Analysis of automated quantification of motor activity in REM sleep behaviour disorder

Contemporary diagnostic visual and automated polysomnographic REM sleep without atonia thresholds in isolated REM sleep behavior disorder

STUDY OBJECTIVES

Accurate diagnosis of isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is crucial due to its injury potential and neurological prognosis. We aimed to analyze visual and automated REM sleep without atonia (RSWA) diagnostic thresholds applicable in varying clinical presentations in a contemporary cohort of patients with iRBD using submentalis (SM) and individual bilateral flexor digitorum superficialis (FDS) and anterior tibialis electromyography limb recordings during polysomnography.

METHODS

We analyzed RSWA in 20 patients with iRBD and 20 age-, REM-, apnea-hypopnea index-matched controls between 2017 and 2022 for phasic burst durations, density of phasic, tonic, and "any" muscle activity (number of 3-second mini-epochs containing phasic or tonic muscle activity divided by the total number of REM sleep 3-second mini-epochs), and automated Ferri REM atonia index (RAI). Group RSWA metrics were comparatively analyzed. Receiver operating characteristic curves determined optimized area under the curve (AUC) and maximized specificity and sensitivity diagnostic iRBD RSWA thresholds.

RESULTS

All mean RSWA metrics were higher in patients with iRBD than in controls (P < .05), except for selected anterior tibialis measures. Optimized, maximal specificity AUC diagnostic cutoffs for coprimary outcomes were: SM "any" 6.5%, 14.0% (AUC = 92.5%) and combined SM+FDS "any" 15.1%, 27.4% (AUC = 95.8%), while SM burst durations were 0.72, and 0.72 seconds (AUC 90.2%) and FDS RAI = 0.930, 0.888 (AUC 92.8%).

CONCLUSIONS

This study provides evidence for current quantitative RSWA diagnostic thresholds in chin and individual 4 limb muscles applicable in different iRBD clinical settings and confirms the key value of SM or SM+FDS to assure accurate iRBD diagnosis. Evolving iRBD recognition underscores the necessity of continuous assessment with future large, prospective, well-harmonized, multicenter polysomnographic analyses.

CITATION

Leclair-Visonneau L, Feemster JC, Bibi N, et al. Contemporary diagnostic visual and automated polysomnographic REM sleep without atonia thresholds in isolated REM sleep behavior disorder. J Clin Sleep Med. 2024;20(2):279-291.

Improved automatic identification of isolated rapid eye movement sleep behavior disorder with a 3D time-of-flight camera

BACKGROUND AND PURPOSE

Automatic 3D video analysis of the lower body during rapid eye movement (REM) sleep has been recently proposed as a novel tool for identifying people with isolated REM sleep behavior disorder (iRBD), but, so far, it has not been validated on unseen subjects. This study aims at validating this technology in a large cohort and at improving its performances by also including an analysis of movements in the head, hands and upper body.

METHODS

Fifty-three people with iRBD and 128 people without RBD (of whom 89 had sleep disorders considered RBD differential diagnoses) were included in the study. An automatic algorithm identified movements from 3D videos during REM sleep in four regions of interest (ROIs): head, hands, upper body and lower body. The movements were divided into categories according to duration: short (0.1-2 s), medium (2-15 s) and long (15-300 s). For each ROI and duration range, features were obtained from the identified movements. Logistic regression models using as predictors the features from one single ROI or a combination of ROIs were trained and tested in a 10-runs 10-fold cross-validation scheme on the task of differentiating people with iRBD from people without RBD.

RESULTS

The best differentiation was achieved using short movements in all four ROIs (test accuracy 0.866 ± 0.007, test F1 score = 0.783 ± 0.010). Single group analyses showed that people with iRBD were distinguished successfully from subjects with RBD differential diagnoses.

CONCLUSIONS

Automatic 3D video analysis might be implemented in clinical routine as a supportive screening tool for identifying people with RBD.

Automatic analysis of muscular activity in the flexor digitorum superficialis muscles: a fast screening method for rapid eye movement sleep without atonia

STUDY OBJECTIVES

To identify a fast and reliable method for rapid eye movement (REM) sleep without atonia (RWA) quantification.

