Microarousals Research Articles

Anything but small: Microarousals stand at the crossroad between noradrenaline signaling and key sleep functions.

Continuous sleep restores the brain and body, whereas fragmented sleep harms cognition and health. Microarousals (MAs), brief (3- to 15-s-long) wake intrusions into sleep, are clinical markers for various sleep disorders. Recent rodent studies show that MAs during healthy non-rapid eye movement (NREM) sleep are driven by infraslow fluctuations of noradrenaline (NA) in coordination with electrophysiological rhythms, vasomotor activity, cerebral blood volume, and glymphatic flow. MAs are hence part of healthy sleep dynamics, raising questions about their biological roles. We propose that MAs bolster NREM sleep's benefits associated with NA fluctuations, according to an inverted U-shaped curve. Weakened noradrenergic fluctuations, as may occur in neurodegenerative diseases or with sleep aids, reduce MAs, whereas exacerbated fluctuations caused by stress fragment NREM sleep and collapse NA signaling. We suggest that MAs are crucial for the restorative and plasticity-promoting functions of sleep and advance our insight into normal and pathological arousal dynamics from sleep.

Pilot study: can machine learning analyses of movement discriminate between leg movements in sleep (LMS) with vs. without cortical arousals?

Clinical and animal studies indicate frequent small micro-arousals (McA) fragment sleep leading to health complications. McA in humans is defined by changes in EEG and EMG during sleep. Complex EEG recordings during the night are usually required to detect McA-limiting large-scale, prospective studies on McA and their impact on health. Even with the use of EEG, reliably measuring McA can be difficult because of low inter-scorer reliability. Surrogate measures in place of EEG could provide easier and possibly more reliable measures of McA. These have usually involved measuring heart rate and arm movements. They have not provided a reliable measurement of McA in part because they cannot adequately detect short wake periods and periods of wake after sleep onset. Leg movements in sleep (LMS) offer an attractive alternative. LMS and cortical arousal, including McA, commonly occur together. Not all McA occur with LMS, but the most clinically significant ones may be those with LMS [1]. Conversely, most LMS do not occur with McA, but LMS vary considerably in their characteristics. Evaluating LMS characteristics may serve to identify the LMS associated with McA. The use of standard machine learning approaches seems appropriate for this particular task. This proof-of-concept pilot project aims to determine the feasibility of detecting McA from machine learning methods analyzing movement characteristics of the LMS. This study uses a small but diverse group of subjects to provide a large variety of LMS and McA adequate for supervised machine learning. LMS measurements were obtained from a new advanced technology in the RestEaZe™ leg band that integrates gyroscope, accelerometer, and capacitance measurements. Eleven RestEaZe™ LMS features were selected for logistic regression analyses. With the optimum logit probability threshold selected, the system accurately detected 76% of the McA matching the accuracy of trained visual inter-scorer reliability (71-76%). The classifier provided a sensitivity of 76% and a specificity of 86% for the identification of the LMS with McA. The classifier identified regions in sleep with high versus low rates of LMS with McA, indicating possible areas of fragmented versus undisturbed restful sleep. These pilot data are encouraging as a preliminary proof-of-concept for using advanced machine learning analyses of LMS to identify sleep fragmented by McA.

Decreased serum potassium may disturb sleep homeostasis in essential hypertensives

The aim is to investigate the association between alterations in the serum potassium (K+) concentration and sleep architecture parameters in essential hypertensives. Two hundred ninety-two hypertensives undergoing polysomnography and providing blood samples were recruited. The sleep architecture was composed of sleep stages 1 (N1), 2 (N2), 3 (N3), 4 (N4) and REM. The light sleep stage (LST) was composed of N1 + N2, and the deep sleep stage (DST) was composed of N3 + N4. The potentialrelationships between electrolytes and sleep parameters were determined via univariate and multivariate analyses. The subjects were divided into two groups via the serum K+ median (3.86 mmol/L). The K+ < 3.86 mmol/L group showed significantly decreased N1 (7.10 ± 4.55% vs 8.61 ± 5.23%, p = 0.002), LST (71.48 ± 11.33% vs 75.92 ± 17.08%, p = 0.013), and periodic leg movement during sleep related to microarousals (MA) /arousal (PLMS-A) [4 (1~10) vs 8 (3~15)/night, p < 0.001] and increased REM (17.38 ± 6.43% vs 15.37 ± 6.18%, p = 0.007) compared to the K+ ≥ 3.86 mmol/L group. A subdivided analysis by gender showed that these changes were more statistically significant in men than in women. Significant positive correlations were identified between K+ and N1 (r = 0.169, p = 0.004), as well as PLMS-A (r = 0.222, p < 0.001) in subjects. Compared to women, a significantly strong correlation was identified between K+ and REM sleep in men (r = 0.158, p = 0.028 vs. r = 0.078, p = 0.442). Multiple linear regression analysis indicated that K+ is significantly associated with N1 in all subjects (p = 0.03) and with REM in men (p = 0.008), even after adjusting for confounders. Decreased K+ may disturb the homeostasis of the sleep architecture, and gender may interfere with their links in the hypertensive population.

