CAP variables and arousals as sleep electroencephalogram markers for primary insomnia

Chronic insomnia, REM sleep instability and emotional dysregulation: A pathway to anxiety and depression?

The world-wide prevalence of insomnia disorder reaches up to 10% of the adult population. Women are more often afflicted than men, and insomnia disorder is a risk factor for somatic and mental illness, especially depression and anxiety disorders. Persistent hyperarousals at the cognitive, emotional, cortical and/or physiological levels are central to most theories regarding the pathophysiology of insomnia. Of the defining features of insomnia disorder, the discrepancy between minor objective polysomnographic alterations of sleep continuity and substantive subjective impairment in insomnia disorder remains enigmatic. Microstructural alterations, especially in rapid eye movement sleep ("rapid eye movement sleep instability"), might explain this mismatch between subjective and objective findings. As rapid eye movement sleep represents the most highly aroused brain state during sleep, it might be particularly prone to fragmentation in individuals with persistent hyperarousal. In consequence, mentation during rapid eye movement sleep may be toned more as conscious-like wake experience, reflecting pre-sleep concerns. It is suggested that this instability of rapid eye movement sleep is involved in the mismatch between subjective and objective measures of sleep in insomnia disorder. Furthermore, as rapid eye movement sleep has been linked in previous works to emotional processing, rapid eye movement sleep instability could play a central role in the close association between insomnia and depressive and anxiety disorders.

Hyperarousal in insomnia disorder: Current evidence and potential mechanisms

Insomnia disorder is among the most frequent mental disorders, making research on its aetiology and pathophysiology particularly important. A unifying element of many aetiological and pathophysiological models is that they support or even centre on the role of some form of hyperarousal. In this theoretical review, we aim to summarise the current evidence on hyperarousal in insomnia. Hyperarousal is discussed as a state of relatively increased arousal in physiological, cortical and cognitive-emotional domains. Regarding physiological hyperarousal, there is no conclusive evidence for the involvement of autonomous variables such as heart rate and heart rate variability, whereas recent evidence points to a pathophysiological role of neuroendocrine variables. In addition, current literature supports a central involvement of cortical arousal, that is, high-frequency electroencephalographic activity. An increasingly important focus in the literature is on the role of other microstructural sleep parameters, especially the existence of microarousals during sleep. Beyond that, a broad range of evidence exists supporting the role of cognitive-emotional hyperarousal in the form of insomnia-related thought and worries, and their concomitant emotional symptoms. Besides being a state marker of insomnia, hyperarousal is considered crucial for the predisposition to insomnia and for the development of comorbid mental disorders. Thus, beyond presenting evidence from cross-sectional studies on markers of hyperarousal in insomnia, hypotheses about the mechanisms of hyperarousal are presented. Nevertheless, longitudinal studies are needed to further elucidate the mechanism of hyperarousal throughout the course of the disorder, and future studies should also focus on similarities and differences in hyperarousal across different diagnostic entities.

Sleep Micro-Macro-structures in Psychophysiological Insomnia. PSG Study

Study Objectives

To address sleep micro-macro-structures in psychophysiological insomnia (PPI) as denoted by cyclic alternating pattern (CAP), Sleep spindles, and hyperarousal as microstructures and sleep characteristics such as sleep stages' variables, and heart rate as macrostructures.

Methods

Two statistical populations, with 20 participants in each, are addressed: good sleepers (GS) and patients with psychophysiological insomnia (PPI). The sleep polysomnography (PSG) for one night was performed and sleep macro-micro-structures extraction was implemented for each participant. Cyclic alternating patterns were scored manually and other structures were monitored by the original PSG's device software. Analytical methods are used to dissect the results.

Result

The findings imply: (a) psychophysiological insomnia is characterized by CAP differences from good sleepers which are associated with hyperarousal; (b) Regarding microstructure, more microarousals in sleep stages caused more number of wake index. (c) The ratio of sleep stages, sleep latency and heart rate as sleep macrostructure are significantly changed. (d) There is no significant difference between PPI and GS groups on spindles length in our research.

Conclusion

Regarding all sleep disorders and especially PPI, CAP variables, EEG arousals, and sleep spindles as microstructures and Total Sleep Time, Sleep Latency, number of waking, REM duration, and Heart Rate as macrostructures were found to be critical for the diagnosis of psychophysiological insomnia The analysis contributes to understanding better approaches in the quantitative specification of psychophysiological insomnia compare to good sleepers.

Cyclic alternating pattern in non-rapid eye movement sleep in patients with vestibular migraine

OBJECTIVE

This study aimed to analyze the microstructural features of sleep in patients with vestibular migraine and migraine, and to hypothesize the pathophysiological mechanism between vestibular migraine and sleep disorders.

METHODS

From March 2021 to June 2022, 35 vestibular migraine patients, 35 migraine patients, and 30 controls were collected from the Vertigo Center & Sleep Center of the Second Affiliated Hospital of Zhengzhou University. The anxiety and depression status, sleep quality, and cyclic alternating pattern (CAP) of the three groups were analyzed and compared using the Pittsburgh Sleep Quality Index, Hamilton Anxiety and Depression Scale, and polysomnography (PSG).