METHODS

We analyzed 36 video-polysomnographies (v-PSGs) of isolated REM sleep behavior disorder (iRBD) patients and 35 controls' v-PSGs. Patients diagnosed with RBD had: i) RWA, quantified with a reference method, i.e. automatic and artifact-corrected 3-s Sleep Innsbruck Barcelona (SINBAR) index in REM sleep periods (RSPs, i.e. manually selected portions of REM sleep); and ii) v-PSG-documented RBD behaviors. We quantified RWA with other (semi)-automated methods requiring less human intervention than the reference one: the indices proposed by the SINBAR group (the 3-s and 30-s phasic flexor digitorum superficialis (FDS), phasic/"any"/tonic mentalis), and the REM atonia, short and long muscle activity indices (in mentalis/submentalis/FDS muscles). They were calculated in whole REM sleep (i.e. REM sleep scored following international guidelines), in RSPs, with and without manual artifact correction. Area under curves (AUC) discriminating iRBD from controls were computed. Using published cut-offs, the indices' sensitivity and specificity for iRBD identification were calculated. Apnea-hypopnea index in REM sleep (AHIREM) was considered in the analyses.

RESULTS

RWA indices from FDS muscles alone had the highest AUCs and all of them had 100% sensitivity. Without manual RSP selection and artifact correction, the "30-s phasic FDS" and the "FDS long muscle activity" had the highest specificity (85%) with AHIREM < 15/h. RWA indices were less reliable when AHIREM≥15/h.

CONCLUSIONS

If AHIREM<15/h, FDS muscular activity in whole REM sleep and without artifact correction is fast and reliable to rule out RWA.

Comparison of rapid eye movement without atonia quantification methods to diagnose rapid eye movement sleep behavior disorder: a systematic review

Study Objectives

Rapid eye movement (REM) sleep without atonia (RWA) is essential for diagnosing REM sleep behavior disorder (RBD). Manual and automatic quantifications of RWA that use different criteria have been validated. This study compared the RWA quantification methods for diagnosing RBD.

Methods

The PubMed, EMBASE, Web of Science, and Cochrane Library databases were systemically searched for studies published from inception to December 2021. The inclusion criteria were cohort, cross-sectional, and case-control studies assessing the sensitivity and specificity of RWA quantification methods. Pooled estimates of the sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were determined. Risk of bias and certainty of evidence was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and the Grading of Recommendations, Assessment, Development, and Evaluations framework, respectively.

Results

Fourteen articles including 402 patients with RBD met the inclusion criteria. Manual methods evaluating any chin and phasic flexor digitorum superficialis (FDS) activity had the highest DOR (138.8, 95% CI = 21.8% to 881.7%) and AUC (0.9686). The automatic REM atonia index (RAI) showed similar or higher sensitivity (89.1%, 95% CI = 84.6% to 92.7%) but a lower specificity (73.5%), DOR (43.1), and AUC (0.9369) than the manual techniques.

Conclusions

In this meta-analysis, manual RWA quantification that employed chin or phasic FDS activity had the best RBD diagnostic performance. The automatic RAI method may be useful for screening patients with RBD. The results should be interpreted carefully because of the high risk of bias in patient selection and significant heterogeneity among the studies.

PROSPERO Registration number

CRD42021276445.

A novel feature relearning method for automatic sleep staging based on single-channel EEG

Correctly identifying sleep stages is essential for assessing sleep quality and treating sleep disorders. However, the current sleep staging methods have the following problems: (1) Manual or semi-automatic extraction of features requires professional knowledge, which is time-consuming and laborious. (2) Due to the similarity of stage features, it is necessary to strengthen the learning of features. (3) Acquisition of a variety of data has high requirements on equipment. Therefore, this paper proposes a novel feature relearning method for automatic sleep staging based on single-channel electroencephalography (EEG) to solve these three problems. Specifically, we design a bottom–up and top–down network and use the attention mechanism to learn EEG information fully. The cascading step with an imbalanced strategy is used to further improve the overall classification performance and realize automatic sleep classification. The experimental results on the public dataset Sleep-EDF show that the proposed method is advanced. The results show that the proposed method outperforms the state-of-the-art methods. The code and supplementary materials are available at GitHub: https://github.com/raintyj/A-novel-feature-relearning-method.

