Alexander A. Borbély

Sleep Homeostasis and Models of Sleep Regulation

According to the two-process model of sleep regulation, the timing and structure of sleep are determined by the interaction of a homeostatic and a circadian process. The original qualitative model was elaborated to quantitative versions that included the ultradian dynamics of sleep in relation to the non-REM-REM sleep cycle. The time course of EEG slow-wave activity, the major marker of non-REM sleep homeostasis, as well as daytime alertness were simulated successfully for a considerable number of experimental protocols. They include sleep after partial sleep deprivation and daytime napping, sleep in habitual short and long sleepers, and alertness in a forced desynchrony protocol or during an extended photoperiod. Simulations revealed that internal desynchronization can be obtained for different shapes of the thresholds. New developments include the analysis of the waking EEG to delineate homeostatic and circadian processes, studies of REM sleep homeostasis, and recent evidence for local, use-dependent sleep processes. Moreover, nonlinear interactions between homeostatic and circadian processes were identified. In the past two decades, models have contributed considerably to conceptualizing and analyzing the major processes underlying sleep regulation, and they are likely to play an important role in future advances in the field.

Sleep deprivation: effect on sleep stages and EEG power density in man.

Sleep was analysed in 8 young adults subjects during two baseline nights and two recovery nights following 40.5 h sleep deprivation. Sleep stages were scored from the polygraph records according to conventional criteria. In addition, the EEG records of the entire nights were subjected to spectral analysis to compute the frequency distribution of the power density in the 0.25-25 Hz range for 0.5 Hz or 1.0 Hz bins. In the first recovery night, the power density in the delta band was significantly higher than baseline for total sleep time as well as for sleep stages 2, 3 and 4, 4 and REM. These changes were not restricted to the delta band, but extended to higher frequency bands. Minor, but significant, effects of sleep deprivation were seen in the power density distribution of the second recovery night. In the baseline nights, a progressive reduction of power density in the delta/theta range was present for successive non-REM-REM sleep cycles for total sleep time and stages 2, 3 and 4, and 4. The results show that effects of sleep deprivation as well as trends within the sleep periods are readily apparent from spectral analysis, but are inadequately reflected by conventional sleep scoring. When the power density values were integrated over the entire frequency range (0.75-25 Hz) for each non-REM-REM sleep cycle, an exponential decline from cycle 1 to cycle 3 was suggested. The present findings support the hypothesis that the EEG power density in the low frequency range is an indicator of a progressively declining process during sleep whose initial value is determined by the duration of prior waking.

Low-frequency (<1 Hz) oscillations in the human sleep electroencephalogram

Low-frequency (< 1 Hz) oscillations in intracellular recordings from cortical neurons were first reported in the anaesthetized cat and then also during natural sleep. The slow sequences of hyperpolarization and depolarization were reflected by slow oscillations in the electroencephalogram. The aim of the present study was to examine whether comparable low-frequency components are present in the human sleep electroencephalogram. All-night sleep recordings from eight healthy young men were subjected to spectral analysis in which the low-frequency attenuation of the amplifier was compensated. During sleep stages with a predominance of slow waves and in the first two episodes of non-rapid-eye-movement sleep, the mean power spectrum showed a peak at 0.7-0.8 Hz (range 0.55-0.95 Hz). The typical decline in delta activity from the first to the second non-rapid-eye-movement sleep episode was not present at frequencies below 2 Hz. To detect very low frequency components in the pattern of slow waves and sleep spindles, a new time series was computed from the mean voltage of successive 0.5 s epochs of the low-pass (< 4.5 Hz) or band-pass (12-15 Hz) filtered electroencephalogram. Spectral analysis revealed a periodicity of 20-30 s in the prevalence of slow waves and a periodicity of 4 s in the occurrence of activity in the spindle frequency range. The results demonstrate that distinct components below 1 Hz are also present in the human sleep electroencephalogram spectrum. The differences in the dynamics between the component with a mean peak value at 0.7-0.8 Hz and delta waves above 2 Hz is in accordance with results from animal experiments.

