Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review.

Purpose: This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in the manual review of predicted hypnograms, a necessity due to the notable inter-scorer variability inherent in polysomnography (PSG) databases. Our efforts target the extent of review required to achieve predefined agreement levels, examining both in-domain (ID) and out-of-domain (OOD) data, and considering subjects' diagnoses. Patients and Methods: A total of 19,578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical database of an additional 8832 PSGs, covering a full spectrum of ages (0-91 years) and sleep-disorders, to refine the U-Sleep, and to evaluate different uncertainty-quantification approaches, including our novel confidence network. The ID data consisted of PSGs scored by over 50 physicians, and the two OOD sets comprised recordings each scored by a unique senior physician. Results: U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID and 73.8-78.8% on OOD data. The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on OOD data. Independently of sleep-disorder status, statistical evaluations revealed significant differences in confidence scores between aligning vs discording predictions, and significant correlations of confidence scores with classification performance metrics. To achieve κ ≥ 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians' workload, and facilitating near-perfect agreement. Conclusion: Inter-scorer variability limits the accuracy of the scoring algorithms to ~80%. By integrating an uncertainty estimation with U-Sleep, we enhance the review of predicted hypnograms, to align with the scoring taste of a responsible physician. Validated across ID and OOD data and various sleep-disorders, our approach offers a strategy to boost automated scoring tools' usability in clinical settings.

SPHYNCS: Feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a multicenter research initiative to identify new biomarkers in central disorders of hypersomnolence (CDH). Whereas narcolepsy type 1 (NT1) is well characterized, other CDH disorders lack precise biomarkers. In SPHYNCS, we utilized Fitbit smartwatches to monitor physical activity, heart rate, and sleep parameters over one year. We examined the feasibility of long-term ambulatory monitoring using the wearable device. We then explored digital biomarkers differentiating patients with NT1 from healthy controls (HC). A total of 115 participants received a Fitbit smartwatch. Using a compliance metric to evaluate the usability of the wearable device, we found an overall compliance rate of 80% over one year. We calculated daily physical activity, heart rate, and sleep parameters from two weeks of greatest compliance to compare NT1 (n=20) and HC (n=9) subjects. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p=0.007) and awakening index (p=0.025), as well as standard deviation of time in bed (p=0.044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p=0.019), and sleep latency (p=0.001), as well as a lower peak heart rate (p=0.008), heart rate standard deviation (p=0.039) and high-intensity activity (p=0.009) compared to HC. This ongoing study demonstrates the feasibility of long-term monitoring with wearable technology in patients with CDH and potentially identifies a digital biomarker profile for NT1. While further validation is needed in larger datasets, these data suggest that long-term wearable technology may play a future role in diagnosing and managing narcolepsy.

Impact of Comorbid Sleep-Disordered Breathing and Atrial Fibrillation on the Long-Term Outcome After Ischemic Stroke.

BACKGROUND: Sleep-disordered breathing (SDB) and atrial fibrillation (AF) are highly prevalent in patients with stroke and are recognized as independent risk factors for stroke. Little is known about the impact of comorbid SDB and AF on long-term outcomes after stroke. METHODS: In this prospective cohort study, 353 patients with acute ischemic stroke or transient ischemic attacks were analyzed. Patients were screened for SDB by respiratory polygraphy during acute hospitalization. Screening for AF was performed using a 7-day ECG up to 3× in the first 6 months. Follow-up visits were scheduled at 1, 3, 12, 24, and 36 months poststroke. Cox regression models adjusted for various factors (age, sex, body mass index, hypertension, diabetes, dyslipidemia, and heart failure) were used to assess the impact of comorbid SDB and AF on subsequent death or cerebro-cardiovascular events. RESULTS: Among 353 patients (299 ischemic stroke and 54 transient ischemic attacks), median age, 67 (interquartile range, 57-74) years with 63% males. Moderate-to-severe SDB (apnea-hypopnea index score, ≥15/h) was present in 118 (33.4%) patients. Among the 56 (15.9%) patients with AF, 28 had comorbid moderate-to-severe SDB and AF. Over 36 months, there were 12 deaths and 67 recurrent cerebro-cardiovascular events. Patients with comorbid moderate-to-severe SDB and AF had a higher risk of subsequent death or cerebro-cardiovascular events compared with those with only moderate-to-severe SDB without AF (hazard ratio, 2.49 [95% CI, 1.18-5.24]) and to those without moderate-to-severe SDB or AF (hazard ratio, 2.25 [95% CI, 1.12-4.50]). However, no significant difference was found between the comorbid moderate-to-severe SDB and AF group and the group with only AF without moderate-to-severe SDB (hazard ratio, 1.64 [95% CI, 0.62-4.36]). CONCLUSIONS: Comorbid moderate-to-severe SDB and AF significantly increase the risk of long-term mortality or recurrent cerebro-cardiovascular events after acute ischemic stroke. Considering both conditions as cumulative and modifiable cerebro-cardiovascular risk factors is of interest for the management of acute stroke. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02559739.

