The function of the brain activity that defines slow wave sleep (SWS) and rapid eye movement (REM) sleep in mammals is unknown. During SWS, the level of electroencephalogram slow wave activity (SWA or 0.5–4.5 Hz power density) increases and decreases as a function of prior time spent awake and asleep, respectively. Such dynamics occur in response to waking brain use, as SWA increases locally in brain regions used more extensively during prior wakefulness. Thus, SWA is thought to reflect homeostatically regulated processes potentially tied to maintaining optimal brain functioning. Interestingly, birds also engage in SWS and REM sleep, a similarity that arose via convergent evolution, as sleeping reptiles and amphibians do not show similar brain activity. Although birds deprived of sleep show global increases in SWA during subsequent sleep, it is unclear whether avian sleep is likewise regulated locally. Here, we provide, to our knowledge, the first electrophysiological evidence for local sleep homeostasis in the avian brain. After staying awake watching David Attenborough's The Life of Birds with only one eye, SWA and the slope of slow waves (a purported marker of synaptic strength) increased only in the hyperpallium—a primary visual processing region—neurologically connected to the stimulated eye. Asymmetries were specific to the hyperpallium, as the non-visual mesopallium showed a symmetric increase in SWA and wave slope. Thus, hypotheses for the function of mammalian SWS that rely on local sleep homeostasis may apply also to birds.
potentiation; slow wave activity; synaptic downscaling; synaptic strength
Slow wave activity (SWA), the EEG power between 0.5 - 4 Hz during NREM sleep, is one of the best characterized markers of sleep need, as it increases as a function of preceding waking duration and decreases during sleep, but the underlying mechanisms remain unknown. We hypothesized that SWA is high at sleep onset because it reflects the occurrence, during the previous waking period, of widespread synaptic potentiation in cortical and subcortical areas. Consistent with this hypothesis, we recently showed that the more rats explore, the stronger is the cortical expression of BDNF during wakefulness, and the larger is the increase in SWA during the subsequent sleep period. There is compelling evidence that BDNF plays a causal role in synaptic potentiation, and exogenous application of BDNF in vivo is sufficient to induce long-term increases in synaptic strength. We therefore performed cortical unilateral microinjections of BDNF in awake rats and measured SWA during the subsequent sleep period. SWA during NREM sleep was higher in the injected hemisphere relative to the contralateral one. The effect was reversible within 2 hours, and did not occur during wakefulness or REM sleep. Asymmetries in NREM SWA did not occur after vehicle injections. Furthermore, microinjections, during wakefulness, of a polyclonal anti-BDNF antibody or K252a, an inhibitor of BDNF TrkB receptors, led to a local SWA decrease during the following sleep period. These effects were also reversible and specific for NREM sleep. These results show a causal link between BDNF expression during wakefulness and subsequent sleep regulation.
sleep homeostasis; cerebral cortex; EEG; rat; BDNF; Synaptic Plasticity
Sleep homeostasis refers to the increase of sleep pressure during waking and the decrease of sleep intensity during sleep. Electroencephalography (EEG) slow-wave activity (SWA; EEG power in the 0.75-4.5 Hz range) is a marker of non-rapid eye movement (NREM) sleep intensity and can be used to model sleep homeostasis (Process S). SWA shows a frontal predominance, and its increase after sleep deprivation is most pronounced in frontal areas. The question arises whether the dynamics of the homeostatic Process S also show regional specificity. Furthermore, the spatial distribution of SWA is characteristic for an individual and may reflect traits of functional anatomy. The aim of the current study was to quantify inter-individual variation in the parameters of Process S and investigate their spatial distribution. Polysomnographic recordings obtained with 27 EEG derivations of a baseline night of sleep and a recovery night of sleep after 40 h of sustained wakefulness were analyzed. Eight healthy young subjects participated in this study. Process S was modeled by a saturating exponential function during wakefulness and an exponential decline during sleep. Empirical mean SWA per NREM sleep episode at episode midpoint served for parameter estimation at each derivation. Time constants were restricted to a physiologically meaningful range.
