Different Effects of Sleep Deprivation and Torpor on EEG Slow-Wave Characteristics in Djungarian Hamsters.

It has been shown previously in Djungarian hamsters that the initial electroencephalography (EEG) slow-wave activity (power in the 0.5-4.0 Hz band; SWA) in non-rapid eye movement (NREM) sleep following an episode of daily torpor is consistently enhanced, similar to the SWA increase after sleep deprivation (SD). However, it is unknown whether the network mechanisms underlying the SWA increase after torpor and SD are similar. EEG slow waves recorded in the neocortex during sleep reflect synchronized transitions between periods of activity and silence among large neuronal populations. We therefore set out to investigate characteristics of individual cortical EEG slow waves recorded during NREM sleep after 4 h SD and during sleep after emergence from an episode of daily torpor in adult male Djungarian hamsters. We found that during the first hour after both SD and torpor, the SWA increase was associated with an increase in slow-wave incidence and amplitude. However, the slopes of single slow waves during NREM sleep were steeper in the first hour after SD but not after torpor, and, in contrast to sleep after SD, the magnitude of change in slopes after torpor was unrelated to the changes in SWA. Furthermore, slow-wave slopes decreased progressively within the first 2 h after SD, while a progressive increase in slow-wave slopes was apparent during the first 2 h after torpor. The data suggest that prolonged waking and torpor have different effects on cortical network activity underlying slow-wave characteristics, while resulting in a similar homeostatic sleep response of SWA. We suggest that sleep plays an important role in network homeostasis after both waking and torpor, consistent with a recovery function for both states.

The EEG microstate topography is predominantly determined by intracortical sources in the alpha band.

Human brain electric activity can be measured at high temporal and fairly good spatial resolution via electroencephalography (EEG). The EEG microstate analysis is an increasingly popular method used to investigate this activity at a millisecond resolution by segmenting it into quasi-stable states of approximately 100 ms duration. These so-called EEG microstates were postulated to represent atoms of thoughts and emotions and can be classified into four classes of topographies A through D, which explain up to 90% of the variance of continuous EEG. The present study investigated whether these topographies are primarily driven by alpha activity originating from the posterior cingulate cortex (all topographies), left and right posterior cortices, and the anterior cingulate cortex (topographies A, B, and C, respectively). We analyzed two 64-channel resting state EEG datasets (N = 61 and N = 78) of healthy participants. Sources of head-surface signals were determined via exact low resolution electromagnetic tomography (eLORETA). The Hilbert transformation was applied to identify instantaneous source strength of four EEG frequency bands (delta through beta). These source strength values were averaged for each participant across time periods belonging to a particular microstate. For each dataset, these averages of the different microstate classes were compared for each voxel. Consistent differences across datasets were identified via a conjunction analysis. The intracortical strength and spatial distribution of alpha band activity mainly determined whether a head-surface topography of EEG microstate class A, B, C, or D was induced. EEG microstate class C was characterized by stronger alpha activity compared to all other classes in large portions of the cortex. Class A was associated with stronger left posterior alpha activity than classes B and D, and class B was associated with stronger right posterior alpha activity than A and D. Previous results indicated that EEG microstate dynamics reflect a fundamental mechanism of the human brain that is altered in different mental states in health and disease. They are characterized by systematic transitions between four head-surface topographies, the EEG microstate classes. Our results show that intra-cortical alpha oscillations, which likely reflect decreased cortical excitability, primarily account for the emergence of these classes. We suggest that microstate class dynamics reflect transitions between four global attractor states that are characterized by selective inhibition of specific intra-cortical regions.

Changes of cerebral tissue oxygen saturation at sleep transitions in adolescents.

In adults, cerebral oxy-([O2Hb]) and deoxyhemoglobin concentrations ([HHb]) change characteristically at transitions of sleep stages. The aims were to assess these changes in adolescents and additionally to measure tissue oxygen saturation (StO2) by near infrared spectroscopy (NIRS). Previously it was reported that in adults [O2Hb] increased and [HHb] decreased at the transition from non-rapid eye movement sleep (NREMS) to REMS and wakefulness. Transitions to NREMS from REMS/wakefulness led to a decrease in [O2Hb] and an increase in [HHb]. We measured [O2Hb], [HHb] and tissue oxygenation (StO2) with NIRS approximately above the left prefrontal cortex in 12 healthy adolescent males (aged 10-16 years). We found comparable signs and magnitudes of changes in [O2Hb] and [HHb] as observed in adults. StO2 increased at the transitions from NREMS to REMS and decreased from REMS to NREMS and at sleep onset (all p < 0.01, linear mixed effects model). Changes in oxygen metabolism during sleep transitions are similar in adolescents and adults. In addition, we show for the first time temporal changes of StO2 at sleep transitions.

