Sleep State Transitions Research Articles

Profiling the sleep architecture of ageing adults using a seven-state continuous-time Markov model.

Sleep is a complex biological process regulated by networks of neurons and environmental factors. As one falls asleep, neurotransmitters from sleep-wake regulating neurones work in synergy to control the switching of different sleep states throughout the night. As sleep disorders or underlying neuropathology can manifest as irregular switching, analysing these patterns is crucial in sleep medicine and neuroscience. While hypnograms represent the switching of sleep states well, current analyses of hypnograms often rely on oversimplified temporal descriptive statistics (TDS, e.g., total time spent in a sleep state), which miss the opportunity to study the sleep state switching by overlooking the complex structures of hypnograms. In this paper, we propose analysing sleep hypnograms using a seven-state continuous-time Markov model (CTMM). This proposed model leverages the CTMM to depict the time-varying sleep-state transitions, and probes three types of insomnia by distinguishing three types of wake states. Fitting the proposed model to data from 2056 ageing adults in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study, we profiled sleep architectures in this population and identified the various associations between the sleep state transitions and demographic factors and subjective sleep questions. Ageing, sex, and race all show distinctive patterns of sleep state transitions. Furthermore, we also found that the perception of insomnia and restless sleep are significantly associated with critical transitions in the sleep architecture. By incorporating three wake states in a continuous-time Markov model, our proposed method reveals interesting insights into the relationships between objective hypnogram data and subjective sleep quality assessments.

Agile C-states: A Core C-state Architecture for Latency Critical Applications Optimizing both Transition and Cold-Start Latency

Latency critical applications running in modern datacenters exhibit irregular request arrival patterns and are implemented using multiple services with strict latency requirements (30 μs –250 μs ). These characteristics render existing energy saving idle CPU sleep states ineffective due to the performance overhead caused by the state’s transition latency. Besides the state transition latency, another important contributor to the performance overhead of sleep states is the cold-start latency, or in other words, the time required to warm-up microarchitectural state (e.g., cache contents, branch predictor metadata) that is flushed or discarded when transitioning to a lower-power state. Both the transition latency and cold-start latency can be particularly detrimental to the performance of latency critical applications with short execution times. While prior work focuses on mitigating the effects of transition and cold-start latency by optimizing request scheduling, in this work, we propose a redesign of the Core C-state architecture for latency-critical applications. In particular, we introduce C6Awarm a new Agile Core C-state that drastically reduces the performance overhead caused by idle sleep state transition latency and cold-start latency, while maintaining significant energy savings. C6Awarm achieves its goals by implementing 1) medium-grained power gating, 2) preserving the microarchitectural state of the core and 3) by keeping the clock generator and PLL active and locked. Our analysis for a set of microservices based on an Intel Skylake server, shows that C6Awarm manages to reduce the energy consumption by up to \(70\% \) with limited performance degradation (at-most \(2\% \) ).

