Automatic quantification of REM sleep without atonia reliably identifies patients with REM sleep behavior disorder: a possible screening tool?

BACKGROUND: REM Sleep Behavior Disorder (RBD) is characterized by absence of physiological muscle atonia during REM sleep (REM sleep without atonia, RWA). Nigro-striatal dopaminergic impairment is a feature of Parkinson disease (PD) and can be identified in prodromal stages as well, such as idiopathic RBD (iRBD). Aims of this study are to explore the efficacy of an automatic RWA quantification in identifying RBD patients and the correlation between RWA and nigro-striatal dopaminergic function. METHODS: Forty-five iRBD, 46 PD with RBD, 24 PD without RBD patients and 11 healthy controls were enrolled in the Genoa Center (group A) and 25 patients with iRBD (group B) were enrolled in the Danish Center. Group A underwent brain [123I]FP-CIT-SPECT and group B underwent brain [18F]PE2I-PET as measures of nigro-striatal dopaminergic function. Chin muscle activity was recorded in all subjects and analyzed by applying a published automatic algorithm. Correlations between RWA and nigro-striatal dopaminergic function were explored. RESULTS: The automatic quantification of RWA significantly differentiated RBD from non-RBD subjects (AUC = 0.86), although with lower accuracy compared with conventional visual scoring (AUC = 0.99). No significant correlation was found between RWA and nigro-striatal dopaminergic function. CONCLUSION: The automatic quantification of RWA is a reliable tool to identify subjects with RBD and may be used as a first-line screening tool, but without correlations with nigro-striatal dopaminergic functioning.

Morbidity, Mortality, and Conversion to Neurodegenerative Diseases in Patients with REM Sleep Behavior Disorder and REM Sleep without Atonia.

INTRODUCTION: The underlying pathophysiology of idiopathic REM sleep behavior disorder (iRBD) is not fully understood, although the condition is currently recognized as an early-stage alpha-synuclein disorder. We evaluated the morbidity, mortality, and rate of conversion to a neurodegenerative disorder in a national group of patients. METHODS: All patients in Denmark with a diagnosis of RBD between 2006 and 2013 were identified from the Danish National Patient Registry (NPR) records. We excluded patients who had received a diagnosis of narcolepsy or any of the following neurodegenerative diseases before their diagnosis of RBD: Parkinson's disease, multiple system atrophy, progressive supranuclear paralysis, Alzheimer's, and Lewy body dementia. We used randomly chosen controls matched for age, gender, and municipality. RESULTS: In total, 246 iRBD patients and 982 matched controls were analyzed. The mortality rate was the same in both groups. The morbidity rate was significantly higher in the years before and after an RBD diagnosis, due to a wide variety of disorders in the following major disease groups: mental/behavioral disorders; endocrine/metabolic diseases; diseases of the eye; diseases of the nervous, digestive, musculoskeletal, circulatory, and respiratory systems; abnormal findings not classified elsewhere; external causes; and factors influencing health status. The conversion rate from RBD to a neurodegenerative disease was 13% over the 8 years after a diagnosis of RBD. CONCLUSIONS: A diagnosis of RBD is associated with increased morbidity several years before and after a diagnosis is made. Patients have a higher risk of converting to a neurodegenerative disorder than matched controls. Mortality rates are unchanged.

Nocturnal eye movements in patients with idiopathic rapid eye movement sleep behaviour disorder and patients with Parkinson's disease.