Correlation between sleep and cognitive functions after hemispheric ischaemic stroke.

BACKGROUND: The aim of this study was to test the hypothesis of a link between sleep and cognitive functions, particularly memory and attention, after stroke. METHODS: We studied 11 consecutive patients with first-ever hemispheric ischaemic stroke within eight days after symptoms onset and nine of them at least three months after stroke. Sleep EEG was recorded with a portable system. Cognitive functions were assessed using a standardized battery of tests allowing the estimation of the most relevant domains of cognition. Five age-matched healthy subjects served as controls. RESULTS: The patients were aged 43 +/- 12 years (18-59). In five patients stroke was right-sided and in six patients left-sided. In the acute stroke phase a correlation between attention and amounts of slow wave sleep (SWS), Rapid eye movement (REM) sleep and sleep efficiency was found. In the recovery phase verbal/figural memory and attention significantly improved in most patients. Furthermore, an association between (i) verbal/figural (non-verbal) memory and amounts of SWS, REM sleep and sleep efficiency, and between (ii) attention and sleep efficiency was observed. CONCLUSIONS: The results point to a link between sleep and cognitive functions and their recovery after hemispheric stroke. Further studies are needed to determine the specific nature of this link.

Sleep homeostasis in the rat in the light and dark period.

Sleep is regulated by the interaction of a homeostatic (Process S) and a circadian component. The duration of prior wakefulness is the main factor influencing subsequent sleep duration and its intensity. We investigated in the rat whether the sleep-wake history before sleep deprivation (SD) contributes to the effects of sleep loss incurred during the SD. A 24-h baseline recording was followed by 6 h SD at light onset (SD-Light, n=7), or at dark onset (SD-Dark, n=8) and 18 h recovery. Both SDs led to a pronounced increase in slow wave activity (SWA, EEG power between 0.75 and 4.0 Hz) in NREM sleep and increased sleep consolidation. The prolongation of sleep episodes was associated with increased intra-episode SWA. The amount of waking before the SD correlated positively with the SWA increase during recovery, and SWA levels before SD were negatively correlated with their subsequent increase. The time-course of SWA (Process S) as well as of single frequency bins within the SWA band was successfully simulated based on vigilance-state distribution. The time constant of the exponential monotonic decay (Td) was higher for the 0.75-1.0 Hz bin compared to all remaining frequency bins of the SWA band, reflecting a slower process determining the slow EEG component during sleep. The data show that the homeostatic response after SD, consisting of increased sleep intensity and sleep consolidation is determined by a combination of SD and the preceding vigilance-state history. The slower dynamics of low frequency delta power compared to fast delta frequencies point to heterogeneity within the traditionally defined SWA band.

Trait-like individual differences in the human sleep electroencephalogram.

We aimed to examine whether commonly observed individual differences in sleep architecture and the sleep electroencephalogram reflect individual traits, which are amenable to a genetic investigation of human sleep. We studied intra-individual stability and inter-individual variation in sleep and sleep electroencephalogram spectra across four baseline recordings of eight healthy young men. A similarity concept based on Euclidean distances between vectors was applied. Visually scored sleep variables served as feature vector components, along with electroencephalogram power spectra in non-rapid-eye-movement and rapid-eye-movement sleep. The distributions of similarity coefficients of feature vectors revealed a clear distinction between high within-subject similarity (i.e. stability), and low between-subject similarity (i.e. variation). Moreover, a cluster analysis based on electroencephalogram spectra in both non-rapid-eye-movement and rapid-eye-movement sleep segregated all four baseline nights of each individual into a distinct cluster. To investigate whether high and low sleep pressure affects the similarity coefficients, normalized non-rapid-eye-movement sleep electroencephalogram spectra of the first and second half of the recordings were compared. Because the electroencephalogram changes systematically in the course of the night, within-subject variation no longer differed from between-subject variation. In conclusion, our data provide evidence for trait-like characteristics in the sleep electroencephalogram. Further studies may help to identify distinct phenotypes to search for genes underlying functional aspects of undisturbed human sleep.

