic sleep structure - specifically sleep stage transitions and sleep cycle characteristics - and intraclass correlation coefficients across nights.

Results

NREM/REM sleep cycles and sleep stage transitions exhibited substantial and stable interindividual differences that were robust across baseline and recovery nights, suggesting that mechanisms underlying the dynamic structure of sleep are phenotypic. In addition, the dynamics of sleep stage transitions were found to be associated with sleep cycle characteristics, with a significant relationship between the length of sleep cycles and the degree to which S2-to-W/S1 and S2-to-SWS transitions were in equilibrium.

Discussion

Our findings are consistent with a model for the underlying mechanisms that involves three subsystems - characterized by S2-to-W/S1, S2-to-SWS, and S2-to-REM transitions - with S2 playing a hub-like role. Furthermore, the balance between the two subsystems within NREM sleep (S2-to-W/S1 and S2-to-SWS) may serve as a basis for the dynamic regulation of sleep structure and may represent a novel target for interventions aiming to improve sleep.

SSAVE: A tool for analysis and visualization of sleep periods using electroencephalography data

Human sleep architecture is structured with repeated episodes of rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep. An overnight sleep study facilitates identification of macro and micro changes in the pattern and duration of sleep stages associated with sleep disorders and other aspects of human mental and physical health. Overnight sleep studies record, in addition to electroencephalography (EEG) and other electro-physiological signals, a sequence of sleep-stage annotations. SSAVE, introduced here, is open-source software that takes sleep-stage annotations and EEG signals as input, identifies and characterizes periods of NREM and REM sleep, and produces a hypnogram and its time-matched EEG spectrogram. SSAVE fills an important gap for the rapidly growing field of sleep medicine by providing an easy-to-use tool for sleep-period identification and visualization. SSAVE can be used as a Python package, a desktop standalone tool or through a web portal. All versions of the SSAVE tool can be found on: https://manticore.niehs.nih.gov/ssave.

A Review of Equine Sleep: Implications for Equine Welfare

Sleep is a significant biological requirement for all living mammals due to its restorative properties and its cognitive role in memory consolidation. Sleep is ubiquitous amongst all mammals but sleep profiles differ between species dependent upon a range of biological and environmental factors. Given the functional importance of sleep, it is important to understand these differences in order to ensure good physical and psychological wellbeing for domesticated animals. This review focuses specifically on the domestic horse and aims to consolidate current information on equine sleep, in relation to other species, in order to (a) identify both quantitatively and qualitatively what constitutes normal sleep in the horse, (b) identify optimal methods to measure equine sleep (logistically and in terms of accuracy), (c) determine whether changes in equine sleep quantity and quality reflect changes in the animal's welfare, and (d) recognize the primary factors that affect the quantity and quality of equine sleep. The review then discusses gaps in current knowledge and uses this information to identify and set the direction of future equine sleep research with the ultimate aim of improving equine performance and welfare. The conclusions from this review are also contextualized within the current discussions around the “social license” of horse use from a welfare perspective.

'SleepCycles' package for R - A free software tool for the detection of sleep cycles from sleep staging

The detection of NREM-REM sleep cycles in human sleep data (i.e., polysomnographically assessed sleep stages) enables fine-grained analyses of ultradian variations in sleep microstructure (e.g., sleep spindles, and arousals), or other amplitude- and frequency-specific electroencephalographic features during sleep. While many laboratories have software that is used internally, reproducibility requires the availability of open-source software. Therefore, we here introduce the ‘SleepCycles’ package for R, an open-source software package that identifies sleep cycles and their respective (non-) rapid eye movement ([N]REM) periods from sleep staging data. Additionally, each (N)REM period is subdivided into parts of equal duration (percentiles), which may be useful for further fine-grained analyses. The detection criteria used in the package are, with some adaptations, largely based on criteria originally proposed by Feinberg and Floyd (1979). The latest version of the package can be downloaded from the Comprehensive R Archives Network (CRAN).•

The package ‘SleepCycles’ for R allows to identify sleep cycles and their respective NREM and REM periods from sleep staging results.

