Electroencephalographic changes associated with subjective under- and overestimation of sleep duration.

Night-to-night variability of obstructive sleep apnea severity is considerable and may depend on the diagnostic modality used. We investigated the night-to-night variability using peripheral arterial tonometry (PAT). Home sleep apnea testing was performed in 51 patients during 3 consecutive nights using PAT. Patients referred to our sleep clinic were screened and prospectively recruited for this study. All recordings were automatically and manually scored according to the PAT scoring guidelines. No systematic differences in PAT-derived apnea-hypopnea index (pAHI) were found between the nights. The night-to-night variability was comparable between manually and automatically scored data. pAHI varied in 35% of patients more than 10 events/h between the nights. The obstructive sleep apnea severity of 24% of patients was misclassified when using 1 night compared to the average of all nights. On average, pAHI varied by 57% from night to night. The variability of pAHI could partially be explained by the variability of time spent in the supine position with more time supine leading to a higher pAHI. On measuring a subsequent night, 12-14% of patients spontaneously fulfilled the commonly accepted criteria for treatment success without any intervention. With repeated recordings of PAT, we found no first night effect. However, there is considerable night-to-night variability similar to values found for polysomnography, which can partially be explained by the variability of time spent in the supine position. Obstructive sleep apnea severity was frequently misclassified due to the night-to-night variability. Our findings make a strong case for multiple testing in the diagnostic work-up of obstructive sleep apnea patients. Tschopp S, Wimmer W, Caversaccio M, Borner U, Tschopp K. Night-to-night variability in obstructive sleep apnea using peripheral arterial tonometry: a case for multiple night testing. J Clin Sleep Med. 2021;17(9):1751-1758.

Conscious experiences and high-density EEG patterns predicting subjective sleep depth

Rapid eye movement sleep behaviour disorder is characterized by dream-enacting behaviour and impaired motor inhibition during rapid eye movement sleep. Rapid eye movement sleep behaviour disorder is commonly associated with neurodegenerative disorders, but also reported in narcolepsy with cataplexy. Most narcolepsy with cataplexy patients lack the sleep-wake, and rapid eye movement sleep, motor-regulating hypocretin neurons in the lateral hypothalamus. In contrast, rapid eye movement sleep behaviour disorder and hypocretin deficiency are rare in narcolepsy without cataplexy. We hypothesized that rapid eye movement sleep behaviour disorder coexists with cataplexy in narcolepsy due to hypocretin deficiency. In our study, rapid eye movement sleep behaviour disorder was diagnosed by the International Classification of Sleep Disorders (2nd edition) criteria in 63 narcolepsy patients with or without cataplexy. Main outcome measures were: rapid eye movement sleep behaviour disorder symptoms; short and long muscle activations per hour rapid eye movement and non-rapid eye movement sleep; and periodic and non-periodic limb movements per hour rapid eye movement and non-rapid eye movement sleep. Outcome variables were analysed in relation to cataplexy and hypocretin deficiency with uni- and multivariate logistic/linear regression models, controlling for possible rapid eye movement sleep behaviour disorder biasing factors (age, gender, disease duration, previous anti-cataplexy medication). Only hypocretin deficiency independently predicted rapid eye movement sleep behaviour disorder symptoms (relative risk = 3.69, P = 0.03), long muscle activations per hour rapid eye movement sleep (ln-coefficient = 0.81, P < 0.01), and short muscle activations per hour rapid eye movement sleep (ln-coefficient = 1.01, P < 0.01). Likewise, periodic limb movements per hour rapid eye movement and non-rapid eye movement sleep were only associated with hypocretin deficiency (P < 0.01). A significant association between hypocretin deficiency and cataplexy was confirmed (P < 0.01). In a sub-analysis, hypocretin deficiency suggested the association of periodic limb movements and rapid eye movement sleep behaviour disorder outcomes (symptoms, non-periodic short and long muscle activity) in rapid eye movement sleep. Our results support the hypothesis that hypocretin deficiency is independently associated with rapid eye movement sleep behaviour disorder in narcolepsy. Thus, hypocretin deficiency is linked to the two major disturbances of rapid eye movement sleep motor regulation in narcolepsy: rapid eye movement sleep behaviour disorder and cataplexy. Hypocretin deficiency is also significantly associated with periodic limb movements in rapid eye movement and non-rapid eye movement sleep, and provides a possible pathophysiological link between rapid eye movement sleep behaviour disorder and periodic limb movements in narcolepsy. The study supports the hypothesis that an impaired hypocretin system causes a general instability of motor regulation during wakefulness, rapid eye movement and non-rapid eye movement sleep in human narcolepsy.