Blood pressure changes associated with periodic leg movements during sleep in healthy subjects

Periodic leg movements during sleep (PLMS) are associated with important blood pressure (BP) increases in restless legs syndrome (RLS) patients. These movements also are highly prevalent in the healthy elderly population. The aims of our study were to evaluate if heart rate (HR) and BP changes associated with PLMS are present in healthy subjects with no report of health concerns and to compare the amplitude of cardiovascular changes in healthy subjects to that of RLS subjects. Fourteen healthy subjects (six men, eight women; 46.6±9.7 y) and 14 RLS subjects (six men, eight women; 47.6±11.8 y) matched for age and gender participated in our study. Beat-to-beat noninvasive BP was continuously recorded during one night of polysomnography. HR, systolic BP (SBP) and diastolic BP (DBP) were measured for 10 beats before and 15 beats after onset of PLMS with and without microarousals (MA). PLMS were associated with sudden and significant increases of HR, SBP and DBP in both groups; however, cardiovascular increases were more pronounced in RLS subjects than in healthy subjects. Because PLMS index increases with age in healthy subjects and aging is associated with higher cardiovascular risk, further studies should investigate the impact of PLMS-related BP changes on the development of cardiovascular diseases in healthy elderly populations.

The hypnospectrogram: An EEG power spectrum based means to concurrently overview the macroscopic and microscopic architecture of human sleep

This study introduces a complementary tool for the description and evaluation of human sleep. The nocturnal sleep electroencephalographic (EEG) time–frequency analysis (TFA) plot (hypnospectrogram for short) is hereby proposed as a means to visualize both the macroscopic and the microscopic architecture of human sleep. It provides the ability to concurrently visually inspect the coarse sleep architecture, that is, the time-course of non-rapid eye movement (NREM) and REM stages, along with finer sleep elements such as slow and fast spindles, NREM delta distribution, REM alpha and beta, microarousals (MAs), and NREM cyclic alternating patterns (CAPs). Furthermore, the hypnospectrogram has the potential to provide visual quality of sleep (QoS) evaluation, as well as reveal the dominant rhythms and their transitions for every cerebral locus – as represented at the electrode space – during the night.

Time of night and first night effects on arousal response in healthy adults

Objective Several factors, such as homeostatic and circadian influences, may affect the density of cortical and subcortical arousals (AR). The purpose of this study was to examine the time-of-night and the first night effect on AR response. Methods AR were classified into microarousals (MA), phases of transitory activation (PAT), delta (D-burst) and K-complex burst (K-burst). The AR density and duration was analyzed during two consecutive nights with the analysis of sleep stage and sleep cycle in thirty-six healthy subjects. Results D- and K-burst showed a trend toward progressive decline across sleep cycles ( p < 0.0001). While MA rate was unaffected throughout sleep cycles, PAT index increased across the night ( p = 0.002). The density and duration of each group of AR exhibited reproducibility without significant differences between nights. An individual inter-night variability in AR density was found independently of night and sleep structure. Conclusions While homeostatic and circadian influences affect nighttime subcortical and MA responses, a wakefulness drive modulates the occurrence of AR with movements. Although the pattern of AR responses was highly reliable from the first to second night, the substantial inter-individual variability suggests the existence of an individual susceptibility. Significance The first night effect on arousal response is affected by individual susceptibility and circadian and homeostatic influences.

