Waking EEG signs of non-restoring sleep in primary insomnia patients

Hyperarousal-state of Insomnia Disorder in Wake-resting State Quantitative Electroencephalography

Objective

: Insomnia is associated with elevated high-frequency electroencephalogram power in the waking state. Although affective symptoms (e.g., depression and anxiety) are commonly comorbid with insomnia, few reports distinguished objective sleep disturbance from affective symptoms. In this study, we investigated whether daytime electroencephalographic activity explains insomnia, even after controlling for the effects of affective symptoms.

Methods

: A total of 107 participants were divided into the insomnia disorder (n = 58) and healthy control (n = 49) groups using the Mini-International Neuropsychiatric Interview and diagnostic criteria for insomnia disorder. The participants underwent daytime resting-state electroencephalography sessions (64 channels, eye-closed).

Results

: The insomnia group showed higher levels of anxiety, depression, and insomnia than the healthy group, as well as increased beta [t(105) = -2.56, p = 0.012] and gamma [t(105) = -2.44, p = 0.016] spectra. Among all participants, insomnia symptoms positively correlated with the intensity of beta (r = 0.28, p ＜ 0.01) and gamma (r = 0.25, p ＜ 0.05) spectra. Through hierarchical multiple regression, the beta power showed the additional ability to predict insomnia symptoms beyond the effect of anxiety (ΔR2 = 0.041, p = 0.018).

Conclusion

: Our results showed a significant relationship between beta electroencephalographic activity and insomnia symptoms, after adjusting for other clinical correlates, and serve as further evidence for the hyperarousal theory of insomnia. Moreover, resting-state quantitative electroencephalography may be a supplementary tool to assess insomnia.

Animal models of human insomnia

Insomnia disorder (chronic sleep continuity disturbance) is a debilitating condition affecting 5%-10% of the adult population worldwide. To date, researchers have attempted to model insomnia in animals through breeding strategies that create pathologically short-sleeping individuals or with drugs and environmental contexts that directly impose sleeplessness. While these approaches have been invaluable for identifying insomnia susceptibility genes and mapping the neural networks that underpin sleep-wake regulation, they fail to capture concurrently several of the core clinical diagnostic features of insomnia disorder in humans, where sleep continuity disturbance is self-perpetuating, occurs despite adequate sleep opportunity, and is often not accompanied by significant changes in sleep duration or architecture. In the present review, we discuss these issues and then outline ways animal models can be used to develop approaches that are more ecologically valid in their recapitulation of chronic insomnia's natural aetiology and pathophysiology. Conditioning of self-generated sleep loss with these methods promises to create a better understanding of the neuroadaptations that maintain insomnia, including potentially within the infralimbic cortex, a substrate at the crossroads of threat habituation and sleep.

Beyond the symptom: the biology of fatigue

A workshop titled "Beyond the Symptom: The Biology of Fatigue" was held virtually September 27-28, 2021. It was jointly organized by the Sleep Research Society and the Neurobiology of Fatigue Working Group of the NIH Blueprint Neuroscience Research Program. For access to the presentations and video recordings, see: https://neuroscienceblueprint.nih.gov/about/event/beyond-symptom-biology-fatigue. The goals of this workshop were to bring together clinicians and scientists who use a variety of research approaches to understand fatigue in multiple conditions and to identify key gaps in our understanding of the biology of fatigue. This workshop summary distills key issues discussed in this workshop and provides a list of promising directions for future research on this topic. We do not attempt to provide a comprehensive review of the state of our understanding of fatigue, nor to provide a comprehensive reprise of the many excellent presentations. Rather, our goal is to highlight key advances and to focus on questions and future approaches to answering them.