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Besides the cycle detection, NREM and REM periods are also split into parts of equal duration (percentiles) thereby allowing for a better temporal resolution across the night and comparisons of sleep cycles with different durations amongst different night recordings.

Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal electroencephalography investigation

BACKGROUND

While the effects of prolonged sleep deprivation (≥24 h) on seizure occurrence has been thoroughly explored, little is known about the effects of day-to-day variations in the duration and quality of sleep on seizure probability. A better understanding of the interaction between sleep and seizures may help to improve seizure management.

METHODS

To explore how sleep and epileptic seizures are associated, we analysed continuous intracranial electroencephalography (EEG) recordings collected from 10 patients with refractory focal epilepsy undergoing ordinary life activities between 2010 and 2012 from three clinical centres (Austin Health, The Royal Melbourne Hospital, and St Vincent's Hospital of the Melbourne University Epilepsy Group). A total of 4340 days of sleep-wake data were analysed (average 434 days per patient). EEG data were sleep scored using a semi-automated machine learning approach into wake, stages one, two, and three non-rapid eye movement sleep, and rapid eye movement sleep categories.

FINDINGS

Seizure probability changes with day-to-day variations in sleep duration. Logistic regression models revealed that an increase in sleep duration, by 1·66 ± 0·52 h, lowered the odds of seizure by 27% in the following 48 h. Following a seizure, patients slept for longer durations and if a seizure occurred during sleep, then sleep quality was also reduced with increased time spent aroused from sleep and reduced rapid eye movement sleep.

INTERPRETATION

Our results suggest that day-to-day deviations from regular sleep duration correlates with changes in seizure probability. Sleeping longer, by 1·66 ± 0·52 h, may offer protective effects for patients with refractory focal epilepsy, reducing seizure risk. Furthermore, the occurrence of a seizure may disrupt sleep patterns by elongating sleep and, if the seizure occurs during sleep, reducing its quality.

Ultradian modulation of cortical arousals during sleep: effects of age and exposure to nighttime transportation noise

STUDY OBJECTIVES

The present study aimed at assessing the temporal non-rapid eye movement (NREM) EEG arousal distribution within and across sleep cycles and its modifications with aging and nighttime transportation noise exposure, factors that typically increase the incidence of EEG arousals.

METHODS

Twenty-six young (19-33 years, 12 women) and 16 older (52-70 years, 8 women) healthy volunteers underwent a 6-day polysomnographic laboratory study. Participants spent two noise-free nights and four transportation noise exposure nights, two with continuous and two characterized by eventful noise (average sound levels of 45 dB, maximum sound levels between 50 and 62 dB for eventful noise). Generalized mixed models were used to model the time course of EEG arousal rates during NREM sleep and included cycle, age, and noise as independent variables.

RESULTS

Arousal rate variation within NREM sleep cycles was best described by a u-shaped course with variations across cycles. Older participants had higher overall arousal rates than the younger individuals with differences for the first and the fourth cycle depending on the age group. During eventful noise nights, overall arousal rates were increased compared to noise-free nights. Additional analyses suggested that the arousal rate time course was partially mediated by slow wave sleep (SWS).

CONCLUSIONS

The characteristic u-shaped arousal rate time course indicates phases of reduced physiological sleep stability both at the beginning and end of NREM cycles. Small effects on the overall arousal rate by eventful noise exposure suggest a preserved physiological within- and across-cycle arousal evolution with noise exposure, while aging affected the shape depending on the cycle.