0039 Investigating the role of striatal dopamine in sleep and narcolepsy-cataplexy

Abstract Introduction Dopamine release in the striatum is known to govern reward, motor, and sleep behaviors. There also exists a link between pharmacological manipulation of dopamine receptors and cataplexy occurrence, yet the precise spatial and temporal dynamics of dopamine release in relation to cataplexy onset are not understood. In the present study, we evaluate dopamine release dynamics in orexin knockout (OX KO) mice during cataplexy and physiological behaviors. Methods OX KO mice were implanted with EEG/EMG electrodes, and dLight1.1 was expressed in the nucleus accumbens core (NAcc), shell (NAcSh), and dorsolateral striatum (DLS) to perform in vivo fiber photometry of dopamine release in freely-moving animals. In NAcc and NAcSh mice, ChrimsonR was retrogradely expressed in the ventral tegmental area (VTA), to allow for precise optogenetic control of midbrain dopamine projections to different regions of the striatum. We also expressed dLight1.1 in the NAcc of wildtype (WT) mice as a control. Three different stimulation parameters were used in independent recordings: phasic 20 Hz stimulation, phasic 10-40 Hz variable stimulation, and tonic 2 Hz stimulation. Results First, we show that while dopamine release in response to rewarding behaviors is muted in OX KO animals, there is no difference in dopamine release across sleep state transitions between OX KO and WT animals. Second, we demonstrate that there is a striking increase in dopamine release in both the NAcc and NAcSh, but not in the DLS, at the onset of cataplexy. This increase occurs independently of the behaviors that the animals were engaged in immediately prior to cataplexy, suggesting that the increase is characteristic of all cataplexy episodes. Finally, we find that while high frequency phasic stimulation did not affect cataplexy frequency, low frequency, tonic stimulation reduced both cataplexy and REM occurrence. Conclusion We show that there are differences in dopamine release in response to rewarding events, but not changes in sleep states, in OX KO and WT animals. We also identify dopamine release in the ventral striatum as a key node in cataplexy regulation, which helps to further our understanding of the limbic circuitry that underlies narcolepsy symptoms. Support (if any) University of Michigan Gilmore Award and Rackham Graduate Research Award.

Simultaneous Microendoscopic Calcium Imaging and EEG Recording of Mouse Brain during Sleep.

Sleep is a conserved biological process in the animal kingdom. Understanding the neural mechanisms underlying sleep state transitions is a fundamental goal of neurobiology, important for the development of new treatments for insomnia and other sleep-related disorders. Yet, brain circuits controlling this process remain poorly understood. A key technique in sleep research is to monitor in vivo neuronal activity in sleep-related brain regions across different sleep states. These sleep-related regions are usually located deeply in the brain. Here, we describe technical details and protocols for in vivo calcium imaging in the brainstem of sleeping mice. In this system, sleep-related neuronal activity in the ventrolateral medulla (VLM) is measured using simultaneous microendoscopic calcium imaging and electroencephalogram (EEG) recording. By aligning calcium and EEG signals, we demonstrate that VLM glutamatergic neurons display increased activity during the transition from wakefulness to non-rapid eye movement (NREM) sleep. The protocol described here can be applied to study neuronal activity in other deep brain regions involved in REM or NREM sleep.

P113 A detailed analysis of multicentric sleep staging inter-rater variabilities

Abstract Introduction Correct identification of sleep stages is important in sleep disorder diagnosis; but is subject to high inter-scorer variability. Thus, we aimed to systematically analyse sleep staging agreements between ten scorers in a multicentric environment to better understand areas that possess high scoring variations. Method Polysomnographic recordings of 50 individuals (mean (±SD) AHI 12.0±13.2) were independently scored by ten sleep technologists from seven sleep centres ( Europe six, Australia one). The epoch-based majority sleep stage was identified from all scorings; then the mean scorer agreement with this majority score was calculated. We also investigated how sleep-state transitions affect the mean scorer agreement. Results In total, 48556 epochs were analysed. The mean (±SD) scorer agreement with majority score was 86.3±2.9% (Randolph’s kappa κ=0.74) overall; and 86.3±9.5% (κ=0.74), 65.6±13.5% (κ=0.39), 86.1±6.2% (κ=0.74), 88.8±11.2% (κ=0.78), and 91.7±4.5% (κ=0.83) for Wake, N1, N2, N3, and REM respectively. Across the ten scorers, 20.0% of epochs were unanimously scored as N2, and the most frequent disagreement was between N2 and N3 (19.7% of epochs). Only 0.3% of epochs were unanimously scored as N1. There was a negative association between the sleep stage transition frequency of the majority score and the mean scorer agreement (r=-0.61). Discussion High mean scorer agreements were obtained with the majority score except for N1. Surprisingly, considerable disparities were observed among scorers in distinguishing N2 and N3; this may reflect both the discrepancy associated with N2/N3 transitions and potentially different scoring perceptions. Greater scoring discrepancies were identified in individuals with more frequent sleep-state transitions.

Changes in the cortical network during sleep stage transitions.