Patients with idiopathic rapid-eye-movement (REM) sleep behaviour disorder (iRBD) have a high risk of converting into manifest α-synucleinopathies. Eye movements (EMs) are controlled by neurons in the lower brainstem, midbrain and frontal areas, and may be affected by the early neurodegenerative process seen in iRBD. Studies have reported impairment of the oculomotor function in patients with Parkinson's disease (PD) during wakefulness, but no studies have investigated EMs during sleep. We aimed to evaluate nocturnal EMs in iRBD and PD, hypothesizing that these patients present abnormal EM distribution during sleep. Twenty-eight patients with periodic limb movement disorder (PLMD), 24 iRBD, 23 PD without RBD (PDwoRBD), 29 PD and RBD (PDwRBD) and 24 controls were included. A validated EM detector automatically identified EM periods between lights off and on. The EM coverage was computed as the percentage of time containing EMs during stable wake after lights off, N1, N2, N3 and REM sleep. Between-group comparisons revealed that PDwRBD had significantly less EM coverage during wake and significantly higher EM coverage during N2 compared to controls and PLMD patients. PDwoRBD showed significantly less EM coverage during wake compared to controls and higher EM coverage during N2 compared to controls and PLMD. Finally, iRBD showed less coverage of EM during wake compared to controls. The same trend was observed between iRBD and controls in N2 but was not significant. The different profiles of EM coverage in iRBD and PD with/without RBD may mirror different stages of central nervous system involvement across neurodegenerative disease progression.

Severe hypersomnia after unilateral infarction in the pulvinar nucleus- a case report.

BACKGROUND: Although a central role of the thalamus for sleep regulation is undisputed, the exact localization of the crucial structures within the thalamus remains controversial. CASE PRESENTATION: Here we report a 35 year old woman with no prior comorbidities who developed severe and persistent hypersomnia with long sleep time after a small right-sided MRI-verified thalamic stroke affecting the dorsal part of the pulvinar and the dorsolateral boarders of the dorsomedial nuclei. CONCLUSION: The observed symptoms suggest a crucial role of posterior thalamus but not the midline parts of the thalamus in sleep-wake control.

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

Rapid eye movement (REM) sleep behaviour disorder (RBD) is characterized by dream enactment and REM sleep without atonia. Atonia is evaluated on the basis of visual criteria, but there is a need for more objective, quantitative measurements. We aimed to define and optimize a method for establishing baseline and all other parameters in automatic quantifying submental motor activity during REM sleep. We analysed the electromyographic activity of the submental muscle in polysomnographs of 29 patients with idiopathic RBD (iRBD), 29 controls and 43 Parkinson's (PD) patients. Six adjustable parameters for motor activity were defined. Motor activity was detected and quantified automatically. The optimal parameters for separating RBD patients from controls were investigated by identifying the greatest area under the receiver operating curve from a total of 648 possible combinations. The optimal parameters were validated on PD patients. Automatic baseline estimation improved characterization of atonia during REM sleep, as it eliminates inter/intra-observer variability and can be standardized across diagnostic centres. We found an optimized method for quantifying motor activity during REM sleep. The method was stable and can be used to differentiate RBD from controls and to quantify motor activity during REM sleep in patients with neurodegeneration. No control had more than 30% of REM sleep with increased motor activity; patients with known RBD had as low activity as 4.5%. We developed and applied a sensitive, quantitative, automatic algorithm to evaluate loss of atonia in RBD patients.

Neurological sleep disorders.

This review summarises the current knowledge of neurological sleep disorders. These disorders are frequent and involve a number of serious diseases associated with complications or precede other serious brain diseases. There is an underdiagnosis of neurological sleep disorders in Denmark. Several of these disorders can be treated, and some of them express a marker for later disease which can be important to diagnose when effective preventive treatment is available.

Health, social, and economic consequences of rapid eye movement sleep behavior disorder: a controlled national study evaluating societal effects.