For both, the buildup and decline of Process S, significant topographic differences were observed: The decline and buildup of Process S were slowest in fronto-central areas while the fastest dynamics were observed in parieto-occipital (decrease) and frontal (buildup) areas. Each individual showed distinct spatial patterns in the parameters of Process S and the parameters differed significantly between individuals.
For the first time, topographical aspects of the buildup of Process S were quantified. Our data provide an additional indication of regional differences in sleep homeostasis and support the notion of local aspects of sleep regulation.
Sleep is regulated by both a circadian and a homeostatic process. The homeostatic process reflects the duration of prior wakefulness: the longer one stays awake, the longer and/or more intense is subsequent sleep. In mammals, the best marker of the homeostatic sleep drive is slow wave activity (SWA), the electroencephalographic (EEG) power spectrum in the 0.5–4 Hz frequency range during non-rapid eye movement (NREM) sleep. In mammals, NREM sleep SWA is high at sleep onset, when sleep pressure is high, and decreases progressively to reach low levels in late sleep. Moreover, SWA increases further with sleep deprivation, when sleep also becomes less fragmented (the duration of sleep episodes increases, and the number of brief awakenings decreases). Although avian and mammalian sleep share several features, the evidence of a clear homeostatic response to sleep loss has been conflicting in the few avian species studied so far. The aim of the current study was therefore to ascertain whether established markers of sleep homeostasis in mammals are also present in the white-crowned sparrow (Zonotrichia leucophrys gambelii), a migratory songbird of the order Passeriformes. To accomplish this goal, we investigated amount of sleep, sleep time course, and measures of sleep intensity in 6 birds during baseline sleep and during recovery sleep following 6 hours of sleep deprivation.
Continuous (24 hours) EEG and video recordings were used to measure baseline sleep and recovery sleep following short-term sleep deprivation. Sleep stages were scored visually based on 4-sec epochs. EEG power spectra (0.5–25 Hz) were calculated on consecutive 4-sec epochs. Four vigilance states were reliably distinguished based on behavior, visual inspection of the EEG, and spectral EEG analysis: Wakefulness (W), Drowsiness (D), slow wave sleep (SWS) and rapid-eye movement (REM) sleep. During baseline, SWA during D, SWS, and NREM sleep (defined as D and SWS combined) was highest at the beginning of the major sleep period and declined thereafter. Moreover, peak SWA in both SWS and NREM sleep increased significantly immediately following sleep deprivation relative to baseline.
As in mammals, sleep deprivation in the white-crowned sparrow increases the intensity of sleep as measured by SWA.
Sleep homeostasis is altered in major depressive disorder (MDD). Pre-to post-sleep decline in waking auditory evoked potential (AEP) amplitude has been correlated with sleep slow wave activity (SWA), suggesting that overnight changes in waking AEP amplitude are homeostatically regulated in healthy individuals. This study investigated whether the overnight change in waking AEP amplitude and its relation to SWA is altered in MDD.
Using 256-channel high-density electroencephalography, all-night sleep polysomnography and single-tone waking AEPs pre-and post-sleep were collected in 15 healthy controls (HC) and 15 non-medicated individuals with MDD.
N1 and P2 amplitudes of the waking AEP declined after sleep in the HC group, but not in MDD. The reduction in N1 amplitude also correlated with fronto-central SWA in the HC group, but a comparable relationship was not found in MDD, despite equivalent SWA between groups. No pre-to post-sleep differences were found for N1 or P2 latencies in either group. These findings were not confounded by varying levels of alertness or differences in sleep variables between groups.
MDD involves altered sleep homeostasis as measured by the overnight change in waking AEP amplitude. Future research is required to determine the clinical implications of these findings.
major depressive disorder; auditory evoked potentials; sleep; homeostasis; slow-wave sleep
Sleep slow wave activity (SWA) is thought to reflect sleep need, increasing in proportion to the length of prior wakefulness and decreasing during sleep. However, the process responsible for SWA regulation is not known. We showed recently that SWA increases locally after a learning task involving a circumscribed brain region, suggesting that SWA may reflect plastic changes triggered by learning.
