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Purpose: Little is known about cognitive complaints (self-reported problems in cognitive functioning) in patients with Obstructive Sleep Apnea (OSA). We compared the prevalence and severity of cognitive complaints in patients with untreated OSA to patients with neurological and respiratory diseases. We also studied risk factors for cognitive complaints across these diseases, including OSA. Methods: We used a convenience sample to compare untreated OSA patients (N = 86) to patients with stroke (N = 166), primary brain tumor (N = 197) and chronic obstructive pulmonary disease (COPD, N = 204) on cognitive complaints (Cognitive Failure Questionnaire, CFQ), anxiety and depression (Hospital Anxiety and Depression Scale, HADS) and cognitive impairments using neuropsychological tests. We combined all patient groups (OSA, stroke, brain tumor and COPD) and studied potential risk factors (demographic variables, anxiety, depression and cognitive impairments) for cognitive complaints across all patient groups using regression analysis. Results: The prevalence of cognitive complaints was higher in OSA patients and complaints of forgetfulness and distractibility were more severe compared to stroke and primary brain tumor patients, but similar to or lower than COPD patients. Regression analysis for the combined sample of all patient groups showed that cognitive complaints were most strongly associated with symptoms of anxiety and depression. Conclusion: A high rate of OSA reported clinically significant cognitive complaints, comparable to other respiratory and neurological patients. Symptoms of anxiety and depression are important risk factors for cognitive complaints in patients with various neurological and respiratory diseases. Future studies should examine the relation between anxiety, depression and cognitive complaints in patients with OSA. [ABSTRACT FROM AUTHOR]

Purpose: Comorbid insomnia often occurs in patients with obstructive sleep apnea (OSA), referred to as COMISA. Cortical arousals manifest as a common feature in both OSA and insomnia, often accompanied by elevated heart rate (HR). Our objective was to evaluate the heart rate response to nocturnal cortical arousals in patients with COMISA and patients with OSA alone. Methods: We analyzed data from patients with COMISA and from patients with OSA matched for apnea-hypopnea index. Sleep staging and analysis of respiratory events and cortical arousals were performed using the Philips Somnolyzer automatic scoring system. Beat-by-beat HR was analyzed from the onset of the cortical arousal to 30 heartbeats afterwards. HR responses were divided into peak and recovery phases. Cortical arousals were separately evaluated according to subtype (related to respiratory events and spontaneous) and duration (3–6 s, 6–10 s, 10–15 s). Results: A total of 72 patients with COMISA and 72 patients with OSA were included in this study. There were no overall group differences in the number of cortical arousals with and without autonomic activation. No significant differences were found for spontaneous cortical arousals. The OSA group had more cortical arousals related to respiratory events (21.0 [14.8–30.0] vs 16.0 [9.0–27.0], p = 0.016). However, the COMISA group had longer cortical arousals (7.2 [6.4–7.8] vs 6.7 [6.2–7.7] s, p = 0.024) and the HR recovery phase was prolonged (52.5 [30.8–82.5] vs 40.0 [21.8–55.5] beats/min, p = 0.017). Both the peak and the recovery phase for longer cortical arousals with a duration of 10–15 s were significantly higher in patients with COMISA compared to patients with OSA (47.0 [27.0–97.5] vs 34.0 [21.0–71.0] beats/min, p = 0.032 and 87.0 [47.0–132.0] vs 71.0 [43.0–103.5] beats/min, p = 0.049, respectively). Conclusions: The HR recovery phase after cortical arousals related to respiratory events is prolonged in patients with COMISA compared to patients with OSA alone. This response could be indicative of the insomnia component in COMISA. [ABSTRACT FROM AUTHOR]