A model of human sleep homeostasis based on EEG slow-wave activity: Quantitative comparison of data and simulations

EEG slow-wave activity (SWA; spectral power in the 0.75-4.5 Hz band) is a function of the duration of prior waking and, thereby, an indicator of sleep homeostasis. We present a model that accounts for both the declining trend of SWA during sleep and for its variation within the successive nonrapid eye movement (non-REM) sleep episodes. The values of the model parameters were estimated by an optimization procedure in which empirical SWA of baseline nights (16 subjects, 26 nights) served as a reference. A sensitivity analysis revealed the model to be quite robust to small changes (+/- 5%) of the parameter values. The estimated parameter values were used to simulate data sets from three different experimental protocols (sleep in the evening or sleep in the morning after prolonged waking, or extended sleep initiated at the habitual bedtime; n = 8 or 9). The timing of the REM trigger parameter was derived from the empirical data. A close fit was obtained between the simulated and empirical SWA data, and even the occasional late SWA peaks during extended sleep could be reproduced. Minor discrepancies suggest indirect or direct circadian influences on SWA. The simulations demonstrate that the concept of sleep homeostasis as proposed in the two-process model of sleep regulation can be refined to account in quantitative terms for empirical data and to predict the changes induced by the prolongation of waking or sleep.

Effect of unilateral somatosensory stimulation prior to sleep on the sleep EEG in humans

SUMMARY  The hypothesis that local activation of brain regions during wakefulness affects the EEG recorded from these regions during sleep was tested by applying vibratory stimuli to one hand prior to sleep. Eight subjects slept in the laboratory for five consecutive nights. During a 6‐h period prior to night 3, either the left or the right hand was vibrated intermittently (20 min on‐8 min off), while prior to night 5 the same treatment was applied to the contralateral hand. The sleep EEG was recorded from frontal, central, parietal and occipital derivations and subjected to spectral analysis. The interhemispheric asymmetry index (IAI) was calculated for spectral power in nonREM sleep in the frequency range 0.25‐25.0 Hz for 0.5‐Hz or 1‐Hz bins. In the first hour of sleep following right‐hand stimulation, the IAI of the central derivation was increased relative to baseline, which corresponds to a shift of power towards the left hemisphere. This effect was most prominent in the delta range, was limited to the first hour of sleep and was restricted to the central derivation situated over the somatosensory cortex. No significant changes were observed following left‐hand stimulation. Although the effect was small, it is consistent with the hypothesis that the activation of specific neuronal populations during wakefulness may have repercussions on their electrical activity pattern during subsequent sleep.

Dual electroencephalogram markers of human sleep homeostasis: correlation between theta activity in waking and slow-wave activity in sleep.

To investigate the relationship between markers of sleep homeostasis during waking and sleep, the electroencephalogram of eight young males was recorded intermittently during a 40-h waking episode, as well as during baseline and recovery sleep. In the course of extended waking, spectral power of the electroencephalogram in the 5-8Hz band (theta activity) increased. In non-rapid eye movement sleep, power in the 0.75-4.5Hz band (slow-wave activity) was enhanced in the recovery night relative to baseline. Comparison of individual records revealed a positive correlation between the rise rate of theta activity during waking and the increase in slow-wave activity in the first non-rapid eye movement sleep episode. A topographic analysis based on 27 derivations showed that both effects were largest in frontal areas. From these results, we suggest that theta activity in waking and slow-wave activity in sleep are markers of a common homeostatic sleep process.

Sleep EEG in the rat as a function of prior waking

Delta activity in non-REM sleep and theta activity in REM sleep in the rat were computed for an 8 h baseline period and for the recovery period after 3, 6, 12 and 24 h sleep deprivation (SD). Delta activity showed a decreasing trend in all schedules and was enhanced as a function of prior waking. Theta activity and REM sleep were increased after 24 h SD.

Effect of sleep deprivation on sleep and EEG power spectra in the rat

EEG power spectra of the rat were computed for consecutive 4-s epochs of the daily light period and matched with the scores of the vigilance states. Sleep was characterized by a progressive decline of low frequency spectral values (i.e. slow wave activity) in non-rapid eye movement (non-REM) sleep, and a progressive increase in the amount of REM sleep. During recovery from 24-h total sleep deprivation (TSD) the following changes were observed: an increase of slow wave activity in non REM sleep with a persisting declining trend; an enhancement of theta activity (7.25-10.0 Hz) both in REM sleep and waking; a decrease of non-REM sleep and an increase of REM sleep. In addition, a slow wave EEG pattern prevailed in the awake and behaving animal during the initial recovery period. In selective sleep deprivation paradigms, either REM sleep or slow wave activity in non-REM sleep was prevented during a 2-h period following upon 24-h TSD. During both procedures, non-REM sleep spectra in the lowest frequency band showed no increase. There was no evidence for a further enhancement of slow wave activity after its selective deprivation. The results indicate that: (1) slow wave activity in non-REM sleep and theta activity in REM sleep may reflect sleep intensity; and (2) REM sleep and active waking, the two states with dominant theta activity, may be functionally related.

