Wake Stages Research Articles

Sleep patterns in table chronic obstructive pulmonary disease patients at a tertiary care center: a hospital-based observational study

Introduction: Sleep has significant adverse consequences for breathing and gas exchange in patients with chronic obstructive pulmonary disease (COPD). This study aimed to assess sleep patterns in patients with stable COPD. Patients and methods: Patients with stable COPD were recruited from a tertiary care hospital. Data collected included demographic profile, clinical characteristics of COPD, neck circumference, Epworth sleepiness scale (ESS) score, and sleep variables using polysomnography. Results: Of recruited 40 patients, 12 fulfilled the inclusion criteria. The mean age of the patients was 57.42 ± 10.98 years and 11 were males. Eight patients (66.7%) had moderately severe COPD, three mild, and one severe COPD. Eleven patients had a normal ESS score. None had excessive day-time sleepiness. High numbers of arousal and sleep-stage change characterized major sleep fragmentation were reported in these individuals. Apnea–hypopnea index was normal, that is, 3.2 ± 1.9 (mean ± standard deviation [SD]). The total sleep time with hemoglobin saturation under 90% (T90) was 48.17 ± 5.79 minutes (mean ± SD). Hemoglobin saturation fell more in rapid eye movement (REM) sleep (87.1% ± 5.5%) than non-REM sleep (88.6% ± 4.8%). Sleep-onset latencies were 19.21 ± 4.68 (mean ± SD) minutes and REM latency was 126 ± 9.39 (mean ± SD) minutes. The mean %wake stage, mean% Stage 1 sleep, and mean% Stage 2 sleep were prolonged in most patients, whereas REM sleep was decreased in all patients. Conclusion: Sleep is characterized by major sleep fragmentation even in patients with stable COPD. Extent and duration of desaturation during sleep are significant. Wake stage, Stage 1, and Stage 2 sleep are prolonged in most patients. REM sleep is decreased in all patients.

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Background:Nonlinear measures like Sample Entropy which is a measure of chaos based on self-similarity extended from dynamic systems & chaos theory can potentially help to interpret time-series data obtained from EEG signals and facilitate understanding of complex profiles seen in ASD. The aim of this study is to characterize EEG patterns in ASD cohorts by extracting non-linear measures (Sample Entropy) from time-frequency data in awake and sleep stages.Methods:Unmedicated Preschool children, ages 2 to 6 years, underwent EEG recording up to 180 mins with 64 Channel EGI-GES400 system after a sleep deprivation protocol. ASD group had 28 children (24M, 4F). The controls were those with ADHD, Specific Speech Delay, and Global Developmental Delay had 10 children (6M, 4F). Awake and sleep stage 1 (N1), 2 (N2) and 3 (N3) recordings were included. Artifact free continuous segment of 60s were selected manually. Each 10s epoch was decomposed into 6 bands (gamma high through delta) by wavelets. Sample Entropy (SE) was computed for each band per channel. Mean Differences were computed between groups in awake & sleep stages. Significance was estimated with p-values (α=0.05).Results and conclusions:Differences in entropies was significant (p < 0.05) in N2 & N3. In higher bands (gamma & beta), ASD was dominant in parietal & frontal particularly Pz. In N3,non-ASD had dominant delta in occipital, parietal & frontal regions.A higher entropy translates to more possible states of being. Delta band is more dynamic in non-ASD group while higher bands are more dynamic in ASD group.

Loss of sensitivity to insulin-like growth factor I in orexin neurons is associated to perturbed sleep patterns during aging.

Sleep disturbances are common during aging. Old mice show altered sleep structure, with changes in both slow and fast electro-corticographic (ECG) activity, and less transitions between sleep and wake stages, as compared to young animals. Insulin-like growth factor I (IGF-I), that is involved in hormonal changes during aging, was previously shown to increase ECG patterns in young mice and monkeys. More recently, we observed that IGF-I shapes sleep architecture through modulation of the activity of mouse orexin neurons in the lateral hypothalamus (LH). We now report that both stimulation of ECG patterns and activation of orexin neurons by systemic IGF-I were abrogated in old mice. Further, activation of orthodromically stimulated LH neurons by either systemic or local IGF-I in young mice was absent in old mice. As orexin neurons of old mice show markedly increased IGF-I receptor (IGF-IR) levels, suggesting loss of sensitivity to IGF-I, we treated them with AIK3a305, a novel IGF-IR sensitizer, and observed restored responses to IGF-I and rejuvenation of ECG patterns. Thus, disturbed sleep structure in aging mice may be related to IGF-I resistance in orexin neurons, reflecting a broader loss of IGF-I activity in the aged mouse brain.

