Statistical properties of sleep-wake behavior in the rat and their relation to circadian and ultradian phases

Development of the sleep-wake switch in rats during the P2-P21 early infancy period

In early infancy, rats randomly alternate between the sleeping and waking states-from postnatal day 2-10 (P2-P10), sleep and wake bouts are both exponentially distributed with increasing means, while from P10-P21 sleep and wake bout means continue to increase, though there is a striking qualitative shift in the distribution of wake bouts from exponential to power law. The behavioral states of sleep and wakefulness correspond to the activity of sleep-active and wake-active neuronal brainstem populations, with reciprocal inhibition between the two ensuring that only one population is active at a time. The locus coeruleus (LC) forms a third component of this circuit that rises in prominence during the P10-P21 period, as experimental evidence shows that an as-of-yet undeciphered interaction of the LC with sleep-active and wake-active populations is responsible for the transformation of the wake bout distribution from exponential to power law. Interestingly, the LC undergoes remarkable physiological changes during the P10-P21 period-gap junctions within the LC are pruned and network-wide oscillatory synchrony declines and vanishes. In this work, we discuss a series of models of sleep-active, wake-active, and the LC populations, and we use these models to postulate the nature of the interaction between these three populations and how these interactions explain empirical observations of sleep and wake bout dynamics. We hypothesize a circuit in which there is reciprocal excitation between the LC and wake-active population with inhibition from the sleep-active population to the LC that suppresses the LC during sleep bouts. During the P2-P10 period, we argue that a noise-based switching mechanism between the sleep-active and wake-active populations provides a simple and natural way to account for exponential bout distributions, and that the locked oscillatory state of the LC prevents it from impacting bout distributions. From P10-P21, we use our models to postulate that, as the LC gradually shifts from a state of synchronized oscillations to a state of continuous firing, reciprocal excitation between the LC and the wake-active population is able to gradually transform the wake bout distribution from exponential to power law.

Sex hormone dose escalation for treating abnormal sleep in ovariectomized rats: in vitro GABA synthesis in sleep-related brain areas

No data in the literature have evaluated sex hormone dose escalation for treating abnormal sleep of ovariectomized rats-nor studies on the role of sex hormones in GABA synthesis of rats' sleep-related areas. The main aim of this study was to determine the maximum tolerated dose (MTD) of estradiol (ET), progesterone (PT), and the mixture of both (EPT) to restore normal sleep in a model of menopause in rats. The second purpose was to describe the in vitro activity of glutamate decarboxylase (GAD) in sleep-related brain areas in the presence or absence of sex hormones. A weekly dose-escalation design of ET, PT, or EPT was implemented in ovariectomized rats (six per group). Dose escalation continued until the dose at which 100% of the rats exhibited a state of "complete somnolence." Doses that were not toxic or did not show side effects were considered. For in vitro experiments, sleep-related brain areas were separated and incubated with radiolabeled glutamate. Estradiol (17β-E2), progesterone (P), and pyridoxal phosphate (PLP) were added to this assay, and GAD activity was determined. Under the same conditions, a second test was carried out, but the P antagonist RU486 was added to assess the role of P in GAD activity. Ovariectomy increased periodic awakenings compared to those determined for the SHAM group. The EPT for ovariectomized rats was very effective by the fifth week in decreasing arousal and achieving a similar sleep behavior to the SHAM-control group. Rats tolerated the ET, PT, and EPT well to the maximum planned dose (0.66 mg/kg and 4.4 mg/kg, respectively). No lethal events occurred; the MTD was reached. The in vitro studies indicated that the presence of 17β-E2 plus P in the assay triggered the activity of isotype 65 GAD in all the studied brain areas. RU486 in the incubation medium blocked such activity; however, the action of isotype 67 GAD was not blocked by RU486. A dose-escalation model was determined; the MTD coincided with the maximum dose of ET and PT used. However, the EPT combination restored normal sleep in the menopause model compared to the SHAMs without toxic effects. The in vitro model demonstrated that 17β-E2 plus P presence in the assay increased the activity of GAD65 in the studied brain tissues.