Dynamics and Ultradian Structure of Human Sleep in Real Life

The temporal dynamics that characterize sleep are difficult to capture outside the sleep laboratory. Therefore, longitudinal studies and big-data approaches assessing sleep dynamics are lacking. Here, we present the first large-scale analysis of human sleep dynamics in real life by making use of longitudinal wrist movement recordings of >16,000 sleep bouts from 573 subjects. Through non-linear conversion of locomotor activity to "Locomotor Inactivity During Sleep" (LIDS), movement patterns are exposed that directly reflect ultradian sleep cycles and replicate the dynamics of laboratory sleep parameters. Our current analyses indicate no sex differences in LIDS-derived sleep dynamics, whereas especially age but also shift work have pronounced effects, specifically on decline rates and ultradian amplitude. In contrast, ultradian period and phase emerged as remarkably stable across the tested variables. Our approach and results provide the necessary quantitative sleep phenotypes for large field studies and outcome assessments in clinical trials.

Biological Rhythm Based Wearable Sleep State Observer

This research aimed to observe human biological rhythm and adjust the human sleep wake pattern based on controlling wakeup timing using low stress system. An ordinary alarm clock operates according to preset time. Biological rhythm determines the human sleep cycle, which affects sleep depth and wakeup timing, which in turn resets the daily rhythm that affects human’s behavior, life-cycle pattern, life-style related disease. We developed a wearable biological rhythm based awakening controller (BRAC) that determines the biological rhythm in the sleep state (sleep cycle) and stimulates the user at a suitable time to enable the person to wakeup refreshed. The proposed system BRAC gauges human’s sleep quality and rhythms from peak to peak interval time of fingertip-pulse waves, that are measured more easily than polysomnography (PSG). In this paper, we detail the method of sleep cycle estimation using a wearable sensor device as the first feature of BRAC, then, in experiments, evaluate the performance of sleep cycle estimation based on a comparison of the BRAC-sleep cycle and the PSG-determined sleep stage.

Awakening from sleep

Awakening is a crucial event for the organism. The transition from sleep to waking implies physiological processes which lead to a new behavioural state. Spontaneous awakenings have varying features which may change as a function of several factors. The latter include intrasleep architecture, circadian phase, time awake, age, or disordered sleep. Despite its clear theoretical and clinical importance, the topic of awakening (in humans) has received little attention so far. This contribution focuses on major issues which relate to awakening from both basic (experimental) and clinical research. Recent knowledge on neurophysiological mechanisms is reported. The experimental data which provide in the human suggestions on the regulation of awakening are discussed, mainly those concerning sleep architecture and homeostatic/circadian factors also in a life-span perspective, since age is a powerful factor which may influence awakening. Clinical contributions will examine two main sleep disorders: insomnia and hypersomnia. Daytime functioning is shown in insomniac patients and compared to other pathologies like sleep apnea. A final section evokes links between some types of night waking and psychological factors.

Spontaneous sleep interruptions during extended nights. Relationships with NREM and REM sleep phases and effects on REM sleep regulation

OBJECTIVES

There is no agreement in the literature as to whether sleep interruption causes rapid eye movement (REM) pressure to increase, and if so, whether this increase is expressed as shortened REM latency, increased REM density, or increased duration of REM sleep. The purpose of the present study was to examine the effect of different durations of spontaneous sleep interruptions on the regulation of REM sleep that occurs after return to sleep.

METHODS

The occurrence of spontaneous periods of wakefulness and their effects on subsequent REM sleep periods were analysed in a total sample of 1189 sleep interruptions which occurred across 364 extended nights in 13 normal subjects.

RESULTS

Compared with sleep interruptions that last less than 10 min, sleep interruptions that last longer than 10 min occur preferentially out of REM sleep. In both the short and long types of sleep interruptions, the duration of REM periods that ended in wakefulness were shorter than the duration of those that were not interrupted by wakefulness. REM densities of the REM periods that terminated in periods of wakefulness were higher than those of uninterrupted REM periods. The proportion of episodes of wakefulness following REM sleep that were long-lasting progressively increased over the course of the extended night period. The sleep episodes that followed the periods of wakefulness were characterised by a short REM latency. REM duration was increased in episodes that followed long sleep interruptions compared to those that followed short sleep interruptions. REM density did not appear to change significantly in the episodes that followed sleep interruption.

