Prolonged Light Exposure Induces Circadian Impairment in Aquaporin-4-Knockout Mice.

Astrocytes are densely present in the suprachiasmatic nucleus (SCN), which is the master circadian oscillator in mammals, and are presumed to play a key role in circadian oscillation. However, specific astrocytic molecules that regulate the circadian clock are not yet well understood. In our study, we found that the water channel aquaporin-4 (AQP4) was abundantly expressed in SCN astrocytes, and we further examined its circadian role using AQP4-knockout mice. There was no prominent difference in circadian behavioral rhythms between Aqp4-/- and Aqp4+/+ mice subjected to light-dark cycles and constant dark conditions. However, exposure to constant light induced a greater decrease in the Aqp4-/- mice rhythmicity. Although the damped rhythm in long-term constant light recovered after transfer to constant dark conditions in both genotypes, the period until the reappearance of original rhythmicity was severely prolonged in Aqp4-/- mice. In conclusion, AQP4 absence exacerbates the prolonged light-induced impairment of circadian oscillations and delays their recovery to normal rhythmicity.

Correction to: Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography.

[This corrects the article DOI: 10.1007/s41105-022-00421-5.].

Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography.

Automatic algorithms are a proposed alternative to manual assessment of polysomnography data for analyzing sleep structure; however, none are acceptably accurate for clinical use. We investigated the feasibility of an automated sleep stage scoring system called Sleep Scope, which is intended for use with portable 1-channel electroencephalograph, and compared it with the traditional polysomnography scoring method. Twenty-six outpatients and fourteen healthy volunteers underwent Sleep Scope and polysomnography assessments simultaneously. Polysomnography records were manually scored by three sleep experts. Sleep Scope records were scored using a dedicated auto-staging algorithm. Sleep parameters, including total sleep time, sleep latency, wake after sleep onset, and sleep efficiency, were calculated. The epoch-by-epoch pairwise concordance based on the classification of sleep into five stages (i.e., wake, rapid eye movement, N1, N2, and N3) was also evaluated after validating homogeneity and bias between Sleep Scope and polysomnography. Compared with polysomnography, Sleep Scope seemed to overestimate sleep latency by approximately 3 min, but there was no consistent tendency in bias in other sleep parameters. The Κ values ranged from 0.66 to 0.75 for experts' inter-rater polysomnography scores and from 0.62 to 0.67 for Sleep Scope versus polysomnography scores, which indicated sufficient agreement in the determination of sleep stages based on the Landis and Koch criteria. We observed sufficient concordance between Sleep Scope and polysomnography despite lower concordance in sleep disorder patients. Thus, this auto-staging system might serve as a novel clinical tool for reducing the time and expenses required of medical staff and patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s41105-022-00421-5.

A light-induced small G-protein gem limits the circadian clock phase-shift magnitude by inhibiting voltage-dependent calcium channels.

Calcium signaling is pivotal to the circadian clockwork in the suprachiasmatic nucleus (SCN), particularly in rhythm entrainment to environmental light-dark cycles. Here, we show that a small G-protein Gem, an endogenous inhibitor of high-voltage-activated voltage-dependent calcium channels (VDCCs), is rapidly induced by light in SCN neurons via the calcium (Ca2+)-mediated CREB/CRE transcriptional pathway. Gem attenuates light-induced calcium signaling through its interaction with VDCCs. The phase-shift magnitude of locomotor activity rhythms by light, at night, increases in Gem-deficient (Gem-/-) mice. Similarly, in SCN slices from Gem-/- mice, depolarizing stimuli induce larger phase shifts of clock gene transcription rhythms that are normalized by the application of an L-type VDCC blocker, nifedipine. Voltage-clamp recordings from SCN neurons reveal that Ca2+ currents through L-type channels increase in Gem-/- mice. Our findings suggest that transcriptionally activated Gem feeds back to suppress excessive light-evoked L-type VDCC activation, adjusting the light-induced phase-shift magnitude to an appropriate level in mammals.

Work Habit-Related Sleep Debt; Insights From Factor Identification Analysis of Actigraphy Data.