Transferability of a Sensing Mattress for Posture Classification from Research into Clinics.

Sleep is crucial in rehabilitation processes, promoting neural plasticity and immune functions. Nocturnal body postures can indicate sleep quality and frequent repositioning is required to prevent bedsores for bedridden patients after a stroke or spinal cord injury. Polysomnography (PSG) is considered the gold standard for sleep assessment. Unobtrusive methods for classifying sleep body postures have been presented with similar accuracy to PSG, but most evaluations have been done in research lab environments. To investigate the challenges in the usability of a previously validated device in a clinical setting, we recorded the sleep posture of 17 patients with a sensorized mattress. Ground-truth labels were collected automatically from a PSG device. In addition, we manually labeled the body postures using video data. This allowed us also to evaluate the quality of the PSG labels. We trained neural networks based on the VGG-3 architecture to classify lying postures and used a self-label correction method to account for noisy labels in the training data. The models trained with the video labels achieved a higher classification accuracy than those trained with the PSG labels (0.79 vs. 0.68). The self-label correction could further increase the models' scores based on video and PSG labels to 0.80 and 0.70, respectively. Unobtrusive sensors validated in clinics can, therefore, potentially improve the quality of care for bedridden patients and advance the field of rehabilitation.

Blood pressure variability, nocturnal heart rate variability and endothelial function predict recurrent cerebro-cardiovascular events following ischemic stroke.

Introduction: Cardiovascular parameters characterizing blood pressure (BP), heart rate (HR), endothelial function and arterial stiffness predict cerebro-cardiovascular events (CCVE) in the general population. Considering the paucity of data in stroke patients, we assessed these parameters as potential predictors of recurrent CCVE at acute stroke stroke. Patients and methods: This is a secondary outcome analysis of a prospective observational longitudinal Sleep Deficiency & Stroke Outcome Study (ClinicalTrials.gov Identifier: NCT02559739). The study consecutively recruited acute ischemic stroke patients. Cardiovascular parameters (blood pressure variability [BPV], heart rate variability [HRV], endothelial function, and arterial stiffness) were assessed within the first week post-stroke. Future CCVE were recorded over a 3-year follow-up. Multivariate Cox regression analysis was used to investigate the prognostic value of 48 cardiovascular parameters regarding CCVE risk. Results: Out of 447 recruited patients, 359 were included in this analysis. 20% of patients developed a future CCVE. A high variability of systolic BP (n = 333) and nocturnal HR (non-linear parameters; n = 187) at acute stroke predicted CCVE risk after adjustment for demographic parameters, cardiovascular risk factors and mean BP or HR, respectively. Endothelial dysfunction (n = 105) at acute stroke predicted CCVE risk after adjustment for age and sex, but not after adjustment for cardiovascular risk factors. Diurnal HR and arterial stiffness at acute stroke were not associated with CCVE risk. Conclusion: High blood pressure variability, high nocturnal HRV and endothelial function contribute to the risk for future CCVE after stroke.

U-Sleep's resilience to AASM guidelines.

AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning-based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies.

Multiple sleep-wake disturbances after stroke predict an increased risk of cardio-cerebrovascular events or death: A prospective cohort study.