RESULTS

The vestibular migraine group had a higher CAP time (mean 173.64 vs. 122.11, P < 0.001), CAP index (mean 54.25 vs. 37.50, P < 0.001), CAP rate (mean 48.41 vs. 32.31, P < 0.001), CAP sequences (mean 42.60 vs. 29.83, P < 0.001), A3% (mean 45.58 vs. 17.50, P < 0.001) and A2%+A3% (mean 68.87 vs. 38.83, P < 0.001) compared to the control group, with a lower A1 index (mean 16.68 vs. 23.87, P < 0.001) and A1% (mean 31.13% vs. 61.17, P < 0.001).

CONCLUSION

Patients with vestibular migraine have poor sleep quality, thalamic-cortical hyperfunction and active arousal system. In addition, high CAP rate and high A2 to A3 ratio make the sleep structure more fragmented.

MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG

Objective. Computerized classification of sleep stages based on single-lead electroencephalography (EEG) signals is important, but still challenging. In this paper, we proposed a deep neural network called MRASleepNet for automatic sleep stage classification using single-channel EEG signals.Approach. The proposed MRASleepNet model consisted of a feature extraction (FE) module, a multi-resolution attention (MRA) module, and a gated multilayer perceptron (gMLP) module, as well as a direct pathway for computing statistical features. The FE, MRA, and gMLP modules were used to extract features, establish feature attention, and obtain temporal relationships between features, respectively. EEG signals were normalized and cut into 30 s segments, and enhanced by incorporating contextual information from adjacent data segments. After data enhancement, the 40 s data segments were input to the MRASleepNet model. The model was evaluated on the SleepEDF and the cyclic alternating pattern (CAP) databases, using such metrics as the accuracy, Kappa, and macro-F1 (MF1).Main results.For the SleepEDF-20 database, the proposed model had an accuracy of 84.5%, an MF1 of 0.789, and a Kappa of 0.786. For the SleepEDF-78 database, the model had an accuracy of 81.4%, an MF1 of 0.754, and a Kappa of 0.743. For the CAP database, the model had an accuracy of 74.3%, an MF1 of 0.656, and a Kappa of 0.652. The proposed model achieved satisfactory performance in automatic sleep stage classification tasks.Significance. The time- and frequency-domain features extracted by the FE module and filtered by the MRA module, together with the temporal features extracted by the gMLP module and the statistical features extracted by the statistical highway, enabled the proposed model to obtain a satisfying performance in sleep staging. The proposed MRASleepNet model may be used as a new deep learning method for automatic sleep stage classification. The code of MRASleepNet will be made available publicly onhttps://github.com/YuRui8879/.

Associations of nonrestorative sleep and insomnia symptoms with incident depressive symptoms over 1-2 years: Longitudinal results from the Hispanic Community Health Study/Study of Latinos and Sueño Ancillary Study

BACKGROUND

Nonrestorative sleep (NRS), defined as insufficiently rested or refreshed sleep, is considered to play an important role in the development of depression. The aim of this study is to investigate the predictive ability of insomnia-related symptoms, including NRS, for incident depressive symptoms (DEPs) in a longitudinal manner.

METHODS

We used data of 1196 samples aged 18-64 years who participated in both the Hispanic Community Health Study/Study of Latinos conducted in 2008-2010 and the follow-up study (Sueño Ancillary Study) conducted in 2010-2013. DEPs and insomnia-related symptoms (difficulty initiating sleep [DIS], difficulty maintaining sleep [DMS], early morning awakening [EMA], difficulty returning to sleep [DRS], and NRS) were evaluated by the 10-item Center for Epidemiologic Studies Depression Scale and the Women's Health Initiative Insomnia Rating Scale, respectively. A logistic regression analysis was used to evaluate the predictive ability of each insomnia-related symptom at baseline for incident DEPs in couple-years.

RESULTS

In the univariate logistic regression analysis, all insomnia-related symptoms had significant associations with incident DEPs (DIS, odds ratio [OR] = 1.6; DMS, OR = 1.6; EMA, OR = 1.5; DRS, OR = 1.9; NRS, OR = 2.5). After adjusting for sociodemographic factors and the confounding effects of other insomnia-related symptoms, only NRS (OR = 2.2, 95% confidence interval = 1.4-3.5, p = .001) was significantly associated with incident DEPs.

CONCLUSIONS

NRS was a risk factor for incident DEPs, which includes a predictive ability for other insomnia-related symptoms. Our results suggest that focusing on NRS is an effective strategy for preventing depression in public health promotions.

REM Sleep: An Unknown Indicator of Sleep Quality

Standard polysomnographic analysis of sleep has not provided evidence of an objective measure of sleep quality; however, factors such as sleep duration and sleep efficiency are those more consistently associated with the subjective perception of sleep quality. Sleep reduction as currently occurs in our 24/7 society has had a profound impact on sleep quality; the habitual sleep period should fit within what is a limited nighttime window and may not be sufficient to satisfy the whole sleep process; moreover, the use of artificial light during the evening and early night hours can delay and disturb the circadian rhythms, especially affecting REM sleep. The correct phase relationship of the sleep period with the circadian pacemaker is an important factor to guarantee adequate restorative sleep duration and sleep continuity, thus providing the necessary background for a good night's sleep. Due to the fact that REM sleep is controlled by the circadian clock, it can provide a window-like mechanism that defines the termination of the sleep period when there is still the necessity to complete the sleep process (not only wake-related homeostasis) and to meet the circadian end of sleep timing. An adequate amount of REM sleep appears necessary to guarantee sleep continuity, while periodically activating the brain and preparing it for the return to consciousness.

Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches

The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis—Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders.

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.

A systematic review and meta-analysis of the cyclic alternating pattern across the lifespan

BACKGROUND

Cyclic alternating pattern (CAP) is the electroencephalogram (EEG) pattern described as a marker of sleep instability and assessed by NREM transient episodes in sleep EEG. It has been associated with brain maturation. The aim of this review was to evaluate the normative data of CAP parameters according to the aging process in healthy subjects through a systematic review and meta-analysis.

METHODS

Two authors independently searched databases using PRISMA guidelines. Discrepancies were reconciled by a third reviewer. Subgroup analysis and tests for heterogeneity were conducted.

RESULTS

Of 286 studies, 10 submitted a total of 168 healthy individuals to CAP analysis. Scoring of CAP can begin at 3 months of life, when K-complexes, delta bursts, or spindles can be recognized. Rate of CAP increased with age, mainly during the first 2 years of life, then decreased in adolescence, and increased in the elderly. The A1 CAP subtype and CAP rate were high in school-aged children during slow-wave sleep (SWS). A1 CAP subtypes were significantly more numerous in adolescents compared with other groups, while the elderly showed the highest amounts of A2 and A3 CAP subtypes. Our meta-analysis registered the lowest CAP rate in infants younger than 2 years old and the highest in the elderly.

CONCLUSIONS

This review summarized the normative data of CAP in NREM sleep during the aging process. The CAP rate increased with age and sleep depth, especially during SWS. Parameters of CAP may reflect gender hormonal effects and neuroplasticity. More reports on CAP subtypes are needed for their reference values establishment.

Functional MRI of arousals in nonrapid eye movement sleep

Arousals commonly occur during human sleep and have been associated with several sleep disorders. Arousals are characterized as an abrupt electroencephalography (EEG) frequency change to higher frequencies during sleep. However, the human brain regions involved in arousal are not yet clear. Simultaneous EEG and functional magnetic resonance imaging (fMRI) data were recorded during the early portion of the sleep period in healthy young adults. Arousals were identified based on the EEG data, and fMRI signal changes associated with 83 arousals from 19 subjects were analyzed. Subcortical regions, including the midbrain, thalamus, basal ganglia, and cerebellum, were activated with arousal. Cortices, including the temporal gyrus, occipital gyrus, and frontal gyrus, were deactivated with arousal. The activations associated with arousal in the subcortical regions were consistent with previous findings of subcortical involvement in behavioral arousal and consciousness. Cortical deactivations may serve as a mechanism to direct incoming sensory stimuli to specific brain regions, thereby monitoring environmental perturbations during sleep.

Characterization of cyclic alternating pattern during sleep in older men and women using large population studies

STUDY OBJECTIVES

To assess the microstructural architecture of non-rapid eye movement (NREM) sleep known as cyclic alternating pattern (CAP) in relation to the age, gender, self-reported sleep quality, and the degree of sleep disruption in large community-based cohort studies of older people.

METHODS

We applied a high-performance automated CAP detection system to characterize CAP in 2,811 men from the Osteoporotic Fractures in Men Sleep Study (MrOS) and 426 women from the Study of Osteoporotic Fractures (SOF). CAP was assessed with respect to age and gender and correlated to obstructive apnea-hypopnea index, arousal index (AI-NREM), and periodic limb movements in sleep index. Further, we evaluated CAP across levels of self-reported sleep quality measures using analysis of covariance.

RESULTS

Age was significantly associated with the number of CAP sequences during NREM sleep (MrOS: p = 0.013, SOF = 0.051). CAP correlated significantly with AI-NREM (MrOS: ρ = 0.30, SOF: ρ = 0.29). CAP rate, especially the A2+A3 index, was inversely related to self-reported quality of sleep, independent of age and sleep disturbance measures. Women experienced significantly fewer A1-phases compared to men, in particular, in slow-wave sleep (N3).

CONCLUSIONS

We demonstrate that automated CAP analysis of large-scale databases can lead to new findings on CAP and its subcomponents. We show that sleep disturbance indices are associated with the CAP rate. Further, the CAP rate is significantly linked to subjectively reported sleep quality, independent from traditionally scored markers of sleep fragmentation. Finally, men and women show differences in the microarchitecture of sleep as identified by CAP, despite similar macro-architecture.

Blood pressure dipping and sleep quality in the Wisconsin Sleep Cohort

AIMS

Nondipping blood pressure (BP) is associated with higher risk for hypertension and advanced target organ damage. Insomnia is the most common sleep complaint in the general population. We sought to investigate the association between sleep quality and insomnia and BP nondipping cross-sectionally and longitudinally in a large, community-based sample.