An alternative measure of sleep fragmentation in clinical practice: the sleep fragmentation index

Background and purpose Micro-arousals (MA) are commonly considered as sleep components reflecting sleep fragmentation. However, their elucidation is time-consuming, with considerable inter-observer variability. The aim of our study was to investigate the usefulness of a sleep fragmentation index (SFI) to detect sleep disruption in a large sample of patients. Patients and methods Five-hundred ninety-eight polysomnographic studies made in controls and patients were examined. The SFI was calculated as the total number of awakenings and sleep stage shifts divided by total sleep time. Results In the whole group a significant correlation was found between the SFI and the MA index (MAI) ( P<0.001) with good agreement across a wide range of values. When patients were stratified according to final diagnosis a significant relation was noted for patients with insomnia ( P<0.001), parasomnia ( P<0.001), circadian schedule disorders ( P<0.001) and sleep related breathing disorders ( P< 0.001). Lower values were found in controls ( P<0.01) and in patients with periodic limb movement disorder and/or restless legs syndrome ( P<0.05). In 111 patients having two consecutive recording nights, a good reproducibility was present with no differences between nights ( P=ns) and with significant correlation ( P<0.001). Conclusions The SFI seems to be an accurate, reproducible and easy method to detect sleep fragmentation in patients with sleep disorders. Further studies are needed to validate the usefulness of this tool in clinical practice.

Influence of sleep stage and wakefulness on spectral EEG activity and heart rate variations around periodic leg movements

Objective: Typical changes in spectral electroencephalographic (EEG) activity and heart rate (HR) have been described in periodic leg movements (PLM) associated with or without microarousals (MA). We aimed to determine the effects of sleep stage and wakefulness on these responses to ascertain whether a common pattern of EEG and HR activation takes place. Methods: The time course of EEG spectral activity and HR variability associated with PLM was analysed in 13 patients during light NREM sleep, rapid-eye-movement (REM) sleep and wakefulness. The same analysis was also conducted for PLM without MA occurring in stage 2. Results: A significant EEG and electrocardiogram (ECG) activation was found associated with PLM during sleep, but not during wakefulness. While in light NREM sleep, an increase in delta and theta bands was detected before the PLM onset, in REM sleep the EEG activation occurred simultaneously with the PLM onset. Moreover, during stage 1 and REM sleep, alpha and fast frequencies tended to remain sustained after the PLM onset. In contrast, during wakefulness, a small and not significant increase in cerebral activity was present, starting at the PLM onset and persisting in the post-movement period. A typical pattern of cardiac response was present during NREM and REM sleep, the autonomic activation being lesser and prolonged during wakefulness. Conclusions: We conclude that the EEG and HR responses to PLM differ between sleep stages and wakefulness with lesser changes found during wakefulness. Significance: These findings suggest that specific sleep state-dependent mechanisms may underlie the occurrence of PLM.

Age and gender effects on heart rate activation associated with periodic leg movements in patients with restless legs syndrome.

Periodic leg movements during sleep (PLMS) are often associated with electroencephalographic (EEG) changes, such as microarousals (MA), and with heart rate (HR) variations. The aim of the present study was to evaluate the effects of age and gender on HR changes associated with PLMS in restless legs syndrome (RLS) patients. Forty-two RLS patients underwent one night of polysomnographic recordings. They were divided into 3 groups of 14 subjects (7 women and 7 men) according to age, i.e. young (25-40 years), middle-aged (41-55 years) and elderly (56-71 years) patients. The RR interval was calculated for 5 intervals before and 15 intervals after the onset of 50 PLMS in each patient. PLMS were associated with HR changes characterized by a tachycardia followed by a bradycardia. However, a reduction in the tachycardia and the bradycardia was observed with age. Moreover, women showed a higher amplitude in the bradycardia than men. No age or gender difference was found for MA index and duration. This study showed age and gender differences in the magnitude of the HR changes associated with PLMS. The knowledge of HR variations during sleep, including rapid HR changes associated with sleep events such as PLMS or MA, may be helpful in understanding the potential mechanisms involved in the increased cardiac risk observed in elderly.

Auto-CPAP Therapy for Obstructive Sleep Apnea: Induction of Microarousals by Automatic Variations of CPAP Pressure?