Sleep state transitions are closely related to insomnia, drowsiness, and sleep maintenance. However, how the cortical network varies during such a transition process remains unclear. Changes in the cortical interaction during the short-term process of sleep stage transitions were investigated. In all, 40 healthy young participants underwent overnight polysomnography. The phase transfer entropy of six frequency bands was obtained from 16 electroencephalography channels to assess the strength and direction of information flow between the cortical regions. Differences in the cortical network between the first and the last 10 s in a 40-s transition period across wakefulness, N1, N2, N3, and rapid eye movement were, respectively, studied. Various frequency bands exhibited different patterns during the sleep stage transitions. It was found that the mutual transitions between the sleep stages were not necessarily the opposite. More significant changes were observed in the sleep deepening process than in the process of sleep awakening. During sleep stage transitions, changes in the inflow and outflow strength of various cortical regions led to regional differences, but for the entire sleep progress, such an imbalance did not intensify, and a dynamic balance was instead observed. The detailed findings of variations in cortical interactions during sleep stage transition promote understanding of sleep mechanism, sleep process, and sleep function. Additionally, it is expected to provide helpful clues for sleep improvement, like reducing the time required to fall asleep and maintaining sleep depth.

Control of non-REM sleep by ventrolateral medulla glutamatergic neurons projecting to the preoptic area

Understanding the neural mechanisms underlying sleep state transitions is a fundamental goal of neurobiology and important for the development of new treatments for insomnia and other sleep disorders. Yet, brain circuits controlling this process remain poorly understood. Here we identify a population of sleep-active glutamatergic neurons in the ventrolateral medulla (VLM) that project to the preoptic area (POA), a prominent sleep-promoting region, in mice. Microendoscopic calcium imaging demonstrate that these VLM glutamatergic neurons display increased activity during the transitions from wakefulness to Non-Rapid Eye Movement (NREM) sleep. Chemogenetic silencing of POA-projecting VLM neurons suppresses NREM sleep, whereas chemogenetic activation of these neurons promotes NREM sleep. Moreover, we show that optogenetic activation of VLM glutamatergic neurons or their projections in the POA initiates NREM sleep in awake mice. Together, our findings uncover an excitatory brainstem-hypothalamic circuit that controls the wake-sleep transitions.

Slow wave synchronization and sleep state transitions.

Spontaneous synchronization over large networks is ubiquitous in nature, ranging from inanimate to biological systems. In the human brain, neuronal synchronization and de-synchronization occur during sleep, with the greatest degree of neuronal synchronization during slow wave sleep (SWS). The current sleep classification schema is based on electroencephalography and provides common criteria for clinicians and researchers to describe stages of non-rapid eye movement (NREM) sleep as well as rapid eye movement (REM) sleep. These sleep stage classifications have been based on convenient heuristic criteria, with little consideration of the accompanying normal physiological changes across those same sleep stages. To begin to resolve those inconsistencies, first focusing only on NREM sleep, we propose a simple cluster synchronization model to explain the emergence of SWS in healthy people without sleep disorders. We apply the empirical mode decomposition (EMD) analysis to quantify slow wave activity in electroencephalograms, and provide quantitative evidence to support our model. Based on this synchronization model, NREM sleep can be classified as SWS and non-SWS, such that NREM sleep can be considered as an intrinsically bistable process. Finally, we develop an automated algorithm for SWS classification. We show that this new approach can unify brain wave dynamics and their corresponding physiologic changes.

Sound quality metrics for improved prediction of hospital sleep disruption

Most policy, including for the healthcare environment, uses A-weighted decibels as a standard metric. A-weighting is, thus, treated as a catch-all intended to represent all or most relevant human factors. However, for protecting sleep in the healthcare environment and its attendant recuperative benefits, A-weighted metrics neglect a great deal of essential information. To illustrate this, a dataset from a prior study of sleep disruption resulting from hospital noises measured using polysomnography [Sleep disruption due to hospital noise, Buxton et al. (2012)] is reanalyzed using measures of loudness, sharpness, and tonality. Linear combinations of these variables result in improved prediction of sleep state transitions relative to the A-weighted measure initially employed. Sound quality metrics can provide additional insight into how best to ensure a sonic environment conducive to a good night’s sleep and can, thus, help decision makers build an environment that delivers restorative sleep in the service of restoration of health. [Work supported by SAND2022-0675 A.]