METHODS

A subset of the Wisconsin Sleep Cohort (n = 502 for cross-sectional analysis and n = 260 for longitudinal analysis) were enrolled in the analysis. Polysomnography measures were used to evaluate sleep quality. Insomnia symptoms were obtained by questionnaire. BP was measured by 24-h ambulatory BP monitoring. Logistic regression models estimated cross-sectional associations of sleep quality and insomnia with BP nondipping. Poisson regression models estimated longitudinal associations between sleep quality and incident nondipping over a mean 7.4 years of follow-up. Systolic and diastolic nondipping were examined separately.

RESULTS

In cross-sectional analyses, difficulty falling asleep, longer waking after sleep onset, shorter and longer total sleep time, lower sleep efficiency and lower rapid eye movement stage sleep were associated with higher risk of SBP and DBP nondipping. In longitudinal analyses, the adjusted relative risks (95% confidence interval) of incident systolic nondipping were 2.1 (1.3-3.5) for 1-h longer waking after sleep onset, 2.1 (1.1-5.1) for 7-8 h total sleep time, and 3.7 (1.3-10.7) for at least 8-h total sleep time (compared with total sleep time 6-7 h), and 1.9 (1.1-3.4) for sleep efficiency less than 0.8, respectively.

CONCLUSION

Clinical features of insomnia and poor sleep quality are associated with nondipping BP. Our findings suggested nondipping might be one possible mechanism by which poor sleep quality was associated with worse cardiovascular outcomes.

Brain mechanisms of insomnia: new perspectives on causes and consequences

While insomnia is the second most common mental disorder, progress in our understanding of underlying neurobiological mechanisms has been limited. The present review addresses the definition and prevalence of insomnia and explores its subjective and objective characteristics across the 24-hour day. Subsequently, the review extensively addresses how the vulnerability to develop insomnia is affected by genetic variants, early life stress, major life events, and brain structure and function. Further supported by the clear mental health risks conveyed by insomnia, the integrated findings suggest that the vulnerability to develop insomnia could rather be found in brain circuits regulating emotion and arousal than in circuits involved in circadian and homeostatic sleep regulation. Finally, a testable model is presented. The model proposes that in people with a vulnerability to develop insomnia, the locus coeruleus is more sensitive to-or receives more input from-the salience network and related circuits, even during rapid eye movement sleep, when it should normally be sound asleep. This vulnerability may ignite a downward spiral of insufficient overnight adaptation to distress, resulting in accumulating hyperarousal, which, in turn, impedes restful sleep and moreover increases the risk of other mental health adversity. Sensitized brain circuits are likely to be subjectively experienced as "sleeping with one eye open". The proposed model opens up the possibility for novel intervention studies and animal studies, thus accelerating the ignition of a neuroscience of insomnia, which is direly needed for better treatment.

Sleep Macrostructure and NREM Sleep Instability Analysis in Pediatric Developmental Coordination Disorder

Developmental Coordination Disorder (DCD) is considered to be abnormal motor skills learning, identified by clumsiness, slowness, and/or motor inaccuracy impairing the daily-life activities in all ages of life, in the absence of sensory, cognitive, or neurological deficits impairment. The present research focuses on studying DCD sleep structure and Cyclic Alternating Pattern (CAP) parameters with a full overnight polysomnography and to study the putative correlations between sleep architecture and CAP parameters with motor coordination skills. The study was a cross-sectional design involving 42 children (26M/16F; mean age 10.12 ± 1.98) selected as a DCD group compared with 79 children (49M/30F; mean age 9.94 ± 2.84) identified as typical (no-DCD) for motor ability and sleep macrostructural parameters according to the MABC-2 and polysomnographic (PSG) evaluations. The two groups (DCD and non-DCD) were similar for age (p = 0.715) and gender (p = 0.854). More significant differences in sleep architecture and CAP parameters were found between two groups and significant correlations were identified between sleep parameters and motor coordination skills in the study population. In conclusion, our data show relevant abnormalities in sleep structure of DCD children and suggest a role for rapid components of A phases on motor coordination development

Data-Driven Analysis of EEG Reveals Concomitant Superficial Sleep During Deep Sleep in Insomnia Disorder

Study Objectives: The subjective suffering of people with Insomnia Disorder (ID) is insufficiently accounted for by traditional sleep classification, which presumes a strict sequential occurrence of global brain states. Recent studies challenged this presumption by showing concurrent sleep- and wake-type neuronal activity. We hypothesized enhanced co-occurrence of diverging EEG vigilance signatures during sleep in ID.

Methods: Electroencephalography (EEG) in 55 cases with ID and 64 controls without sleep complaints was subjected to a Latent Dirichlet Allocation topic model describing each 30 s epoch as a mixture of six vigilance states called Topics (T), ranked from N3-related T1 and T2 to wakefulness-related T6. For each stable epoch we determined topic dominance (the probability of the most likely topic), topic co-occurrence (the probability of the remaining topics), and epoch-to-epoch transition probabilities.

