Humans ; Sleep, REM ; Memory ; Sleep ; REM Sleep Behavior Disorder

Objective We aimed to investigate whether antagonism of the cannabinoid CB₁ receptor (CB₁R) could affect novel object recognition (NOR) memory in chronically rapid eye movement sleep-deprived (RSD) rats.Methods The animals were examined for recognition memory following a 7-day chronic partial RSD paradigm using the multiple platform technique. The CB₁R antagonist rimonabant (1 or 3 mg/kg, i.p.) was administered either at one hour prior to the sample phase for acquisition, or immediately after the sample phase for consolidation, or at one hour before the test phase for retrieval of NOR memory. For the reconsolidation task, rimonabant was administered immediately after the second sample phase.Results The RSD episode impaired acquisition, consolidation, and retrieval, but it did not affect the reconsolidation of NOR memory. Rimonabant administration did not affect acquisition, consolidation, and reconsolidation; however, it attenuated impairment of the retrieval of NOR memory induced by chronic RSD.Conclusions These findings, along with our previous report, would seem to suggest that RSD may affect different phases of recognition memory based on its duration. Importantly, it seems that the CB₁R may, at least in part, be involved in the adverse effects of chronic RSD on the retrieval, but not in the acquisition, consolidation, and reconsolidation, of NOR memory.
Rats ; Animals ; Rimonabant/pharmacology* ; Memory ; Sleep, REM ; Receptors, Cannabinoid ; Cannabinoids/pharmacology*

Humans ; Cataplexy/drug therapy* ; Sleep, REM ; Dopamine ; Narcolepsy/drug therapy*

Aiming at the limitations of clinical diagnosis of Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor's detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.
Electroencephalography ; Humans ; Intelligence ; Parkinson Disease/diagnosis* ; Polysomnography ; REM Sleep Behavior Disorder/diagnosis*

BACKGROUND: Sleep disorders are common but under-researched symptoms in patients with multiple system atrophy (MSA). We investigated the frequency and factors associated with sleep-related symptoms in patients with MSA and the impact of sleep disturbances on disease severity. METHODS: This cross-sectional study involved 165 patients with MSA. Three sleep-related symptoms, namely Parkinson's disease (PD)-related sleep problems (PD-SP), excessive daytime sleepiness (EDS), and rapid eye movement sleep behavior disorder (RBD), were evaluated using the PD Sleep Scale-2 (PDSS-2), Epworth Sleepiness Scale (ESS), and RBD Screening Questionnaire (RBDSQ), respectively. Disease severity was evaluated using the Unified MSA Rating Scale (UMSARS). RESULTS: The frequency of PD-SP (PDSS-2 score of ≥18), EDS (ESS score of ≥10), and RBD (RBDSQ score of ≥5) in patients with MSA was 18.8%, 27.3%, and 49.7%, respectively. The frequency of coexistence of all three sleep-related symptoms was 7.3%. Compared with the cerebellar subtype of MSA (MSA-C), the parkinsonism subtype of MSA (MSA-P) was associated with a higher frequency of PD-SP and EDS, but not of RBD. Binary logistic regression revealed that the MSA-P subtype, a higher total UMSARS score, and anxiety were associated with PD-SP; that male sex, a higher total UMSARS score, the MSA-P subtype, and fatigue were associated with EDS; and that male sex, a higher total UMSARS score, and autonomic onset were associated with RBD in patients with MSA. Stepwise linear regression showed that the number of sleep-related symptoms (PD-SP, EDS, and RBD), disease duration, depression, fatigue, and total Montreal Cognitive Assessment score were predictors of disease severity in patients with MSA. CONCLUSIONS Sleep-related disorders were associated with both MSA subtypes and the severity of disease in patients with MSA, indicating that sleep disorders may reflect the distribution and degree of dopaminergic/non-dopaminergic neuron degeneration in MSA.
Cross-Sectional Studies ; Humans ; Male ; Multiple System Atrophy ; REM Sleep Behavior Disorder ; Severity of Illness Index ; Sleep