To test this hypothesis directly, we used transcranial magnetic stimulation (TMS) in conjunction with high-density EEG in humans. We show that 5-Hz TMS applied to motor cortex induces a localized potentiation of TMS-evoked cortical EEG responses. We then show that, in the sleep episode following 5-Hz TMS, SWA increases markedly (+39.1±17.4%, p<0.01, n = 10). Electrode coregistration with magnetic resonance images localized the increase in SWA to the same premotor site as the maximum TMS-induced potentiation during wakefulness. Moreover, the magnitude of potentiation during wakefulness predicts the local increase in SWA during sleep.
These results provide direct evidence for a link between plastic changes and the local regulation of sleep need.
This review summarizes the brain mechanisms controlling sleep and wakefulness. Wakefulness promoting systems cause low-voltage, fast activity in the electroencephalogram (EEG). Multiple interacting neurotransmitter systems in the brain stem, hypothalamus, and basal forebrain converge onto common effector systems in the thalamus and cortex. Sleep results from the inhibition of wake-promoting systems by homeostatic sleep factors such as adenosine and nitric oxide and GABAergic neurons in the preoptic area of the hypothalamus, resulting in large-amplitude, slow EEG oscillations. Local, activity-dependent factors modulate the amplitude and frequency of cortical slow oscillations. Non-rapid-eye-movement (NREM) sleep results in conservation of brain energy and facilitates memory consolidation through the modulation of synaptic weights. Rapid-eye-movement (REM) sleep results from the interaction of brain stem cholinergic, aminergic, and GABAergic neurons which control the activity of glutamatergic reticular formation neurons leading to REM sleep phenomena such as muscle atonia, REMs, dreaming, and cortical activation. Strong activation of limbic regions during REM sleep suggests a role in regulation of emotion. Genetic studies suggest that brain mechanisms controlling waking and NREM sleep are strongly conserved throughout evolution, underscoring their enormous importance for brain function. Sleep disruption interferes with the normal restorative functions of NREM and REM sleep, resulting in disruptions of breathing and cardiovascular function, changes in emotional reactivity, and cognitive impairments in attention, memory, and decision making.
When the brain is awake, neurons in the cerebral cortex fire irregularly and the electroencephalogram (EEG) displays low amplitude, high frequency fluctuations. After falling asleep, neurons start oscillating between ON periods, when they fire as during wake, and OFF periods, when they stop firing altogether, and the EEG displays high amplitude slow waves. But what happens to neuronal firing after a long period of wake? We show here in freely behaving rats that, after prolonged wake, cortical neurons can go briefly “OFF line” as they do in sleep, accompanied by slower waves in the local EEG. Strikingly, neurons often go OFF line in one cortical area and not in another. During these periods of “local sleep”, whose incidence increases with wake duration, rats appear awake, active, and display a wake EEG. However, they are progressively impaired in a sugar pellet reaching task. Thus, though both the EEG and behavior indicate wakefulness, local populations of neurons in the cortex may be falling asleep, with negative consequences on performance.
slow wave sleep; slow oscillations; EEG; cerebral cortex; multi-unit recording; reaching task; sleep deprivation
In a recent series of experiments, we demonstrated that a visuomotor adaptation task, 12 hours of left arm immobilization, and rapid transcranial magnetic stimulation (rTMS) during waking can each induce local changes in the topography of electroencephalographic (EEG) slow wave activity (SWA) during subsequent non-rapid eye movement (NREM) sleep. However, the poor spatial resolution of EEG and the difficulty of relating scalp potentials to the activity of the underlying cortex limited the interpretation of these results. In order to better understand local cortical regulation of sleep, we used source modeling to show that plastic changes in specific cortical areas during waking produce correlated changes in SWA during sleep in those same areas. We found that implicit learning of a visuomotor adaptation task induced an increase in SWA in right premotor and sensorimotor cortices when compared to a motor control. These same areas have previously been shown to be selectively involved in the performance of this task. We also found that arm immobilization resulted in a decrease in SWA in sensorimotor cortex. Inducing cortical potentiation with repetitive transcranial magnetic stimulation (rTMS) caused an increase in SWA in the targeted area and a decrease in SWA in the contralateral cortex. Finally, we report the first evidence that these modulations in SWA may be related to the dynamics of individual slow waves. We conclude that there is a local, plasticity dependent component to sleep regulation and confirm previous inferences made from the scalp data.