Video-polysomnography procedures for diagnosis of rapid eye movement sleep behavior disorder (RBD) and the identification of its prodromal stages: guidelines from the International RBD Study Group

Video-polysomnography (v-PSG) is essential for diagnosing rapid eye movement (REM) sleep behavior disorder (RBD). Although there are current American Academy of Sleep Medicine standards to diagnose RBD, several aspects need to be addressed to achieve harmonization across sleep centers. Prodromal RBD is a stage in which symptoms and signs of evolving RBD are present, but do not yet meet established diagnostic criteria for RBD. However, the boundary between prodromal and definite RBD is still unclear. As a common effort of the Neurophysiology Working Group of the International RBD Study Group, this manuscript addresses the need for comprehensive and unambiguous v-PSG recommendations to diagnose RBD and identify prodromal RBD. These include: (1) standardized v-PSG technical settings; (2) specific considerations for REM sleep scoring; (3) harmonized methods for scoring REM sleep without atonia; (4) consistent methods to analyze video and audio recorded during v-PSGs and to classify movements and vocalizations; (5) clear v-PSG guidelines to diagnose RBD and identify prodromal RBD. Each section follows a common template: The current recommendations and methods are presented, their limitations are outlined, and new recommendations are described. Finally, future directions are presented. These v-PSG recommendations are intended for both practicing clinicians and researchers. Classification and quantification of motor events, RBD episodes, and vocalizations are however intended for research purposes only. These v-PSG guidelines will allow collection of homogeneous data, providing objective v-PSG measures and making future harmonized multicentric studies and clinical trials possible.

Assessing REM Sleep Behaviour Disorder: From Machine Learning Classification to the Definition of a Continuous Dissociation Index

Objectives: Rapid Eye Movement Sleep Behaviour Disorder (RBD) is regarded as a prodrome of neurodegeneration, with a high conversion rate to α-synucleinopathies such as Parkinson's Disease (PD). The clinical diagnosis of RBD co-exists with evidence of REM Sleep Without Atonia (RSWA), a parasomnia that features loss of physiological muscular atonia during REM sleep. The objectives of this study are to implement an automatic detection of RSWA from polysomnographic traces, and to propose a continuous index (the Dissociation Index) to assess the level of dissociation between REM sleep stage and atonia. This is performed using Euclidean distance in proper vector spaces. Each subject is assigned a dissociation degree based on their distance from a reference, encompassing healthy subjects and clinically diagnosed RBD patients at the two extremes. Methods: Machine Learning models were employed to perform automatic identification of patients with RSWA through clinical polysomnographic scores, together with variables derived from electromyography. Proper distance metrics are proposed and tested to achieve a dissociation measure. Results: The method proved efficient in classifying RSWA vs. not-RSWA subjects, achieving an overall accuracy, sensitivity and precision of 87%, 93% and 87.5%, respectively. On its part, the Dissociation Index proved to be promising in measuring the impairment level of patients. Conclusions: The proposed method moves a step forward in the direction of automatically identifying REM sleep disorders and evaluating the impairment degree. We believe that this index may be correlated with the patients' neurodegeneration process; this assumption will undergo a robust clinical validation process involving healthy, RSWA, RBD and PD subjects.

Validation of Visually Identified Muscle Potentials during Human Sleep Using High Frequency/Low Frequency Spectral Power Ratios

Surface electromyography (EMG), typically recorded from muscle groups such as the mentalis (chin/mentum) and anterior tibialis (lower leg/crus), is often performed in human subjects undergoing overnight polysomnography. Such signals have great importance, not only in aiding in the definitions of normal sleep stages, but also in defining certain disease states with abnormal EMG activity during rapid eye movement (REM) sleep, e.g., REM sleep behavior disorder and parkinsonism. Gold standard approaches to evaluation of such EMG signals in the clinical realm are typically qualitative, and therefore burdensome and subject to individual interpretation. We originally developed a digitized, signal processing method using the ratio of high frequency to low frequency spectral power and validated this method against expert human scorer interpretation of transient muscle activation of the EMG signal. Herein, we further refine and validate our initial approach, applying this to EMG activity across 1,618,842 s of polysomnography recorded REM sleep acquired from 461 human participants. These data demonstrate a significant association between visual interpretation and the spectrally processed signals, indicating a highly accurate approach to detecting and quantifying abnormally high levels of EMG activity during REM sleep. Accordingly, our automated approach to EMG quantification during human sleep recording is practical, feasible, and may provide a much-needed clinical tool for the screening of REM sleep behavior disorder and parkinsonism.