Sleep-wake dynamics pre- and post-exposure to chronic social stress

An analytical approach combining the statistical distributions of the sleep-wake bouts and the Markov transition matrix is used to explain the under-examined association between the microarchitecture of the sleep-wake cycle and susceptibility to chronic social stress in C57BL/6J mice. We separated the sleep-wake transitions into distinct sleep-wake sequences, NREM↔Wake and NREM→REM→Wake, which are controlled by independent neural circuits. Our findings imply greater pull toward the wake leading to early termination and fragmentation of the sleep bouts in the light in both sleep-wake sequences pre- and post-stress. Moreover, the stability of NREM in the NREM↔Wake transition was lower, and the probability of transitioning to wake was higher in susceptible relative to resilient or stress-naïve mice pre- and post-stress. Our findings help elucidate the mechanistic interplay between sleep and mood by suggesting the potential neural underpinnings of sleep disturbances responsible the aberrant transitions of sleep-wake bouts exhibited by the stress-susceptible phenotype.

A Focal Inactivation and Computational Study of Ventrolateral Periaqueductal Gray and Deep Mesencephalic Reticular Nucleus Involvement in Sleep State Switching and Bistability

Neurons of the ventrolateral periaqueductal gray (vlPAG) and adjacent deep mesencephalic reticular nucleus (DpMe) are implicated in the control of sleep-wake state and are hypothesized components of a flip-flop circuit that maintains sleep bistability by preventing the overexpression of non-rapid eye movement (NREM)/REM sleep intermediary states (NRt). To determine the contribution of vlPAG/DpMe neurons in maintaining sleep bistability we combined computer simulations of flip-flop circuitry with focal inactivation of vlPAG/DpMe neurons by microdialysis delivery of the GABA_A receptor agonist muscimol in freely behaving male rats (n = 25) instrumented for electroencephalographic and electromyographic recording. REM sleep was enhanced by muscimol at the vlPAG/DpMe, consistent with previous studies; however, our analyses of NRt dynamics in vivo and those produced by flop-flop circuit simulations show that current thinking is too narrowly focused on the contribution of REM sleep-inactive populations toward vlPAG/DpMe involvement in REM sleep control. We found that much of the muscimol-mediated increase in REM sleep was more appropriately classified as NRt. This loss of sleep bistability was accompanied by fragmentation of REM sleep, as evidenced by an increased number of short REM sleep bouts. REM sleep fragmentation stemmed from an increased number and duration of NRt bouts originating in REM sleep. By contrast, NREM sleep bouts were not likewise fragmented by vlPAG/DpMe inactivation. In flip-flop circuit simulations, these changes could not be replicated through inhibition of the REM sleep-inactive population alone. Instead, combined suppression of REM sleep active and inactive vlPAG/DpMe subpopulations was required to replicate the changes in NRt dynamics.

Regulation of Rest, Rather Than Activity, Underlies Day-Night Activity Differences in Mice

The suprachiasmatic nucleus (SCN), which serves as the central pacemaker in mammals, regulates the 24-h rhythm in behavioral activity. However, it is currently unclear whether and how bouts of activity and rest are regulated within the 24-h cycle (i.e., over ultradian time scales). Therefore, we used passive infrared sensors to measure temporal behavior in mice housed under either a light–dark (LD) cycle or continuous darkness (DD). We found that a probabilistic Markov model captures the ultradian changes in the behavioral state over a 24-h cycle. In this model, the animal’s behavioral state in the next time interval is determined solely by the animal’s current behavioral state and by the “toss” of a proverbial “biased coin.” We found that the bias of this “coin” is regulated by light input and by the phase of the clock. Moreover, the bias of this “coin” for an animal is related to the average length of rest and activity bouts in that animal. In LD conditions, the average length of rest bouts was greater during the day compared to during the night, whereas the average length of activity bouts was greater during the night compared to during the day. Importantly, we also found that day-night changes in the rest bout lengths were significantly greater than day-night changes in the activity bout lengths. Finally, in DD conditions, the activity and rest bouts also differed between subjective night and subjective day, albeit to a lesser extent compared to LD conditions. The ultradian regulation represented by the model does not result in ultradian rhythms, although some weak ultradian rhythms are present in the data. The persistent differences in bout length over the circadian cycle following loss of the external LD cycle indicate that the central pacemaker plays a role in regulating rest and activity bouts on an ultradian time scale.

Pharmacosynthetic Deconstruction of Sleep-Wake Circuits in the Brain

Over the past decade, basic sleep research investigating the circuitry controlling sleep and wakefulness has been boosted by pharmacosynthetic approaches, including chemogenetic techniques using designed receptors exclusively activated by designer drugs (DREADD). DREADD offers a series of tools that selectively control neuronal activity as a way to probe causal relationship between neuronal sub-populations and the regulation of the sleep-wake cycle. Following the path opened by optogenetics, DREADD tools applied to discrete neuronal sub-populations in numerous brain areas quickly made their contribution to the discovery and the expansion of our understanding of critical brain structures involved in a wide variety of behaviors and in the control of vigilance state architecture.