It has been hypothesized that slow wave activity, a well established measure of sleep homeostasis that increases after waking and decreases after sleep, may reflect changes in cortical synaptic strength. If so, the amplitude of sensory evoked responses should also vary as a function of time awake and asleep in a way that reflects sleep homeostasis.
Using 256-channel, high-density electroencephalography (EEG) in 12 subjects, auditory evoked potentials (AEP) and spontaneous waking data were collected during wakefulness before and after sleep.
The amplitudes of the N1 and P2 waves of the AEP were reduced after a night of sleep. In addition, the decline in N1 amplitude correlated with low-frequency EEG power during non-rapid eye movement sleep and spontaneous wakefulness, both homeostatically regulated measures of sleep need.
The decline in AEP amplitude after a night of sleep may reflect a homeostatic reduction in synaptic strength.
These findings provide further evidence for a connection between synaptic plasticity and sleep homeostasis.
Auditory evoked potentials; sleep homeostasis; synaptic plasticity; slow wave sleep; N1 and P2; electroencephalogram
The amount and architecture of vigilance states are governed by two distinct processes, which occur at different time scales. The first, a slow one, is related to a wake/sleep dependent homeostatic Process S, which occurs on a time scale of hours, and is reflected in the dynamics of NREM sleep EEG slow-wave activity. The second, a fast one, is manifested in a regular alternation of two sleep states – NREM and REM sleep, which occur, in rodents, on a time scale of ∼5–10 minutes. Neither the mechanisms underlying the time constants of these two processes – the slow one and the fast one, nor their functional significance are understood. Notably, both processes are primarily apparent during sleep, while their potential manifestation during wakefulness is obscured by ongoing behaviour. Here, we find, in mice provided with running wheels, that the two sleep processes become clearly apparent also during waking at the level of behavior and brain activity. Specifically, the slow process was manifested in the total duration of waking periods starting from dark onset, while the fast process was apparent in a regular occurrence of running bouts during the waking periods. The dynamics of both processes were stable within individual animals, but showed large interindividual variability. Importantly, the two processes were not independent: the periodic structure of waking behaviour (fast process) appeared to be a strong predictor of the capacity to sustain continuous wakefulness (slow process). The data indicate that the temporal organization of vigilance states on both the fast and the slow time scales may arise from a common neurophysiologic mechanism.
Substantial evidence suggests that brain regions that have been disproportionately used during waking will require a greater intensity and/or duration of subsequent sleep. For example, rats use their whiskers in the dark and their eyes during the light which manifests as a greater magnitude of electroencephalogram (EEG) slow wave activity in the somatosensory and visual cortex during sleep in the corresponding light and dark periods respectively. The parsimonious interpretation of such findings is that sleep is distributed across local brain regions and is use-dependent. The fundamental properties of sleep can also be experimentally defined locally at the level of small neural assemblies such as cortical columns. In this view, sleep is orchestrated, but not fundamentally driven, by central mechanisms. We explore two physiological markers of local, use-dependent sleep, namely, an electrical marker apparent as a change in the size and shape of an electrical evoked response, and a metabolic marker evident as an evoked change in blood volume and oxygenation delivered to activated tissue. Both markers, applied to cortical columns, provide a means to investigate physiological mechanisms for the distributed homeostatic regulation of sleep, and may yield new insights into the consequences of sleep loss and sleep pathologies on waking brain function.