Neurophysiological Aspects of REM Sleep Behavior Disorder (RBD): A Narrative Review

REM sleep without atonia (RSWA) is the polysomnographic (PSG) hallmark of rapid eye movement (REM) sleep behavior disorder (RBD), a feature essential for the diagnosis of this condition. Several additional neurophysiological aspects of this complex disorder have also recently been investigated in depth, which constitute the focus of this narrative review, together with RSWA. First, we describe the complex neural network underlying REM sleep and its muscle atonia, focusing on the disordered mechanisms leading to RSWA. RSWA is then described in terms of its polysomnographic features, and the methods (visual and automatic) currently available for its scoring and quantification are exposed and discussed. Subsequently, more recent and advanced neurophysiological features of RBD are described, such as electroencephalography during wakefulness and sleep, transcranial magnetic stimulation, and vestibular evoked myogenic potentials. The role of the assessment of neurophysiological features in the study of RBD is then carefully discussed, highlighting their usefulness and sensitivity in detecting neurodegeneration in the early or prodromal stages of RBD, as well as their relationship with other proposed biomarkers for the diagnosis, prognosis, and monitoring of this condition. Finally, a future research agenda is proposed to help clarify the many still unclear aspects of RBD.

Neurophysiological Aspects of REM Sleep Behavior Disorder (RBD): A Narrative Review

REM sleep without atonia (RSWA) is the polysomnographic (PSG) hallmark of rapid eye movement (REM) sleep behavior disorder (RBD), a feature essential for the diagnosis of this condition. Several additional neurophysiological aspects of this complex disorder have also recently been investigated in depth, which constitute the focus of this narrative review, together with RSWA. First, we describe the complex neural network underlying REM sleep and its muscle atonia, focusing on the disordered mechanisms leading to RSWA. RSWA is then described in terms of its polysomnographic features, and the methods (visual and automatic) currently available for its scoring and quantification are exposed and discussed. Subsequently, more recent and advanced neurophysiological features of RBD are described, such as electroencephalography during wakefulness and sleep, transcranial magnetic stimulation, and vestibular evoked myogenic potentials. The role of the assessment of neurophysiological features in the study of RBD is then carefully discussed, highlighting their usefulness and sensitivity in detecting neurodegeneration in the early or prodromal stages of RBD, as well as their relationship with other proposed biomarkers for the diagnosis, prognosis, and monitoring of this condition. Finally, a future research agenda is proposed to help clarify the many still unclear aspects of RBD.

Automatic 3D Video Analysis of Upper and Lower Body Movements to Identify Isolated REM Sleep Behavior Disorder: A Pilot Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:7050-7053. [PMID: 34892726 DOI: 10.1109/embc46164.2021.9630011]

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by dream enactment, abnormal jerks and movements during REM sleep. Isolated RBD (iRBD) is recognized as the early stage of alpha-synucleinopathies, i.e. dementia with Lewy bodies, Parkinson's disease and multiple system atrophy. The certain diagnosis of iRBD requires video-polysomnography, evaluated by experts with time-consuming visual analyses. In this study, we propose automatic analysis of movements detected with 3D contactless video as a promising technology to assist sleep experts in the identification of patients with iRBD. By using automatically detected upper and lower body movements occurring during REM sleep with a duration between 4s and 5s, we could discriminate 20 iRBD patients from 24 patients with sleep-disordered breathing with an accuracy of 0.91 and F1-score of 0.90. This pilot study shows that 3D contactless video can be successfully used as a non-invasive technology to assist clinicians in identifying abnormal movements during REM sleep, and therefore to recognize patients with iRBD. Future investigations in larger cohorts are needed to validate the proposed technology and methodology.