CONCLUSIONS

REM sleep mechanisms appear to be the main force controlling sleep after a spontaneous sleep interruption, presumably because during the second half of the night, where more sleep interruptions occur, the pressure for non-rapid eye movement sleep is reduced and the circadian rhythm in REM sleep propensity reaches its peak. Processes promoting REM sleep at the end of the night are consistent with the Pittendrigh and Daan dual oscillator model of the circadian pacemaker.

Combined influence of cyclic arousability and EEG synchrony on generalized interictal discharges within the sleep cycle

PURPOSE

to analyze the activating role of cyclic alternating pattern (CAP) and EEG synchrony on generalized interictal paroxysms in the first part of the night, when all sleep patterns are represented.

METHODS

nocturnal polysomnographic investigation was accomplished on a randomized series of 18 subjects with an active form of primary generalized epilepsy (PGE), but only six patients showed a complete and regular profile of the first two sleep cycles (SCs). Completeness and regularity of the selected SCs consisted in the absence of intervening wakefulness, in the presence of all sleep stages, and in the identification of three main units, (a) a descending branch, dominated by the build-up of EEG synchrony in the transition from light to deep non-rapid eye movement (NREM) sleep; (b) a trough, where the magnitude of EEG synchrony is greatest and gives rise to stages 3 and 4; (c) an ascending branch characterized by a decrease of EEG synchrony preceding the onset of rapid eye movement (REM) sleep. Generalized paroxysms were evaluated in terms of discharge rates (number of interictal bursts per minute of sleep) and distribution within the investigated sleep parameters.

RESULTS

the discharge rates decreased from SC1 to SC2, with higher values quantified during NREM sleep (mean, 2.8) compared with REM sleep (mean, 0.8). Both SCs showed a progressive decrease of activation across the three units, from the highest discharge rates reached during the descending branches (mean, 3.6) to the more attenuated discharge rates during the troughs (mean, 2.4) down to the lowest rates during the ascending limbs (mean, 1.1). The magnitude of activation during the descending branches was closely related to the CAP condition (mean, 5.2) and to the powerful effect of phase A (mean, 13.9). The great majority (82%) of EEG discharges occurring in phase A were distributed within the A1 subtypes (identified by sequences of k-complexes or delta bursts).

CONCLUSIONS

within the first two SCs, the features of NREM sleep endowed with the major activating power on generalized bursts are represented by the rise of EEG synchrony (descending branch) and by the A phases of CAP involved in the regulation of its build-up.

CAP components and EEG synchronization in the first 3 sleep cycles

OBJECTIVE

There is consolidated evidence that stage changes in sleep are closely related to spontaneous EEG fluctuations centered on the 20-40 periodicity of the cyclic alternating pattern (CAP). The present investigation aimed at assessing the involvement of the different components of CAP in the process of build-up, maintenance and demolition of deep non-REM (NREM) sleep.

METHODS

CAP parameters were quantified in the first 3 sleep cycles (SC1, SC2, SC3), selected from polysomnographic recordings of 25 healthy sound sleepers belonging to an extensive age range (10-49 years). Only ideal SCs were selected, i.e. the ones uninterrupted by intervening wakefulness and in which all stages were represented and linked in a regular succession of a descending branch, a trough and an ascending branch.

RESULTS

Among the first 3 SCs, a total amount of 45 (SC1, 16; SC2, 13; SC3, 16) met the inclusion requirements. SCI contained the highest amount of slow wave sleep (43.7 min) and the lowest values of CAP rate (31.6%). The number of phase A1 subtypes remained unmodified across the 3 SCs (SC1, 48; SC2, 48; SC3, 48), whereas both subtypes A2 (SC1, 9; SC2, 14; SC3, 14) and A3 (SC1, 2; SC2, 8; SC3, 10) increased significantly (P<0.028 and P<0.0001, respectively). The A1 subtypes composed more than 90% of all the A phases collected in the descending branches and in the troughs, while the A2 and A3 subtypes were the major representatives (64.3%) of the A phases occurring in the ascending branches.