Evoked Response Potential; Model; Homeostasis; Optical; Hemodynamic Response
There is a general consensus that sleep is strictly linked to memory, learning, and, in general, to the mechanisms of neural plasticity, and that this link may directly affect recovery processes. In fact, a coherent pattern of empirical findings points to beneficial effect of sleep on learning and plastic processes, and changes in synaptic plasticity during wakefulness induce coherent modifications in EEG slow wave cortical topography during subsequent sleep. However, the specific nature of the relation between sleep and synaptic plasticity is not clear yet. We reported findings in line with two models conflicting with respect to the underlying mechanisms, that is, the “synaptic homeostasis hypothesis” and the “consolidation” hypothesis, and some recent results that may reconcile them. Independently from the specific mechanisms involved, sleep loss is associated with detrimental effects on plastic processes at a molecular and electrophysiological level. Finally, we reviewed growing evidence supporting the notion that plasticity-dependent recovery could be improved managing sleep quality, while monitoring EEG during sleep may help to explain how specific rehabilitative paradigms work. We conclude that a better understanding of the sleep-plasticity link could be crucial from a rehabilitative point of view.
Sleep slow wave activity (SWA) is thought to reflect sleep need, increasing in proportion to the prior time awake and decreasing during sleep, though the underlying mechanisms are unclear. Recent studies have shown that procedures presumably leading to local plastic changes in the cerebral cortex can lead to local changes in SWA during subsequent sleep. To further investigate the connection between cortical plasticity and sleep SWA, in this study we employed a paired associative stimulation (PAS) protocol, in which median nerve stimuli were followed at different intervals (25 or 10 ms) by transcranial magnetic stimulation (TMS) pulses to the contralateral cortical hand area. As expected, such a protocol lead to a sustained increase (LTP-like) or decrease (LTD-like) of cortical excitability as measured by motor evoked potentials. By employing a TMS-compatible high-density electroencephalographic (EEG) system, we also found that, in individual subjects, TMS-evoked cortical responses over sensorimotor cortex changed with different interstimulus intervals. Moreover, during subsequent sleep, SWA increased locally in subjects whose TMS-evoked cortical responses had increased after PAS, and decreased in subjects whose cortical responses had decreased. Changes in TMS-evoked cortical EEG response and change in sleep SWA were localized to similar cortical regions and were positively correlated. Together, these results suggest that changes in cortical excitability in opposite directions lead to corresponding changes in local sleep regulation, as reflected by SWA, providing evidence for a tight relationship between cortical plasticity and sleep intensity.
sleep homeostasis; synaptic plasticity; high-density EEG; transcranial magnetic stimulation; slow oscillations; cortical excitability
Cortical spreading depression (CSD) is an electrophysiological phenomenon first described by Leao in 1944 as a suppression of spontaneous electroencephalographic activity, traveling across the cerebral cortex. In vitro studies suggest that CSD may induce synaptic potentiation. One recent study also found that CSD is followed by a non-rapid eye movement (NREM) sleep duration increase, suggesting an increased need for sleep. Recent experiments in animals and humans show that the occurrence of synaptic potentiation increases subsequent sleep need as measured by larger slow wave activity (SWA) during NREM sleep, prompting the question whether CSD can affect NREM SWA. Here, we find that, in freely moving rats, local CSD induction increases corticocortical evoked responses and strongly induces brain derived neurotrophic factor (BDNF) in the affected cortical hemisphere but not in the contralateral one, consistent with synaptic potentiation in vivo. Moreover, for several hours after CSD, large slow waves occur in the affected hemisphere during rapid eye movement sleep and quiet waking but disappear during active exploration. Finally, we find that CSD increases NREM sleep duration and SWA, the latter specifically in the affected hemisphere. These effects are consistent with an increase in synaptic strength triggered by CSD, although nonphysiological phenomena associated with CSD may also play a role.
cerebral cortex; EEG; rat; slow wave activity
The consolidation of memories in a variety of learning processes benefits from post-training sleep, and recent work has suggested a role for sleep slow wave activity (SWA). Previous studies using a visuomotor learning task showed a local increase in sleep SWA in right parietal cortex, which was correlated with post-sleep performance enhancement. In these as in most similar studies, learning took place in the evening, shortly before sleep. Thus, it is currently unknown whether learning a task in the morning, followed by the usual daily activities, would also result in a local increase in sleep SWA during the night, and in a correlated enhancement in performance the next day. To answer this question, a group of subjects performed a visuomotor learning task in the morning and was retested the following morning. Whole night sleep was recorded with high-density EEG. We found an increase of SWA over the right posterior parietal areas that was most evident during the second sleep cycle. Performance improved significantly the following morning, and the improvement was positively correlated with the SWA increase in the second sleep cycle. These results suggest that training-induced changes in sleep SWA and post-sleep improvements do not depend upon the time interval between original training and sleep.