BACKGROUND AND PURPOSE: Contradictory evidence on the impact of single sleep-wake-disturbances (SWD), such as sleep-disorderd breating (SDB) or insomnia, in patients with stroke, on the risk of subsequent cardio- and cerebrovascular events (CCE) and death, exists. Very recent studies in the general population suggest that the presence of multiple SWD increases cardio-cerebrovascular risk. Hence, the aim of this study was to asssess whether a novel score capturing the burden of multiple SWD, a so called "sleep burden index", is predictive for subsequent CCE including death in a prospectively followed cohort of stroke patients. METHODS: Patients with acute ischemic stroke or transient ischemic attack (TIA) were prospectively recruited. Four SWD were analyzed: (i) SDB with respirography; (ii) insomnia (defined using the insomnia severity index [ISI]); (iii) restless legs syndrome (RLS; defined using the International RLS Study Group rating scale); and (iv) self-estimated sleep duration at 1 and 3 months. A "sleep burden index", calculated using the mean of z-transformed values from assessments of these four SWD, was created. The occurrence of CCE was recorded over a mean ± standard deviation (SD) follow-up of 3.2 ± 0.3 years. RESULTS: We assessed 437 patients (87% ischemic stroke, 13% TIA, 64% males) with a mean ± SD age of 65.1 ± 13.0 years. SDB (respiratory event index ≥ 5/h) was present in 66.2% of these patients. Insomnia (ISI ≥ 10), RLS and extreme sleep duration affected 26.2%, 6.4% and 13.7% of the patients 3 months post-stroke. Seventy out of the 437 patients (16%) had at least one CCE during the follow-up. The sleep burden index was associated with a higher risk for subsequent CCE, including death (odds ratio 1.80 per index unit, 95% confidence interval 1.19-2.72; p = 0.0056). CONCLUSION: The presence of multiple SWDs constitutes a risk for subsequent CCE (including death) within the first 3 years following stroke. Larger systematic studies should assess the utility of the sleep burden index for patients' risk stratification in clinical practice.

REM sleep and muscle atonia in brainstem stroke: A quantitative polysomnographic and lesion analysis study.

Important brainstem regions are involved in the regulation of rapid eye movement sleep. We hypothesized that brainstem stroke is associated with dysregulated rapid eye movement sleep and related muscle activity. We compared quantitative/qualitative polysomnography features of rapid eye movement sleep and muscle activity (any, phasic, tonic) between 15 patients with brainstem stroke (N = 46 rapid eye movement periods), 16 patients with lacunar/non-brainstem stroke (N = 40 rapid eye movement periods), 15 healthy controls (N = 62 rapid eye movement periods), and patients with Parkinson's disease and polysomnography-confirmed rapid eye movement sleep behaviour disorder. Further, in the brainstem group, we performed a magnetic resonance imaging-based lesion overlap analysis. The mean ratio of muscle activity to rapid eye movement sleep epoch in the brainstem group ("any" muscle activity 0.09 ± 0.15; phasic muscle activity 0.08 ± 0.14) was significantly lower than in the lacunar group ("any" muscle activity 0.17 ± 0.2, p < 0.05; phasic muscle activity 0.16 ± 0.19, p < 0.05), and also lower than in the control group ("any" muscle activity 0.15 ± 0.17, p < 0.05). Magnetic resonance imaging-based lesion analysis indicated an area of maximum overlap in the medioventral pontine region for patients with reduced phasic muscle activity index. For all groups, mean values of muscle activity were significantly lower than in the patients with Parkinson's disease and polysomnography-confirmed REM sleep behaviour disorder group ("any" activity 0.51 ± 0.26, p < 0.0001 for all groups; phasic muscle activity 0.42 ± 0.21, p < 0.0001 for all groups). For the tonic muscle activity in the mentalis muscle, no significant differences were found between the groups. In the brainstem group, contrary to the lacunar and the control groups, "any" muscle activity index during rapid eye movement sleep was significantly reduced after the third rapid eye movement sleep phase. This study reports on the impact of brainstem stroke on rapid eye movement atonia features in a human cohort. Our findings highlight the important role of the human brainstem, in particular the medioventral pontine regions, in the regulation of phasic muscle activity during rapid eye movement sleep and the ultradian distribution of rapid eye movement-related muscle activity.

Frequency and evolution of sleep-wake disturbances after ischemic stroke: A 2-year prospective study of 437 patients.