STUDY OBJECTIVES: Parkinson's disease (PD) causes significant socioeconomic burdens. One of the strongest predictors of PD is rapid eye movement (REM) sleep behavior disorder (RBD; when there is no known other cause of RBD, referred to as idiopathic RBD [iRBD]), but there is no information about its factual welfare burden. We estimated the direct and indirect total costs of iRBD in a national sample of patients, based on a national register-based cohort study with matched controls. METHODS: Using records from the Danish National Patient Registry, patient's diagnosis with RBD from 2006 to 2016 were identified. We excluded patients with a prior diagnosis of narcolepsy, PD, and other neurodegenerative diseases. We identified and compared randomly chosen controls matched for age, gender, geographic area, and civil status. Direct costs included frequencies of primary and secondary sector contacts and procedures, and medication. Indirect costs included the effect on labor supply. Social-transfer payments were included to illustrate the effect on national accounts. RESULTS: A total of 246 iRBD patients and 982 matched controls were registered. iRBD patients had significantly higher rates of health-related contacts and of medication use, and higher socioeconomic costs than controls. The total additional direct net healthcare costs after the diagnosis (general practitioner services, hospital services, and medication) and indirect costs (loss of labor market income) was €13,088 for patients compared with controls. Patients already exhibited a negative social- and health-related status several years before the first diagnosis. CONCLUSIONS: Diagnoses of iRBD have major socioeconomic consequences for patients, their partners, and society.

Sleep stability and transitions in patients with idiopathic REM sleep behavior disorder and patients with Parkinson's disease.

OBJECTIVE: Patients with idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) are at high risk of developing Parkinson's disease (PD). As wake/sleep-regulation is thought to involve neurons located in the brainstem and hypothalamic areas, we hypothesize that the neurodegeneration in iRBD/PD is likely to affect wake/sleep and REM/non-REM (NREM) sleep transitions. METHODS: We determined the frequency of wake/sleep and REM/NREM sleep transitions and the stability of wake (W), REM and NREM sleep as measured by polysomnography (PSG) in 27 patients with PD, 23 patients with iRBD, 25 patients with periodic leg movement disorder (PLMD) and 23 controls. Measures were computed based on manual scorings and data-driven labeled sleep staging. RESULTS: Patients with PD showed significantly lower REM stability than controls and patients with PLMD. Patients with iRBD had significantly lower REM stability compared with controls. Patients with PD and RBD showed significantly lower NREM stability and significantly more REM/NREM transitions than controls. CONCLUSIONS: We conclude that W, NREM and REM stability and transitions are progressively affected in iRBD and PD, probably reflecting the successive involvement of brain stem areas from early on in the disease. SIGNIFICANCE: Sleep stability and transitions determined by a data-driven approach could support the evaluation of iRBD and PD patients.

Sleep spindle alterations in patients with Parkinson's disease.

UNLABELLED: The aim of this study was to identify changes of sleep spindles (SS) in the EEG of patients with Parkinson's disease (PD). Five sleep experts manually identified SS at a central scalp location (C3-A2) in 15 PD and 15 age- and sex-matched control subjects. Each SS was given a confidence score, and by using a group consensus rule, 901 SS were identified and characterized by their (1) duration, (2) oscillation frequency, (3) maximum peak-to-peak amplitude, (4) percent-to-peak amplitude, and (5) density. Between-group comparisons were made for all SS characteristics computed, and significant changes for PD patients vs. control subjects were found for duration, oscillation frequency, maximum peak-to-peak amplitude and density. Specifically, SS density was lower, duration was longer, oscillation frequency slower and maximum peak-to-peak amplitude higher in patients vs. CONTROLS: We also computed inter-expert reliability in SS scoring and found a significantly lower reliability in scoring definite SS in patients when compared to controls. How neurodegeneration in PD could influence SS characteristics is discussed. We also note that the SS morphological changes observed here may affect automatic detection of SS in patients with PD or other neurodegenerative disorders (NDDs).

Morbidity in early Parkinson's disease and prior to diagnosis.

BACKGROUND: Nonmotor symptoms are probably present prior to, early on, and following, a diagnosis of Parkinson's disease. Nonmotor symptoms may hold important information about the progression of Parkinson's disease. OBJECTIVE: To evaluated the total early and prediagnostic morbidities in the 3 years before a hospital contact leading to a diagnosis of Parkinson's disease. METHODS: Retrospective morbidity data from Danish National Patient Registry records (1997-2007) of 10,490 adult patients with a secondary care diagnosis of Parkinson's disease were compared with 42,505 control cases. RESULTS: Parkinson's disease was associated with significantly higher morbidity rates associated with conditions in the following categories: mental and psychiatric, nervous system, gastrointestinal, musculoskeletal system and connective tissue, genitourinary, abnormal clinical and laboratory findings, injury, poisoning and certain other external causes, and other factors influencing health status and contact with health services. It was negatively associated with neoplasm, cardiovascular, and respiratory diseases. CONCLUSIONS: Patients with a diagnosis of Parkinson's disease present significant differences in morbidities early on, following, and prior to, their diagnosis, compared with healthy controls.