Modeling circadian rhythm generation in the suprachiasmatic nucleus with locally coupled self-sustained oscillators: phase shifts and phase response curves.

Circadian rhythm generation in the suprachiasmatic nucleus was modeled by locally coupled self-sustained oscillators. The model is composed of 10,000 oscillators, arranged in a square array. Coupling between oscillators and standard deviation of (randomly determined) intrinsic oscillator periods were varied. A stable overall rhythm emerged. The model behavior was investigated for phase shifts of a 24-h zeitgeber cycle. Prolongation of either the dark or the light phase resulted in a lengthening of the period, whereas shortening of the dark or the light phase shortened the period. The model's response to shifts in the light-dark cycle was dependent only on the extent of the shift and was insensitive to changes in parameters. Phase response curves (PRC) and amplitude response curves were determined for single and triple 5-h light pulses (1000 lux). Single pulses lead to type 1 PRCs with larger phase shifts for weak coupling. Triple pulses generally evoked type 1 PRCs with the exception of weak coupling, where a type 0 PRC was observed.

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.

Commentary: future considerations for models of human neurobehavioral function.

Modeling human neurobehavioral functions has the goal of identifying work-rest schedules that are safer and more productive. The models of Folkard et al. and of Jewett and Kronauer illustrate excellent progress toward this goal. Examination of these models reveals four additional areas that need to be addressed to facilitate continued development of accurate models of neurobehavioral functions. (1) The choice of neurobehavioral metrics may have a significant influence on model development. The lack of correlation among different neurobehavioral measures may make comparisons of models difficult. Many neurobehavioral measures are confounded by secondary and random error variance that can lead to model distortion. Although different models may ultimately be required for different neurobehavioral functions, measures that have been extensively validated to be sensitive to circadian variation and sleep loss should take priority in model development. (2) Because error variance in neurobehavioral outcomes can be substantial in uncontrolled environments, model validation should proceed from controlled laboratory protocols to real-world scenarios. Once validated, the ability of a model to predict field data can be tested. (3) While neurobehavioral models have been developed to predict behavior over time (i.e., within-subjects), to be useful in the real world, models will also ultimately have to provide estimates of between-subject variation in vulnerability to neurobehavioral dysfunction during night work or sleep loss (e.g., younger versus older workers). (4) Finally, to be theoretically accurate and practically useful, models of human neurobehavioral functions should be able to predict both cumulative effects (i.e., across days or weeks) and the influence of countermeasures (e.g., light, naps, caffeine).

Simulation of human sleep: ultradian dynamics of electroencephalographic slow-wave activity.

The typical declining trend of electroencephalographic (EEG) slow-wave activity (SWA) within a sleep period is represented in the two-process model of sleep regulation by an exponentially decaying process (Process S). The model has been further elaborated to simulate not only the global changes of SWA, but also the dynamics within non-rapid-eye-movement (non-REM) sleep episodes. In this new model, the initial intraepisodic buildup of SWA is determined by the combined action of an exponentially increasing process and a saturation process, whereas its fall at the end of an episode is due to an exponentially decreasing process. The global declining trend of SWA over consecutive episodes results from the monotonic decay of the intraepisodic saturation level. In contrast to Process S in the two-process model, this decay is not represented by an exponential function, but is proportional to the momentary level of SWA. REM sleep episodes are triggered by an external function. The model allows one to simulate the ultradian pattern of SWA for baseline nights as well as changes induced by a prolonged waking period, a daytime nap, a partial slow-wave sleep deprivation, or an antidepressant drug.

Sleep initiation and initial sleep intensity: interactions of homeostatic and circadian mechanisms.