learning; memory; consolidation; motor learning; SWA
During sleep, the mammalian CNS undergoes widespread, synchronized slow wave activity (SWA) that directly varies with prior waking duration (Borbely, 1982;Dijk et al., 1990a). When sleep is restricted, an enhanced SWA response follows in the next sleep period. The enhancement of SWA is associated with improved cognitive performance (Huber et al., 2004c), but it is unclear either how the SWA is enhanced or whether SWA is needed to maintain normal cognitive performance. A conditional, CNS knockout of the adenosine receptor, AdoA1R gene, shows selective attenuation of the SWA rebound response to restricted sleep, but sleep duration is not affected. During sleep restriction, wild phenotype animals, express a rebound SWA response and maintain cognitive performance in a working memory task. However, the knockout animals not only show a reduced rebound SWA response but they also fail to maintain normal cognitive function, although this function is normal when sleep is not restricted. Thus, AdoA1R activation is needed for normal rebound SWA, and when the SWA rebound is reduced, there is a failure to maintain working memory function suggesting a functional role for SWA homeostasis.
Sleep; Delta; Adenosine; working memory; Hippocampal function; Memory; Cre-transgenic; metabolism
Mammalian sleep varies widely, ranging from frequent napping in rodents to consolidated blocks in primates and unihemispheric sleep in cetaceans. In humans, rats, mice and cats, sleep patterns are orchestrated by homeostatic and circadian drives to the sleep–wake switch, but it is not known whether this system is ubiquitous among mammals. Here, changes of just two parameters in a recent quantitative model of this switch are shown to reproduce typical sleep patterns for 17 species across 7 orders. Furthermore, the parameter variations are found to be consistent with the assumptions that homeostatic production and clearance scale as brain volume and surface area, respectively. Modeling an additional inhibitory connection between sleep-active neuronal populations on opposite sides of the brain generates unihemispheric sleep, providing a testable hypothetical mechanism for this poorly understood phenomenon. Neuromodulation of this connection alone is shown to account for the ability of fur seals to transition between bihemispheric sleep on land and unihemispheric sleep in water. Determining what aspects of mammalian sleep patterns can be explained within a single framework, and are thus universal, is essential to understanding the evolution and function of mammalian sleep. This is the first demonstration of a single model reproducing sleep patterns for multiple different species. These wide-ranging findings suggest that the core physiological mechanisms controlling sleep are common to many mammalian orders, with slight evolutionary modifications accounting for interspecies differences.
The field of sleep physiology has made huge strides in recent years, uncovering the neurological structures which are critical to sleep regulation. However, given the small number of species studied in such detail in the laboratory, it remains to be seen how universal these mechanisms are across the whole mammalian order. Mammalian sleep is extremely diverse, and the unihemispheric sleep of dolphins is nothing like the rapidly cycling sleep of rodents, or the single daily block of humans. Here, we use a mathematical model to demonstrate that the established sleep physiology can indeed account for the sleep of a wide range of mammals. Furthermore, the model gives insight into why the sleep patterns of different species are so distinct: smaller animals burn energy more rapidly, resulting in more rapid sleep–wake cycling. We also show that mammals that sleep unihemispherically may have a single additional neuronal pathway which prevents sleep-promoting neurons on opposite sides of the hypothalamus from activating simultaneously. These findings suggest that the basic physiology controlling sleep evolved before mammals, and illustrate the functional flexibility of this simple system.