Rationale: Sleep-disordered breathing (SDB) occurring primarily during rapid eye movement (REM) sleep is a common clinical problem. The natural history of REM-related SDB and the associated cardiovascular sequelae of disease progression remain to be determined.Objectives: The objective of the current study was to describe the natural history of REM-related SDB, ascertain predictors of progression, and determine whether the evolution of REM-related SDB into non-REM (NREM) sleep is associated with incident cardiovascular events.Methods: Participants from the Sleep Heart Health Study with a baseline NREM apnea-hypopnea index (NREM-AHI) of <5 events/h and data from a follow-up sleep study along with information on incident cardiovascular disease were included in the study. Bivariate logistic regression was used to jointly model the predictors of disease progression based on the presence or absence of SDB during NREM and REM sleep using a cut-point of 5 events/h. Explanatory variables such as age, race, body mass index (BMI), change in BMI, and baseline REM-AHI were considered. Proportional hazards regression was then used to establish whether the development of SDB during NREM sleep was associated with incident cardiovascular disease.Results: The majority of the 1,908 participants included in the study did not develop SDB during NREM sleep. The likelihood of progression of SDB into NREM sleep did increase with higher baseline REM-AHI. BMI and an increase in BMI predicted progression of SDB in both NREM and REM sleep in men but not in women. There was a strong interdependence between developing a NREM-AHI of ≥5 events/h and worsening REM-AHI at follow-up with odds ratios of 6.01 and 4.47, in women and men, respectively. Moreover, the relative risk for incident cardiovascular events among those who developed a NREM-AHI of ≥5 events/h at the follow-up visit was elevated only in women with REM-related SDB at baseline.Conclusions: SDB during REM sleep is a relatively stable condition and does not progress in the majority of individuals. Progression of SDB into NREM sleep is associated with sex, weight, and age. SDB during REM and NREM sleep tends to develop concurrently. Finally, the development of SDB during NREM sleep is associated with incident cardiovascular events, but only in women with REM-related SDB at baseline.

Introduction Although obstructive sleep apnea (OSA) is associated with abnormal glycometabolism, current evidence on the association between hyperglycemia and abnormal sleep structure is still limited among patients with OSA. We sought to evaluate whether hyperglycemia was associated with abnormal sleep architecture. Methods A total of 452 patients with OSA, who were free of previously diagnosed diabetes mellitus, were consecutively recruited. All participants underwent overnight polysomnography and 75-g oral glucose tolerance test. Patients were divided into normal glucose tolerance (NGT) and hyperglycemia (i.e. prediabetes and type 2 diabetes) according to the ADA criteria. The association between hyperglycemia and sleep architecture was examined using logistic regression model. Results Of 452 patients, 283 (63%) had hyperglycemia (age 43.9 ± 11.1) and 169 (37%) had NGT (age 40.1 ± 11.1). Compared to the NGT group, the hyperglycemia group had older age (P &amp;lt; 0.05), higher body mass index (27.5 ± 4.1 vs. 26.33 ± 4.4; P &amp;lt; 0.05) and higher AHI (apnea-hypopnea index) (57.41 ± 28.6 vs. 48.3 ± 28.2; P &amp;lt; 0.05). There were no differences in total sleep time, the percentage of time spent in rapid eye movement (REM) or non-rapid eye movement (NREM) sleep between groups. However, patients with hyperglycemia had more microarousal events, especially during the NREM sleep (214 (range 19-662) events/h vs. 148 (range 37-600) events/h; P &amp;lt; 0.05). In addition, sleep variables related to oxygen saturation measures, such as the percentage of time spent with oxygen saturation ≤80%, were significantly greater during the REM sleep in patients with hyperglycemia (1.4 (total range 0-91.1) % vs. 1.1(0-78.6) %; P &amp;lt; 0.05). After adjusting potential confounders, logistic regression analyses showed that the presence of hyperglycemia was independently associated with the number of microarousals in NREM sleep (OR = 1.01, 95% CI = 1.00-1.02, P = 0.02). Conclusion Hyperglycemia is independently associated with abnormal sleep architecture among patients with OSA. Patients with hyperglycemia have significantly increased sleep fragmentation in NREM sleep and significantly increased hypoxia in REM sleep. Support This work was supported by the National Natural Science Foundation of China (81700087).