Sleep initiation and sleep intensity in humans show a dissimilar time course. The propensity of sleep initiation (PSI), as measured by the multiple sleep latency test, remains at a relatively constant level throughout the habitual period of waking or exhibits a midafternoon peak. When waking is extended into the sleep period, PSI rises rapidly within a few hours. In contrast, sleep intensity, as measured by electroencephalographic slow-wave activity during naps, shows a gradual increase during the period of habitual waking. In the two-process model of sleep regulation, it corresponds to the rising limb of the homeostatic Process S. We propose that PSI is determined by the difference between Process S and the threshold H defining sleep onset, which is modulated by the circadian process C. In contrast to a previous version of the model, the parameters of H (amplitude, phase, skewness) differ from those of threshold L, which defines sleep termination. The present model is able to simulate the time course of PSI under baseline conditions as well as following recovery sleep after extended sleep deprivation. The simulations suggest that during the regular period of waking, a circadian process counteracts the increasing sleep propensity induced by a homeostatic process. Data obtained in the rat indicate that during the circadian period of predominant waking, a circadian process prevents a major intrusion of sleep.

Individual 'fingerprints' in human sleep EEG topography.

The sleep EEG of eight healthy young men was recorded from 27 derivations during a baseline night and a recovery night after 40 h of waking. Individual power maps of the nonREM sleep EEG were calculated for the delta, theta, alpha, sigma and beta range. The comparison of the normalized individual maps for baseline and recovery sleep revealed very similar individual patterns within each frequency band. This high correspondence was quantified and statistically confirmed by calculating the Manhattan distance between all pairs of maps within and between individuals. Although prolonged waking enhanced power in the low-frequency range (0.75-10.5 Hz) and reduced power in the high-frequency range (13.25-25 Hz), only minor effects on the individual topography were observed. Nevertheless, statistical analysis revealed frequency-specific regional effects of sleep deprivation. The results demonstrate that the pattern of the EEG power distribution in nonREM sleep is characteristic for an individual and may reflect individual traits of functional anatomy.

Serotonin-2 receptors and human sleep: effect of a selective antagonist on EEG power spectra.

To investigate the effect on the sleep EEG, a 1-mg oral dose of SR 46349B, a novel 5-HT2 antagonist, was administered three hours before bedtime. The drug enhanced slow wave sleep (SWS) and reduced stage 2 without affecting subjective sleep quality. In nonREM sleep (NREMS) EEG slow-wave activity (SWA; power within 0.75-4.5 Hz) was increased and spindle frequency activity (SFA; power within 12.25-15 Hz) was decreased. The relative NREMS power spectrum showed a bimodal pattern with the main peak at 1.5 Hz and a secondary peak at 6 Hz. A regional analysis based on bipolar derivations along the antero-posterior axis revealed significant 'treatment' x 'derivation' interactions within the 9-16 Hz range. In enhancing SWA and attenuating SFA, the 5-HT2 receptor antagonist mimicked the effect of sleep deprivation, whereas the pattern of the NREMS spectrum differed.

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.

Coherence analysis of the human sleep electroencephalogram.

Animal studies have shown that the sleep-related oscillations in the frequency range of spindles and slow-waves, and in the gamma band occur synchronously over large parts of the cerebral cortex. Coherence analysis was used to investigate these oscillations in the human sleep electroencephalogram. In all-night electroencephalogram recordings from eight young subjects power and coherence spectra within and between cerebral hemispheres were computed from bipolar derivations placed bilaterally along the antero-posterior axis. The 0.75-50 Hz range was examined with a resolution of 0.25 Hz. Distinct peaks in coherence were present in non-rapid eye movement sleep but not in rapid eye movement sleep. The most prominent and consistent peak was seen in the range of sleep spindles (13-14 Hz), and additional peaks were present in the alpha band (9-10 Hz) and low delta band (1-2 Hz). Whereas coherence in the spindle range was highest in stage 2, the alpha peak was most prominent in slow-wave sleep (stages 3 and 4). Interhemispheric coherence at 30 Hz was higher in rapid eye movement sleep than in non-rapid eye movement sleep. There were also marked sleep state-independent regional differences. Coherence between homologous interhemispheric derivations was high in the low frequency range and declined with increasing frequencies, whereas coherence of intrahemispheric and non-homologous interhemispheric derivations was at a low level throughout the spectra. It is concluded that coherence analysis may provide insights into large-scale functional connectivities of brain regions during sleep. The high coherence of sleep spindles is an indication for their widespread and quasi-synchronous occurrence throughout the cortex and may point to their specific role in the sleep process.

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.

Interhemispheric coherence of the sleep electroencephalogram in mice with congenital callosal dysgenesis.