AM-251, A Cannabinoid Antagonist, Modifies the Dynamics of Sleep-Wake Cycles in Rats

Study Objectives: (a) To describe the microarchitecture of wakefulness and sleep following administrations of 5- and 10-mg/kg AM-251 in rats. (b) To develop a new statistical method to follow bout-to-bout dynamics. Method: Wistar rats (n = 6) had been equipped with electroencephalography (EEG) and electromyography (EMG) electrodes. Following their recovery and habituation after the surgery, the animals were injected with vehicle and 5- and 10-mg/kg AM-251 intraperitoneally and EEG, EMG, and motor activity were analyzed for the subsequent 3 h. Results: AM-251 induced a dose- and time-dependent increase in the number of bouts in active wake (AW), and it decreased this number in all other vigilance states except in passive wake (PW). In contrast, the bout duration in PW compensatory decreased. The effect of AM-251 on the sleep transition dynamics was monitored with a new tool we call "transition heatmap." The analysis of bout trajectories with transition heatmaps reveals a highly organized pattern. Conclusion: AM-251 selectively influences the frequency of vigilance state transitions, but it has no direct impact on the state lengths. AM-251 markedly changed the state transition dynamics, which was visualized with the help of state transition heatmaps.

Can absence seizures be predicted by vigilance states?: Advanced analysis of sleep-wake states and spike-wave discharges' occurrence in rats

Spike-wave discharges (SWDs) are the main manifestation of absence epilepsy. Their occurrence is dependent on the behavioral state, and they preferentially occur during unstable vigilance periods. The present study investigated whether the occurrence of SWDs can be predicted by the preceding behavioral state and whether this relationship is different between the light and the dark phases of the 24-h day. Twenty-four-hour (12:12 light/dark phases) electroencephalographic (EEG) recordings of 12 Wistar Albino Glaxo, originally bred in Rijswijk (WAG/Rij) rats, a well-known genetic model of absence epilepsy, were analyzed and transformed into sequences of 2-s length intervals of the following 6 possible states: active wakefulness (AW), passive wakefulness (PW), deep slow-wave sleep (DSWS), light slow-wave sleep (LSWS), rapid eye movement (REM) sleep, and SWDs, given discrete series of categorical data. Probabilities of all transitions between states and Shannon entropy of transitions were calculated for the light and dark phases separately and statistically analyzed. Common differences between the light and the dark phases were found regarding the time spent in AW, LSWS, DSWS, and SWDs. The most probable transitions were that AW was preceded and followed by PW and vice versa regardless of the phase of the photoperiod. A similar relationship was found for light and deep slow-wave sleep. The most probable transitions to and from SWDs were AW and LSWS, respectively, with these transition likelihoods being consistent across both circadian phases. The second most probable transitions around SWDs appeared more variable between light and dark. During the light phase, SWDs occurred around PW and participated exclusively in sleep initiation; in the dark phase, SWDs were seen on both, ascending and descending steps towards and from sleep. Conditional Shannon entropy showed that AW and DSWS are the most predictable events, while the possible prediction horizon of SWDs is not larger than 4 s and despite the higher occurrence of SWDs in the dark phase, did not differ between phases. It can be concluded that although SWDs show a stable, strong circadian rhythm with a peak in number during the dark phase, their occurrence cannot be reliably predicted by the preceding behavioral state, except at a very short time base.

Analysis of variability in length of sleep state bouts reveals memory-free sleep subcomponents consistent among primary insomnia patients