Results: In stable epochs where the N1-related T4 was dominant, T4 was more dominant in ID than in controls, and patients showed an almost doubled co-occurrence of T4 during epochs where the N3-related T1 was dominant. Furthermore, patients had a higher probability of switching from T1- to T4-dominated epochs, at the cost of switching to N3-related T2-dominated epochs, and a higher probability of switching from N2-related T3- to wakefulness-related T6-dominated epochs.

Conclusion: Even during their deepest sleep, the EEG of people with ID express more N1-related vigilance signatures than good sleepers do. People with ID are moreover more likely to switch from deep to light sleep and from N2 sleep to wakefulness. The findings suggest that hyperarousal never rests in ID.

Impact Of Phenotypic Heterogeneity Of Insomnia On The Patients' Response To Cognitive-Behavioral Therapy For Insomnia: Current Perspectives

Insomnia is one of the most common mental disorders and the most frequent sleep disorder encountered in clinical practice, with a prevalence of about 7% in the European population. Insomnia Disorder (ID) is defined as a disturbance of sleep initiation or maintenance, followed by a feeling of non-restorative sleep and several diurnal consequences ranging from occupational and social difficulties to cognitive impairment. Cognitive-Behavioral Therapy for Insomnia (CBT-I) is considered the first-choice therapy for this disorder because its effectiveness has been proven to be greater in the long term with fewer side effects in comparison to pharmacotherapy. Although its effectiveness has been well established, it has been reported that nearly 40% of patients do not achieve remission after treatment. This finding could be the consequence of heterogeneity of ID between patients. It has been proposed that this heterogeneity might be ascribable to indices that are not related to sleep quality and quantity, such as comorbidities, life events, and personality traits. However, several works focused on the role of sleep markers, in particular objective total sleep time, for the phenotypization of ID and treatment response. The aim of this work is to summarize the available scientific literature regarding the impact of ID subtype on CBT-I response.

Impact of migraine on the clinical presentation of insomnia: a population-based study

BACKGROUND

Insomnia and migraine are closely related; insomnia aggravates migraine symptoms. This study was conducted to investigate the impact of migraine on the clinical presentation of insomnia symptoms.

METHODS

The data of the Korean Headache-Sleep Study (KHSS) were used in the present study. The KHSS is a nation-wide cross-sectional population-based survey regarding headache and sleep in Korean adults aged 19 to 69 years. If a participant's Insomnia Severity Index (ISI) score ≥ 10, she/he was classified as having insomnia. The clinical presentation of insomnia symptoms was assessed using total and subcomponent scores of the ISI.

RESULTS

Of 2695 participants, 290 (10.8%) and 143 (5.3%) individuals were assigned as having insomnia and migraine, respectively. The proportions of migraine (12.8% vs. 4.4%, p <  0.001) and non-migraine headache (59.0% vs. 39.9%, p <  0.001) were higher among individuals with insomnia compared to those without insomnia. Among participants with insomnia, total ISI scores were not significantly different among participants with migraine, non-migraine, and non-headache [median and interquartile range: 13.0 (11.0-17.5) vs. 13.0 (11.0-17.5) vs. 12.0 (11.0-16.0), p = 0.245]. ISI scores for noticeability of sleep problems to others were significantly higher among participants with migraine [3.0 (2.0-4.0) vs. 2.0 (2.0-3.0), p = 0.011] and non-migraine headache [3.0 (2.0-4.0) vs. 2.0 (2.0-3.0), p = 0.001] compared to those without headache history. Other ISI subcomponent scores did not significantly differ between headache status groups.

CONCLUSIONS

Participants with insomnia had an increased risk of migraine and non-migraine headache compared to those without insomnia. Among participants with insomnia, overall insomnia severity was not significantly influenced by the headache status.

Sleep Stage Transition Dynamics Reveal Specific Stage 2 Vulnerability in Insomnia

Study Objectives

Objective sleep impairments in insomnia disorder (ID) are insufficiently understood. The present study evaluated whether whole-night sleep stage dynamics derived from polysomnography (PSG) differ between people with ID and matched controls and whether sleep stage dynamic features discriminate them better than conventional sleep parameters.

Methods

Eighty-eight participants aged 21-70 years, including 46 with ID and 42 age- and sex-matched controls without sleep complaints, were recruited through www.sleepregistry.nl and completed two nights of laboratory PSG. Data of 100 people with ID and 100 age- and sex-matched controls from a previously reported study were used to validate the generalizability of findings. The second night was used to obtain, in addition to conventional sleep parameters, probabilities of transitions between stages and bout duration distributions of each stage. Group differences were evaluated with nonparametric tests.

Results

People with ID showed higher empirical probabilities to transition from stage N2 to the lighter sleep stage N1 or wakefulness and a faster decaying stage N2 bout survival function. The increased transition probability from stage N2 to stage N1 discriminated people with ID better than any of their deviations in conventional sleep parameters, including less total sleep time, less sleep efficiency, more stage N1, and more wake after sleep onset. Moreover, adding this transition probability significantly improved the discriminating power of a multiple logistic regression model based on conventional sleep parameters.

Conclusions

Quantification of sleep stage dynamics revealed a particular vulnerability of stage N2 in insomnia. The feature characterizes insomnia better than-and independently of-any conventional sleep parameter.