CONCLUSIONS

Within the dynamic organization of sleep, the non-random distribution of CAP sequences, with their succession of slow (subtypes A1) and rapid (subtypes A2 and A3) EEG shifts, seem to be responsible for sculpturing EEG synchrony under the driving and alternating forces of NREM and REM sleep.

Spectral characteristics of sleep EEG in chronic insomnia

To determine whether the spectral characteristics of the sleep electroencephalogram (EEG) of insomniacs differ from that of healthy subjects, we compared in each of the first four non-rapid eye movement (NREM) and rapid eye movement (REM) episodes: (a) the time courses of absolute power, averaged over the subjects in each group, for the delta, theta, alpha, sigma and beta frequency bands; (b) the relationship between these time courses; and (c) the overnight trend of integrated power in each frequency band. The results show that NREM power, for all frequencies below the beta range, has slower rise rates and reaches lower levels in the insomniac group, whereas beta power is significantly increased. In REM, insomniacs show lower levels in the delta and theta bands, whereas power in the faster frequency bands is significantly increased. Thus, the pathophysiology of insomnia is characterized not only by the generally acknowledged slow wave deficiency, but also by an excessive hyperarousal of the central nervous system throughout the night, affecting both REM and NREM sleep. This hyperarousal is interpreted in terms of the neuronal group theory of sleep which provides a possible explanation for the discrepancies observed between subjective impressions and objective measures of sleep. Also, it is suggested that the progressive hyperpolarization of the thalamocortical neurons as sleep deepens is slower in the patient population and that this may explain the observed slow wave deficiency. The homeostatic control of slow wave activity, on the other hand, would appear to be intact in the patient population.

Evolution of delta activity within the nonREM sleep episode: a biphasic hypothesis

The time course of delta activity within nonREM (NREM) episodes is measured for 24 healthy subjects with normal REM latencies. The first two NREM episodes in particular, show two very clearly separated peaks for about 35% of the subjects. Another 25% show two less well separated peaks. These double peak patterns are also prevalent in the literature, but there has been a tendency to dismiss them as a skipped REM effect. They are, however, still evident even when the data are averaged over the 24 subjects, indicating a systematic phenomenon. These averaged data are well fitted by an analytic function given by the sum of two consecutive overlapping Gaussian curves. The well-behaved residuals also, are an indication that a biphasic model of this kind is statistically appropriate. The model proposed is simple, with parameters related to physiological phenomena, and it suggests that there may be an underlying process with delta waves emanating from two separate signal sources. Recent neurophysiological findings suggest that delta oscillations are generated both in the thalamus and in the cortex and show that excessive synchronization of slow oscillations may lead to seizures. Hence the speculation that the biphasic process may emanate from cortical and thalamic sources and be protective in the sense that it permits smaller delta amplitudes at each source while retaining the integral delta energy necessary to satisfy sleep pressure. It is significant that the two peaks are most evident in the first two NREM episodes where delta power is high.

Effects of different sleep duration on delta sleep in recovery nights

The study assessed the effects of different amounts of sleep restriction on slow wave sleep (SWS) in the ensuing recovery nights. After one adaptation night and an 8-hr baseline night, six healthy men were individually studied during and following five nights in which sleep was reduced to 5, 4, 3, 2, and 1 hr with a 1-week interval between conditions. Each sleep reduction was followed by an 8-hr recovery night. Finally, a second 8-hr baseline night was recorded. A trend analysis revealed that SWS amount in recovery nights increases with decreasing previous sleep duration. Regression analyses showed that, within each participant, the rebound of SWS after a sleep reduction is predicted better by the total duration of sleep than by the specific amount of SWS lost.