Glutamatergic Neurons of the Paraventricular Nucleus are Critical for the Control of Wakefulness

Normal sleep–wake behavior is extremely important for humans to maintain basic physiological and cognitive activities. However, the neural mechanisms underlying sleep–wake regulation are not fully understood. The paraventricular nucleus (PVN) of the hypothalamus has been classically defined as a region for the regulation of the hypothalamoneurohypophysial system and autonomic nervous system. Here, we identify the glutamatergic neurons in the PVN that play a unique role in sleep–wake regulation. Firstly using in vivo fiber photometry, we found altered calcium activities of PVN glutamatergic neurons during three sleep state transitions in freely behaving mice. The calcium activities of PVN glutamatergic neurons began to increase before non-rapid-eye movement (NREM) sleep to wake transitions and NREM sleep to rapid-eye-movement (REM) sleep transitions and began to decrease before wake to NREM sleep transitions. Then we used chemogenetic manipulations together with polysomnographic recordings, activation of PVN neurons increased wakefulness and decreased NREM sleep, while inhibition of PVN neurons caused a reduction in wakefulness and an increase in NREM sleep. Altogether, our findings revealed an important role for PVN glutamatergic neurons in the regulation of wake state.

Astrocytic Ca2+ signaling is reduced during sleep and is involved in the regulation of slow wave sleep

Astrocytic Ca2+ signaling has been intensively studied in health and disease but has not been quantified during natural sleep. Here, we employ an activity-based algorithm to assess astrocytic Ca2+ signals in the neocortex of awake and naturally sleeping mice while monitoring neuronal Ca2+ activity, brain rhythms and behavior. We show that astrocytic Ca2+ signals exhibit distinct features across the sleep-wake cycle and are reduced during sleep compared to wakefulness. Moreover, an increase in astrocytic Ca2+ signaling precedes transitions from slow wave sleep to wakefulness, with a peak upon awakening exceeding the levels during whisking and locomotion. Finally, genetic ablation of an important astrocytic Ca2+ signaling pathway impairs slow wave sleep and results in an increased number of microarousals, abnormal brain rhythms, and an increased frequency of slow wave sleep state transitions and sleep spindles. Our findings demonstrate an essential role for astrocytic Ca2+ signaling in regulating slow wave sleep.

0941 Defining Disrupted Nighttime Sleep in Pediatric Narcolepsy

Abstract Introduction Disrupted nighttime sleep (DNS) is a core narcolepsy symptom subjectively described as spontaneous awakenings during the night, but researchers use varied polysomnogram (PSG) definitions based on sleep state transitions, NREM 1% and poor sleep efficiency. These sleep measures have yet to be validated to determine the best objective measure of DNS. Furthermore, it unknown to what extent DNS occurs in pediatric narcolepsy as children have greater sleep drive than adults. Here, we assess the construct validity of various DNS objective measures and evaluate its diagnostic utility for pediatric Narcolepsy Type 1 (NT1) when combined with a nocturnal Sleep Onset REM period (nSOREMP) in a large cohort of pediatric patients with CNS hypersomnias. Methods Retrospective, cross-sectional study of consecutive PSGs and multiple sleep latency tests (MSLTs) obtained at Boston Children’s Hospital and University of Bologna. Participants were drug-free or drug naïve, ages 6-18 years and slept at least 6 hours during the PSG. We analyzed associations between objective DNS measures and outcomes of self-reported sleep disturbance, Epworth Sleepiness Score, mean sleep latency, NT1 diagnosis, and CSF hypocretin values when available. We then combined the best performing DNS measure with the presence of a nSOREMP to determine the diagnostic value for NT1 using bootstrap analysis. We included n=151 NT1, n=21 narcolepsy type 2 (NT2), n=27 idiopathic hypersomnia (IH) and n= 117 subjectively sleepy controls in this analysis. Results Across groups, the Wake and NREM 1 bouts index had the most robust associations with objective sleepiness, subjective sleep disturbance and CSF hypocretin levels (p’s <0.0001). From 1000 bootstrap samples, the Wake/N1 index and presence of a nSOREMP have greater diagnostic accuracy for NT1 than the nSOREMP alone (p<0.0001). Conclusion Among a variety of sleep quality measures, we find that a Wake and NREM 1 bout index is the best objective measure of DNS. In combination with a nSOREMP, this DNS measure can aid in pediatric NT1 diagnosis using PSG alone and potentially reduce diagnostic delays. Support This study was supported by K23 National Institutes of Health (NINDS, K23 NS104267-01A1) grant and Investigator Initiated Research grant from Jazz Pharmaceuticals, Inc. to Dr. Maski