Zolpidem's use for insomnia

Zolpidem is a short-acting non-benzodiazepine hypnotic drug that belongs to the imidazopyridine class. In addition to immediate-release (IR) and extended-release (ER) formulations, the new delivery forms including two sublingual tablets [standard dose (SD) and low dose (LD)], and an oral spray form have been recently developed which bypass the gastrointestinal tract. So far, Zolpidem has been studied in several clinical populations: cases poor sleepers, transient insomnia, elderly and non-elderly patients with chronic primary insomnia, and in comorbid insomnia. Peak plasma concentration (Tmax) of zolpidem-IR occurs in 45 to 60min, with the terminal elimination half-life (t½) equating to 2.4h. The extended-release formulation results in a higher concentration over a period of more than 6h. Peak plasma concentration is somewhat shorter for the sublingual forms and the oral spray, while their t½ is comparable to that of zolpidem-IR. Zolpidem-IR reduces sleep latency (SL) at recommended doses of 5mg and 10mg in elderly and non-elderly patients, respectively. Zolpidem-ER at doses of 6.25mg and 12.5mg, improves sleep maintenance in elderly and non-elderly patients, respectively, 4h after its administration. Sublingual zolpidem-LD (5mg) and zolpidem oral spray are indicated for middle-of-the-night (MOTN) wakefulness and difficulty returning to sleep, while sublingual zolpidem-SD (10mg) is marketed for difficulty falling asleep. With their array of therapeutic uses and their popularity among physicians and patients; this review describes the clinical pharmacology, indications and uses, identifying withdrawal symptoms, abuse and dependence potentials, and adverse drug reactions are discussed.

Sleep state instabilities in major depressive disorder: Detection and quantification with electrocardiogram-based cardiopulmonary coupling analysis

Sleep disruption is an important aspect of major depressive disorder but lacks an objective and inexpensive means of assessment. We evaluated the utility of electrocardiogram (ECG)-based cardiopulmonary coupling analysis to quantify physiologic sleep stability in patients with major depression. Relative to controls, unmedicated depressed patients had a reduction in high-frequency coupling, an index of stable sleep, an increase in low-frequency coupling, an index of unstable sleep, and an increase in very-low-frequency coupling, an index of wakefulness/REM sleep. The medicated depressed group showed a restoration of stable sleep to a level comparable with that of the control group. ECG-based cardiopulmonary coupling analysis may provide a simple, cost-efficient point-of-care method to quantify sleep quality/stability and to objectively evaluate the severity of insomnia in patients with major depression.

Maintenance of Wakefulness and Occupational Injuries among Workers of an Italian Teaching Hospital

BACKGROUND

To assess in a laboratory setting the ability to stay awake in a sample of workers of an Italian hospital and to investigate the association between that ability and the risk of occupational injury.

METHODS

Nine workers at the University Hospital of Udine who reported an occupational injury in the study period (cases), and seven noninjured workers (controls) underwent a polysomnography and four 40-minute maintenance of wakefulness tests (MWT). Differences in sleep characteristics and in wakefulness maintenance were assessed using Wilcoxon's rank sums tests and Fisher's exact tests.

RESULTS

Controls had greater sleep latency, lower total sleep time, fewer leg movements, and a higher percentage ratio of cycling alternating pattern, were more likely not to fall asleep during the MWT and were less likely to have two or more sleep onsets. Although not all the differences reached statistical significance, cases had lower sleep onset times in Trials 1-3.

CONCLUSION

In the literature, the evidence of an association between MWT results and real life risk of accidents is weak. Our results suggest a relationship between the MWT results and the risk of injury among hospital workers.

Depressive symptoms predict the quality of sleep in patients with partial epilepsy--A combined retrospective and prospective study

INTRODUCTION

Epilepsy is one of the most common neurological diseases and has many detrimental effects on the patients' well-being as well as sleep quality. The aim of this study was to assess the subjective quality of sleep and influencing factors on subjective sleep quality in patients with partial epilepsy using a combined retrospective and prospective study design.

METHODS

We conducted a combined retrospective and prospective study in patients with partial epilepsy and analyzed subjective ratings of sleep quality in 32 patients (17 female, 15 male; mean age: 40.41 ± 12.67 years, range: 20-64) with partial epilepsy (mean duration of epilepsy diagnosis: 18.31 ± 13.26 years) and 32 healthy gender-matched and age-matched controls. All patients filled out a seizure diary for 90 days, which included the number, duration, and type (partial vs. secondary generalized) of epileptic seizures and intake of antiepileptic and sleep medications. At baseline, all participants completed the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Beck's Depression Inventory (BDI), and Beck Anxiety Inventory (BAI). Poor sleepers were defined by a PSQI score of ≥ 5.