OBJECTIVE: In the absence of systematic and longitudinal data, this study prospectively assessed both frequency and evolution of sleep-wake disturbances (SWD) after stroke. METHODS: In 437 consecutively recruited patients with ischemic stroke or transient ischemic attack (TIA), stroke characteristics and outcome were assessed within the 1st week and 3.2 ± 0.3 years (M±SD) after the acute event. SWD were assessed by interview and questionnaires at 1 and 3 months as well as 1 and 2 years after the acute event. Sleep disordered breathing (SDB) was assessed by respirography in the acute phase and repeated in one fifth of the participants 3 months and 1 year later. RESULTS: Patients (63.8% male, 87% ischemic stroke and mean age 65.1 ± 13.0 years) presented with mean NIHSS-score of 3.5 ± 4.5 at admission. In the acute phase, respiratory event index was >15/h in 34% and >30/h in 15% of patients. Over the entire observation period, the frequencies of excessive daytime sleepiness (EDS), fatigue and insomnia varied between 10-14%, 22-28% and 20-28%, respectively. Mean insomnia and EDS scores decreased from acute to chronic stroke, whereas restless legs syndrome (RLS) percentages (6-9%) and mean fatigue scores remained similar. Mean self-reported sleep duration was enhanced at acute stroke (month 1: 07:54 ± 01:27h) and decreased at chronic stage (year 2: 07:43 ± 01:20h). CONCLUSIONS: This study documents a high frequency of SDB, insomnia, fatigue and a prolonged sleep duration after stroke/TIA, which can persist for years. Considering the negative effects of SWD on physical, brain and mental health these data suggest the need for a systematic assessment and management of post-stroke SWD.

Energetic costs and benefits of sleep.

Energy derived from food is a precious resource to animals. Those finite calories are often well-earned through exhaustive foraging effort, which can dominate waking hours, to support physiological processes (e.g. body maintenance and growth) and ecological necessities (e.g. predator avoidance and courting) that are pertinent to the production of progeny. So, it is unsurprising to find that animals have evolved strategies to guard against the gratuitous waste of hard-won caloric energy. Yet, it remains surprising to find such diversity, and elegant creativity, in those solutions. Brief examples of energy-saving innovation could include the very shape of animals and how they move, from streamlined swimming sharks to skyward-soaring seabirds; or the evolutionary appearance of various states of dormancy, such as endothermic animals sacrificing high body temperature through modest (torpor) or severe (hibernation) curtailments to metabolic heat production. Another reversibly dormant state with energetic benefits is sleep.

Gender differences in narcolepsy: What are recent findings telling us?

Three papers currently published in SLEEP using two different mouse models of narcolepsy, including either Hcrt-tTa;TetO diptheria toxin-A (DTA) or Hypocretin knock-out (Hcrt-KO) mice, suggest important gender differences in narcolepsy expression. Specifically, these recent data corroborate previous findings in mice demonstrating that females show more cataplexy events and more total cataplexy expression than males. Moreover, in the neurotoxic DTA mouse model, females show earlier onset of cataplexy expression than males during active Hcrt cell loss. Finally, females show a doubling of cataplexy during estrous compared to other phases of the estrous cycle. These findings are reviewed in the broader context of prior published literature, including reported gender differences in Hcrt expression and hormonal influences on sleep and wakefulness. Although similar findings have not been reported in humans, a systematic evaluation of gender differences in human narcolepsy has yet to be performed. Taken together, these animal data suggest that more research exploring gender differences in human narcolepsy is warranted.

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering.

BACKGROUND AND OBJECTIVES: Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. METHODS: We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups. RESULTS: We included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters. DISCUSSION: Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features.

A genetically encoded sensor for in vivo imaging of orexin neuropeptides.

Orexins (also called hypocretins) are hypothalamic neuropeptides that carry out essential functions in the central nervous system; however, little is known about their release and range of action in vivo owing to the limited resolution of current detection technologies. Here we developed a genetically encoded orexin sensor (OxLight1) based on the engineering of circularly permutated green fluorescent protein into the human type-2 orexin receptor. In mice OxLight1 detects optogenetically evoked release of endogenous orexins in vivo with high sensitivity. Photometry recordings of OxLight1 in mice show rapid orexin release associated with spontaneous running behavior, acute stress and sleep-to-wake transitions in different brain areas. Moreover, two-photon imaging of OxLight1 reveals orexin release in layer 2/3 of the mouse somatosensory cortex during emergence from anesthesia. Thus, OxLight1 enables sensitive and direct optical detection of orexin neuropeptides with high spatiotemporal resolution in living animals.