Regional differences in the effect of sleep deprivation on the sleep electroencephalogram (EEG) may be related to interhemispheric synchronization. To investigate the role of the corpus callosum in interhemispheric EEG synchronization, coherence spectra were computed in mice with congenital callosal dysgenesis (B1) under baseline conditions and after 6-h sleep deprivation, and compared with the spectra of a control strain (C57BL/6). In B1 mice coherence was lower than in controls in all vigilance states. The level of coherence in each of the three totally acallosal mice was lower than in the mice with only partial callosal dysgenesis. The difference between B1 and control mice was present over the entire 0.5-25 Hz frequency range in non-rapid eye movement sleep (NREM sleep), and in all frequencies except for the high delta and low theta band (3-7 Hz) in rapid eye movement (REM) sleep and waking. In control mice, sleep deprivation induced a rise of coherence in the Delta band of NREM sleep in the first 2 h of recovery. This effect was absent in B1 mice with total callosal dysgenesis and attenuated in mice with partial callosal dysgenesis. In both strains the effect of sleep deprivation dissipated within 4 h. The results show that EEG synchronization between the hemispheres in sleep and waking is mediated to a large part by the corpus callosum. This applies also to the functional changes induced by sleep deprivation in NREM sleep. In contrast, interhemispheric synchronisation of theta oscillations in waking and REM sleep may be mediated by direct interhippocampal connections.

Sleep and rest facilitate auditory learning.

Sleep is superior to waking for promoting performance improvements between sessions of visual perceptual and motor learning tasks. Few studies have investigated possible effects of sleep on auditory learning. A key issue is whether sleep specifically promotes learning, or whether restful waking yields similar benefits. According to the "interference hypothesis," sleep facilitates learning because it prevents interference from ongoing sensory input, learning and other cognitive activities that normally occur during waking. We tested this hypothesis by comparing effects of sleep, busy waking (watching a film) and restful waking (lying in the dark) on auditory tone sequence learning. Consistent with recent findings for human language learning, we found that compared with busy waking, sleep between sessions of auditory tone sequence learning enhanced performance improvements. Restful waking provided similar benefits, as predicted based on the interference hypothesis. These findings indicate that physiological, behavioral and environmental conditions that accompany restful waking are sufficient to facilitate learning and may contribute to the facilitation of learning that occurs during sleep.

Human versus porcine insulin in patients with insulin-dependent diabetes mellitus: differences in sleep and the sleep EEG during near-normoglycemia.

To investigate whether porcine insulin (PI) and human insulin (HI) have different effects on brain functions outside of hypoglycemia, sleep and the sleep EEG were recorded in eight insulin-dependent diabetes mellitus (IDDM) patients in three separate sessions of 2 consecutive nights. Near-normoglycemia was confirmed by measurements of capillary blood glucose before and after sleep and at 0145 hours. The treatment effect (PI compared to HI) consisted of a change in the NREM sleep EEG in the spindle frequency range. Spectral power density in the 14-Hz bin was reduced upon transfer from PI (session 1) to HI (session 2) in all subjects, and increased upon reversal to PI (session 3) in all but one subject. There were no significant treatment effects on any other sleep EEG variable or on sleep stages. The subjects rated their sleep as more sound and their state in the morning as more relaxed during PI treatment. They were, however, not blinded to the type of insulin they were using. Porcine insulin and human insulin may exert differential effects on spindle-generating mechanisms in the thalamocortical system. The results indicate that human insulin may affect brain functions differently compared to animal insulin under near-normoglycemic conditions.

Brain topography of the human sleep EEG: antero-posterior shifts of spectral power.

To investigate the brain topography of the human sleep EEG along the antero-posterior axis, spectra (0.25-25 Hz; 1 Hz bins) were computed from all-night EEG recordings (n = 20 subjects) obtained from an anterior F3-C3) and a posterior (P3-O1) derivation. State-dependent and frequency-dependent topographic differences were observed. In non-rapid eye movement (REM) sleep, power in the anterior derivation was higher than in the posterior derivation in the 2 Hz bin, and lower in the 4-10 Hz bins. In REM sleep, a posterior dominance was present in most bins below 18 Hz. The 2-6 Hz bins exhibited an antero-posterior shift of power over consecutive non-REM sleep episodes. Consistent shifts of power were also present within non-REM sleep episodes. The results suggest that anterior and posterior cortical regions may be differently involved in the sleep process.