Sleep disturbance plays an important role in major depressive disorder (MDD). Prior investigations have demonstrated that slow wave activity (SWA) during sleep is altered in MDD; however, results have not been consistent across studies, which may be due in part to sex-related differences in SWA and/or limited spatial resolution of spectral analyses. This study sought to characterize SWA in MDD utilizing high-density electroencephalography (hdEEG) to examine the topography of SWA across the cortex in MDD, as well as sex-related variation in SWA topography in the disorder.
All-night recordings with 256 channel hdEEG were collected in 30 unipolar MDD subjects (19 women) and 30 age and sex-matched control subjects. Spectral analyses of SWA were performed to determine group differences. SWA was compared between MDD and controls, including analyses stratified by sex, using statistical non-parametric mapping to correct for multiple comparisons of topographic data.
As a group, MDD subjects demonstrated significant increases in all-night SWA primarily in bilateral prefrontal channels. When stratified by sex, MDD women demonstrated global increases in SWA relative to age-matched controls that were most consistent in bilateral prefrontal regions; however, MDD men showed no significant differences relative to age-matched controls. Further analyses demonstrated increased SWA in MDD women was most prominent in the first portion of the night.
Women, but not men with MDD demonstrate significant increases in SWA in multiple cortical areas relative to control subjects. Further research is warranted to investigate the role of SWA in MDD, and to clarify how increased SWA in women with MDD is related to the pathophysiology of the disorder.
Sleep deprivation disrupts significantly sleep pattern and cause poor quality of sleep. The aim the present study was to explore role of Withania somniferra root extract in sleep-disturbed rats. Male wistar rats (n=5-6/group) were sleep deprived for 24 h using grid suspended over water method. Withania somniferra extract (100 mg/kg) was administered intraperitoneally (i.p.) 30 min before actual recording (EEG and EMG) recording and electrophysiological recordings are further classified as- sleep latency, slow wave sleep, paradoxical sleep, total sleep, wakefulness. One day (24 h) sleep deprivation delayed latency sleep, reduced duration of slow wave sleep, rapid eye movement sleep, total sleep time and increased total waking as compared to animals placed on saw dust (P<0.05). Pretreatment with Withania somniferra extract (100 mg/kg) and diazepam (0.5 mg/kg) significantly improved electrophysiological parameters, which was further reversed by picrotoxin (2 mg/kg) and potentiated by muscimol (0.05 mg/kg). Flumazenil (2 mg/kg) did not produce any significant effect on the sleep parameters of Withania somnifera root extract. Present study suggests the involvement of GABAergic mechanism in the sleep promoting effect of Withania somniferra in sleep-disturbed state.
Sleep; sleep deprivation; EEG; EMG
Slow wave oscillations in the electroencephalogram (EEG) during sleep may reflect both sleep need and intensity, which are implied in homeostatic regulation. Adenosine is strongly implicated in sleep homeostasis, and a single nucleotide polymorphism in the adenosine deaminase gene (ADA G22A) has been associated with deeper and more efficient sleep. The present study verified the association between the ADA G22A polymorphism and changes in sleep EEG spectral power (from C3-A2, C4-A1, O1-A2, and O2-A1 derivations) in the Epidemiologic Sleep Study (EPISONO) sample from São Paulo, Brazil. Eight-hundred individuals were subjected to full-night polysomnography and ADA G22A genotyping. Spectral analysis of the EEG was carried out in all individuals using fast Fourier transformation of the signals from each EEG electrode. The genotype groups were compared in the whole sample and in a subsample of 120 individuals matched according to ADA genotype for age, gender, body mass index, caffeine intake status, presence of sleep disturbance, and sleep-disturbing medication. When compared with homozygous GG genotype carriers, A allele carriers showed higher delta spectral power in Stage 1 and Stages 3+4 of sleep, and increased theta spectral power in Stages 1, 2 and REM sleep. These changes were seen both in the whole sample and in the matched subset. The higher EEG spectral power indicates that the sleep of individuals carrying the A allele may be more intense. Therefore, this polymorphism may be an important source of variation in sleep homeostasis in humans, through modulation of specific components of the sleep EEG.