The present study investigates the factors of "Weekday sleep debt (WSD)" by comparing activity data collected from persons with and without WSD. Since it has been reported that the amount of sleep debt as well the difference between the social clock and the biological clock is associated with WSD, specifying the factors of WSD other than chronotype may contribute to sleep debt prevention. We recruited 324 healthy male employees working at the same company and collected their 1-week wrist actigraphy data and answers to questionnaires. Because 106 participants were excluded due to measurement failure of the actigraphy data, the remaining 218 participants were included in the analysis. All participants were classified into WSD or non-WSD groups, in which persons had WDS if the difference between their weekend sleep duration and the mean weekday sleep duration was more than 120 min. We evaluated multiple measurements derived from the collected actigraphy data and trained a classifier that predicts the presence of WSD using these measurements. A support vector machine (SVM) was adopted as the classifier. In addition, to evaluate the contribution of each indicator to WSD, permutation feature importance was calculated based on the trained classifier. Our analysis results showed significant importance of the following three out of the tested 32 factors: (1) WSD was significantly related to persons with evening tendency. (2) Daily activity rhythms and sleep were less stable in the WSD group than in the non-WSD group. (3) A specific day of the week had the highest importance in our data, suggesting that work habit contributes to WSD. These findings indicate some WSD factors: evening chronotype, instability of the daily activity rhythm, and differences in work habits on the specific day of the week. Thus, it is necessary to evaluate the rhythms of diurnal activities as well as sleep conditions to identify the WSD factors. In particular, the diurnal activity rhythm influences WSD. It is suggested that proper management of activity rhythm may contribute to the prevention of sleep debt.

Role of α2δ3 in Cellular Synchronization of the Suprachiasmatic Nucleus Under Constant Light Conditions.

By the effort to identify candidate signaling molecules important for the formation of robust circadian rhythms in the suprachiasmatic nucleus (SCN), the mammalian circadian center, here we characterize the role of α2δ proteins, synaptic molecules initially identified as an auxiliary subunit of the voltage dependent calcium channel, in circadian rhythm formation. In situ hybridization study demonstrated that type 3 α2δ gene (α2δ3) was strongly expressed in the SCN. Mice without this isoform (Cacna2d3-/-) did not maintain proper circadian locomotor activity rhythms under a constant light (LL) condition, whereas under a constant dark (DD) condition, these mice showed a similar period length and similar light-responsiveness as compared to wild type mice. Reflecting this behavioral phenotype, Cacna2d3-/- mice showed a severely impaired Per1 expression rhythm in the SCN under LL, but not under DD. Cultured SCN slices from Per1-luc transgenic Cacna2d3-/- mice revealed reduced synchrony of Per1-luc gene expression rhythms among SCN neurons. These findings suggest that α2δ3 is essential for synchronized cellular oscillations in the SCN and thereby contributes to enhancing the sustainability of circadian rhythms in behavior.

Screening of sleep apnea based on heart rate variability and long short-term memory.

PURPOSE: Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of the risk of lifestyle-related disease development as well as daytime sleepiness. Although SAS is a common sleep disorder, most patients remain undiagnosed because the gold standard test polysomnography (PSG), is high-cost and unavailable in many hospitals. Thus, an SAS screening system that can be used easily at home is needed. METHODS: Apnea during sleep affects changes in the autonomic nervous function, which causes fluctuation of the heart rate. In this study, we propose a new SAS screening method that combines heart rate measurement and long short-term memory (LSTM) which is a type of recurrent neural network (RNN). We analyzed the data of intervals between adjacent R waves (R-R interval; RRI) on the electrocardiogram (ECG) records, and used an LSTM model whose inputs are the RRI data is trained to discriminate the respiratory condition during sleep. RESULTS: The application of the proposed method to clinical data showed that it distinguished between patients with moderate-to-severe SAS with a sensitivity of 100% and specificity of 100%, results which are superior to any other existing SAS screening methods. CONCLUSION: Since the RRI data can be easily measured by means of wearable heart rate sensors, our method may prove to be useful as an SAS screening system at home.

Resting Heart Rate Variability Is Associated With Subsequent Orthostatic Hypotension: Comparison Between Healthy Older People and Patients With Rapid Eye Movement Sleep Behavior Disorder.