REM sleep behavior disorder: Mimics and variants

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia with dream-enactment behaviors occurring during REM sleep and associated with the lack of the physiological REM sleep muscle atonia. It can be isolated and secondary to other neurological or medical conditions. Isolated RBD heralds in most cases a neurodegenerative condition due to an underlying synucleinopathy and consequently its recognition is crucial for prognostic implications. REM sleep without atonia on polysomnography is a mandatory diagnostic criterion. Different conditions may mimic RBD, the most frequent being obstructive sleep apnea during sleep, non-REM parasomnia, and sleep-related hypermotor epilepsy. These diseases might also be comorbid with RBD, challenging the evaluation of disease severity, the treatment choices and the response to treatment evaluation. Video-PSG is the gold standard for a correct diagnosis and will distinguish between different or comorbid sleep disorders. Careful history taking together with actigraphy may give important clues for the differential diagnosis. The extreme boundaries of RBD might also be seen in more severe and complex conditions like status dissociatus or in the sleep disorders' scenario of anti IgLON5 disease, but in the latter both clinical and neurophysiological features will differ. A step-by-step approach is suggested to guide the differential diagnosis.

Flexor digitorum superficialis muscular activity is more reliable than mentalis muscular activity for rapid eye movement sleep without atonia quantification

STUDY OBJECTIVES

To evaluate interrater reliability for artefact correction in the context of semi-automated quantification of rapid eye movement (REM) sleep without atonia (RWA) in the mentalis and flexor digitorum superficialis (FDS) muscles.

METHODS

We included video-polysomnographies of 14 subjects with apnea-hypopnea-index in REM sleep (AHIREM)<15/h and 11 subjects with AHIREM≥15/h. Eight subjects had isolated REM sleep behavior disorder. A validated algorithm (www.osg.be) automatically scored phasic and "any" EMG activity in the mentalis muscle, and phasic EMG activity in the FDS muscles. Four independent expert scorers performed artefact correction according to the SINBAR (Sleep Innsbruck Barcelona) recommendations. Interrater reliability for artefact correction was computed with B-statistics. The variability across scorers of four RWA indices (phasic mentalis, "any" mentalis, phasic FDS and SINBAR - i.e. "any" mentalis and/or phasic FDS - EMG activity indices) was computed. With Friedman tests we compared B-statistics obtained for mentalis and FDS muscles, and the variability of the RWA indices. Influence of AHIREM and RBD diagnosis on the RWA indices variability was evaluated with linear regressions.

RESULTS

Interrater reliability for artefact correction was higher in the FDS than in the mentalis muscle (p<0.001). Phasic FDS activity was minimally affected by artefacts. Accordingly, the phasic FDS EMG activity index had the lowest variability across scorers (p<0.001). Variability across scorers of the RWA indices including the mentalis muscle increased with AHIREM and was independent from RBD diagnosis.

CONCLUSIONS

Due to the consistently found low number of artefacts, phasic FDS activity is a reliable measure of RWA.

Association of neurocognitive functioning with sleep stage dissociation and REM sleep instability in medicated patients with schizophrenia

Many patients with schizophrenia present with impaired cognitive functioning and sleep disturbances. Dissociated stages of sleep represent instability within distinct sleep regulatory cerebral networks. Previous studies found increased rates of rapid eye movement (REM) sleep abnormalities in patients with schizophrenia and a positive association with psychopathology. In this study, we examined if sleep stage dissociation and REM sleep instability was associated with neurocognitive functioning in a sample of medicated patients with schizophrenia. The analyses were performed on 31 baseline polysomnographic recordings as well as baseline data on neurocognitive performance. Regression models were built with the cognitive composite score as primary dependent variable and measures of sleep stage dissociation, including REM sleep without atonia (RSWA), REM sleep without eye movements, non-REM sleep with eye movements, REM sleep percentage in REM periods and REM sleep stability as independent variables. Analyses were adjusted for age, gender, total antipsychotic dose, total benzodiazepine dose, and symptom severity. After correction for multiple testing, we found that the neurocognitive composite score was inversely associated with the degree of RSWA. Exploratory analyses with the cognitive sub scores as dependent variables showed that RSWA was associated with cognitive performance across several sub domains. Dissociated sleep stages, specifically the RSWA feature, might represent a new treatment target for improving cognitive impairment in patients with schizophrenia.