Sleep apnoea and ischaemic stroke: current knowledge and future directions.

Sleep apnoea, one of the most common chronic diseases, is a risk factor for ischaemic stroke, stroke recurrence, and poor functional recovery after stroke. More than half of stroke survivors present with sleep apnoea during the acute phase after stroke, with obstructive sleep apnoea being the most common subtype. Following a stroke, sleep apnoea frequency and severity might decrease over time, but moderate to severe sleep apnoea is nevertheless present in up to a third of patients in the chronic phase after an ischaemic stroke. Over the past few decades evidence suggests that treatment for sleep apnoea is feasible during the acute phase of stroke and might favourably affect recovery and long-term outcomes. Nevertheless, sleep apnoea still remains underdiagnosed and untreated in many cases, due to challenges in the detection and prediction of post-stroke sleep apnoea, uncertainty as to the optimal timing for its diagnosis, and a scarcity of clear treatment guidelines (ie, uncertainty on when to treat and the optimal treatment strategy). Moreover, the pathophysiology of sleep apnoea associated with stroke, the proportion of stroke survivors with obstructive and central sleep apnoea, and the temporal evolution of sleep apnoea subtypes following stroke remain to be clarified. To address these shortcomings, the management of sleep apnoea associated with stroke should be integrated into a multidisciplinary diagnostic, treatment, and follow-up strategy.

Measuring Sleep, Wakefulness, and Circadian Functions in Neurologic Disorders.

Neurologic disorders impact the ability of the brain to regulate sleep, wake, and circadian functions, including state generation, components of state (such as rapid eye movement sleep muscle atonia, state transitions) and electroencephalographic microarchitecture. At its most extreme, extensive brain damage may even prevent differentiation of sleep stages from wakefulness (eg, status dissociatus). Given that comorbid sleep-wake-circadian disorders are common and can adversely impact the occurrence, evolution, and management of underlying neurologic conditions, new technologies for long-term monitoring of neurologic patients may potentially usher in new diagnostic strategies and optimization of clinical management.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort study (SPHYNCS): Study protocol for a prospective, multicentre cohort observational study.

Narcolepsy type 1 (NT1) is a disorder with well-established markers and a suspected autoimmune aetiology. Conversely, the narcoleptic borderland (NBL) disorders, including narcolepsy type 2, idiopathic hypersomnia, insufficient sleep syndrome and hypersomnia associated with a psychiatric disorder, lack well-defined markers and remain controversial in terms of aetiology, diagnosis and management. The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a comprehensive multicentre cohort study, which will investigate the clinical picture, pathophysiology and long-term course of NT1 and the NBL. The primary aim is to validate new and reappraise well-known markers for the characterization of the NBL, facilitating the diagnostic process. Seven Swiss sleep centres, belonging to the Swiss Narcolepsy Network (SNaNe), joined the study and will prospectively enrol over 500 patients with recent onset of excessive daytime sleepiness (EDS), hypersomnia or a suspected central disorder of hypersomnolence (CDH) during a 3-year recruitment phase. Healthy controls and patients with EDS due to severe sleep-disordered breathing, improving after therapy, will represent two control groups of over 50 patients each. Clinical and electrophysiological (polysomnography, multiple sleep latency test, maintenance of wakefulness test) information, and information on psychomotor vigilance and a sustained attention to response task, actigraphy and wearable devices (long-term monitoring), and responses to questionnaires will be collected at baseline and after 6, 12, 24 and 36 months. Potential disease markers will be searched for in blood, cerebrospinal fluid and stool. Analyses will include quantitative hypocretin measurements, proteomics/peptidomics, and immunological, genetic and microbiota studies. SPHYNCS will increase our understanding of CDH and the relationship between NT1 and the NBL. The identification of new disease markers is expected to lead to better and earlier diagnosis, better prognosis and personalized management of CDH.

Effect of early sleep apnoea treatment with adaptive servo-ventilation in acute stroke patients on cerebral lesion evolution and neurological outcomes: study protocol for a multicentre, randomized controlled, rater-blinded, clinical trial (eSATIS: early Sleep Apnoea Treatment in Stroke).