Background: Orthostatic hypotension (OH) caused by autonomic dysfunction is a common symptom in older people and patients with idiopathic rapid eye movement sleep behavior disorder (iRBD). The orthostatic challenge test is a standard autonomic function test that measures a decrease of blood pressure during a postural change from supine to standing positions. Although previous studies have reported that changes in heart rate variability (HRV) are associated with autonomic dysfunction, no study has investigated the relationship between HRV before standing and the occurrence of OH in an orthostatic challenge test. This study aims to examine the connection between HRV in the supine position and the occurrence of OH in an orthostatic challenge test. Methods: We measured the electrocardiograms of patients with iRBD and healthy older people during an orthostatic challenge test, in which the supine and standing positions were held for 15 min, respectively. The subjects were divided into three groups: healthy controls (HC), OH-negative iRBD [OH (-) iRBD], and OH-positive iRBD [OH (+) iRBD]. HRV measured in the supine position during the test were calculated by time-domain analysis and Poincaré plots to evaluate the autonomic dysfunction. Results: Forty-two HC, 12 OH (-) iRBD, and nine OH (+) iRBD subjects were included. HRV indices in the OH (-) and the OH (+) iRBD groups were significantly smaller than those in the HC group. The multivariate logistic regression analysis for OH identification for the iRBD groups showed the model whose inputs were the HRV indices, i.e., standard deviation 2 (SD2) and the percentage of adjacent intervals that varied by more than 50 ms (pNN50), had a receiver operating characteristic curve with area under the curve of 0.840, the sensitivity to OH (+) of 1.000, and the specificity to OH (-) of 0.583 (p = 0.023). Conclusions: This study showed the possibility that short-term HRV indices in the supine position would predict subsequent OH in iRBD patients. Our results are of clinical importance in terms of showing the possibility that OH can be predicted using only HRV in the supine position without an orthostatic challenge test, which would improve the efficiency and safety of testing.

Self-Isolation Due to COVID-19 Is Linked to Small One-Year Changes in Depression, Sleepiness, and Insomnia: Results from a Clinic for Sleep Disorders in Shiga Prefecture, Japan.

We aimed to analyze (a) the changes in depression, sleepiness, insomnia, and sleep habits in relation to the degree of self-isolation and (b) the effects of changes in sleep habits and social interactions on depression, insomnia, and sleepiness during the coronavirus disease 2019 (COVID-19) pandemic. We enrolled 164 patients who visited the sleep outpatient clinic in Shiga University of Medical Science Hospital. We compared the sleep habits, depression (Patient Health Questionnaire-9: PHQ-9), insomnia (Athens Insomnia Scale: AIS), and sleepiness (Epworth Sleepiness Scale: ESS) of patients during the period from April to July 2019 vs. May 2020 (a period of self-isolation due to COVID-19). A Wilcoxon signed-rank test indicated no significant differences in PHQ-9, ESS, and AIS scores between 2019 and 2020 within both the strong self-isolation group and no/little self-isolation group. With respect to sleep habits, earlier bedtime (p = 0.006) and increased sleep duration (p = 0.014) were found in the strong self-isolation group. The former (p = 0.009) was also found in the no/little self-isolation group, but we found significant differences in sleep duration between the no/little self-isolation group and the strong self-isolation group (p = 0.047). Therefore, self-isolation due to COVID-19 had relatively small one-year effects on depression, sleepiness, and insomnia in a clinical population.

Evaluation of Severity Levels of the Athens Insomnia Scale Based on the Criterion of Insomnia Severity Index.

The Athens Insomnia Scale (AIS) can be regarded as a highly useful instrument in both clinical and research settings, except for when assessing the severity level. This study aims to determine the severity criteria for AIS by using the Insomnia Severity Index (ISI). A total of 1666 government employees aged 20 years or older were evaluated using the AIS and ISI, the Patient Health Questionnaire for depressive symptoms, the Epworth Sleepiness Scale for daytime sleepiness, and the Short Form Health Survey of the Medical Outcomes Study for health-related quality of life (QoL). A significant positive correlation (r) was found between the AIS and the ISI (r = 0.80, p < 0.001). As a result of describing receiver-operator curves, the severity criteria of the AIS are capable of categorizing insomnia severity as follows: absence of insomnia (0-5), mild insomnia (6-9), moderate insomnia (10-15), and severe insomnia (16-24). In addition, compared to all scales across groups categorized by AIS or ISI, it was revealed that similar results could be obtained (all p < 0.05). Therefore, the identification of the severity of AIS in this study is important in linking the findings of epidemiological studies with those of clinical studies.

Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform.

Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.

Obstructive sleep apnea screening by heart rate variability-based apnea/normal respiration discriminant model.