Current Concepts and Controversies in the Management of REM Sleep Behavior Disorder

Rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by dream enactment and the loss of muscle atonia during REM sleep, known as REM sleep without atonia (RSWA). RBD can result in significant injuries, prompting patients to seek medical attention. However, in others, it may present only as non-violent behaviors noted as an incidental finding during polysomnography (PSG). RBD typically occurs in the context of synuclein-based neurodegenerative disorders but can also be seen accompanying brain lesions and be exacerbated by medications, particularly antidepressants. There is also an increasing appreciation regarding isolated or idiopathic RBD (iRBD). Symptomatic treatment of RBD is a priority to prevent injurious complications, with usual choices being melatonin or clonazepam. The discovery that iRBD represents a prodromal stage of incurable synucleinopathies has galvanized the research community into delineating the pathophysiology of RBD and defining biomarkers of neurodegeneration that will facilitate future disease-modifying trials in iRBD. Despite many advances, there has been no progress in available symptomatic or neuroprotective therapies for RBD, with recent negative trials highlighting several challenges that need to be addressed to prepare for definitive therapeutic trials for patients with this disorder. These challenges relate to i) the diagnostic and screening strategies applied to RBD, ii) the limited evidence base for symptomatic therapies, (iii) the existence of possible subtypes of RBD, (iv) the relevance of triggering medications, (v) the absence of objective markers of severity, (vi) the optimal design of disease-modifying trials, and vii) the implications around disclosing the risk of future neurodegeneration in otherwise healthy individuals. Here, we review the current concepts in the therapeutics of RBD as it relates to the above challenges and identify pertinent research questions to be addressed by future work.

Automated 3D video analysis of lower limb movements during REM sleep: a new diagnostic tool for isolated REM sleep behavior disorder

STUDY OBJECTIVES

The differentiation of isolated rapid eye movement (REM) sleep behavior disorder (iRBD) or its prodromal phase (prodromal RBD) from other disorders with motor activity during sleep is critical for identifying α-synucleinopathy in an early stage. Currently, definite RBD diagnosis requires video polysomnography (vPSG). The aim of this study was to evaluate automated 3D video analysis of leg movements during REM sleep as objective diagnostic tool for iRBD.

METHODS

A total of 122 participants (40 iRBD, 18 prodromal RBD, 64 participants with other disorders with motor activity during sleep) were recruited among patients undergoing vPSG at the Sleep Disorders Unit, Department of Neurology, Medical University of Innsbruck. 3D videos synchronous to vPSG were recorded. Lower limb movements rate, duration, extent, and intensity were computed using a newly developed software.

RESULTS

The analyzed 3D movement features were significantly increased in subjects with iRBD compared to prodromal RBD and other disorders with motor activity during sleep. Minor leg jerks with a duration < 2 seconds discriminated with the highest accuracy (90.4%) iRBD from other motor activity during sleep. Automatic 3D analysis did not differentiate between prodromal RBD and other disorders with motor activity during sleep.

CONCLUSIONS

Automated 3D video analysis of leg movements during REM sleep is a promising diagnostic tool for identifying subjects with iRBD in a sleep laboratory population and is able to distinguish iRBD from subjects with other motor activities during sleep. For future application as a screening, further studies should investigate usefulness of this tool when no information about sleep stages from vPSG is available and in the home environment.

Detection of REM sleep behaviour disorder by automated polysomnography analysis

OBJECTIVE

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification.

METHODS

Analysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent.

RESULTS

Automated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved from 86% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging.

CONCLUSIONS

This study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation.

SIGNIFICANCE

This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.

REM sleep behaviour disorder

Rapid eye movement (REM) sleep behaviour disorder (RBD) is a parasomnia that is characterized by loss of muscle atonia during REM sleep (known as REM sleep without atonia, or RSWA) and abnormal behaviours occurring during REM sleep, often as dream enactments that can cause injury. RBD is categorized as either idiopathic RBD or symptomatic (also known as secondary) RBD; the latter is associated with antidepressant use or with neurological diseases, especially α-synucleinopathies (such as Parkinson disease, dementia with Lewy bodies and multiple system atrophy) but also narcolepsy type 1. A clinical history of dream enactment or complex motor behaviours together with the presence of muscle activity during REM sleep confirmed by video polysomnography are mandatory for a definite RBD diagnosis. Management involves clonazepam and/or melatonin and counselling and aims to suppress unpleasant dreams and behaviours and improve bedpartner quality of life. RSWA and RBD are now recognized as manifestations of an α-synucleinopathy; most older adults with idiopathic RBD will eventually develop an overt neurodegenerative syndrome. In the future, studies will likely evaluate neuroprotective therapies in patients with idiopathic RBD to prevent or delay α-synucleinopathy-related motor and cognitive decline.