New variables for defining sleep continuity

One very synthetic way to represent a night's sleep is by way of a hypnogram: a graphical representation of the sleep stages as a function of time. The hypnogram is generally quantified by a series of variables that measure the durations and latencies of the various sleep stages including wake. These variables, however, do not fully account for all the information contained in the hypnogram, in particular information on sleep continuity. A series of variables that measure and localize disruption of this continuity are proposed and their utility validated on three groups of patients presenting sleep disorders. Utility is established if the variable is capable of differentiating between patients and healthy controls. Two sets of variables are examined: those that use the entire sleep period as unit of measurement, and those that are measured within each consecutive NREM-REM sleep cycle. The results show that the variables proposed are able to differentiate between groups and, therefore, are useful measures reflecting the hypnogram more precisely. They also show that fragmentation of REM sleep does not present a systematic trend across the night, but that fragmentation of NREM sleep goes up linearly.

Human slow wave sleep: a review and appraisal of recent findings, with implications for sleep functions, and psychiatric illness

Recent findings concerning human slow wave sleep (hSWS-stages 3 + 4; delta EEG activity) are critically reviewed. Areas covered include the significance of the first hSWS cycle; hSWS in extended sleep; relationship between hSWS, prior wakefulness and sleep loss; hSWS influence on sleep length; problems with hSWS deprivation; influence of the circadian rhythm; individual differences in hSWS, especially, age, gender and constitutional variables such as physical fitness and body composition. Transient increases in hSWS can be produced by increasing both the quality and quantity of prior wakefulness, with an underlying mechanism perhaps relating to the waking level of brain metabolism. Whilst there may also be thermoregulatory influences on hSWS, hypotheses that energy conservation and brain cooling are major roles for hSWS are debatable. hSWS seems to offer some form of cerebral recovery, with the prefrontal cortex being particularly implicated. The hSWS characteristics of certain forms of major psychiatric disorders may well endorse this prefrontal link.

A study of the interrupted REM episode

Brief interruptions of REM sleep are considered to be part of the REM episode. The maximum allowable duration of such an interruption, which is used to define the end of the REM episode, is currently a matter of debate. Making measurements on individual REM cycles, inter-REM interval analysis was carried out to determine whether the generally adopted 15 minute empirical rule for this maximum needs to be extended to 25 minutes as suggested by several including Kobayashi et al. Our results show that there is no reason to alter the 15 minute rule and that measurements which do not take into account the time-of-night effect may be misleading. The proportion of interrupted REM episodes observed in our population of healthy adults is high. We have therefore also examined in some detail the phenomenology of the temporal evolution of the structure and content of the interrupted REM episodes. Both showed a definite change over the night: the interruptions in the earlier episodes tend to return the system to slow wave sleep while those in the later episodes tend to return it to wake. It is hypothesized that these interruptions reflect a measure of REM sleep pressure and its interaction with both slow wave sleep and wake pressures.

Modifications of sleep structure by brief forced awakenings at different times of the night

Four subjects were awakened once a night for 10 min at either 01.30, 03.30 or 05.30 h. During the waking intervals, they performed a mental task while remaining in bed. The awakenings did not significantly modify the amount of different stages during subsequent sleep with no effect of time of occurrence in the night. In contrast, the timing of the awakening within the cycle had a significant influence on REM cycle structure. If awakening occurred during a REM episode or shortly thereafter, the following inter-REM interval was shortened; if it occurred late in the cycle, that is shortly before a REM episode, it increased the inter-REM interval beyond the reference length of the corresponding uninterrupted cycle. An explanation based on a model of sleep which implies the simultaneous activity of REM-on and REM-off neurones is proposed.

The intrasleep relationship between wake and stage 4 examined by transition probability analysis

The relationship between wake and stage 4 of slow-wave sleep (SWS), in particular the previously observed deficiency in SWS accompanying sleep containing long-wake periods, is examined in this study of 147 health subjects. Stage shift comportment is compared between those NREM/REM cycles with wake periods greater than 3 minutes and those with less, using the method of transition probabilities. It is shown that these long wake interruptions occur preferentially in light sleep, and systematically disrupt the regular normal descent towards SWS, but do not significantly reduce the number of SWS episodes. There is at the same time, however, a reduction in the average duration of stage 4 periods of SWS which accounts for the observed reduction in the total amount of SWS.