OBJECTIVE: Obstructive sleep apnea (OSA) is a common sleep disorder; however, most patients are undiagnosed and untreated because it is difficult for patients themselves to notice OSA in daily living. Polysomnography (PSG), which is the gold standard test for sleep disorder diagnosis, cannot be performed in many hospitals. This fact motivates us to develop a simple system for screening OSA at home. APPROACH: The autonomic nervous system changes during apnea, and such changes affect heart rate variability (HRV). This work develops a new apnea screening method based on HRV analysis and machine learning technologies. An apnea/normal respiration (A/N) discriminant model is built for respiration condition estimation for every heart rate measurement, and an apnea/sleep ratio is introduced for final diagnosis. A random forest is adopted for the A/N discriminant model construction, which is trained with the PhysioNet apnea-ECG database. MAIN RESULTS: The screening performance of the proposed method was evaluated by applying it to clinical PSG data. Sensitivity and specificity achieved 76% and 92%, respectively, which are comparable to existing portable sleep monitoring devices used in sleep laboratories. SIGNIFICANCE: Since the proposed OSA screening method can be used more easily than existing devices, it will contribute to OSA treatment.

Background Music Dependent Reduction of Aversive Perception and Its Relation to P3 Amplitude Reduction and Increased Heart Rate.

Music is commonly used to modify mood and has attracted attention as a potential therapeutic intervention. Despite the well-recognized effects of music on mood, changes in affective perception due to music remain majorly unknown. Here, we examined if the perception of aversive stimuli could be altered by mood-changing background music. Using subjective scoring data from 17 healthy volunteers, we assessed the effect of relaxing background music (RelaxBGM), busy background music (BusyBGM), or no background music (NoBGM) conditions on response to aversive white noise stimulation. Interestingly, affective response to the white noise was selectively alleviated, and white noise-related P3 component amplitude was reduced in BusyBGM. However, affective responses as well as P3 amplitude to reference pure tone stimuli were similar regardless of background music conditions. Interestingly, heart rate (HR) increased in BusyBGM, whereas no increase in HR was found in similar distress, NoBGM condition. These findings suggest that increase in HR, which happens during BusyBGM exposure, can be a reflecting feature of music that ameliorates the affective response to aversive stimuli, possibly through selective reduction in neurophysiological responses.

Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG.

OBJECTIVE: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. METHODS: Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. RESULT: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. CONCLUSION: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. SIGNIFICANCE: The proposed method can contribute to preventing accidents caused by drowsy driving.

Development of a Sleep Apnea Detection Algorithm Using Long Short-Term Memory and Heart Rate Variability.

Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with the increased risk of lifestyle diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with high sensitivity (100%) and specificity (100%).

Association of different neural processes during different emotional perceptions of white noise and pure tone auditory stimuli.

Sound is a sensory stimulant ubiquitously found throughout our environment. Humans have evolved a system that effectively and automatically converts sound sensory inputs into emotions. Although different emotional responses to sounds with different frequency characteristics are empirically recognized, there is a paucity of studies addressing different emotional responses to these sounds and the underlying neural mechanisms. In this study, we examined effects of pure tone (PT) and white noise (WN) inputs at ordinary loudness levels on emotional responses. We found that WN stimuli produced more aversive responses than PT stimuli. This difference was endorsed by larger late posterior positivity (LPP). In a source localization study, we found increased neural activity in the parietal lobe prior to LPP. These findings show that WN stimuli produce aversive perceptions compared with PT stimuli, at typical loudness levels. In addition, different emotional responses were processed in a similar manner as visual stimulations, as reflected by increased LPP activation. Various emotional effects of WN and PT stimuli, at ordinary loudness levels, could expand our understanding of adverse effects of noise as well as favorable effects associated with music.

Comparisons of Portable Sleep Monitors of Different Modalities: Potential as Naturalistic Sleep Recorders.