Electroencephalogram variability analysis for monitoring depth of anesthesia

Objective. In this paper, a new approach of extracting and measuring the variability in electroencephalogram (EEG) was proposed to assess the depth of anesthesia (DOA) under general anesthesia. Approach. The EEG variability (EEGV) was extracted as a fluctuation in time interval that occurs between two local maxima of EEG. Eight parameters related to EEGV were measured in time and frequency domains, and compared with state-of-the-art DOA estimation parameters, including sample entropy, permutation entropy, median frequency and spectral edge frequency of EEG. The area under the receiver-operator characteristics curve (AUC) and Pearson correlation coefficient were used to validate its performance on 56 patients. Main results. Our proposed EEGV-derived parameters yield significant difference for discriminating between awake and anesthesia stages at a significance level of 0.05, as well as improvement in AUC and correlation coefficient on average, which surpasses the conventional features of EEG in detection accuracy of unconscious state and tracking the level of consciousness. Significance. To sum up, EEGV analysis provides a new perspective in quantifying EEG and corresponding parameters are powerful and promising for monitoring DOA under clinical situations.

Associations among sleep-disordered breathing, arousal response, and risk of mild cognitive impairment in a northern Taiwan population.

Dementia is associated with sleep disorders. However, the relationship between dementia and sleep arousal remains unclear. This study explored the associations among sleep parameters, arousal responses, and risk of mild cognitive impairment (MCI). Participants with the chief complaints of memory problems and sleep disorders, from the sleep center database of Taipei Medical University Shuang-Ho Hospital, were screened, and the parameters related to the Cognitive Abilities Screening Instrument, Clinical Dementia Rating, and polysomnography were determined. All examinations were conducted within 6 months and without a particular order. The participants were divided into those without cognitive impairment (Clinical Dementia Rating = 0) and those with MCI (Clinical Dementia Rating = 0.5). Mean comparison, linear regression models, and logistic regression models were employed to investigate the associations among obtained variables. This study included 31 participants without MCI and 37 with MCI (17 with amnestic MCI, 20 with multidomain MCI). Patients with MCI had significantly higher mean values of the spontaneous arousal index and spontaneous arousal index in the non-rapid eye movement stage than those without MCI. An increased risk of MCI was significantly associated with increased spontaneous arousal index and spontaneous arousal index in the non-rapid eye movement stage with various adjustments. Significant associations between the Cognitive Abilities Screening Instrument scores and the oximetry parameters and sleep disorder indexes were observed. Repetitive respiratory events with hypoxia were associated with cognitive dysfunction. Spontaneous arousal, especially in non-rapid eye movement sleep, was related to the risk of MCI. However, additional longitudinal studies are required to confirm their causality. Tsai C-Y, Hsu W-H, Lin Y-T, etal. Associations among sleep-disordered breathing, arousal response, and risk of mild cognitive impairment in a northern Taiwan population. J Clin Sleep Med. 2022;18(4): 1003-1012.

Sleep Apnea Syndrome Detection Based on Degree of Convexity of Logarithmic Spectrum Calculated from Overnight Bio-vibration Data of Mattress Sensor.

This paper proposes the novel Sleep Apnea Syndrome (SAS) detection method based on the frequency analysis of the overnight bio-vibration data acquired from mattress sensor. Concretely, this paper designs the index called Degree of Convexity of the Logarithmic Spectrum (DCLS), which quantifies the degree of convexity by computing the difference between the waveform of the averaged logarithmic spectrum and the waveform of its approximation formula, and employs it to detect SAS. Through the human subject experiment on the SAS detection, the following implications have been revealed: (1) the SAS subjects tend to have the large density around 3Hz, and the average of DCLS in SAS subjects and healthy subjects are 98.6±10.1 and 48.2±6.8 respectively, which succeeds to correctly separate the nine SAS subjects and the nine healthy subjects; and (2) the characteristics of the WAKE stage are different between the SAS and healthy subjects.

Automatic Sleep Staging in Children with Sleep Apnea using Photoplethysmography and Convolutional Neural Networks.