Evaluation of maturity of sleep states in preterm infants using conventional and amplitude-integrated electroencephalography

ObjectiveTo determine whether sleep state maturity can be estimated accurately using conventional electroencephalography (cEEG) or amplitude-integrated electroencephalography (aEEG) features concerning sleep in neurologically unimpaired preterm infants. MethodsA total of 51 preterm infants were monitored with cEEG-polygraphy and simultaneous aEEG. Sleep state maturity of EEG corresponded to specific postmenstrual age (PMA). PMA on cEEG was blindly estimated according to cEEG patterns (indicated as background continuity, frequencies, and voltages) as well as developmental markers in specific states. PMA on aEEG was blindly estimated based on the cycling score (cycling representing sleep state transitions) according to a pre-established scoring system. ResultsA total of 51 EEGs recorded between 32 and 37 weeks PMA were analysed. A significant relationship between estimated PMA (ePMA) and actual chronological PMA (cPMA) was shown by linear regression both on cEEG (r = 0.93, β = 0.98, 95% confidence interval (CI) 0.87–1.09, p < 0.001) and aEEG (r = 0.85, β = 0.83, 95% CI 0.69–0.98, p < 0.001). The estimation gap (defined as ePMA minus cPMA) was between −2 and +2 weeks both on cEEG and aEEG. The percentage of estimation gap between −1 and +1 weeks was 96% for cEEG, which was higher than the estimate of 88% for aEEG. ConclusionEstimated maturity of sleep state was well correlated with cPMA both on cEEG and aEEG. PMA corresponding to state maturity could be estimated within two weeks of actual cPMA using either of these two tools. However, cEEG had higher accuracy compared with aEEG in the evaluation of sleep state maturity.

Automated EEG sleep staging in the term-age baby using a generative modelling approach

Objective. We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38–40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification. Approach. EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording’s feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen’s kappa agreement calculated between the estimates and clinicians’ visual labels. Main results. For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively. Significance. This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may lead to learning problems by school age.

Sleep–wake stability in narcolepsy patients with normal, low and unmeasurable hypocretin levels

ObjectiveTo compare diurnal and nocturnal electrophysiological data from narcolepsy patients with undetectable (<20 pg/mL), low (20–110 pg/mL) and normal (>110 pg/mL) cerebrospinal fluid (CSF) hypocretin-1 levels. Patients/MethodsA total of 109 narcolepsy patients and 37 controls were studied; all had available CSF hypocretin-1 measurements. The sleep laboratory studies were conducted between 2008 and 2014. The study retrospectively examined measurements of sleep stage transitions in diurnal and nocturnal continuous polysomnography. The percentage distribution of time awake and rapid eye movement (REM) sleep, and the occurrence of sleep onset REM (SOREM) in the nocturnal polysomnography were also measured. ResultsParticipants with undetectable hypocretin-1 levels had significantly higher frequencies of transitions than controls and those with normal hypocretin-1 levels. Participants with low hypocretin-1 levels showed more transitions than controls and, in some cases, also more than those with normal hypocretin-1. Participants with normal hypocretin-1 failed to show any significant difference from the controls, except in the overall diurnal transitions. ConclusionUndetectable hypocretin-1 levels in particular, but also low hypocretin-1 levels, were associated with a less stable phenotype featuring more sleep state transitions and SOREM episodes. In addition, there was a distinction between nocturnal and diurnal REM sleep in hypocretin-deficient participants, expressed as increased diurnal REM sleep, which was not reflected in nocturnal sleep.