This study describes a computationally efficient algorithm for 4-class sleep staging based on cardiac activity and body movements. Using an accelerometer to calculate gross body movements and a reflective photoplethysmographic (PPG) sensor to determine interbeat intervals and a corresponding instantaneous heart rate signal, a neural network was trained to classify between wake, combined N1 and N2, N3 and REM sleep in epochs of 30 s. The classifier was validated on a hold-out set by comparing the output against manually scored sleep stages based on polysomnography (PSG). In addition, the execution time was compared with that of a previously developed heart rate variability (HRV) feature-based sleep staging algorithm. With a median epoch-per-epoch κ of 0.638 and accuracy of 77.8% the algorithm achieved an equivalent performance when compared to the previously developed HRV-based approach, but with a 50-times faster execution time. This shows how a neural network, without leveraging any a priori knowledge of the domain, can automatically "discover" a suitable mapping between cardiac activity and body movements, and sleep stages, even in patients with different sleep pathologies. In addition to the high performance, the reduced complexity of the algorithm makes practical implementation feasible, opening up new avenues in sleep diagnostics. [ABSTRACT FROM AUTHOR]

Rationale: The mechanisms underlying impaired sleep quality in insomnia are not fully known, but an important role for sleep fragmentation has been proposed. Objectives: The aim of this study is to explore potential mechanisms of sleep fragmentation influencing alterations of perceived sleep quality. Methods: We analyzed polysomnography (PSG) recordings from a double-blind crossover study with zopiclone 7.5 mg and placebo, in elderly participants with insomnia complaints and age-matched healthy controls. We compared survival dynamics of sleep and wake across group and treatment. Subsequently, we used a previously proposed model to estimate the amount of sleep onset latency (SOL) misperception from PSG-defined sleep fragmentation. Self-reported and model-estimated amount of SOL misperception were compared across group and treatment, as well as model prediction errors. Results: In the zopiclone night, the average segment length of NREM sleep was increased (group F = 1.16, p = 0.32; treatment F = 8.89, p< 0.01; group x treatment F = 0.44, p = 0.65), while the segment length of wake was decreased (group F = 1.48, p = 0.23; treatment F = 11.49, p< 0.01; group x treatment F = 0.36, p = 0.70). The self-reported and model-estimated amount of SOL misperception were lower during the zopiclone night (self-reported group F = 6.08, p< 0.01, treatment F = 10.8, p< 0.01, group x treatment F = 2.49, p = 0.09; model-estimated F = 1.70, p = 0.19, treatment F = 16.1, p< 0.001, group x treatment F = 0.60, p = 0.55). The prediction error was not altered (group F = 1.62, p = 0.20; treatment F = 0.20, p = 0.65; group x treatment F = 1.01, p = 0.37). Conclusions: Impaired subjective sleep quality is associated with decreased NREM stability, together with increased stability of wake. Furthermore, we conclude that zopiclone-induced changes in SOL misperception can be largely attributed to predictable changes of sleep architecture. [ABSTRACT FROM AUTHOR]

A large part of the worldwide population suffers from obstructive sleep apnea (OSA), a disorder impairing the restorative function of sleep and constituting a risk factor for several cardiovascular pathologies. The standard diagnostic metric to define OSA is the apnea–hypopnea index (AHI), typically obtained by manually annotating polysomnographic recordings. However, this clinical procedure cannot be employed for screening and for long-term monitoring of OSA due to its obtrusiveness and cost. Here, we propose an automatic unobtrusive AHI estimation method fully based on wrist-worn reflective photoplethysmography (rPPG), employing a deep learning model exploiting cardiorespiratory and sleep information extracted from the rPPG signal trained with 250 recordings. We tested our method with an independent set of 188 heterogeneously disordered clinical recordings and we found it estimates the AHI with a good agreement to the gold standard polysomnography reference (correlation = 0.61, estimation error = 3±10 events/h). The estimated AHI was shown to reliably assess OSA severity (weighted Cohen's kappa = 0.51) and screen for OSA (ROC–AUC = 0.84/0.86/0.85 for mild/moderate/severe OSA). These findings suggest that wrist-worn rPPG measurements that can be implemented in wearables such as smartwatches, have the potential to complement standard OSA diagnostic techniques by allowing unobtrusive sleep and respiratory monitoring. [ABSTRACT FROM AUTHOR]