RESULTS

Twenty-three patients (72%) reported 15.17 ± 25.54 seizures in the previous three months, and nine (28%) patients reported being seizure-free. During the 90-day diary period, twenty-two patients (69%) documented a total of 319 epileptic seizures, while ten patients (31%) reported that they were seizure-free. The mean PSQI score of all patients was 4.88 ± 2.92 (range: 1-14) and the mean ESS score was 5.25 ± 2.98 (range: 0-10). The mean PSQI score of the control group was 3.25 ± 1.57 (range: 1-6), and their mean ESS score was 6.72 ± 3.48 (range: 0-14). The comparison of the two groups showed a significantly higher PSQI score in the patient group (t = 2.778, p = 0.008), but no statistically significant difference regarding their ESS score (t = -1.811, p = 0.075). Sixteen (50%) patients were poor sleepers. Good sleepers showed a significantly lower PSQI (2.69 ± 1.08 vs. 7.06 ± 2.49; p < 0.001) and BDI scores (2.38 ± 2.50 vs. 9.63 ± 7.63; p < 0.002) than poor sleepers. Linear regression analysis showed that the BDI score was the significant predictor for the PSQI score (estimate: 0.2019; p = 0.00819) and for the ESS score (estimate: 0.2251; p = 0.0321).

CONCLUSION

In patients with partial epilepsy, a higher depression score was the best predictor for a poor subjective sleep quality and increased daytime sleepiness.

High cardiac vagal control is related to better subjective and objective sleep quality

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Cardiac vagal control (CVC) was measured during an extended standardized baseline.

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Subjective and polysomnographic variables of sleep quality were assessed.

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Higher CVC was found to be associated with better subjective and objective sleep quality.

Cardiac vagal control (CVC) has been linked to both physical and mental health. One critical aspect of health, that has not received much attention, is sleep. We hypothesized that adults with higher CVC – operationalized by high-frequency heart rate variability (HF-HRV) – will exhibit better sleep quality assessed both subjectively (i.e., with Pittsburgh Sleep Quality Index) and objectively (i.e., with polysomnography). HF-HRV was measured in 29 healthy young women during an extended neutral film clip. Participants then underwent full polysomnography to obtain objective measures of sleep quality and HF-HRV during a night of sleep. As expected, higher resting HF-HRV was associated with higher subjective and objective sleep quality (i.e., shorter sleep latency and fewer arousals). HF-HRV during sleep (overall or separated by sleep phases) showed less consistent relationships with sleep quality. These findings indicate that high waking CVC may be a key predictor of healthy sleep.

Bupropion response on sleep quality in patients with depression: implications for increased cardiovascular disease risk

Depression could be an independent risk factor for cardiovascular disease. We assessed bupropion response in depressed patients by polysomnography (PSG) and cardiopulmonary coupling (CPC) variables. Nineteen subjects participated in a two-session, two consecutive night PSG protocol. Participants received either placebo or bupropion-SR 150 mg, orally, in a randomized, double-blind cross-over fashion on night two. Outcome variables were: sleep stages, REM latency, stable, unstable sleep and very low frequency coupling (VLFC). CPC analysis uses heart rate variability and the electrocardiogram's R-wave amplitude fluctuations associated with respiration to generate frequency maps. Bupropion increased REM latency (p=0.043) but did not impact PSG sleep continuity, architecture and CPC variables. A trend (p=0.092) was observed towards increasing VLFC duration. Bupropion increased the number of stable-unstable sleep transitions (p=0.036). Moderate to strong correlations between PSG and CPC variables were found on placebo and bupropion nights. Limitations include a small sample size, limited power to detect CPC changes and lack of normal controls for comparison. Increased stable-unstable sleep transitions and VLFC duration may indicate vulnerability to cardiovascular disease due to their association with low heart rate variability that has been associated with increased mortality raising the question whether the beneficial effects of the antidepressant medication outweighs the impact on cardiopulmonary dynamics.

Guidelines for the recording and evaluation of pharmaco-sleep studies in man: the International Pharmaco-EEG Society (IPEG)

The International Pharmaco-EEG Society (IPEG) presents guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-sleep data in man. Over the past years, technical and data-processing methods have advanced steadily, thus enhancing data quality and expanding the palette of sleep assessment tools that can be used to investigate the activity of drugs on the central nervous system (CNS), determine the time course of effects and pharmacodynamic properties of novel therapeutics, hence enabling the study of the pharmacokinetic/pharmacodynamic relationship, and evaluate the CNS penetration or toxicity of compounds. However, despite the presence of robust guidelines on the scoring of polysomnography -recordings, a review of the literature reveals inconsistent -aspects in the operating procedures from one study to another. While this fact does not invalidate results, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. The present guidelines are intended to assist investigators, who are using pharmaco-sleep measures in clinical research, in an effort to provide clear and concise recommendations and thereby to standardise methodology and facilitate comparability of data across laboratories.

Physiological correlates of insomnia

Insomnia is a prevalent sleep disorder that is typically comorbid with medical, psychiatric, and other sleep disorders. Yet, it is a disorder with its own course and morbidity that can persist if untreated. This chapter describes the physiological correlates of insomnia expressed during sleep and during the daytime. Together, the data from nighttime and daytime electrophysiology, event-related brain potential recording, neuroimaging studies, sympathetic nervous system, and HPA axis monitoring all suggest that insomnia is a 24 h disorder of hyperarousal.