BACKGROUND: Humans spend more than one-fourth of their life sleeping, and sleep quality has been significantly linked to health. However, the objective examination of ambulatory sleep quality remains a challenge, since sleep is a state of unconsciousness, which limits the reliability of self-reports. Therefore, a non-invasive, continuous, and objective method for the recording and analysis of naturalistic sleep is required. OBJECTIVE: Portable sleep recording devices provide a suitable solution for the ambulatory analysis of sleep quality. In this study, the performance of two activity-based sleep monitors (Actiwatch and MTN-210) and a single-channel electroencephalography (EEG)-based sleep monitor (SleepScope) were compared in order to examine their reliability for the assessment of sleep quality. METHODS: Twenty healthy adults were recruited for this study. First, data from daily activity recorded by Actiwatch and MTN-210 were compared to determine whether MTN-210, a more affordable device, could yield data similar to Actiwatch, the de facto standard. In addition, sleep detection ability was examined using data obtained by polysomnography as reference. One simple analysis included comparing the sleep/wake detection ability of Actiwatch, MTN-210, and SleepScope. Furthermore, the fidelity of sleep stage determination was examined using SleepScope in finer time resolution. RESULTS: The results indicate that MTN-210 demonstrates an activity pattern comparable to that of Actiwatch, although their sensitivity preferences were not identical. Moreover, MTN-210 provides assessment of sleep duration comparable to that of the wrist-worn Actiwatch when MTN-210 was attached to the body. SleepScope featured superior overall sleep detection performance among the three methods tested. Furthermore, SleepScope was able to provide information regarding sleep architecture, although systemic bias was found. CONCLUSION: The present results suggest that single-channel EEG-based sleep monitors are the superior option for the examination of naturalistic sleep. The current results pave a possible future use for reliable portable sleep assessment methods in an ambulatory rather than a laboratory setting.

Development of sleep apnea syndrome screening algorithm by using heart rate variability analysis and support vector machine.

Although sleep apnea syndrome (SAS) is a common sleep disorder, most patients with sleep apnea are undiagnosed and untreated because it is difficult for patients themselves to notice SAS in daily living. Polysomnography (PSG) is a gold standard test for sleep disorder diagnosis, however PSG cannot be performed in many hospitals. This fact motivates us to develop an SAS screening system that can be used easily at home. The autonomic nervous function of a patient changes during apnea. Since changes in the autonomic nervous function affect fluctuation of the R-R interval (RRI) of an electrocardiogram (ECG), called heart rate variability (HRV), SAS can be detected through monitoring HRV. The present work proposes a new HRV-based SAS screening algorithm by utilizing support vector machine (SVM), which is a well-known pattern recognition method. In the proposed algorithm, various HRV features are derived from RRI data in both apnea and normal respiration periods of patients and healthy people, and an apnea/normal respiration (A/N) discriminant model is built from the derived HRV features by SVM. The result of applying the proposed SAS screening algorithm to clinical data demonstrates that it can discriminate patients with sleep apnea and healthy people appropriately. The sensitivity and the specificity of the proposed algorithm were 100% and 86%, respectively.

Association of sleep-disordered breathing with decreased cognitive function among patients with dementia.

Sleep is known to be essential for proper cognitive functioning. Sleep disturbance, especially respiratory disturbance during sleep, is a risk factor for the development of dementia. However, it is not known whether hypopnoea during sleep is related to severity of cognitive function in patients already diagnosed with dementia. Considering the high prevalence of sleep problems in aged people, it is important to determine if hypopnoea during sleep contributes to dementia. In addition, it would be desirable to develop a feasible method for objectively evaluating sleep in patients with dementia. For this purpose, a simple sleep recorder that employs single or dual bioparameter recording, which is defined as a type-4 portable monitor, is suitable. In this study, a type-4 sleep recorder was used to evaluate respiratory function during sleep in 111 patients with dementia, and data suggesting a possible relationship with cognitive function levels were examined. Multivariate logistic regression was used to investigate the association of severity of dementia with sleep-disordered breathing, age, diabetes, dyslipidaemia and hypertension. It was found that the respiratory disturbance index was associated with severity of cognitive dysfunction in our subjects. Furthermore, patients younger than 80 years were more susceptible to lower cognitive function associated with sleep-disordered breathing than patients 80 years old or over, because an increase in the respiratory disturbance index was associated with deteriorated cognitive function only in the former age group. These results suggest that proper treatment of sleep apnea is important for the preservation of cognitive function, especially in patients with early-stage dementia.

RGS2 is a feedback inhibitor of melatonin production in the pineal gland.

The 24-h rhythmic production of melatonin by the pineal gland is essential for coordinating circadian physiology. Melatonin production increases at night in response to the release of norepinephrine from sympathetic nerve processes which innervate the pineal gland. This signal is transduced through G-protein-coupled adrenergic receptors. Here, we found that the abundance of regulator of G-protein signaling 2 (RGS2) increases at night, that expression is increased by norepinephrine and that this protein has a negative feedback effect on melatonin production. These data are consistent with the conclusion that RGS2 functions on a daily basis to negatively modulate melatonin production.