To investigate the frequency of microarousals (MA) associated with pressure changes during auto-CPAP therapy (APAP) for obstructive sleep apnea (OSA). Patients with OSA were studied by polysomnography during APAP therapy (Somnosmart). The MA were classified on the basis of concomitant changes in APAP pressure. Sleep laboratory of a university hospital 30 patients with moderate to severe OSA. The mean AHI during APAP was 4.7+/-4.7, the mean arousal index was 14.5+/-6.6 per hour. During epochs with a pressure variation greater than 0.5 mbar, significantly more MA occurred (0.30+/-0.17 MA per epoch) than in epochs with constant treatment pressure (0.10+/-0.054 MA per epoch; p<0.001). There were more MA during pressure-increase epochs than during pressure-decrease epochs (0.42+/-0.24 vs. 0.16+/-0.12 MA per epoch; p<0.001). 82.5 percent of the MA were not preceded by a significant change in pressure (at least 0.5 mbar within 30 sec.), 10.6% were associated with a significant prior increase and 6.9% with a significant prior decrease in pressure. The percentage of MA preceded by a significant pressure variation varied between 2.3% and 61%, with a mean of 18.9%. The overall frequency of MA was low, and in most individuals the relative amount of "pressure-associated MA" was not significant. However in some individuals it cannot be excluded that some additional MA may have been induced by pressure variations. Should it prove possible to prevent such "pressure-associated MA" by optimizing the regulation of APAP pressure, the overall clinical effect of APAP treatment may be improved.

Transient cardiorespiratory events during NREM sleep: a feline model for human microarousals.

Microarousals (MAs) are brief transient events that occur during normal sleep in humans and with increased frequency in disordered sleep, especially in association with sleep apnoea. In a feline model, we discovered transient cardiorespiratory events during nonrapid eye movement (NREM) sleep that exhibited consistent features with similarities to human MAs. It was observed that MAs have two distinct phases. Phase I (MAI) is characterized by an abrupt increase in electromyogram (EMG) amplitude (> 50%), increased electrooculogram (EOG) activity and accelerated frequency of hippocampal electroencephalographic (EEG) activity. MAI lasts 4.1 +/- 0.3 s. Phase II (MAII), lasting 9.8 +/- 0.8 s, is characterized by high frequency EEG activity, but EMG, EOG and hippocampal activity remain at baseline levels. Mean inspiratory rate begins to increase 15 s before the onset of the MA, followed 10 s later by the increase in mean heart rate. Mean respiratory rate decreases sharply through MAII, and returns to baseline levels 15 s after the MA. During MAII mean heart rate decreases quickly; there is increased respiratory irregularity, followed by a prolonged ventilatory overshoot. The abrupt shift in heart rate is coincident with the change in breath timing seen during MAII. Heart rate returns to baseline levels 10 s following the MA. Integrating our findings with those described previously in humans, we propose that MAs may serve as a homeostatic mechanism which is designed to restore cardiorespiratory function allowing the continuity of sleep.

EEG and cardiac activation during periodic leg movements in sleep: support for a hierarchy of arousal responses.

To investigate other physiologic changes that occur with periodic leg movements during sleep (PLMS) that might be considered to be more sensitive indices of sleep fragmentation. Although PLMS are associated with recurrent microarousals (MA), the frequency of PLMS with MA does not correlate with objective daytime sleepiness. It is postulated that the lack of correlation results from the low sensitivity of the standard criteria used to score MA. Ten drug-free patients with a polygraphic and clinical diagnosis of restless legs syndrome (RLS) and PLMS were examined. The EEG correlates of PLMS were analyzed by visual scoring and spectral analysis during PLMS that ended in a visible microarousal (PLMS with MA) or not (PLMS without MA). The R-R interval in the EKG signal was also examined. A total of 34% of PLMS were associated with MA lasting >3 seconds, and 3% of PLMS were associated with MA lasting <3 seconds. Although PLMS with MA were associated with an increase in alpha activity, for PLMS without MA a significant increase in delta and theta activity was present. Both types of PLMS induced a shortening of the R-R interval; this was particularly more marked for PLMS with MA. First, visual scoring of MA that include a duration of less than 3 seconds has little effect on the detection of PLMS with MA. Second, EEG activation and tachycardia are present during both types of PLMS. Third, a hierarchy in the arousal response is present-going from autonomic activation to bursts of delta activity to alpha activity to a full awakening.