Sleep staging is of paramount importance in children with suspicion of pediatric obstructive sleep apnea (OSA). Complexity, cost, and intrusiveness of overnight polysomnography (PSG), the gold standard, have led to the search for alternative tests. In this sense, the photoplethysmography signal (PPG) carries useful information about the autonomous nervous activity associated to sleep stages and can be easily acquired in pediatric sleep apnea home tests with a pulse oximeter. In this study, we use the PPG signal along with convolutional neural networks (CNN), a deep-learning technique, for the automatic identification of the three main levels of sleep: wake (W), rapid eye movement (REM), and non-REM sleep. A database of 366 PPG recordings from pediatric OSA patients is involved in the study. A CNN architecture was trained using 30-s epochs from the PPG signal for three-stage sleep classification. This model showed a promising diagnostic performance in an independent test set, with 78.2% accuracy and 0.57 Cohen's kappa for W/NREM/REM classification. Furthermore, the percentage of time in wake stage obtained for each subject showed no statistically significant differences with the manually scored from PSG. These results were superior to the only state-of-the-art study focused on the analysis of the PPG signal in the automated detection of sleep stages in children suffering from OSA. This suggests that CNN can be used along with PPG recordings for sleep stages scoring in pediatric home sleep apnea tests.

Capturing the non-stationarity of whole-brain dynamics underlying human brain states

Brain dynamics depicts an extremely complex energy landscape that changes over time, and its characterisation is a central unsolved problem in neuroscience. We approximate the non-stationary landscape sustained by the human brain through a novel mathematical formalism that allows us characterise the attractor structure, i.e. the stationary points and their connections. Due to its time-varying nature, the structure of the global attractor and the corresponding number of energy levels changes over time. We apply this formalism to distinguish quantitatively between the different human brain states of wakefulness and different stages of sleep, as a step towards future clinical applications.

Sleep Pattern and Night-Time Muscle Activity in Children With Cerebral Palsy Compared to Typically Developing Peers

Objectives: To compare night-time muscle activity in children with cerebral palsy (CP) with that in typical developing peers. Methods: Polysomnography with electroencephalography (EEG), electrocardiogram, electromyography (EMG), respiration, and electrooculography movements recorded during one night were used to characterize sleep stages in a group of children with CP (Gross Motor Function Classification Scale I–III) and a group of typically developing (TD) children (aged 3–13 years). Periods of EMG activity for the tibialis anterior and soleus (SOL) muscles were identified, and the coherence between EEG and EMG was measured to assess corticomuscular drive during sleep. Results: There were no significant differences between the groups in total sleep time, average time spent awake, or rapid eye movement, N1, and N3 sleep. Children with CP spent significantly less time in N2 than their healthy peers (43% vs. 51%, p=0.03). There was only a significant difference between TD and CP in SOL muscle activity during the wake stage. Otherwise, there were no differences between groups in coherence in EEG and EMG signals for any sleep stage. Conclusion: Mildly affected children with CP do not show altered night sleep or muscle activity patterns as compared to TD peers. Abnormal muscle activity is unlikely to contribute to sleep disturbance, development of contractures, joint deformation, pain, or general discomfort in this group of children with CP.

Autonomic nervous function and low-grade inflammation in children with sleep-disordered breathing.

The objective of the study was to assess the relationship between autonomic nervous function and low-grade inflammation in children with sleep-disordered breathing. We enrolled habitually snoring children aged 3-14 years for overnight polysomnography (PSG) and high-sensitivity C-reactive protein (hsCRP) measurement. Low-grade inflammation was defined as hsCRP >1.0 mg/L to <10.0 mg/L. An electrocardiogram recording was extracted from PSG. Heart rate variability was analyzed using time and frequency domain methods. In total, 190 children were included, with 61 having primary snoring (PS), 39 mild obstructive sleep apnea (OSA), and 90 moderate-to-severe OSA. The average RR interval displayed a significant decline, whereas the low frequency/high frequency (LF/HF) ratio showed an increasing tendency in children with PS, mild OSA, and moderate-to-severe OSA. Mean RR was mainly influenced by age and the apnea hypopnea index (AHI) (all P < 0.01). AHI was an independent risk factor for the altered LF/HF ratio at all sleep stages except N3 stage (all P < 0.05). In the wake stage, low-grade inflammation was an independent risk factor of altered LF/HF ratio (P = 0.014). Autonomic nervous function was impaired in children with OSA. The sympathetic-vagal balance was influenced by low-grade inflammation in the wake stage, whereas it was only affected by AHI when falling asleep. We found that autonomic nervous function was impaired in children with OSA. We found that there was a negative correlation between systemic inflammation and autonomic nervous function in children with SDB only at wake stage. A negative association between systemic inflammation and autonomic nervous function was demonstrated in children in this study. Furthermore, altered LF/HF ratio maybe a good indicator of autonomic nervous dysfunction in children as it only correlated with the SDB severity, not with age.

