This study assessed feasibility and effects of a randomised two-week, at-home intervention with phase-targeted auditory stimulation (PTAS) of slow waves in Parkinson's disease (PD). We reproduced previously reported enhancement of slow wave activity and confirmed indications of improved patient-reported outcomes. Together with the high adherence and data quality, this suggests that home-based PTAS protocols are a feasible method for investigating the long-term therapeutic potential of deep sleep enhancement in PD.

Study Objectives:Approximately 4.7 million subjects worldwide suffer from narcolepsy (type 1 and 2) and idiopathic hypersomnia and are impaired in their sleep and wakefulness behavior. However, the role of the circadian rhythm in their sleep-wake behavior remains largely unknown. With this study, we investigated the influence of the internal clock on vigilance and sleep in central disorders of hypersomnolence. Methods:We implemented a nap protocol with 10 cycles of 80 minutes sleep and 160 minutes wake time and compared various sleep and wake parameters (vigilance, subjective sleepiness, sleep efficiency, stages of sleep, slow wave activity) of patients with idiopathic hypersomnia (N=12, ∅25.8 ± 4.1 years, 10 females) and narcolepsy type 1 (N=12, ∅25.0 ± 4.4 years, 8 females) with a healthy control group (N=12, ∅26.8 ± 4.7 years, 6 females) using a linear mixed-model analysis. Results:Our protocol successfully disentangled the circadian rhythm from homeostatic sleep pressure and revealed an intact circadian melatonin pattern as assessed by dim light melatonin onset and offset in both patient groups. Patients with narcolepsy showed high sleep efficiencies of ∅94.3 ± 5.3% over all naps (group effect p < 0.001) and responded positively to the short sleep episodes with an increase in vigilance in the late afternoon (PVT speed @wake seven 2.5 ± 1.0 vs @wake ten 3.9 ± 0.6, p < 0.001). Patients with idiopathic hypersomnia showed an increased subjective sleepiness (group effect p < 0.001), yet practically no statistically significant differences in sleep parameters compared to healthy controls. Surprisingly, we observed a high number of SOREMPs in the healthy control group under low sleep pressure - a finding not previously reported in literature. Conclusion:These findings argue against circadian disruption as a primary mechanism in idiopathic hypersomnia and point toward other underlying causes, such as altered sleep homeostasis or neurochemical dysregulation.

Poor sleep quality might contribute to the risk and progression of neurodegenerative disorders via deficient cerebral waste clearance functions during sleep. In this retrospective cross-sectional study, we explore the link between enlarged perivascular spaces (PVS), a putative marker of sleep-dependent glymphatic clearance, with sleep quality and motor symptoms in patients with Parkinson's disease (PD). T2-weighted magnetic resonance imaging (MRI) images of 20 patients and 17 healthy control participants were estimated visually for PVS in the basal ganglia (BG) and centrum semiovale (CSO). The patient group additionally underwent a single-night polysomnography. Readouts included polysomnographic sleep features and slow-wave activity (SWA), a quantitative EEG marker of sleep depth. Associations between PVS counts, PD symptoms (MDS-UPDRS scores), and sleep parameters were evaluated using correlation and regression analyses. Intra- and inter-rater reproducibility was assessed with weighted Cohen`s kappa coefficient. BG and CSO PVS counts in both patients and controls did not differ significantly between groups. In patients, PVS in both brain regions was negatively associated with SWA (1-2 Hz; BG: r(15) = -.58, p(adj) = .015 and CSO: r(15) = -.6, p(adj) = .015). Basal ganglia PVS counts were positively associated with motor symptoms of daily living (IRR = 1.05, CI [1.01, 1.09], p = .007, p(adj) = .026) and antidepressant use (IRR = 1.37, CI [1.05, 1.80], p = .021, p(adj) = .043) after controlling for age. Centrum Semiovale PVS counts in patients were positively associated with a diagnosis of REM sleep behavior disorder (IRR = 1.39, CI [1.06, 1.84], p = .018, p(adj) = .11). These results add to evidence that sleep deterioration may play a role in impairing glymphatic clearance via altered perivascular function, potentially contributing to disease severity in PD patients.