Relevance of the metabotropic glutamate receptor (mGluR5) in the regulation of NREM-REM sleep cycle and homeostasis: Evidence from mGluR5 (−/−) mice

Sleep is a homeostatically regulated behavior and sleep loss evokes a proportional increase in sleep time and delta slow wave activity. Glutamate and pharmacological modulation of the metabotropic glutamate receptors (mGluR) signaling have been implicated in the organization of vigilance states. Here, the role of the mGluR5 on homeostatic regulation of sleep-wake cycle and electroencephalographic (EEG) activity was examined in mGluR5 (−/−) mice. We first characterized the sleep-wake EEG phenotype in mGluR5 (−/−) and wild-type (WT) littermates mice by continuous recording for 72h of EEG, body temperature (BT) and locomotor activity (LMA). Next, we investigated the influence of sleep deprivation on the recovery sleep and EEG slow wave activity (1–4Hz) during NREM sleep to assess whether mGluR5 deletion affects the sleep homeostasis process. Like the control animals, mGluR5 (−/−) mice exhibited a clear-cut circadian sleep-wake architecture, however they showed reduced REM sleep time during the light phase with shorter REM sleep bouts and reduced state transitions in the NREM sleep-REM sleep cycle during the first and last 24h of the spontaneous 72h recording period. In addition, mGluR5 (−/−) mice had decreased slow EEG delta power during NREM sleep and enhanced LMA associated with elevated BT during the dark phase. Moreover, mGluR5 (−/−) mice exhibited reduced slow wave activity and sleep drive after sleep deprivation, indicating altered sleep homeostatic processes. The findings strongly indicate that mGluR5 is involved in shaping the stability of NREM sleep-REM sleep state transitions, NREM slow wave activity and homeostatic response to sleep loss.

Sleep fragmentation has differential effects on obese and lean mice.

Chronic sleep fragmentation (SF), common in patients with sleep apnea, correlates with the development of obesity. We hypothesized that SF differentially affects neurobehavior in lean wild-type (WT) and obese pan-leptin receptor knockout (POKO) mice fed the same normal diet. First, we established an SF paradigm by interrupting sleep every 2min during the inactive light span. The maneuver was effective in decreasing sleep duration and bout length, and in increasing sleep state transition and waking, without significant rebound sleep in the dark span. Changes of sleep architecture were evident in the light span and consistent across days 1-10 of SF. There was reduced NREM, shortened sleep latency, and increased state transitions. During the light span of the first day of SF, there also was reduction of REM and increased delta power of slow-wave sleep. Potential effects of SF on thermal pain threshold, locomotor activity, and anxiety were then tested. POKO mice had a lower circadian amplitude of pain latency than WT mice in the hot plate test, and both groups had lowest tolerance at 4pm (zeitgeber time (ZT) 10) and longest latency at 4am (ZT 22). SF increased the pain threshold in WT but not in POKO mice when tested at 8a.m. (ZT 2). Both the POKO mutation and SF resulted in reduced physical activity and increased anxiety, but there was no additive effect of these two factors. Overall, SF and the POKO mutation differentially regulate mouse behavior. The results suggest that obesity can blunt neurobehavioral responses to SF.