The statistical distributions of bout lengths for the different (macro) sleep states (wake, N1, N2, N3, and REM sleep) are essential to understanding whether any memory-free subcomponent ("micro state") is involved in the organization of sleep. Micro state detection can be prevented by the fusion of data including various sources of variability, in particular by the differences in sleep architecture between individuals, along sleep time (or nighttime), or between different nights. In this analysis, a mathematical model of sleep was adopted to disentangle these features and advance the understanding of the dynamics and mechanisms of sleep and its states. The analysis involved 116 primary insomnia patients taking placebo before going to bed and undergoing polysomnography for one night. The individual sequences of macro sleep states had been previously modeled with a mixed-effect nonhomogeneous modified Markov chain model, from which individual conditional probability distributions for the bout durations were derived in this analysis as functions of sleep time. The probability distributions, affected by neither subject, night-time, nor multiple-night pooling, substantially changed at ¼ and ¾ sleep time, had modified exponential shape, and were best described as the sum of one to four exponentials, depending on the sleep state. The time constants and proportions of bouts contributing to each exponential were similar in the different subjects, changing over sleep time. Variability in bout durations thus indicated the presence of multiple memory-free sleep subcomponents whose mean residence times and access probabilities could be identified and shown to be consistent among the studied subjects. NEW & NOTEWORTHY We present a new methodology for deriving, from polysomnography data, the individual conditional probability for the duration of the bouts of wake, N1, N2, N3, and REM sleep. We evaluated the variability of this probability within and between primary insomnia patients and along sleep time. The multiexponential shapes of the probability distributions within the individuals revealed memory-free mechanisms and sleep subcomponents with consistent features in the studied population.

A four-state Markov model of sleep-wakefulness dynamics along light/dark cycle in mice

Behavioral states alternate between wakefulness (wk), rapid eye movement (rem) and non-rem (nrem) sleep at time scale of hours i.e., light and dark cycle rhythms and from several tens of minutes to seconds (i.e., brief awakenings during sleep). Using statistical analysis of bout duration, Markov chains of sleep-wk dynamics and quantitative EEG analysis, we evaluated the influence of light/dark (ld) changes on brain function along the sleep-wk cycle. Bout duration (bd) histograms and Kaplan-Meier (km) survival curves of wk showed a bimodal statistical distribution, suggesting that two types of wk do exist: brief-wk (wkb) and long-wk (wkl). Light changes modulated specifically wkl bouts, increasing its duration during active/dark period. In contrast, wkb, nrem and rembd histograms and km curves did not change significantly along ld cycle. Hippocampal eeg of both types of wk were different: in comparison wkb showed a lower spectral power in fast gamma and fast theta bands and less emg tone. After fitting a four-states Markov chain to mice hypnograms, moreover in states transition probabilities matrix was found that: in dark/active period, state-maintenance probability of wkl increased, and probability of wkl to nrem transition decreased; the opposite was found in light period, favoring the hypothesis of the participation of brief wk into nrem-rem intrinsic sleep cycle, and the role of wkl in SWS homeostasis. In conclusion, we propose an extended Markov model of sleep using four stages (wkl, nrem, rem, wkb) as a fully adequate model accounting for both modulation of sleep-wake dynamics based on the differential regulation of long-wk (high gamma/theta) epochs during dark and light phases.

Role of the locus coeruleus in the emergence of power law wake bouts in a model of the brainstem sleep-wake system through early infancy

Infant rats randomly cycle between the sleeping and waking states, which are tightly correlated with the activity of mutually inhibitory brainstem sleep and wake populations. Bouts of sleep and wakefulness are random; from P2-P10, sleep and wake bout lengths are exponentially distributed with increasing means, while during P10-P21, the sleep bout distribution remains exponential while the distribution of wake bouts gradually transforms to power law. The locus coeruleus (LC), via an undeciphered interaction with sleep and wake populations, has been shown experimentally to be responsible for the exponential to power law transition. Concurrently during P10-P21, the LC undergoes striking physiological changes - the LC exhibits strong global 0.3 Hz oscillations up to P10, but the oscillation frequency gradually rises and synchrony diminishes from P10-P21, with oscillations and synchrony vanishing at P21 and beyond. In this work, we construct a biologically plausible Wilson Cowan-style model consisting of the LC along with sleep and wake populations. We show that external noise and strong reciprocal inhibition can lead to switching between sleep and wake populations and exponentially distributed sleep and wake bout durations as during P2-P10, with the parameters of inhibition between the sleep and wake populations controlling mean bout lengths. Furthermore, we show that the changing physiology of the LC from P10-P21, coupled with reciprocal excitation between the LC and wake population, can explain the shift from exponential to power law of the wake bout distribution. To our knowledge, this is the first study that proposes a plausible biological mechanism, which incorporates the known changing physiology of the LC, for tying the developing sleep-wake circuit and its interaction with the LC to the transformation of sleep and wake bout dynamics from P2-P21.