We assessed subjective sleep measures in a cohort of 146 patients with chronic insomnia and hypothesized that these measures may differ depending on the presence as well as the type of comorbid psychiatric disorders. All patients were consecutively referred to a third line sleep medicine center and underwent an extensive clinical intake by both a sleep physician and a psychologist. Psychodiagnostics were performed according to the DSM-IV criteria. Subjective sleep was assessed by sleep diaries and the insomnia severity index. Insomniacs with anxiety disorders showed a relatively higher total sleep time and higher sleep efficiency than the two other groups. Furthermore, the number of awakenings was higher and time in bed after the final awakening was lower in patients with comorbid anxiety disorders when compared to the two other groups. Although subjective total bedtimes where comparable, patients with comorbid anxiety disorders on average reported a striking 2 h more sleep per night and around a 20% higher sleep efficiency than patients with comorbid mood disorders. Patients with comorbid mood disorders showed a trend towards a higher Insomnia Severity Index-score when compared to primary insomniacs. Insomniacs with comorbid anxiety disorders present with a markedly higher sleep efficiency and total sleep time. On the other hand, these patients describe more fragmented sleep. Our findings may have practical implications for more tailored cognitive behavioral treatment in insomniacs with and without different types of psychiatric comorbidity. [ABSTRACT FROM AUTHOR]

Sleep disturbances are among the most frequent and incapacitating non-motor symptoms of Parkinson's disease (PD), and are increasingly recognized as an important determinant of impaired quality of life. Here we review several recent developments regarding the recognition and diagnosis of sleep disorders in PD. In addition, we provide a practical and easily applicable approach to the diagnostic process as a basis for tailored therapeutic interventions. This includes a stepwise scheme that guides the clinical interview and subsequent ancillary investigations. In this scheme, the various possible sleep disorders are arranged not in order of prevalence, but in a 'differential diagnostic' order. We also provide recommendations for the use of sleep registrations such as polysomnography. Furthermore, we point out when a sleep specialist could be consulted to provide additional diagnostic and therapeutic input. This structured approach facilitates early detection of sleep disturbances in PD, so treatment can be initiated promptly. [ABSTRACT FROM AUTHOR]

Patients with Parkinson's disease (PD) are likely to become physically inactive, because of their motor, mental, and emotional symptoms. However, specific studies on physical activity in PD are scarce, and results are conflicting. Here, we quantified daily physical activities in a large cohort of PD patients and another large cohort of matched controls. Moreover, we investigated the influence of disease-related factors on daily physical activities in PD patients. Daily physical activity data of PD patients ( n = 699) were collected in the ParkinsonNet trial and of controls ( n = 1,959) in the Longitudinal Aging Study Amsterdam (LASA); data were determined using the LAPAQ, a validated physical activity questionnaire. In addition, variables that may affect daily physical activities in PD were recorded, including motor symptoms, depression, disability in daily life, and comorbidity. Patients were physically less active; a reduction of 29% compared to controls (95% CI, 10-44%). Multivariate regression analyses demonstrated that greater disease severity, gait impairment, and greater disability in daily living were associated with less daily physical activity in PD ( R = 24%). In this large study, we show that PD patients are about one-third less active compared to controls. While disease severity, gait, and disability in daily living predicted part of the inactivity, a portion of the variance remained unexplained, suggesting that additional determinants may also affect daily physical activities in PD. Because physical inactivity has many adverse consequences, work is needed to develop safe and enjoyable exercise programs for patients with PD. [ABSTRACT FROM AUTHOR]

Narcolepsy is a rare sleep disorder with the strongest human leukocyte antigen (HLA) association ever reported. Since the associated HLA-DRB1*1501-DQB1*0602 haplotype is common in the general population (15–25%), it has been suggested that it is almost necessary but not sufficient for developing narcolepsy. To further define the genetic basis of narcolepsy risk, we performed a genome-wide association study (GWAS) in 562 European individuals with narcolepsy (cases) and 702 ethnically matched controls, with independent replication in 370 cases and 495 controls, all heterozygous for DRB1*1501-DQB1*0602. We found association with a protective variant near HLA-DQA2 (rs2858884; P < 3 × 10−8). Further analysis revealed that rs2858884 is strongly linked to DRB1*03-DQB1*02 (P < 4 × 10−43) and DRB1*1301-DQB1*0603 (P < 3 × 10−7). Cases almost never carried a trans DRB1*1301-DQB1*0603 haplotype (odds ratio = 0.02; P < 6 × 10−14). This unexpected protective HLA haplotype suggests a virtually causal involvement of the HLA region in narcolepsy susceptibility. [ABSTRACT FROM AUTHOR]