IntroductionAbnormal rapid eye movement (REM) sleep is often symptomatic of chronic disorders, however polysomnography, the gold standard method to measure REM sleep, is expensive and often impractical. Attempts to develop cost-effective ambulatory systems to measure REM sleep have had limited success. As elevated twitching is often observed during REM sleep in some distal muscles, the aim of this study was to assess the potential for a finger-mounted device to measure finger twitches, and thereby differentiate periods of REM and non-REM (NREM) sleep.MethodsOne night of sleep data was collected by polysomnography from each of 18 (3f, 15m) healthy adults aged 23.2 ± 3.3 (mean ± SD) years. Finger movement was detected using a piezo-electric limb sensor taped to the index finger of each participant. Finger twitch densities were calculated for each stage of sleep.ResultsFinger twitch density was greater in REM than in NREM sleep (p < 0.001). Each sleep stage had a unique finger twitch density, except for REM and stage N1 sleep which were similar. Finger twitch density was greater in late REM than in early REM sleep (p = 0.005), and there was a time–state interaction: the difference between finger twitch densities in REM and NREM sleep was greater in late sleep than in early sleep (p = 0.007).ConclusionFinger twitching is more frequent in REM sleep than in NREM sleep and becomes more distinguishable as sleep progresses. Finger twitches appear to be too infrequent to make definitive 30-second epoch determinations of sleep stage. However, an algorithm informed by measures of finger twitch density has the potential to detect periods of REM sleep and provide estimates of total REM sleep time and percentage.

Birds exhibit two types of sleep that are in many respects similar to mammalian rapid eye movement (REM) and non-REM (NREM) sleep. As in mammals, several aspects of avian sleep can occur in a local manner within the brain. Electrophysiological evidence of NREM sleep occurring more deeply in one hemisphere, or only in one hemisphere – the latter being a phenomenon most pronounced in dolphins – was actually first described in birds. Such asymmetric or unihemispheric NREM sleep occurs with one eye open, enabling birds to visually monitor their environment for predators. Frigatebirds primarily engage in this form of sleep in flight, perhaps to avoid collisions with other birds. In addition to interhemispheric differences in NREM sleep intensity, the intensity of NREM sleep is homeostatically regulated in a local, use-depended manner within each hemisphere. Furthermore, the intensity and temporo-spatial distribution of NREM sleep-related slow waves varies across layers of the avian hyperpallium – a primary visual area – with the slow waves occurring first in, and propagating through and outward from, thalamic input layers. Slow waves also have the greatest amplitude in these layers. Although most research has focused on NREM sleep, there are also local aspects to avian REM sleep. REM sleep-related reductions in skeletal muscle tone appear largely restricted to muscles involved in maintaining head posture. Other local aspects of sleep manifest as a mixture of features of NREM and REM sleep occurring simultaneously in different parts of the neuroaxis. Like monotreme mammals, ostriches often exhibit brainstem-mediated features of REM sleep (muscle atonia and REMs) while the hyperpallium shows EEG slow waves typical of NREM sleep. Finally, although mice show slow waves in thalamic input layers of primary sensory cortices during REM sleep, this is not the case in the hyperpallium of pigeons, suggesting that this phenomenon is not a universal feature of REM sleep. Collectively, the local aspects of sleep described in birds and mammals reveal that wakefulness, NREM sleep, and REM sleep are not always discrete states.

Introduction Sleep is a fundamental necessity for health and is commonly disrupted in the perioperative period. Technological improvements leveraging dry electroencephalographic (EEG) sensors have opened the door for large-scale quantitative assessments of sleep in relation to perioperative outcomes. Methods Patients utilized the Dreem (Rhythm, New York USA), a wireless EEG headband, to acquire their own preoperative nocturnal sleep records at home. Following cardiac surgery, postoperative recordings were obtained with staff assistance until postoperative night 7. Sleep records were scored as rapid eye movement (REM) and non-rapid eye movement (NREM) stages N1-N3, using modified American Academy of Sleep Medicine guidelines. Results Of 100 patients enrolled for perioperative sleep recordings, 74 patients provided 132 preoperative records; 80% were scorable with a median total sleep time (TST) of 209.8 minutes. TST was distributed as 8.3% N1, 70.6% N2, 2.1% N3 and 19% REM, consistent with expected sleep structure in geriatric populations. EEG markers for staging sleep were evaluated in the scorable records: 92% with sleep spindles, 98% with K-complexes, 69% with slow waves, 92% with sawtooth waves, and 80% with rapid eye movements. Among 26 patients with multiple preoperative sleep recordings, no significant within-subject differences in sleep structure were observed (all p &amp;gt; 0.05, paired Wilcoxon sign-rank test). 270 postoperative nocturnal sleep recordings were obtained from 83 patients, 70% of which were scorable. TST in scorable postoperative records was distributed as 14.9% N1, 78.6% N2, 0.9% N3 and 5.6% REM. Durations of REM and N3 sleep were significantly reduced in postoperative (POD 1-4) overnight recordings compared to preoperative measurements (Skillings–Mack test, p &amp;lt; 0.001 and p = 0.02 for REM and N3, respectively). Conclusion Wireless EEG devices enhance the feasibility of assaying perioperative sleep. A single night of unattended, ambulatory sleep monitoring is sufficient to establish a preoperative baseline. Multiple preoperative and postoperative sleep studies were tolerated by patients, which showed reductions of N3 and REM sleep in the early postoperative period. This study demonstrates the feasibility of using the Dreem for monitoring sleep macro- and microstructural EEG elements in the perioperative setting. Support (if any):