Shorter rapid eye movement sleep duration in children with attention-deficit /hyperactivity disorder: the impact on quality of life

ObjectivesThe relationship of sleep with attention deficit hyperactivity disorder (ADHD) remains complex and unclear. The current study aimed to compare the subjective and objective sleep parameters among children with and without ADHD. Moreover, to address the effect of sleep parameters on the quality of life (QoL). MethodsWe assessed 42 participants who were children with ADHD (aged 6–12 years), with predominant combined presentation (23), compared to 42 healthy children, who underwent a sleep study assessment subjectively (the Children's Sleep Habits Questionnaire), and objectively (one-night Polysomnographic recording). Also, parents completed the pediatric quality of life inventory (PedsQL-P). ResultsChildren with ADHD had more subjective sleep problems. ADHD patients also had a significant decrease in total sleep time and sleep efficiency, spending more time in wake and N1 sleep stages. Moreover, they had significantly lower rapid eye movement (REM)sleep duration and less duration of deep sleep stages. Multivariate regression analysis showed that REM sleep duration was the most contributing and predictive factor to QoL and school function impairment. ConclusionChildren with ADHD have a significantly lower sleep quantity and poor sleep quality. In addition, short REM sleep duration independently contributes to further deterioration in different areas of QoL.

Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep

Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.

Measuring the effects of sleep on epileptogenicity with multifrequency entropy

ObjectiveWe demonstrate that multifrequency entropy gives insight into the relationship between epileptogenicity and sleep, and forms the basis for an improved measure of medical assessment of sleep impairment in epilepsy patients. MethodsMultifrequency entropy was computed from electroencephalography measurements taken from 31 children with Benign Epilepsy with Centrotemporal Spikes and 31 non-epileptic controls while awake and during sleep. Values were compared in the epileptic zone and away from the epileptic zone in various sleep stages. ResultsWe find that (I) in lower frequencies, multifrequency entropy decreases during non-rapid eye movement sleep stages when compared with wakefulness in a general population of pediatric patients, (II) patients with Benign Epilepsy with Centrotemporal Spikes had lower multifrequency entropy across stages of sleep and wakefulness, and (III) the epileptic regions of the brain exhibit lower multifrequency entropy patterns than the rest of the brain in epilepsy patients. ConclusionsOur results show that multifrequency entropy decreases during sleep, particularly sleep stage 2, confirming, in a pediatric population, an association between sleep, lower multifrequency entropy, and increased likelihood of seizure. SignificanceWe observed a correlation between lowered multifrequency entropy and increased epileptogenicity that lays preliminary groundwork for the detection of a digital biomarker for epileptogenicity.

Application of High-Frequency Oscillations on Scalp EEG in Infant Spasm: A Prospective Controlled Study.

ObjectiveWe quantitatively analyzed high-frequency oscillations (HFOs) using scalp electroencephalography (EEG) in patients with infantile spasms (IS).MethodsWe enrolled 60 children with IS hospitalized from January 2019 to August 2020. Sixty healthy age-matched children comprised the control group. Time–frequency analysis was used to quantify γ, ripple, and fast ripple (FR) oscillation energy changes.Resultsγ, ripple, and FR oscillations dominated in the temporal and frontal lobes. The average HFO energy of the sleep stage is lower than that of the wake stage in the same frequency bands in both the normal control (NC) and IS groups (P < 0.05). The average HFO energy of the IS group was significantly higher than that of the NC group in γ band during sleep stage (P < 0.01). The average HFO energy of S and Post-S stage were higher than that of sleep stage in γ band (P < 0.05). In the ripple band, the average HFO energy of Pre-S, S, and Post-S stage was higher than that of sleep stage (P < 0.05). Before treatment, there was no significant difference in BASED score between the effective and ineffective groups. The interaction of curative efficacy × frequency and the interaction of curative efficacy × state are statistically significant. The average HFO energy of the effective group was lower than that of the ineffective group in the sleep stage (P < 0.05). For the 16 children deemed “effective” in the IS group, the average HFO energy of three frequency bands was not significantly different before compared with after treatment.SignificanceScalp EEG can record HFOs. The energy of HFOs can distinguish physiological HFOs from pathological ones more accurately than frequency. On scalp EEG, γ oscillations can better detect susceptibility to epilepsy than ripple and FR oscillations. HFOs can trigger spasms. The analysis of average HFO energy can be used as a predictor of the effectiveness of epilepsy treatment.