Significance of the zero sum principle for circadian, homeostatic and allostatic regulation of sleep-wake state in the rat

Sleep-wake behavior exhibits diurnal rhythmicity, rebound responses to acute total sleep deprivation (TSD), and attenuated rebounds following chronic sleep restriction (CSR). We investigated how these long-term patterns of behavior emerge from stochastic short-term dynamics of state transition. Male Sprague-Dawley rats were subjected to TSD (1day×24h, N=9), or CSR (10days×18h TSD, N=7) using a rodent walking-wheel apparatus. One baseline day and one recovery day following TSD and CSR were analyzed. The implications of the zero sum principle were evaluated using a Markov model of sleep-wake state transition. Wake bout duration (a combined function of the probability of wake maintenance and proportional representations of brief and long wake) was a key variable mediating the baseline diurnal rhythms and post-TSD responses of all three states, and the attenuation of the post-CSR rebounds. Post-NREM state transition trajectory was an important factor in REM rebounds. The zero sum constraint ensures that a change in any transition probability always affects bout frequency and cumulative time of at least two, and usually all three, of wakefulness, NREM and REM. Neural mechanisms controlling wake maintenance may play a pivotal role in regulation and dysregulation of all three states.

Behavioral sleep-wake homeostasis and EEG delta power are decoupled by chronic sleep restriction in the rat

STUDY OBJECTIVES

Chronic sleep restriction (CSR) is prevalent in society and is linked to adverse consequences that might be ameliorated by acclimation of homeostatic drive. This study was designed to test the hypothesis that the sleep-wake homeostat will acclimatize to CSR.

DESIGN

A four-parameter model of proportional control was used to quantify sleep homeostasis with and without recourse to a sleep intensity function.

SETTING

Animal laboratory, rodent walking-wheel apparatus.

SUBJECTS

Male Sprague-Dawley rats.

INTERVENTIONS

Acute total sleep deprivation (TSD, 1 day × 18 or 24 h, N = 12), CSR (10 days × 18 h TSD, N = 5, or 5 days × 20 h TSD, N = 6).

MEASUREMENTS AND RESULTS

Behavioral rebounds were consistent with model predictions for proportional control of cumulative times in wake, nonrapid eye movement (NREM) and rapid eye movement (REM). Delta (D) energy homeostasis was secondary to behavioral homeostasis; a biphasic NREM D power rebound contributed to the dynamics (rapid response) but not to the magnitude of the rebound in D energy. REM behavioral homeostasis was little affected by CSR. NREM behavioral homeostasis was attenuated in proportion to cumulative NREM deficit, whereas the biphasic NREM D power rebound was only slightly suppressed, indicating decoupled regulatory mechanisms following CSR.

CONCLUSIONS

We conclude that sleep homeostasis is achieved through behavioral regulation, that the NREM behavioral homeostat is susceptible to attenuation during CSR and that the concept of sleep intensity is not essential in a model of sleep-wake regulation.

STUDY OBJECTIVES

Chronic sleep restriction (CSR) is prevalent in society and is linked to adverse consequences that might be ameliorated by acclimation of homeostatic drive. This study was designed to test the hypothesis that the sleep-wake homeostat will acclimatize to CSR.

DESIGN

A four-parameter model of proportional control was used to quantify sleep homeostasis with and without recourse to a sleep intensity function.

SETTING

Animal laboratory, rodent walking-wheel apparatus.

SUBJECTS

Male Sprague-Dawley rats.

INTERVENTIONS

Acute total sleep deprivation (TSD, 1 day × 18 or 24 h, N = 12), CSR (10 days × 18 h TSD, N = 5, or 5 days × 20 h TSD, N = 6).

MEASUREMENTS AND RESULTS

Behavioral rebounds were consistent with model predictions for proportional control of cumulative times in wake, nonrapid eye movement (NREM) and rapid eye movement (REM). Delta (D) energy homeostasis was secondary to behavioral homeostasis; a biphasic NREM D power rebound contributed to the dynamics (rapid response) but not to the magnitude of the rebound in D energy. REM behavioral homeostasis was little affected by CSR. NREM behavioral homeostasis was attenuated in proportion to cumulative NREM deficit, whereas the biphasic NREM D power rebound was only slightly suppressed, indicating decoupled regulatory mechanisms following CSR.

CONCLUSIONS

We conclude that sleep homeostasis is achieved through behavioral regulation, that the NREM behavioral homeostat is susceptible to attenuation during CSR and that the concept of sleep intensity is not essential in a model of sleep-wake regulation.