BACKGROUND: Many Parkinson disease (PD) patients complain about chronic fatigue and sleep disturbances during the night. The objective of this study is to determine the relationship between fatigue and sleep disturbances by using polysomnography (PSG) in PD patients. METHODS: Two hundred and thirty-two PD patients (152 with mild fatigue and 80 with severe fatigue) were recruited in this study. Demographic information and clinical symptoms were collected. Fatigue severity scale (FSS) was applied to evaluate the severity of fatigue, and PSG was conducted in all PD patients. FSS ≥4 was defined as severe fatigue, and FSS <4 was defined as mild fatigue. Multivariate logistic regression and linear regression models were used to investigate the associations between fatigue and sleep disturbances. RESULTS: Patients with severe fatigue tended to have a longer duration of disease, higher Unified Parkinson Disease Rating Scale score, more advanced Hoehn and Yahr stage, higher daily levodopa equivalent dose, worse depression, anxiety, and higher daytime sleepiness score. In addition, they had lower percentage of rapid eye movement (REM) sleep (P = 0.009) and were more likely to have REM sleep behavior disorder (RBD) (P = 0.018). Multivariate logistic regression analyses found that the presence of RBD and proportion of REM sleep were the independent predictors for fatigue. After the adjustment of age, sex, duration, body mass index, severity of disease, scores of Hamilton Rating Scale for Depression, Hamilton Anxiety Rating Scale, and other sleep disorders, proportion of REM sleep and degree of REM sleep without atonia in patients with PD were still associated with FSS score. CONCLUSION Considering the association between fatigue, RBD, and the altered sleep architecture, fatigue is a special subtype in PD and more studies should be focused on this debilitating symptom.
Humans ; Parkinson Disease/complications* ; Polysomnography ; REM Sleep Behavior Disorder ; Sleep ; Sleep Wake Disorders/etiology*

Recent studies have developed simple techniques for monitoring and assessing sleep. However, several issues remain to be solved for example high-cost sensor and algorithm as a home-use device. In this study, we aimed to develop an inexpensive and simple sleep monitoring system using a camera and video processing. Polysomnography (PSG) recordings were performed in six subjects for four consecutive nights. Subjects' body movements were simultaneously recorded by the web camera. Body movement was extracted by video processing from the video data and fi ve parameters were calculated for machine learning. Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these fi ve parameters to a support vector machine. The overall estimation accuracy was 70.3 ± 11.3% with the highest accuracy for DEEP (82.8 ± 4.7%) and the lowest for LIGHT (53.0 ± 4.0%) compared with correct sleep stages manually scored on PSG data by a sleep technician. Estimation accuracy for REM sleep was 68.0 ± 6.8%. The kappa was 0.19 ± 0.04 for all subjects. The present non-contact sleep monitoring system showed suffi cient accuracy in sleep stage estimation with REM sleep detection being accomplished. Low-cost computing power of this system can be advantageous for mobile application and modularization into home-device.
Machine Learning ; Methods ; Mobile Applications ; Polysomnography ; Sleep Stages ; Sleep, REM ; Support Vector Machine

A close relationship has emerged between obstructive sleep apnea (OSA) and cardiac arrhythmia. However, transient sinus arrest or atrioventricular (AV) conduction disturbance during rapid eye movement (REM) sleep was rarely reported. This sleep stage specific arrhythmia has been referred to as REM sleep-related bradyarrhythmia syndrome. The differential diagnosis between OSA-related arrhythmia and REM sleep-related bradyarrhythmia syndrome is important in determining the treatment strategy for the underlying disease and its complication, especially in patient with a history of OSA. Here, we report a case with both REM sleep-related AV block and severe OSA, whose REM sleep-related AV block was not improved with continuous positive airway pressure treatment.
Arrhythmias, Cardiac ; Atrioventricular Block ; Bradycardia ; Continuous Positive Airway Pressure ; Diagnosis, Differential ; Humans ; Sleep Apnea, Obstructive ; Sleep Stages ; Sleep, REM