Functional topography of the human nonREM sleep electroencephalogram

The sleep EEG of healthy young men was recorded during baseline and recovery sleep after 40 h of waking. To analyse the EEG topography, power spectra were computed from 27 derivations. Mean power maps of the nonREM sleep EEG were calculated for 1‐Hz bins between 1.0 and 24.75 Hz. Cluster analysis revealed a topographic segregation into distinct frequency bands which were similar for baseline and recovery sleep, and corresponded closely to the traditional frequency bands. Hallmarks of the power maps were the frontal predominance in the delta and alpha band, the occipital predominance in the theta band, and the sharply delineated vertex maximum in the sigma band. The effect of sleep deprivation on EEG topography was determined by calculating the recovery/baseline ratio of the power spectra. Prolonged waking induced an increase in power in the low‐frequency range (1–10.75 Hz) which was largest over the frontal region, and a decrease in power in the sigma band (13–15.75 Hz) which was most pronounced over the vertex. The topographic pattern of the recovery/baseline power ratio was similar to the power ratio between the first and second half of the baseline night. These results indicate that changes in sleep propensity are reflected by specific regional differences in EEG power. The predominant increase of low‐frequency power in frontal areas may be due to a high ‘recovery need’ of the frontal heteromodal association areas of the cortex.

Sleep-deprivation: Effects on sleep and EEG in the rat

Summary1.The vigilance states (waking, rapid eye movement (REM) sleep, and non-REM (NREM) sleep), motor activity, food intake and water intake were continuously recorded by telemetry in unrestrained rats. In addition, an amplitude measure and a frequency measure (number of zero-crossings (ZCR) per 10 s) of the telemetered EEG-signal was obtained. The animals were recorded during a control day, then subjected to 12-h or 24-h sleep-deprivation (SD) by means of a slowly rotating cylinder, and subsequently recorded for further 1–2 days. The EEG-parameters were recorded also during SD.2.On the control day, the EEG-amplitude of NREM-sleep exhibited a decreasing trend in the 12-h light-phase (Figs. 3, 4). The occurence of slow wave sleep (SWS; defined as the NREM-sleep fraction with less than 40 ZCR/10 s) was practically limited to the first part of the light-phase (Figs. 2, 4). Cumulative plots of the zero-crossing bands (Fig. 2) revealed a prominent daily rhythm in the EEG-frequency distributionwithin NREM-sleep.3.The percentage of NREM-sleep and REM-sleep was little affected by the 12-h SD, but the amount of SWS and the EEG-amplitude of NREM-sleep were increased (Figs. 4, 6). After a 24-h SD period terminating before light-onset, NREM-sleep was reduced and REM-sleep was markedly enhanced (Figs. 4, 6; Table 1). Both the duration and frequency of REM-sleep episodes were increased, and episodes of total sleep prolonged (Table 2). The amount of SWS was significantly more increased after 24-h SD than after 12-h SD, whereas the EEG-amplitude of NREM-sleep was enhanced to a similar extent after both SD-schedules (Tables 1, 3 Fig. 6).4.After a 24-h SD period terminating before dark-onset, sleep (particularly REM-sleep) was enhanced in the first hours of the dark-phase, yet the usual high activity bouts prevailed in the later part of the dark-phase (Figs. 7, 8; Table 1). The extent and time-course of REM-sleep rebound was similar after the two 24-SD schedules, whereas SWS-rebound was different: SWS exhibited a one-stage rebound when recovery started in the light-phase, and a two-stage rebound when recovery started in the dark-phase (Fig. 9).5.A comparison of the effects of 12-h SD performed with the usual and with the double cylinder rotation rate, showed only small differences, indicating that forced locomotion was a minor factor in comparison to sleep-deprivation (Fig. 10; Table 1).6.The daily pattern of SWS on control days, and the marked increase of SWS after SD correspond to the results from other animal and human studies. It is proposed that due to the existence of an intensity dimension, NREM-sleep is finely regulated around its baseline level, and thus may be readily and accurately adjusted to current ‘needs’, whereas REM-sleep, lacking an apparent intensity gradient, is regulated around a level which is considerably below baseline. Thus, in contrast to NREM-sleep, REM-sleep compensation can occur only by an increase in the time devoted to this state, thereby curtailing the time available for other activities.