Idiopathic REM sleep behaviour disorder and neurodegeneration - an update

So-called idiopathic rapid eye movement (REM) sleep behaviour disorder (RBD), formerly seen as a rare parasomnia, is now recognized as the prodromal stage of an α-synucleinopathy. Given the very high risk that patients with idiopathic RBD have of developing α-synucleinopathies, such as Parkinson disease (PD), PD dementia, dementia with Lewy bodies or multiple system atrophy, and the outstandingly high specificity and very long interval between the onset of idiopathic RBD and the clinical manifestations of α-synucleinopathies, the prodromal phase of this disorder represents a unique opportunity for potentially disease-modifying intervention. This Review provides an update on classic and novel biomarkers of α-synuclein-related neurodegeneration in patients with idiopathic RBD, focusing on advances in imaging and neurophysiological, cognitive, autonomic, tissue-specific and other biomarkers. We discuss the strengths, potential weaknesses and suitability of these biomarkers for identifying RBD and neurodegeneration, with an emphasis on predicting progression to overt α-synucleinopathy. The role of video polysomnography in providing quantifiable and potentially treatment-responsive biomarkers of neurodegeneration is highlighted. In light of all these advances, and the now understood role of idiopathic RBD as an early manifestation of α-synuclein disease, we call for idiopathic RBD to be reconceptualized as isolated RBD.

Increased Motor Activity During REM Sleep Is Linked with Dopamine Function in Idiopathic REM Sleep Behavior Disorder and Parkinson Disease

STUDY OBJECTIVES

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by impaired motor inhibition during REM sleep, and dream-enacting behavior. RBD is especially associated with α-synucleinopathies, such as Parkinson disease (PD). Follow-up studies have shown that patients with idiopathic RBD (iRBD) have an increased risk of developing an α-synucleinopathy in later life. Although abundant studies have shown that degeneration of the nigrostriatal dopaminergic system is associated with daytime motor function in Parkinson disease, only few studies have investigated the relation between this system and electromyographic (EMG) activity during sleep. The objective of this study was to investigate the relationship between the nigrostriatal dopamine system and muscle activity during sleep in iRBD and PD.

METHODS

10 iRBD patients, 10 PD patients with PD, 10 PD patients without RBD, and 10 healthy controls were included and assessed with (123)I-N-omega-fluoropropyl-2-beta-carboxymethoxy-3beta-(4-iodophenyl) nortropane ((123)I-FP-CIT) Single-photon emission computed tomography (SPECT) scanning ((123)I-FP-CIT SPECT), neurological examination, and polysomnography.

RESULTS

iRBD patients and PD patients with RBD had increased EMG-activity compared to healthy controls. (123)I-FP-CIT uptake in the putamen-region was highest in controls, followed by iRBD patients, and lowest in PD patients. In iRBD patients, EMG-activity in the mentalis muscle was correlated to (123)I-FP-CIT uptake in the putamen. In PD patients, EMG-activity was correlated to anti-Parkinson medication.

CONCLUSIONS

Our results support the hypothesis that increased EMG-activity during REM sleep is at least partly linked to the nigrostriatal dopamine system in iRBD, and with dopamine function in PD.

Neurophysiological basis of rapid eye movement sleep behavior disorder: informing future drug development

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by a history of recurrent nocturnal dream enactment behavior and loss of skeletal muscle atonia and increased phasic muscle activity during REM sleep: REM sleep without atonia. RBD and associated comorbidities have recently been identified as one of the most specific and potentially sensitive risk factors for later development of any of the alpha-synucleinopathies: Parkinson's disease, dementia with Lewy bodies, and other atypical parkinsonian syndromes. Several other sleep-related abnormalities have recently been identified in patients with RBD/Parkinson's disease who experience abnormalities in sleep electroencephalographic frequencies, sleep-wake transitions, wake and sleep stability, occurrence and morphology of sleep spindles, and electrooculography measures. These findings suggest a gradual involvement of the brainstem and other structures, which is in line with the gradual involvement known in these disorders. We propose that these findings may help identify biomarkers of individuals at high risk of subsequent conversion to parkinsonism.