Sleep and circadian rhythm disruption in psychiatric and neurodegenerative disease

How to deal with sleep problems during heatwaves: practical recommendations from the European Insomnia Network

Heatwaves are occurring more frequently and are known to affect particularly night-time temperatures. We review here literature on how night-time ambient temperature changes affect body temperature and sleep quality. We then discuss how these temperature effects impact particularly vulnerable populations such as older adults, children, pregnant women, and those with psychiatric conditions. Several ways of dealing with sleep problems in the context of heatwaves are then suggested, adapted from elements of cognitive behavioural therapy for insomnia, with more specific advice for vulnerable populations. By better dealing with sleep problems during heatwaves, general health effects of heatwaves may be more limited. However, given the sparse literature, many links addressed in this review on sleep problems affected by temperature changes should be the focus of future research.

Control of Emotion and Wakefulness by Neurotensinergic Neurons in the Parabrachial Nucleus

The parabrachial nucleus (PBN) integrates interoceptive and exteroceptive information to control various behavioral and physiological processes including breathing, emotion, and sleep/wake regulation through the neural circuits that connect to the forebrain and the brainstem. However, the precise identity and function of distinct PBN subpopulations are still largely unknown. Here, we leveraged molecular characterization, retrograde tracing, optogenetics, chemogenetics, and electrocortical recording approaches to identify a small subpopulation of neurotensin-expressing neurons in the PBN that largely project to the emotional control regions in the forebrain, rather than the medulla. Their activation induces freezing and anxiety-like behaviors, which in turn result in tachypnea. In addition, optogenetic and chemogenetic manipulations of these neurons revealed their function in promoting wakefulness and maintaining sleep architecture. We propose that these neurons comprise a PBN subpopulation with specific gene expression, connectivity, and function, which play essential roles in behavioral and physiological regulation.

Does the smartphone's eye protection mode work?

People spend about 5-8 hours per day on phones, causing circadian disruption and eye fatigue, thus raising a great need for comfort and health. Most phones have eye protection modes, claiming a potential eye protection effect. To examine the effectiveness, we investigated the color quality, namely gamut area and just noticeable color difference (JNCD), and circadian effect, namely equivalent melanopic lux (EML) and melanopic daylight efficacy ratio (MDER), characteristics of two smartphones: iPhone 13 and HUAWEI P30, in normal and eye protection mode. The results show that the circadian effect is inversely proportional to color quality when the iPhone 13 and HUAWEI P30 changed from normal to eye protection mode. The gamut area changed from 102.51% to 82.5% sRGB and 100.36% to 84.55% sRGB, respectively. The EML and MDER decreased by 13 and 15, and, 0.50 and 0.38, respectively, affected by the eye protection mode and screen luminance. The EML and JNCD results in different modes show that the eye protection mode benefits the nighttime circadian effect at the cost of the image quality. This study provides a way to precisely assess the image quality and circadian effect of displays and elucidates the tradeoff relationship between them.

The GABAA receptor modulator zolpidem augments hippocampal-prefrontal coupling during non-REM sleep

Benzodiazepines and 'Z-drugs' (including zolpidem and zopiclone) are GABAA receptor (GABAAR) positive modulators commonly prescribed as hypnotics to treat insomnia and/or anxiety. However, alongside sedation, augmenting GABAAR function may also alter coordinated neuronal activity during sleep, thereby influencing sleep-dependent processes including memory consolidation. We used simultaneous recordings of neural population activity from the medial prelimbic cortex (PrL) and CA1 of the dorsal hippocampus (dCA1) of naturally sleeping rats to detail the effects of zolpidem on network activity during the cardinal oscillations of non-REM sleep. For comparison, we also characterized the effects of diazepam and 4,5,6,7-tetrahydroisoxazolo(5,4-c)pyridin-3-ol (THIP/gaboxadol), which acts predominantly at extra-synaptic GABAARs. Zolpidem and THIP significantly increased the amplitudes of slow-waves, which were attenuated by diazepam. Zolpidem increased hippocampal ripple density whereas diazepam decreased both ripple density and intrinsic frequency. While none of the drugs affected thalamocortical spindles in isolation, zolpidem augmented the temporal coordination between slow-waves and spindles. At the cellular level, analyses of spiking activity from 523 PrL and 579 dCA1 neurons revealed that zolpidem significantly enhanced synchronized pauses in cortical firing during slow-wave down states, while increasing correlated activity within and between dCA1 and PrL populations. Of the drugs compared here, zolpidem was unique in augmenting coordinated activity within and between hippocampus and neocortex during non-REM sleep. Zolpidem's enhancement of hippocampal-prefrontal coupling may reflect the cellular basis of its potential to modulate offline memory processing.