A Nationally Representative Survey Assessing Restorative Sleep in US Adults

Restorative sleep is a commonly used term but a poorly defined construct. Few studies have assessed restorative sleep in nationally representative samples. We convened a panel of 7 expert physicians and researchers to evaluate and enhance available measures of restorative sleep. We then developed the revised Restorative Sleep Questionnaire (REST-Q), which comprises 9 items assessing feelings resulting from the prior sleep episode, each with 5-point Likert response scales. Finally, we assessed the prevalence of high, somewhat, and low REST-Q scores in a nationally representative sample of US adults (n= 1,055) and examined the relationship of REST-Q scores with other sleep and demographic characteristics. Pairwise correlations were performed between the REST-Q scores and other self-reported sleep measures. Weighted logistic regression analyses were conducted to compare scores on the REST-Q with demographic variables. The prevalence of higher REST-Q scores (4 or 5 on the Likert scale) was 28.1% in the nationally representative sample. REST-Q scores positively correlated with sleep quality (r=0.61) and sleep duration (r=0.32), and negatively correlated with both difficulty falling asleep (r=-0.40) and falling back asleep after waking (r=-0.41). Higher restorative sleep scores (indicating more feelings of restoration upon waking) were more common among those who were: ≥60 years of age (OR=4.20, 95%CI: 1.92-9.17); widowed (OR=2.35, 95%CI:1.01-5.42), and retired (OR=2.02, 95%CI:1.30-3.14). Higher restorative sleep scores were less frequent among those who were not working (OR=0.36, 95%CI: 0.10-1.00) and living in a household with two or more persons (OR=0.51,95%CI:0.29-0.87). Our findings suggest that the REST-Q may be useful for assessing restorative sleep.

Single-channel EEG based insomnia detection with domain adaptation

Insomnia is one of the most common sleep disorders which can dramatically impair life quality and negatively affect an individual's physical and mental health. Recently, various deep learning based methods have been proposed for automatic and objective insomnia detection, owing to the great success of deep learning techniques. However, due to the scarcity of public insomnia data, a deep learning model trained on a dataset with a small number of insomnia subjects may compromise the generalization capacity of the model and eventually limit the performance of insomnia detection. Meanwhile, there have been a number of public EEG datasets collected from a large number of healthy subjects for various sleep research tasks such as sleep staging. Therefore, to utilize such abundant EEG datasets for addressing the data scarcity issue in insomnia detection, in this paper we propose a domain adaptation based model to better extract insomnia related features of the target domain by leveraging stage annotations from the source domain. For each domain, two pairs of common encoder and private encoder are firstly trained to extract sleep related features and sleep irrelevant features, respectively. In order to further discriminate source domain and target domain, a domain classifier is introduced. Then, the common encoder of the target domain will be used together with the Long Short Term Memory (LSTM) network for insomnia detection. To the best of our knowledge, this is the first deep learning based domain adaptation model using single channel raw EEG signals to detect insomnia at subject level. We use the Montreal Archive of Sleep Studies (MASS) dataset which contains only healthy subjects as source domain and two datasets which contain both healthy and insomnia subjects as target domain to validate our model's generalizability. Experimental results on the two target domain datasets (a public one and an in-house one) demonstrate that our model generalizes well on two target domain datasets with different sampling rates. In particular, our proposed method is able to improve insomnia detection performance from 50.0% to 90.9% and 66.7%-79.2% in terms of accuracy on the two target domain datasets, respectively.

Enhanced Vigilance Stability during Daytime in Insomnia Disorder

Central nervous hyperarousal is as a key component of current pathophysiological concepts of chronic insomnia disorder. However, there are still open questions regarding its exact nature and the mechanisms linking hyperarousal to sleep disturbance. Here, we aimed at studying waking state hyperarousal in insomnia by the perspective of resting-state vigilance dynamics. The VIGALL (Vigilance Algorithm Leipzig) algorithm has been developed to investigate resting-state vigilance dynamics, and it revealed, for example, enhanced vigilance stability in depressive patients. We hypothesized that patients with insomnia also show a more stable vigilance regulation. Thirty-four unmedicated patients with chronic insomnia and 25 healthy controls participated in a twenty-minute resting-state electroencephalography (EEG) measurement following a night of polysomnography. Insomnia patients showed enhanced EEG vigilance stability as compared to controls. The pattern of vigilance hyperstability differed from that reported previously in depressive patients. Vigilance hyperstability was also present in insomnia patients showing only mildly reduced sleep efficiency. In this subgroup, vigilance hyperstability correlated with measures of disturbed sleep continuity and arousal. Our data indicate that insomnia disorder is characterized by hyperarousal at night as well as during daytime.