Both subjective and electroencephalographic arousal diminish as a function of the duration of prior wakefulness. Data reported here suggest that the major criteria for a neural sleep factor mediating the somnogenic effects of prolonged wakefulness are satisfied by adenosine, a neuromodulator whose extracellular concentration increases with brain metabolism and which, in vitro, inhibits basal forebrain cholinergic neurons. In vivo microdialysis measurements in freely behaving cats showed that adenosine extracellular concentrations in the basal forebrain cholinergic region increased during spontaneous wakefulness as contrasted with slow wave sleep; exhibited progressive increases during sustained, prolonged wakefulness; and declined slowly during recovery sleep. Furthermore, the sleep-wakefulness profile occurring after prolonged wakefulness was mimicked by increased extracellular adenosine induced by microdialysis perfusion of an adenosine transport inhibitor in the cholinergic basal forebrain but not by perfusion in a control noncholinergic region.
Purpose of review
Sleep–wake problems such as night wakings, excessive crying, or difficulties in falling asleep are frequent behavioral issues during childhood. Maturational changes in sleep and circadian regulation likely contribute to the development and maintenance of such problems. This review highlights the recent research examining bioregulatory sleep mechanisms during development and provides a model for predicting sleep–wake behavior in young humans.
Findings demonstrate that circadian and sleep homeostatic processes exhibit maturational changes during the first two decades of life. The developing interaction of both processes may be a key determinant of sleep–wake and crying behavior in infancy. Evidence shows that the dynamics of sleep homeostatic processes slow down in the course of childhood (i.e., sleep pressure accumulates more slowly with increasing age) enabling children to be awake for consolidated periods during the day. Another current topic is the adolescent sleep phase delay, which appears to be driven primarily by maturational changes in sleep homeostatic and circadian processes.
The two-process model of sleep regulation is a valuable framework for understanding and predicting sleep–wake behavior in young humans. Such knowledge is important for improving anticipatory guidance, parental education, and patient care, as well as for developing appropriate social policies.
adolescence; children; excessive crying; sleep behavior; sleep homeostasis
Evidence that electroencephalography (EEG) slow-wave activity (SWA) (EEG spectral power in the 1– 4.5 Hz band) during non-rapid eye movement sleep (NREM) reflects plastic changes is increasing (Tononi and Cirelli, 2006). Regional assessment of gray matter development from neuroimaging studies reveals a posteroanterior trajectory of cortical maturation in the first three decades of life (Shaw et al., 2008). Our aim was to test whether this regional cortical maturation is reflected in regional changes of sleep SWA. We evaluated all-night high-density EEG (128 channels) in 55 healthy human subjects (2.4 –19.4 years) and assessed age-related changes in NREM sleep topography. As in adults, we observed frequency-specific topographical distributions of sleep EEG power in all subjects. However, from early childhood to late adolescence, the location on the scalp showing maximal SWA underwent a shift from posterior to anterior regions. This shift along the posteroanterior axis was only present in the SWA frequency range and remained stable across the night. Changes in the topography of SWA during sleep parallel neuroimaging study findings indicating cortical maturation starts early in posterior areas and spreads rostrally over the frontal cortex. Thus, SWA might reflect the underlying processes of cortical maturation. In the future, sleep SWA assessments may be used as a clinical tool to detect aberrations in cortical maturation.
The need to sleep grows with the duration of wakefulness and dissipates with time spent asleep, a process called sleep homeostasis. What are the consequences of staying awake on brain cells, and why is sleep needed? Surprisingly, we do not know whether the firing of cortical neurons is affected by how long an animal has been awake or asleep. Here we found that after sustained wakefulness cortical neurons fire at higher frequencies in all behavioral states. During early NREM sleep after sustained wakefulness, periods of population activity (ON) are short, frequent, and associated with synchronous firing, while periods of neuronal silence are long and frequent. After sustained sleep, firing rates and synchrony decrease, while the duration of ON periods increases. Changes in firing patterns in NREM sleep correlate with changes in slow-wave-activity, a marker of sleep homeostasis. Thus, the systematic increase of firing during wakefulness is counterbalanced by staying asleep.
slow wave sleep; slow oscillations; EEG; rat; cerebral cortex; multi-unit recording