INTRODUCTION:In neurodegenerative Parkinsonism, biomarkers of α-synucleinopathy (Syn) or tauopathy (Tau) are an unmet need. Rapid eye movement (REM) sleep behavior disorder (RBD) strongly indicates Syn. However, it remains unknown if sleep features other than RBD could reflect underlying neuropathology. Here we assess sleep phenotypes of Syn or Tau in neurodegenerative Parkinsonism and explore their properties as potential biomarkers. METHODS:We retrospectively analyzed polysomnography recordings from 198 patients with clinically diagnosed Parkinsonism (20 DLB, 100 PD, 45 MSA, 27 PSP, 6 CBS). We compared sleep features between clinical diagnoses and between Syn (DLB + PD + MSA) and Tau (PSP + CBS) patients. We established linear discriminant analysis-informed parsimonious logistic regression models for differentiating Syn and Tau proteinopathies. RESULTS:Sleep architecture was more disturbed in Tau compared to Syn patients, with less REM and non-REM stage 2 sleep, lower sleep efficiency, and more wake after sleep onset. Stridor was unique to MSA, with a prevalence of 42 %. Parsimonious modeling identified sleep features sufficient to differentiate Tau from Syn patients; Diagnostic accuracy was robust with RBD (AUC = 0.78) but even higher after adding more polysomnography features (AUC = 0.83) and demographic variables to the model (AUC = 0.9). The best classification model of Syn vs. Tau is available online for exploration and custom data input at SynTauSleepTool. CONCLUSION:Distinct sleep phenotypes characterize neurodegenerative Parkinsonism with Syn or Tau. Pending pathological confirmation, our data suggests that neurodegeneration could affect sleep-wake regulatory brain systems in a proteinopathy-dependent manner. Sleep phenotypes hold promise as non-invasive biomarkers of Syn or Tau in Parkinsonism.

Wearable devices that monitor sleep stages and heart rate offer the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington’s disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD. Ten participants with manifest HD wore an FB during overnight hospital-based PSG, and 9 of these participants continued to wear the FB for 7 nights at home. Sleep stages (30-second epochs) and minute-by-minute heart rate were extracted and compared against PSG data. FB-estimated total sleep and wake times and sleep stage times were in good agreement with PSG, with intraclass correlations of 0.79–0.96. However, poor agreement was observed for wake after sleep onset and the number of awakenings. FB detected waking with 68.6 ± 15.5

BACKGROUND:Little is known about the characteristics and occurrence frequencies of rapid eye movements (REMs) during REM sleep in movement disorders. OBJECTIVES:The aim of this study was to detect and characterize REMs during polysomnographically defined REM sleep as recorded by electro-oculography (EOG) in 12 patients with progressive supranuclear palsy (PSP), 13 patients with Parkinson's disease (PD) and 12 healthy controls. METHODS:Using a modified EOG montage, we developed an algorithm that automatically detects and characterizes REMs during REM sleep based on their presumptive saccadic kinematics. RESULTS:Compared to PD and healthy controls, REM densities and REM peak velocities were significantly reduced in PSP. These effects were most pronounced in vertical REMs. CONCLUSION:Ocular motor dysfunction, one of the cardinal features of PSP, seems to be equally at play during REM sleep and wakefulness. For future studies, we provide a novel tool for the unbiased analysis of REMs during REM sleep in movement disorders.