EEG and Sleep Effects of Tramadol Suggest Potential Antidepressant Effects with Different Mechanisms of Action.

Tramadol is a widely used, centrally acting, opioid analgesic compound, with additional inhibitory effects on the synaptic reuptake of serotonin and noradrenaline, as well as on the 5-HT2 and NMDA receptors. Preclinical and clinical evidence also suggests its therapeutic potential in the treatment of depression and anxiety. The effects of most widely used antidepressants on sleep and quantitative electroencephalogram (qEEG) are well characterized; however, such studies of tramadol are scarce. Our aim was to characterize the effects of tramadol on sleep architecture and qEEG in different sleep–wake stages. EEG-equipped Wistar rats were treated with tramadol (0, 5, 15 and 45 mg/kg) at the beginning of the passive phase, and EEG, electromyogram and motor activity were recorded. Tramadol dose-dependently reduced the time spent in rapid eye movement (REM) sleep and increased the REM onset latency. Lower doses of tramadol had wake-promoting effects in the first hours, while 45 mg/kg of tramadol promoted sleep first, but induced wakefulness thereafter. During non-REM sleep, tramadol (15 and 45 mg/kg) increased delta and decreased alpha power, while all doses increased gamma power. In conclusion, the sleep-related and qEEG effects of tramadol suggest antidepressant-like properties, including specific beneficial effects in selected patient groups, and raise the possibility of a faster acting antidepressant action.

253 Better and faster automatic sleep staging with artificial intelligence

Abstract Introduction Sleep staging of polysomnography (PSG) is a time-consuming task, it requires significant training, and significant variability among scorers is expected. A new software (MEBsleep by Medibio Limited) was developed to automatically perform sleep scoring based on machine learning algorithms. This study aimed to perform an extensive investigation of its agreement with expert sleep technicians. Methods Forty polysomnography recordings of patients that were referred for sleep evaluation to three sleep clinics were retrospectively collected. Three experienced technicians independently staged the recording complying with the scoring rules of the American Academy of Sleep Medicine guidelines. Positive Percent Agreement (PPA), Positive Predictive Value (PPV), and other agreement statistics between the automatic and manual staging, among the staging performed by the three technicians, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and statistical significance of the differences. Results Automatic staging took less than two minutes per PSG on a consumer laptop. The automatic staging resulted for the most comparable (PPA difference of N1, N3, and REM; PPV difference of N1, N2, N3, and REM) or statistically significantly more in agreement with the technicians’ staging than the between-technician agreement (PPA difference of N2: 3.90%, 95% bootstrap CI 1.79%-6.01%; PPV difference of Wake: 1.16%, 95% bootstrap CI 0.64%/1.67%), with the sole exception of a partial reduction in the positive percent agreement of the Wake stage (PPA difference of Wake -7.04%, 95% bootstrap CI -10.40%/-3.85%). The automatic staging also demonstrated very high accuracy in an indirect comparison with other similar software. Conclusion Given these promising results, the use of this software may support sleep clinicians by improving efficiency in sleep scoring. Support (if any):

Attention-Based LSTM for Non-Contact Sleep Stage Classification Using IR-UWB Radar.

Manual scoring of sleep stages from polysomnography (PSG) records is essential to understand the sleep quality and architecture. Since the PSG requires specialized personnel, a lab environment, and uncomfortable sensors, non-contact sleep staging methods based on machine learning techniques have been investigated over the past years. In this study, we propose an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model for automatic sleep stage scoring using an impulse-radio ultra-wideband (IR-UWB) radar which can remotely detect vital signs. Sixty-five young (30.0 ± 8.6 yrs.) and healthy volunteers underwent nocturnal PSG and IR-UWB radar measurement simultaneously; From 51 recordings, 26 were used for training, 8 for validation, and 17 for testing. Sixteen features including movement-, respiration-, and heart rate variability-related indices were extracted from the raw IR-UWB signals in each 30-s epoch. Sleep stage classification performances of Attention Bi-LSTM model with optimized hyperparameters were evaluated and compared with those of conventional LSTM networks for same test dataset. In the results, we achieved an accuracy of 82.6 ± 6.7% and a Cohen's kappa coefficient of 0.73 ± 0.11 in the classification of wake stage, REM sleep, light (N1+N2) sleep, and deep (N3) sleep which is significantly higher than the conventional LSTM networks (p < 0.01). Moreover, the classification performances were higher than those reported in comparative studies, demonstrating the effectiveness of the attention mechanism coupled with bi-LSTM networks for the sleep staging using cardiorespiratory signals.