We explored the role of hypocretins in human narcolepsy through histopathology of six narcolepsy brains and mutation screening of Hcrt, Hcrtr1 and Hcrtr2 in 74 patients of various human leukocyte antigen and family history status. One Hcrt mutation, impairing peptide trafficking and processing, was found in a single case with early onset narcolepsy. In situ hybridization of the perifornical area and peptide radioimmunoassays indicated global loss of hypocretins, without gliosis or signs of inflammation in all human cases examined. Although hypocretin loci do not contribute significantly to genetic predisposition, most cases of human narcolepsy are associated with a deficient hypocretin system. [ABSTRACT FROM AUTHOR]

In the first column of table 2, it says: 'REM Weibull scale, 1/ʎ'. NREM Weibull scale 1/ʎ, and Wake Weibull scale 1/ʎ. [ABSTRACT FROM AUTHOR]

A letter to the editor about high frequency repetitive transcranial magnetic stimulation over the motor cortex as diagnostic value for narcolepsy is presented.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper. [ABSTRACT FROM AUTHOR]

Narcolepsy type 1 is a chronic sleep disorder caused by a deficiency of the orexin (hypocretin) neuropeptides. In addition to sleep regulation, orexin is important for motivated control processes. Weight gain and obesity are common in narcolepsy. However, the neurocognitive processes associated with food-related control and overeating in narcolepsy are unknown. We explored the neural correlates of general and food-related attentional control in narcolepsy-type-1 patients (n = 23) and healthy BMI-matched controls (n = 20). We measured attentional bias to food words with a Food Stroop task and general executive control with a Classic Stroop task during fMRI. Moreover, using multiple linear regression, we assessed the relative contribution of neural responses during Food Stroop and Classic Stroop to spontaneous snack intake. Relative to healthy controls, narcolepsy patients showed enhanced ventral medial prefrontal cortex responses and connectivity with motor cortex during the Food Stroop task, but attenuated dorsal medial prefrontal cortex responses during the Classic Stroop task. Moreover, the former activity but not the latter, was a significant predictor of spontaneous snack intake. These findings demonstrate that narcolepsy, characterized by orexin deficiency, is associated with decreased dorsal medial prefrontal cortex responses during general executive control and enhanced ventral medial prefrontal cortex responses during food-driven attention. [ABSTRACT FROM AUTHOR]

A correction to the article "Genome-wide Association Study Identifies New HLA Class II Haplotypes Strongly Protective Against Narcolepsy" that was published in the October 27, 2010 issue is presented.

Circadian rhythms are physiological and behavioural cycles generated by an endogenous biological clock, the suprachiasmatic nucleus. The circadian system influences the majority of physiological processes, including sleep-wake homeostasis. Impaired sleep and alertness are common symptoms of neurodegenerative disorders, and circadian dysfunction might exacerbate the disease process. The pathophysiology of sleep-wake disturbances in these disorders remains largely unknown, and is presumably multifactorial. Circadian rhythm dysfunction is often observed in patients with Alzheimer disease, in whom it has a major impact on quality of life and represents one of the most important factors leading to institutionalization of patients. Similarly, sleep and circadian problems represent common nonmotor features of Parkinson disease and Huntington disease. Clinical studies and experiments in animal models of neurodegenerative disorders have revealed the progressive nature of circadian dysfunction throughout the course of neurodegeneration, and suggest strategies for the restoration of circadian rhythmicity involving behavioural and pharmacological interventions that target the sleep-wake cycle. In this Review, we discuss the role of the circadian system in the regulation of the sleep-wake cycle, and outline the implications of disrupted circadian timekeeping in neurodegenerative diseases. [ABSTRACT FROM AUTHOR]