Study Objectives Excessive daytime sleepiness (EDS) is a common and devastating symptom in Parkinson disease (PD), but surprisingly most studies showed that EDS is independent from nocturnal sleep disturbance measured with polysomnography. Quantitative electroencephalography (EEG) may reveal additional insights by measuring the EEG hallmarks of non-rapid eye movement (NREM) sleep, namely slow waves and spindles. Here, we tested the hypothesis that EDS in PD is associated with nocturnal sleep disturbance revealed by quantitative NREM sleep EEG markers. Methods Patients with PD (n = 130) underwent polysomnography followed by spectral analysis to calculate spindle frequency activity, slow-wave activity (SWA), and overnight SWA decline, which reflects the dissipation of homeostatic sleep pressure. We used the Epworth Sleepiness Scale (ESS) to assess subjective daytime sleepiness and define EDS (ESS > 10). All examinations were part of an evaluation for deep brain stimulation. Results Patients with EDS (n = 46) showed reduced overnight decline of SWA (p = 0.036) and reduced spindle frequency activity (p = 0.032) compared with patients without EDS. Likewise, more severe daytime sleepiness was associated with reduced SWA decline (ss= -0.24 p = 0.008) and reduced spindle frequency activity (ss= -0.42, p < 0.001) across all patients. Reduced SWA decline, but not daytime sleepiness, was associated with poor sleep quality and continuity at polysomnography. Conclusions Our data suggest that daytime sleepiness in PD patients is associated with sleep disturbance revealed by quantitative EEG, namely reduced overnight SWA decline and reduced spindle frequency activity. These findings could indicate that poor sleep quality, with incomplete dissipation of homeostatic sleep pressure, may contribute to EDS in PD.

Objective: According to current practical guidelines, naps of the Mean Sleep Latency Test (MSLT) must be terminated 15 min after sleep onset, which requires ad hoc scoring. For clinical convenience, some sleep clinics use a simplified protocol with fixed nap lengths of 20min. Its diagnostic accuracy remains unknown. Methods: A subset of MSLT naps of 56 narcolepsy type 1 (NT1), 98 Parkinson's disease (PD), 117 sleep disordered breathing (SDB), 22 insufficient sleep syndrome (ISS) patients, and 24 patients with idiopathic hypersomnia (IH), originally performed according to the simplified protocol, were retrospectively adjusted to standard protocol (nap termination 15min after sleep onset or after 20min when no sleep occurs). This was feasible in 60% of MSLT naps; in this subset, we compared sensitivity and specificity of both MSLT protocols for identification of patients with and without NT1. Results: Sensitivity of classical MSLT criteria for NT1, i.e. mean sleep latency & LE;8.0min and & GE;2 sleep onset rapid eye movement periods (SOREMPs), did not differ between protocols (95%). Specificity, however, was slightly lower (88.1% vs. 89.7%) in the simplified nap termination protocol, with 3 SDB patients and 1 ISS patient having false-positive MSLT findings in the simplified but not in the standard protocol. Conclusions: The use of a simplified MSLT protocol with fixed nap duration had no impact on MSLT sensitivity for NT1, but the longer sleep periods in the simplified protocol increased the likelihood of REM sleep occurrence particularly in non-NT1 conditions, resulting in a slightly lower MSLT specificity compared to the standard protocol.

OBJECTIVE:Previous studies suggest that intermittent deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) affects physiological sleep architecture. Here, we investigated the impact of continuous ANT DBS on sleep in epilepsy patients in a multicenter crossover study in 10 patients.METHODS:We assessed sleep stage distribution, delta power, delta energy, and total sleep time in standardized 10/20 polysomnographic investigations before and 12 months after DBS lead implantation.RESULTS:In contrast to previous studies, we found no disruption of sleep architecture or alterations of sleep stage distribution under active ANT DBS (p = .76). On the contrary, we observed more consolidated and deeper slow wave sleep (SWS) under continuous high-frequency DBS as compared to baseline sleep prior to DBS lead implantation. In particular, biomarkers of deep sleep (delta power and delta energy) showed a significant increase post-DBS as compared to baseline (36.67 ± 13.68 μV2 /Hz and 799.86 ± 407.56 μV2 *s, p < .001). Furthermore, the observed increase in delta power was related to the location of the active stimulation contact within the ANT; we found higher delta power and higher delta energy in patients with active stimulation in more superior contacts as compared to inferior ANT stimulation. We also observed significantly fewer nocturnal electroencephalographic discharges in DBS ON condition. In conclusion, our findings suggest that continuous ANT DBS in the most cranial part of the target region leads to more consolidated SWS.SIGNIFICANCE:From a clinical perspective, these findings suggest that patients with sleep disruption under cyclic ANT DBS could benefit from an adaptation of stimulation parameters to more superior contacts and continuous mode stimulation.