Insomnia types and sleep microstructure dynamics

This work aims to investigate sleep microstructure as expressed by Cyclic Alternating Pattern (CAP), and its possible alterations in pathological sleep. Three groups, of 10 subjects each, are considered: a) normal sleep, b) psychophysiological insomnia, and c) sleep misperception. One night sleep PSG and sleep macro- micro structure annotations were available per subject. The statistical properties and the dynamics of CAP events are in focus. Multiscale and non-linear methods are presented for the analysis of the microstructure event time series, applied for each type of CAP events, and their combination. The results suggest that a) both types of insomnia present CAP differences from normal sleep related to hyperarousal, b) sleep misperception presents more extensive differences from normal, potentially reflecting multiple sleep mechanisms, c) there are differences between the two types of insomnia as regard to the intertwining of events of different subtypes. The analysis constitutes a contribution towards new markers for the quantitative characterization of insomnia, and its subtypes.

CAP sleep in insomnia: new methodological aspects for sleep microstructure analysis

This work aims to propose new methodologies for the quantitative characterization of insomnia. Sleep microstructure, as expressed by Cyclic Alternatic pattern (CAP) sleep, is studied and differences between normal sleepers and insomniacs are investigated. The dynamic in the structure of CAP activation events is studied by use of wavelet analysis and the content of events, i.e. EEG dynamics, is studied in terms of complexity analysis. Both in structure and content, features exhibiting statistically significant differences are proposed, opening new perspectives for the understanding and the quantitative characterization of sleep and its disorders.

The sleep phenotypes of attention deficit hyperactivity disorder: the role of arousal during sleep and implications for treatment

About 25-50% of children and adolescents with attention-deficit hyperactivity disorder (ADHD) experience sleep problems. An appropriate assessment and treatment of such problems might improve the quality of life in such patients and reduce both the severity of ADHD and the impairment it causes. According to data in the literature and to the overall complexity of the interaction between ADHD and sleep, five sleep phenotypes may be identified in ADHD: (i) a sleep phenotype characterized mainly by a hypo-arousal state, resembling narcolepsy, which may be considered a "primary" form of ADHD (i.e. without the interference of other sleep disorders); (ii) a phenotype associated with delayed sleep onset latency and with a higher risk of bipolar disorder; (iii) a phenotype associated with sleep disordered breathing (SDB); (iv) another phenotype related to restless legs syndrome (RLS) and/or periodic limb movements; (v) lastly, a phenotype related to epilepsy/or EEG interictal discharges. Each sleep phenotype is characterized by peculiar sleep alterations expressed by either an increased or decreased level of arousal during sleep that have important treatment implications. Treatment with stimulants is recommended above all in the primary form of ADHD, whereas treatment of the main sleep disorders or of co-morbidities (i.e. bipolar disorders and epilepsy) is preferred in the other sleep phenotypes. All the sleep phenotypes, except the primary form of ADHD and those related to focal benign epilepsy or focal EEG discharges, are associated with an increased level of arousal during sleep. Recent studies have demonstrated that both an increase and a decrease in arousal are ascribable to executive dysfunctions controlled by prefrontal cortical regions (the main cortical areas implicated in the pathogenesis of ADHD), and that the arousal system, which may be hyperactivated or hypoactivated depending on the form of ADHD/sleep phenotype.

Cyclic alternating pattern (CAP): the marker of sleep instability

Cyclic alternating pattern CAP is the EEG marker of unstable sleep, a concept which is poorly appreciated among the metrics of sleep physiology. Besides, duration, depth and continuity, sleep restorative properties depend on the capacity of the brain to create periods of sustained stable sleep. This issue is not confined only to the EEG activities but reverberates upon the ongoing autonomic activity and behavioral functions, which are mutually entrained in a synchronized oscillation. CAP can be identified both in adult and children sleep and therefore represents a sensitive tool for the investigation of sleep disorders across the lifespan. The present review illustrates the story of CAP in the last 25 years, the standardized scoring criteria, the basic physiological properties and how the dimension of sleep instability has provided new insight into pathophysiolology and management of sleep disorders.

Characterization of A phases during the cyclic alternating pattern of sleep

OBJECTIVE

This study aims to identify, starting from a single EEG trace, quantitative distinctive features characterizing the A phases of the Cyclic Alternating Pattern (CAP).

METHODS

The C3-A2 or C4-A1 EEG leads of the night recording of eight healthy adult subjects were used for this analysis. CAP was scored by an expert and the portions relative to NREM were selected. Nine descriptors were computed: band descriptors (low delta, high delta, theta, alpha, sigma and beta); Hjorth activity in the low delta and high delta bands; differential variance of the EEG signal. The information content of each descriptor in recognizing the A phases was evaluated through the computation of the ROC curves and the statistics sensitivity, specificity and accuracy.

RESULTS

The ROC curves show that all the descriptors have a certain significance in characterizing A phases. The average accuracy obtained by thresholding the descriptors ranges from 59.89 (sigma descriptor) to 72.44 (differential EEG variance).

CONCLUSIONS

The results show that it is possible to attribute a significant quantitative value to the information content of the descriptors.

SIGNIFICANCE

This study gives a mathematical confirm to the features of CAP generally described qualitatively, and puts the bases for the creation of automatic detection methods.