Increased sleep fragmentation in experimental autoimmune encephalomyelitis

Sleep disturbance in patients with multiple sclerosis is prevalent and has multifactorial causes. In mice with experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis, we determined the dynamic changes of sleep architecture and the interactions between sleep changes and EAE symptoms. The changes of sleep patterns were mainly reflected by altered sleep stage distribution and increased sleep fragmentation. Increased waking and decreased non-rapid eye movement sleep occurred after EAE onset and persisted through the symptomatic phase. There also was increased sleep state transition, indicating a reduction of sleep cohesiveness. Furthermore, the extent of sleep fragmentation correlated with the severity of disease. This is the first study of sleep characteristics in EAE mice demarcating specific changes related to the autoimmune disorder without confounding factors such as psychosocial impact and treatment effects. The reduction of sleep efficiency and cohesiveness supports the notion that enhancing sleep might facilitate the recovery of mice from EAE, pertinent to the multimodality treatment of multiple sclerosis.

Time- and state-dependent analysis of autonomic control in narcolepsy: higher heart rate with normal heart rate variability

Introduction Narcolepsy with hypocretin deficiency is known to alter cardiovascular control during sleep. The underlying etiology has been disputed. To discriminate the effects of sleep architecture on cardiovascular autonomic control from the consequences of hypocretin-deficiency per se, the present study examined heart rate modulations while taking into account the effects of sleep state duration, and sleep state transitions. Materials and methods One-night ambulatory polysomnography was recorded from 12 hypocretin-deficient narcolepsy patients with cataplexy (11 male, 16–53 years) and 12 healthy controls (11 male, 19–55 years). Heart rate and its variability were calculated per 30-s epoch. Heart rate variability was computed through fast Fourier transforms. Respiratory frequency was derived from a thoracic respiratory band. Results Heart rate was continuously elevated in narcolepsy patients compared to controls. This effect was found in sleep stage 1 (Mean ± SEM, controls vs. patients: 54.3 ± 2.2 vs. 60.4 ± 3.1 ), sleep stage 2 ( 51.0 ± 2.1 vs. 57.8 ± 3.0 ), slow wave sleep ( 52.1 ± 2.3 vs. 59.5 ± 3.2 ), and rapid eye movement (REM) sleep ( 53.5 ± 2.1 vs. 61.5 ± 3.0 ). No substantial changes in sympathovagal balance were found between patients and controls, even if differences in respiration frequency were taken into account. Heart rate alterations after sleep state onset and during transitions from non-REM to REM sleep were comparable between cases and controls. However, patients showed a blunted heart rate augmentation in response to transitions from NREM to wake. Conclusion Heart rate was continuously elevated in patients, and was not only explained by sleep fragmentation. This indicates that autonomic changes are may also be attributable to a direct effect of hypocretin-deficiency. Acknowledgements We would like to thank Noortje van der Kleij – Corssmit, Hanno Pijl and Nienke Biermasz for their contribution to the data collection.

State-Dependent Modulation of Breathing in Urethane-Anesthetized Rats

Respiratory activity is most fragile during sleep, in particular during paradoxical [or rapid eye movement (REM)] sleep and sleep state transitions. Rats are commonly used to study respiratory neuromodulation, but rodent sleep is characterized by a highly fragmented sleep pattern, thus making it very challenging to examine different sleep states and potential pharmacological manipulations within them. Sleep-like brain-state alternations occur in rats under urethane anesthesia and may be an effective and efficient model for sleep itself. The present study assessed state-dependent changes in breathing and respiratory muscle modulation under urethane anesthesia to determine their similarity to those occurring during natural sleep. Rats were anesthetized with urethane and respiratory airflow, as well as electromyographic activity in respiratory muscles were recorded in combination with local field potentials in neocortex and hippocampus to determine how breathing pattern and muscle activity are modulated with brain state. Measurements were made in normoxic, hypoxic, and hypercapnic conditions. Results were compared with recordings made from rats during natural sleep. Brain-state alternations under urethane anesthesia were closely correlated with changes in breathing rate and variability and with modulation of respiratory muscle tone. These changes closely mimicked those observed in natural sleep. Of great interest was that, during both REM and REM-like states, genioglossus muscle activity was strongly depressed and abdominal muscle activity showed potent expiratory modulation. We demonstrate that, in urethane-anesthetized rats, respiratory airflow and muscle activity are closely correlated with brain-state transitions and parallel those shown in natural sleep, providing a useful model to systematically study sleep-related changes in respiratory control.