Daytime Neurophysiological Hyperarousal in Chronic Insomnia: A Study of qEEG

Background: The hyperarousal model demonstrates that instability of sleep-wake regulation leads to insomnia symptoms and various neurophysiological hyperarousal states. Previous studies have shown that hyperarousal states that appear in chronic insomnia patients are not limited to sleep at nighttime but are stable characteristics that extend into the daytime. However, this phenomenon is mainly measured at bedtime, so it hard to determine whether it is maintained throughout a 24 h cycle or if it just appears at bedtime. Methods: We examined the resting state qEEG (quantitative electroencephalogram) and ECG (electrocardiogram) of chronic insomnia patients (n = 24) compared to good sleepers (n = 22) during the daytime. Results: As compared with controls, participants with insomnia showed a clearly high beta band activity in eyes closed condition at all brain areas. They showed a low frequency band at the frontal area; high frequency bands at the central and parietal areas were found in eyes open condition. Significantly higher heart rates were also found in the chronic insomnia group. Conclusion: These findings suggest that chronic insomnia patients were in a state of neurophysiological hyperarousal during the middle of the day due to abnormal arousal regulation.

Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis

BACKGROUND

Nonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, the performance of their use in EEG data analysis has not been fully compared. Here, we provide a comparison of four NMF algorithms in terms of accuracy of estimation, stability (repeatability of the results) and time complexity of algorithms with simulated data. In the practical application of NMF algorithms, stability plays an important role, which was an emphasis in the comparison. A Hierarchical clustering algorithm was implemented to evaluate the stability of NMF algorithms.

RESULTS

In simulation-based comprehensive analysis of fit, stability, accuracy of estimation and time complexity, hierarchical alternating least squares (HALS) low-rank NMF algorithm (lraNMF_HALS) outperformed the other three NMF algorithms. In the application of lraNMF_HALS for real resting-state EEG data analysis, stable and interpretable features were extracted.

CONCLUSION

Based on the results of assessment, our recommendation is to use lraNMF_HALS, providing the most accurate and robust estimation.

Biofeedback Therapies for Chronic Insomnia: A Systematic Review

The treatment of insomnia is still a challenge in clinical practice. This systematic review of randomized and quasi-randomized clinical trials aims to summarize the evidence for the use of biofeedback techniques in the treatment of chronic insomnia. Studies that compared biofeedback with other techniques of cognitive behavioral therapy, placebo, or absence of treatment were selected. The outcomes evaluated included sleep onset latency, total sleep time, sleep fragmentation, sleep efficiency and subjective sleep quality. Comparing to placebo and absence of treatment, some studies suggest possible benefits from the use of biofeedback for chronic insomnia in decreasing sleep onset latency and number of awakenings; however, there was marked divergence among included studies. There was no evidence of improvement in total sleep time, sleep efficiency and subjective sleep quality. Moreover, the maintenance of long-term benefits lacks evidence for any outcome. In the majority of outcomes evaluated, no significant differences in the effectiveness of biofeedback compared with other cognitive behavioral therapy techniques were observed. This systematic review found conflicting evidence for the effectiveness of biofeedback techniques in the treatment of chronic insomnia. Inter- and intra-group clinical heterogeneity among studies could be a reasonable explanation for the divergent results. These findings emphasize the need of performing further randomized clinical trials of higher methodological quality in order to better delineate the effectiveness of biofeedback on chronic insomnia treatment.