Temporal and spatial evolution of vortex shedding for flow around a cylinder close to a wall

Temporal and spatial vortex shedding evolution for flow around a circular cylinder near a plane boundary is investigated using three-dimensional direct numerical simulation, with a parameter space of boundary layer thickness-to-diameter δ/D = 0–1.4, gap-to-diameter G/D = 0.4 and Re = 350. For each δ/D, three consecutive wake stages are divided with time based on the dominated flow structures – mode A and mode B, of which spanwise wavelength is quantitatively measured by the autocorrelation function (ACF). In the first stage, mode A structures spread from two spanwise ends to the middle region and from upstream to downstream wake simultaneously. This stage is only dominated by ordered mode A structures and shows a weak three-dimensionality. In the second stage, mode B structures start to propagate in the same spanwise but opposite streamwise direction as mode A, and squeeze the mode A region until its disappearance. A mixture of mode A and mode B structures dominate the second stage and the flow is in the strongest three-dimensionality. In the third stage, the wake transits from a symmetric distribution of ordered mode B structure to an asymmetric distribution of disordered mode B structure, showing a relatively strong three-dimensionality. The typical vortex shedding patterns are analyzed by dynamic mode decomposition (DMD). When δ/D > 0, the dominant mode frequency (f0) of vortex shedding, larger than that of an isolated cylinder, decreases with the increase of δ/D. Due to suppression of the lower-surface vortex shedding, the flow structures with f0 are greatly reduced both in size and regularity with the increase of δ/D. Scattered mode components are reduced and the energy of each mode is more concentrated with increasing δ/D.

Cell-Type-Specific Dynamics of Calcium Activity in Cortical Circuits over the Course of Slow-Wave Sleep and Rapid Eye Movement Sleep.

Sleep shapes cortical network activity, fostering global homeostatic downregulation of excitability while maintaining or even upregulating excitability in selected networks in a manner that supports memory consolidation. Here, we used two-photon calcium imaging of cortical layer 2/3 neurons in sleeping male mice to examine how these seemingly opposing dynamics are balanced in cortical networks. During slow-wave sleep (SWS) episodes, mean calcium activity of excitatory pyramidal (Pyr) cells decreased. Simultaneously, however, variance in Pyr population calcium activity increased, contradicting the notion of a homogenous downregulation of network activity. Indeed, we identified a subpopulation of Pyr cells distinctly upregulating calcium activity during SWS, which were highly active during sleep spindles known to support mnemonic processing. Rapid eye movement (REM) episodes following SWS were associated with a general downregulation of Pyr cells, including the subpopulation of Pyr cells active during spindles, which persisted into following stages of sleep and wakefulness. Parvalbumin-positive inhibitory interneurons (PV-In) showed an increase in calcium activity during SWS episodes, while activity remained unchanged during REM sleep episodes. This supports the view that downregulation of Pyr calcium activity during SWS results from increased somatic inhibition via PV-In, whereas downregulation during REM sleep is achieved independently of such inhibitory activity. Overall, our findings show that SWS enables upregulation of select cortical circuits (likely those which were involved in mnemonic processing) through a spindle-related process, whereas REM sleep mediates general downregulation, possibly through synaptic re-normalization.SIGNIFICANCE STATEMENT Sleep is thought to globally downregulate cortical excitability and, concurrently, to upregulate synaptic connections in neuron ensembles with newly encoded memory, with upregulation representing a function of sleep spindles. Using in vivo two-photon calcium imaging in combination with surface EEG recordings, we classified cells based on their calcium activity during sleep spindles. Spindle-active pyramidal (Pyr) cells persistently increased calcium activity during slow-wave sleep (SWS) episodes while spindle-inactive cells decreased calcium activity. Subsequent rapid eye movement (REM) sleep episodes profoundly reduced calcium activity in both cell clusters. Results indicate that SWS allows for a spindle-related differential upregulation of ensembles whereas REM sleep functions to globally downregulate networks.

Night-to-night variability in obstructive sleep apnea using peripheral arterial tonometry: a case for multiple night testing.

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.