The knowledge of the distribution of sleep and wake over a 24-h day is essential for a comprehensive image of sleep-wake rhythms. Current sleep-wake scoring algorithms for wrist-worn actigraphy suffer from low specificities, which leads to an underestimation of the time staying awake. The goal of this study (ClinicalTrials.gov Identifier: NCT03356938) was to develop a sleep-wake classifier with increased specificity. By artificially balancing the training dataset to contain as much wake as sleep epochs from day- and nighttime measurements from 12 subjects, we optimized the classification parameters to an optimal trade-off between sensitivity and specificity. The resulting sleep-wake classifier achieved high specificity of 80.4% and sensitivity of 88.6% on the balanced dataset containing 3079.9 h of actimeter data. In the validation on night sleep of separate adaptation recordings from 19 healthy subjects, the sleep-wake classifier achieved 89.4% sensitivity and 64.6% specificity and estimated accurately total sleep time and sleep efficiency with a mean difference of 12.16 min and 2.83%, respectively. This new, device-independent method allows to rid sleep-wake classifiers from their bias towards sleep detection and lay a foundation for more accurate assessments in everyday life, which could be applied to monitor patients with fragmented sleep-wake rhythms.

The Swiss Narcolepsy Network (SNaNe) was founded in 2017 as a non-profit organization with the vision of improving the care of patients with narcolepsy, central disorders of hypersomnolence (CDH), and rare sleep disorders. The SNaNe aims at maximizing the speed of diagnosis, minimizing difficulties stemming from the rare nature of these conditions, and providing patients with optimum health care throughout the course of their disease. In addition, the SNaNe promotes education, awareness, and research on CDH and rare sleep disorders. The article reports the current structure, organization, and the following main activities of the SNaNe: (1) the discussion of complex patient cases; (2) the organization of the Swiss Narcolepsy Days; (3) the coordination of multicenter research projects (e.g., SPHYNCS and iSPHYNCS studies); (4) the establishment of an anonymous Swiss registry for CDH patients (SNaNe Data Registry); (5) the collaboration with the national patients’ organization (SNAG); and (6) the collaboration with other national and international scientific, professional, and patients’ (eNAP) organizations.

Objective: There is a great interest in observing breathing patterns during sleep, as sleep disturbances can be caused by respiratory irregularity and cessations. In this paper, we introduce the first steps to an accelerometer-based screening tool for respiratory rate estimation and a novel approach towards detecting breathing cessations such as apnea/hypopnea, by extending and combining established signal processing routines with machine learning. Methods: From a single chest-worn accelerometer, we estimate the respiratory rate based on the inhalation/exhalation movements of the chest and carry out a full overnight validation. On this basis, we build a set of features customized to detect irregular respiratory activity, including a novel feature: the respiratory peak variance (RPV). From thirteen healthy subjects, a classification model was trained, validated, and tested with over 98 h of PSG-labeled accelerometer data. Results: The algorithm estimated the respiratory rate with a mean difference of 1.8 breaths per minute compared to respiratory inductance plethysmography during overnight PSGs. The machine learning classifier detected respiratory cessations with a sensitivity and specificity of 76.05% and 70.05% respectively, with an overall accuracy of 70.95%. Conclusion: We successfully demonstrated the potential of a novel respiratory feature set in a preliminary application with young healthy volunteers for respiratory rate estimation and in identifying apnea/hypopnea events during overnight sleep. Significance: We present a simple and unobtrusive wearable system that can serve as a home screening tool for sleep-related breathing disorders.