Ginseng Extract Regulates the Alterations of Sleep Architecture and EEG Power Spectra in Restraint Stressed Rats

Chapter 2 NREM–REM sleep

A salient feature of mammalian sleep is the alternation between rapid eye movement (REM) and non-REM (NREM) sleep. However, how these two sleep stages influence each other and thereby regulate the timing of REM sleep episodes is still largely unresolved. Here, we developed a statistical model that specifies the relationship between REM and subsequent NREM sleep to quantify how REM sleep affects the following NREM sleep duration and its electrophysiological features in mice. We show that a lognormal mixture model well describes how the preceding REM sleep duration influences the amount of NREM sleep till the next REM sleep episode. The model supports the existence of two different types of sleep cycles: Short cycles form closely interspaced sequences of REM sleep episodes, whereas during long cycles, REM sleep is first followed by an interval of NREM sleep during which transitions to REM sleep are extremely unlikely. This refractory period is characterized by low power in the theta and sigma range of the electroencephalogram (EEG), low spindle rate and frequent microarousals, and its duration proportionally increases with the preceding REM sleep duration. Using our model, we estimated the propensity for REM sleep at the transition from NREM to REM sleep and found that entering REM sleep with higher propensity resulted in longer REM sleep episodes with reduced EEG power. Compared with the light phase, the buildup of REM sleep propensity was slower during the dark phase. Our data-driven modeling approach uncovered basic principles underlying the timing and duration of REM sleep episodes in mice and provides a flexible framework to describe the ultradian regulation of REM sleep in health and disease.

A salient feature of mammalian sleep is the alternation between rapid eye movement (REM) and non-REM (NREM) sleep. However, how these two sleep stages influence each other and thereby regulate the timing of REM sleep episodes is still largely unresolved. Here, we developed a statistical model that specifies the relationship between REM and subsequent NREM sleep to quantify how REM sleep affects the following NREM sleep duration and its electrophysiological features in mice. We show that a lognormal mixture model well describes how the preceding REM sleep duration influences the amount of NREM sleep till the next REM sleep episode. The model supports the existence of two different types of sleep cycles: Short cycles form closely interspaced sequences of REM sleep episodes, whereas during long cycles, REM sleep is first followed by an interval of NREM sleep during which transitions to REM sleep are extremely unlikely. This refractory period is characterized by low power in the theta and sigma range of the electroencephalogram (EEG), low spindle rate and frequent microarousals, and its duration proportionally increases with the preceding REM sleep duration. Using our model, we estimated the propensity for REM sleep at the transition from NREM to REM sleep and found that entering REM sleep with higher propensity resulted in longer REM sleep episodes with reduced EEG power. Compared with the light phase, the buildup of REM sleep propensity was slower during the dark phase. Our data-driven modeling approach uncovered basic principles underlying the timing and duration of REM sleep episodes in mice and provides a flexible framework to describe the ultradian regulation of REM sleep in health and disease.

Ischemic stroke selectively inhibits REM sleep of rats

1. In thirty full-term infants in the first week of life, nursed in a constant volume, closed-circuit metabolism chamber in a neutral thermal environment (31.5-33.5 degrees C), measurements were made of oxygen consumption ( V(O2)) during periods of rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep.2. The mean V(O2) during REM sleep was 5.97 ml. kg(-1). min(-1). In NREM sleep the mean V(O2) was 5.72 ml. kg(-1). min(-1). This difference was significant (paired t test P < 0.05).3. When the direction of sleep state change was taken into account the difference in V(O2) between the two states was much less when REM sleep preceded NREM than when the change was in the opposite direction. In nineteen infants in whom the change was from REM to NREM the difference in V(O2) (6.18 and 6.03 ml. kg(-1). min(-1)) was not significant (P > 0.05). The mean difference when the sleep state change was from NREM to REM was significant (P < 0.01), the values being 5.54 and 5.81 ml. kg(-1). min(-1) respectively.4. In the NREM state, a gradual diminution of V(O2) with time was consistently found. This was not the case in REM sleep.5. In twelve infants studied in a cool environment (29 +/- 0.5 degrees C) V(O2) during REM sleep was 7.77, and during NREM sleep it was 6.58 ml. kg(-1). min(-1), (P < 0.001). Thus even the maximum difference found in a neutral thermal environment of 6.6% was significantly increased to 14.9% (P < 0.01) with mild thermal stress.6. No consistent changes in V(O2) with time were found in either REM or NREM sleep in twelve infants studied in a cool environment, in contrast to the findings in thermal neutrality

Facial muscle contractions during REM sleep and its association to emotional dreamed content