Increased all-cause mortality with psychotropic medication in Parkinson's disease and controls: a national register-based study.

AIM: Use of medication and polypharmacy is common as the population ages and its disease burden increases. We evaluated the association of antidepressants, benzodiazepines, antipsychotics and combinations of psychotropic drugs with all-cause mortality in patients with Parkinson's disease (PD) and a matched group without PD. METHOD: We identified 5861 PD patients and 31,395 control subjects matched by age, gender and marital status, and obtained register data on medication use and vital status between 1997 and 2007. RESULTS: All-cause mortality was significantly higher with the use of most groups of psychotropic medication in PD patients and controls. Hazard ratios were as follows for the medication types: selective serotonin reuptake inhibitors or serotonin-noradrenalin reuptake inhibitors, PD HR = 1.19, 95% CI = 1.04-1.36; Control HR = 1.77, 95% CI = 1.64-1.91; benzodiazepines, PD HR = 1.17, 95% CI = 0.99-1.38; Control HR = 1.39, 95% CI = 1.29-1.51; benzodiazepine-like drugs, PD HR = 1.33, 95% CI = 1.11-1.59; Control HR = 1.27, 95% CI = 1.18-1.37; first-generation antipsychotics, PD HR = 1.89, 95% CI = 1.42-2.53; Control HR = 2.12, 95% CI = 1.82-2.47; second-generation antipsychotics, PD HR = 1.46, 95% CI = 1.20-1.76; Control HR = 2.00, 95% CI 1.66-2.43; and combinations of these drugs compared with non-medicated PD patients and controls. Discontinuation of medication was associated with decreased mortality in both groups. CONCLUSIONS: The use of psychotropic medication in the elderly is associated with increased mortality, independent of concurrent neurodegeneration due to PD. Confounding by indication may partly explain the higher hazard ratios in medicated controls compared with medicated PD patients. Our findings indicate that neurodegeneration should not be a separate contraindication per se for the use of psychotropic drug in patients with PD, but its use should be based on careful clinical evaluation and follow-up.

Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease.

BACKGROUND: Manual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases. NEW METHOD: This study evaluates sleep using a topic modeling and unsupervised learning approach to identify sleep topics directly from electroencephalography (EEG) and electrooculography (EOG). PSG data from control subjects were used to develop an EOG and an EEG topic model. The models were applied to PSG data from 23 control subjects, 25 patients with periodic leg movements (PLMs), 31 patients with idiopathic REM sleep behavior disorder (iRBD) and 36 patients with Parkinson's disease (PD). The data were divided into training and validation datasets and features reflecting EEG and EOG characteristics based on topics were computed. The most discriminative feature subset for separating iRBD/PD and PLM/controls was estimated using a Lasso-regularized regression model. RESULTS: The features with highest discriminability were the number and stability of EEG topics linked to REM and N3, respectively. Validation of the model indicated a sensitivity of 91.4% and a specificity of 68.8% when classifying iRBD/PD patients. COMPARISON WITH EXISTING METHOD: The topics showed visual accordance with the manually scored sleep stages, and the features revealed sleep characteristics containing information indicative of neurodegeneration. CONCLUSIONS: This study suggests that the amount of N3 and the ability to maintain NREM and REM sleep have potential as early PD biomarkers. Data-driven analysis of sleep may contribute to the evaluation of neurodegenerative patients.

Automatic sleep classification using a data-driven topic model reveals latent sleep states.