Recent behavioral evidence from a virtual reality (VR) study indicates that awake sleepwalkers show dissociation of motor control and motor awareness. This dissociation resembles the nocturnal disintegration of motor awareness and movement during episodes of sleepwalking. Here, we set out to examine the neural underpinnings of altered motor awareness in sleepwalkers by measuring EEG modulation during redirected walking in VR. To this end, we measured scalp EEG during ongoing motor behavior to provide information on motor processing and its modulation in VR. Using this approach, we discovered distinct EEG patterns associated to dual tasking and sub-threshold motor control in sleepwalkers compared to control subjects. These observations provide further electrophysiological evidence for the proposed brain-body dissociation in awake sleepwalkers. This study shows proof-of-principle that EEG biomarkers of movement in a VR setting add to the understanding of altered motor awareness in sleepwalkers. In a broader perspective, we confirm the feasibility of using the additional dimensionality in VR providing novel diagnostic biomarkers not accessible to conventional clinical investigations. In future studies, this approach could contribute to the diagnostic work-up of patients with a broad spectrum of neurological diseases.

Objectives: Sleep-wake misperception has mainly been reported in insomnia patients. Conversely, the present study aimed to assess the prevalence and correlates of sleep-wake misperception in a large cohort of patients with various sleep-wake disorders, all diagnosed along the third version of the International Classification of Sleep Disorders. Methods: We retrospectively included 2738 patients examined by polysomnography, who in addition estimated upon awakening their total sleep time, sleep onset latency and Wake after sleep onset (WASO). We computed subjective-objective mismatch by the formula (subjective objective value)/objective value x100; negative and positive values indicated under-and overestimation, respectively. Results: In the entire sample, the magnitude of under-and overestimation of total sleep time was similar, but varied significantly between diagnostic groups, with insomnia and insufficient sleep syndrome showing the most pronounced underestimation and REM parasomnia and circadian rhythm disorders showing the most pronounced overestimation of total sleep time. In all diagnostic categories, a majority tended to overestimate their sleep onset latency and to underestimate the amount of WASO. Younger age was independently correlated with underestimation of total sleep time and WASO, and with overestimation of sleep onset latency. Overestimation of sleep onset latency independently correlated to an increased latency to N3 sleep stage on polysomnography. Conclusions: While sleep-wake misperception is highly prevalent in all sleep-wake disorders, significant differences exist in magnitude of under-and overestimation between distinct diagnostic groups. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Background Auditory stimulation has emerged as a promising tool to enhance non-invasively sleep slow waves, deep sleep brain oscillations that are tightly linked to sleep restoration and are diminished with age. While auditory stimulation showed a beneficial effect in lab-based studies, it remains unclear whether this stimulation approach could translate to real-life settings. Methods We present a fully remote, randomized, cross-over trial in healthy adults aged 62–78 years (clinicaltrials.gov: NCT03420677). We assessed slow wave activity as the primary outcome and sleep architecture and daily functions, e.g., vigilance and mood as secondary outcomes, after a two-week mobile auditory slow wave stimulation period and a two-week Sham period, interleaved with a two-week washout period. Participants were randomized in terms of which intervention condition will take place first using a blocked design to guarantee balance. Participants and experimenters performing the assessments were blinded to the condition. Results Out of 33 enrolled and screened participants, we report data of 16 participants that received identical intervention. We demonstrate a robust and significant enhancement of slow wave activity on the group-level based on two different auditory stimulation approaches with minor effects on sleep architecture and daily functions. We further highlight the existence of pronounced inter- and intra-individual differences in the slow wave response to auditory stimulation and establish predictions thereof. Conclusions While slow wave enhancement in healthy older adults is possible in fully remote settings, pronounced inter-individual differences in the response to auditory stimulation exist. Novel personalization solutions are needed to address these differences and our findings will guide future designs to effectively deliver auditory sleep stimulations using wearable technology.