OBJECTIVES: Night shift workers suffer from sleep and daytime disturbances due to circadian misalignment. To investigate the role of environmental light in daytime sleep following 12 h-night shift work. METHODS: We enrolled 12 h-shift female nurses working at one university-affiliated hospital (n=10, mean age 26.6 years, shift work duration 3.8 years). This is a cross-over study to compare sleep between under light exposure (30 lux) and in the dark (<5 lux) following 12 h-night duty. Two sessions of experiments were underwent and the interval between sessions was about a month. Psychomotor vigilance test (PVT) had performed on awakening from sleep at each session and sleep-wake pattern had been monitored by actigraphy throughout the study period. Daytime sleep was also compared with night sleep of age-and gender matched daytime workers (n=10). RESULTS: Sleep parameters and PVT scores were not different between two light conditions. Activities during sleep seemed to be more abundant under 30 lux condition than in the dark, which was not significant. Compared to night sleep, daytime sleep of shift workers was different in terms of rapid eye movement (REM) sleep. Three shift workers showed sleep onset REM sleep and first REM sleep period was the longest during daytime sleep. CONCLUSIONS: Unexpectedly, daytime sleep of 12 h night shift workers was well-maintained regardless of light exposure. Early occurrence of REM sleep and shorter sleep latency during daytime sleep suggest that shift workers meet with misalignment of circadian rhythm as well as increased homeostatic sleep pressure drive.
Actigraphy ; Circadian Rhythm ; Cross-Over Studies ; Female ; Humans ; Polysomnography ; Sleep, REM

OBJECTIVES: To develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratorysounds recorded during polysomnography during all sleep stages between sleep onset and offset. METHODS: Patients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audiorecordings were performed with an air-conduction microphone during polysomnography. Analyses included allsleep stages (i.e., N1, N2, N3, rapid eye movement, and waking). After noise reduction preprocessing, data were segmentedinto 5-s windows and sound features were extracted. Prediction models were established and validated with10-fold cross-validation by using simple logistic regression. Binary classifications were separately conducted for threedifferent threshold criteria at apnea hypopnea index (AHI) of 5, 15, or 30. Prediction model characteristics, includingaccuracy, sensitivity, specificity, positive predictive value (precision), negative predictive value, and area under thecurve (AUC) of the receiver operating characteristic were computed. RESULTS: A total of 116 subjects were included; their mean age, body mass index, and AHI were 50.4 years, 25.5 kg/m2, and23.0/hr, respectively. A total of 508 sound features were extracted from respiratory sounds recorded throughoutsleep. Accuracies of binary classifiers at AHIs of 5, 15, and 30 were 82.7%, 84.4%, and 85.3%, respectively. Predictionperformances for the classifiers at AHIs of 5, 15, and 30 were AUC, 0.83, 0.901, and 0.91; sensitivity, 87.5%,81.6%, and 60%; and specificity, 67.8%, 87.5%, and 94.1%. Respective precision values of the classifiers were89.5%, 87.5%, and 78.2% for AHIs of 5, 15, and 30. CONCLUSION: This study showed that our binary classifier predicted patients with AHI of ≥15 with sensitivity and specificityof >80% by using respiratory sounds during sleep. Since our prediction model included all sleep stage data, algorithmsbased on respiratory sounds may have a high value for prescreening OSA with mobile devices.
Apnea ; Area Under Curve ; Body Mass Index ; Classification ; Humans ; Logistic Models ; Machine Learning ; Noise ; Polysomnography ; Respiratory Sounds ; ROC Curve ; Sensitivity and Specificity ; Sleep Apnea, Obstructive ; Sleep Stages ; Sleep, REM