BACKGROUND: Sleep-disordered breathing (SDB) is highly prevalent in acute ischaemic stroke and is associated with worse functional outcome and increased risk of recurrence. Recent meta-analyses suggest the possibility of beneficial effects of nocturnal ventilatory treatments (continuous positive airway pressure (CPAP) or adaptive servo-ventilation (ASV)) in stroke patients with SDB. The evidence for a favourable effect of early SDB treatment in acute stroke patients remains, however, uncertain. METHODS: eSATIS is an open-label, multicentre (6 centres in 4 countries), interventional, randomized controlled trial in patients with acute ischaemic stroke and significant SDB. Primary outcome of the study is the impact of immediate SDB treatment with non-invasive ASV on infarct progression measured with magnetic resonance imaging in the first 3 months after stroke. Secondary outcomes are the effects of immediate SDB treatment vs non-treatment on clinical outcome (independence in daily functioning, new cardio-/cerebrovascular events including death, cognition) and physiological parameters (blood pressure, endothelial functioning/arterial stiffness). After respiratory polygraphy in the first night after stroke, patients are classified as having significant SDB (apnoea-hypopnoea index (AHI) > 20/h) or no SDB (AHI < 5/h). Patients with significant SDB are randomized to treatment (ASV+ group) or no treatment (ASV- group) from the second night after stroke. In all patients, clinical, physiological and magnetic resonance imaging studies are performed between day 1 (visit 1) and days 4-7 (visit 4) and repeated at day 90 ± 7 (visit 6) after stroke. DISCUSSION: The trial will give information on the feasibility and efficacy of ASV treatment in patients with acute stroke and SDB and allows assessing the impact of SDB on stroke outcome. Diagnosing and treating SDB during the acute phase of stroke is not yet current medical practice. Evidence in favour of ASV treatment from a randomized multicentre trial may lead to a change in stroke care and to improved outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT02554487 , retrospectively registered on 16 September 2015 (actual study start date, 13 August 2015), and www.kofam.ch (SNCTP000001521).

What Is REM Sleep?

For many decades, sleep researchers have sought to determine which species 'have' rapid eye movement (REM) sleep. In doing so, they relied predominantly on a template derived from the expression of REM sleep in the adults of a small number of mammalian species. Here, we argue for a different approach that focuses less on a binary decision about haves and have nots, and more on the diverse expression of REM sleep components over development and across species. By focusing on the components of REM sleep and discouraging continued reliance on a restricted template, we aim to promote a richer and more biologically grounded developmental-comparative approach that spans behavioral, physiological, neural, and ecological domains.

Dynamic REM Sleep Modulation by Ambient Temperature and the Critical Role of the Melanin-Concentrating Hormone System.

Ambient temperature (Ta) warming toward the high end of the thermoneutral zone (TNZ) preferentially increases rapid eye movement (REM) sleep over non-REM (NREM) sleep across species. The control and function of this temperature-induced REM sleep expression have remained unknown. Melanin-concentrating hormone (MCH) neurons play an important role in REM sleep control. We hypothesize that the MCH system may modulate REM sleep as a function of Ta. Here, we show that wild-type (WT) mice dynamically increased REM sleep durations specifically during warm Ta pulsing within the TNZ, compared to both the TNZ cool and baseline constant Ta conditions, without significantly affecting either wake or NREM sleep durations. However, genetically engineered MCH receptor-1 knockout (MCHR1-KO) mice showed no significant changes in REM sleep as a function of Ta, even with increased sleep pressure following a 4-h sleep deprivation. Using MCH-cre mice transduced with channelrhodopsin, we then optogenetically activated MCH neurons time locked with Ta warming, showing an increase in REM sleep expression beyond what Ta warming in yellow fluorescent protein (YFP) control mice achieved. Finally, in mice transduced with archaerhodopsin-T, semi-chronic optogenetic MCH neuronal silencing during Ta warming completely blocked the increase in REM sleep seen in YFP controls. These data demonstrate a previously unknown role for the MCH system in the dynamic output expression of REM sleep during Ta manipulation. These findings are consistent with the energy allocation hypothesis of sleep function, suggesting that endotherms have evolved neural circuits to opportunistically express REM sleep when the need for thermoregulatory defense is minimized.