BackgroundWearable devices enable long-term home sleep monitoring, allowing changes in sleep quality and sleep patterns to be examined in Huntington’s Disease (HD).AimTo establish the accuracy of Fitbit Charge 4 sleep metrics and heart rate during sleep compared to polysomnography (PSG) in participants with HD.MethodsTen participants with HD (Unified HD Rating Scale motor scores 5-51 points) wore a Fitbit Charge 4 during overnight PSG. PSG sleep stages were scored by an expert sleep physiologist. Fitbit sleep stages (8 participants; 30s epochs), and Fitbit heart rate data (all participants; 60s epochs) were extracted. Total sleep time, total wake time, time in each sleep stage (REM, Light and Deep) were calculated using sleep staging data. Sleep fragmentation index was calculated as number of awakenings per hour. The mean and coefficient of variation in heart rate were calculated for each sleep. Agreement was assessed using Bland Altman plots, and the sensitivity and specificity of Fitbit to each sleep stage were calculated.ResultsCompared to PSG, Fitbit (Figure 1) overestimated total sleep time by 5±26.8 mins and REM sleep by 5.21±22.35 mins, and underestimated deep sleep by 4.1±21.8 mins. Fitbit sensitivity and specificity to sleep was 90% and 73%, REM was 76% and 96%, and deep sleep was 54% and 94%. Heart rate during sleep was estimated by Fitbit with a mean error of -0.32±0.54 bpm compared with PSG.ConclusionsPreliminary results suggest that Fitbit Charge 4 may be suitable to monitor sleep stages and heart rate during sleep in HD.

OBJECTIVE:Unilateral manifestation of motor dysfunction is a prominent hallmark of Parkinson's disease (PD). We investigated how the motor laterality of the disorder affects sleep neural asymmetry before and after Deep Brain Stimulation (DBS).METHODS:Twenty-seven PD patients of the akinetic-rigid subtype were studied; 11 with right dominant (RD) and 16 with left dominant (LD) motor symptoms. Neuronal sleep asymmetry was computed as the difference of sleep slow-wave energy (SWE) between left and right hemispheres. We used linear mixed models to assess the relationship between symptomatic profile and SWE asymmetry.RESULTS:LD PD patients exhibited frontal electroencephalographic (EEG) asymmetry and motor laterality pre-DBS with increased SWE contralateral to their affected body side, which diminished post-DBS. The RD group did not exhibit neither neural asymmetry nor motor laterality pre- and post-DBS. There was a significant negative correlation between the motor laterality and sleep EEG asymmetry.CONCLUSIONS:Our results suggest evidence for a local use-dependent modulation of SWE as a result of the lateralized pathological motor profile. More bilateral motor symptoms and optimized treatment contribute to diminished sleep EEG asymmetry.SIGNIFICANCE:These novel findings about the association between symptomatic motor laterality and sleep neural asymmetry may provide targeted therapeutic insights.

Background: Early brainstem neurodegeneration is common in Parkinson's disease (PD) and progressive supranuclear palsy (PSP). While previous work showed abnormalities in vestibular evoked myogenic potentials (VEMPs) in patients with either disorder as compared to healthy humans, it remains unclear whether ocular and cervical VEMPs differ between PD and PSP patients.Methods: We prospectively included 12 PD and 11 PSP patients, performed ocular and cervical VEMPs, and calculated specific VEMP scores (0 = normal, 12 = most pathological) based on latencies, amplitude, and absent responses. In addition, we assessed disease duration, presence of imbalance, motor asymmetry, and motor disability using the Movement Disorder Society Unified Parkinson's Disease Rating Scale, part III (MDS-UPDRS III). Moreover, we ascertained various sleep parameters by video-polysomnography.Results: PSP and PD patients had similar oVEMP scores (6 [3–6] vs. 3 [1.3–6], p = 0.06), but PSP patients had higher cVEMP scores (3 [0–6] vs. 0 [0–2.8], p = 0.03) and total VEMP scores (9 [5–12] vs. 4 [2–7.5], p = 0.01). Moreover, total VEMP scores >10 were only observed in PSP patients (45%, p = 0.01). MDS-UPDRS III correlated with cVEMP scores (rho = 0.77, p = 0.01) in PSP, but not in PD. In PD, but not in PSP, polysomnographic markers of disturbed sleep, including decreased rapid eye movement sleep, showed significant correlations with VEMP scores.Conclusions: Our findings suggest that central vestibular pathways are more severely damaged in PSP than in PD, as indicated by higher cervical and total VEMP scores in PSP than PD in a between-groups analysis. Meaningful correlations between VEMPs and motor and non-motor symptoms further encourage its use in neurodegenerative Parkinsonian syndromes.