BACKGROUND: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. NEW METHOD: To meet the criticism and reveal the latent sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group. The model was optimized using 50 subjects and validated on 76 subjects. RESULTS: The optimized sleep model used six topics, and the topic probabilities changed smoothly during transitions. According to the manual scorings, the model scored an overall subject-specific accuracy of 68.3 ± 7.44 (% µ ± σ) and group specific accuracies of 69.0 ± 4.62 (control), 70.1 ± 5.10 (PLM), 67.2 ± 8.30 (iRBD) and 67.7 ± 9.07 (PD). COMPARISON WITH EXISTING METHOD: Statistics of the latent sleep state content showed accordances to the sleep stages defined in the golden standard. However, this study indicates that sleep contains six diverse latent sleep states and that state transitions are continuous processes. CONCLUSIONS: The model is generally applicable and may contribute to the research in neurodegenerative diseases and sleep disorders.

Classification of iRBD and Parkinson's disease patients based on eye movements during sleep.

Patients suffering from the sleep disorder idiopathic rapid-eye-movement sleep behavior disorder (iRBD) have been observed to be in high risk of developing Parkinson's disease (PD). This makes it essential to analyze them in the search for PD biomarkers. This study aims at classifying patients suffering from iRBD or PD based on features reflecting eye movements (EMs) during sleep. A Latent Dirichlet Allocation (LDA) topic model was developed based on features extracted from two electrooculographic (EOG) signals measured as parts in full night polysomnographic (PSG) recordings from ten control subjects. The trained model was tested on ten other control subjects, ten iRBD patients and ten PD patients, obtaining a EM topic mixture diagram for each subject in the test dataset. Three features were extracted from the topic mixture diagrams, reflecting "certainty", "fragmentation" and "stability" in the timely distribution of the EM topics. Using a Naive Bayes (NB) classifier and the features "certainty" and "stability" yielded the best classification result and the subjects were classified with a sensitivity of 95 %, a specificity of 80% and an accuracy of 90 %. This study demonstrates in a data-driven approach, that iRBD and PD patients may exhibit abnorm form and/or timely distribution of EMs during sleep.

Classification of iRBD and Parkinson's patients using a general data-driven sleep staging model built on EEG.

Sleep analysis is an important diagnostic tool for sleep disorders. However, the current manual sleep scoring is time-consuming as it is a crude discretization in time and stages. This study changes Esbroeck and Westover's [1] latent sleep staging model into a global model. The proposed data-driven method trained a topic mixture model on 10 control subjects and was applied on 10 other control subjects, 10 iRBD patients and 10 Parkinson's patients. In that way 30 topic mixture diagrams were obtained from which features reflecting distinct sleep architectures between control subjects and patients were extracted. Two features calculated on basis of two latent sleep states classified subjects as "control" or "patient" by a simple clustering algorithm. The mean sleep staging accuracy compared to classical AASM scoring was 72.4% for control subjects and a clustering of the derived features resulted in a sensitivity of 95% and a specificity of 80 %. This study demonstrates that frequency analysis of sleep EEG can be used for data-driven global sleep classification and that topic features separates iRBD and Parkinson's patients from control subjects.

Separation of Parkinson's patients in early and mature stages from control subjects using one EOG channel.

In this study, polysomnographic left side EOG signals from ten control subjects, ten iRBD patients and ten Parkinson's patients were decomposed in time and frequency using wavelet transformation. A total of 28 features were computed as the means and standard deviations in energy measures from different reconstructed detail subbands across all sleep epochs during a whole night of sleep. A subset of features was chosen based on a cross validated Shrunken Centroids Regularized Discriminant Analysis, where the controls were treated as one group and the patients as another. Classification of the subjects was done by a leave-one-out validation approach using same method, and reached a sensitivity of 95%, a specificity of 70% and an accuracy of 86.7%. It was found that in the optimal subset of features, two hold lower frequencies reflecting the rapid eye movements and two hold higher frequencies reflecting EMG activity. This study demonstrates that both analysis of eye movements during sleep as well as EMG activity measured at the EOG channel hold potential of being biomarkers for Parkinson's disease.