Lithium affects the circadian clock in the choroid plexus - A new role for an old mechanism

Lithium is an effective mood stabilizer, but the mechanism of its therapeutic action is not well understood. We investigated the effect of lithium on the circadian clock located in the ventricle barrier complex containing the choroid plexus (CP), a part of the glymphatic system that influences gross brain function via the production of cerebrospinal fluid. The mPer2^Luc mice were injected with lithium chloride (LiCl) or vehicle, and their effects on the clock gene Nr1d1 in CP were detected by RT qPCR. CP organotypic explants were prepared to monitor bioluminescence rhythms in real time and examine the responses of the CP clock to LiCl and inhibitors of glycogen synthase kinase-3 (CHIR-99021) and protein kinase C (chelerythrine). LiCl affected Nr1d1 expression levels in CP in vivo and dose-dependently delayed the phase and prolonged the period of the CP clock in vitro. LiCl and CHIR-99021 had different effects on 1] CP clock parameters (amplitude, period, phase), 2] dexamethasone-induced phase shifts of the CP clock, and 3] dynamics of PER2 degradation and de novo accumulation. LiCl-induced phase delays were significantly reduced by chelerythrine, suggesting the involvement of PKC activity. The effects on the CP clock may be involved in the therapeutic effects of lithium and hypothetically improve brain function in psychiatric patients by aligning the function of the CP clock-related glymphatic system with the sleep-wake cycle. Importantly, our data argue for personalized timing of lithium treatment in BD patients.

Do Sleep Disturbances have a Dual Effect on Alzheimer's Disease?

Sleep disturbances and Alzheimer's disease have deleterious effects on various physiological and cognitive functions including synaptic plasticity, oxidative stress, neuroinflammation, and memory. In addition, clock genes expression is significantly altered following sleep disturbances, which may be involved in the pathogenesis of Alzheimer's disease. In this review article, we aimed to discuss the role of sleep disturbances and Alzheimer's disease in the regulation of synaptic plasticity, oxidative stress, neuroinflammation, and clock genes expression. Also, we aimed to find significant relationships between sleep disturbances and Alzheimer's disease in the modulation of these mechanisms. We referred to the controversial effects of sleep disturbances (particularly those related to the duration of sleep deprivation) on the modulation of synaptic function and neuroinflammation. We aimed to know that, do sleep disturbances have a dual effect on the progression of Alzheimer's disease? Although numerous studies have discussed the association between sleep disturbances and Alzheimer's disease, the new point of this study was to focus on the controversial effects of sleep disturbances on different biological functions, and to evaluate the potential dualistic role of sleep disturbances in the pathogenesis of Alzheimer's disease.

Aging, circadian disruption and neurodegeneration: Interesting interplay

The circadian system is an intricate molecular network of coordinating circadian clocks that organize the internal synchrony of the organism in response to the environment. These rhythms are maintained by genetically programmed positive and negative auto-regulated transcriptional and translational feedback loops that sustain 24-hour oscillations in mRNA and protein components of the endogenous circadian clock. Since inter and intracellular activity of the central pacemaker appears to reduce with aging, the interaction between the circadian clock and aging continues to elude our understanding. In this review article, we discuss circadian clock components at the molecular level and how aging adversely affects circadian clock functioning in rodents and humans. The natural decline in melatonin levels with aging strongly contributes to circadian dysregulation resulting in the development of neurological anomalies. Additionally, inappropriate environmental conditions such as Artificial Light at Night (ALAN) can cause circadian disruption or chronodisruption (CD) which can result in a variety of pathological diseases, including premature aging. Furthermore, we summarize recent evidence suggesting that CD may also be a predisposing factor for the development of age-related neurodegenerative diseases (NDDs) such as Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD), although more investigation is required to prove this link. Finally, certain chrono-enhancement approaches have been offered as intervention strategies to prevent, alleviate, or mitigate the impacts of CD. This review thus aims to bring together recent advancements in the chronobiology of the aging process, as well as its role in NDDs.

Abnormal sleep features in adolescent MDD and its potential in diagnosis and prediction of early efficacy

INTRODUCTION

Previous studies have shown that abnormal sleep architectures are the important indicator for diagnosing MDD and predicting the efficacy of antidepressants. However, few studies have focused specifically on adolescents.

OBJECTIVE

To explore the relationship between abnormal sleep features, including PSG parameters and scale evaluation, and the onset of adolescent MDD, as well as early SSRIs efficacy.

METHODS

102 adolescent MDD patients (age 12 to 19-year-old) and 41 similarly age-marched controls were recruited. Demographic data, the HAMD₂₄ and the PSQI scale assessment scores were collected at baseline, latter two were also collected at follow-up. Part of the participants underwent a minimum 7-d medication-free period, and two consecutive night polysomnography. In the follow-up study, MDD patients were treated with standardized SSRIs. Treatment response was assessed every two weeks.

RESULTS

MDD subjects' parental marital status, REM-sleep latency, N2, N2%, N3, REM-sleep duration, REM % showed significant differences at baseline. REM-sleep latency showed significant prediction of the onset of MDD. The HAMD₂₄ and PSQI scale assessment scores decreased over time in the follow-up study. Specifically, the sleep disorder factor score of HAMD₂₄, the scores of PSQI sleep latency, sleep disorder, sleep efficiency and total score showed significantly differences between responder and non-responder groups. PSQI baseline moderate group showed significant prediction of the early efficacy of SSRIs.

CONCLUSION

Abnormal sleep PSG parameters and self-evaluation could be predictors for the adolescent MDD onset and early SSRIs efficacy.

The evolving role of quantitative actigraphy in clinical sleep medicine

Actigraphy has a consolidated role in Insomnia and Circadian Rhythm Sleep-Wake Disorders (CRSWD) and recent studies have highlighted the use of actigraphy for narcolepsy and REM sleep behaviour disorder (RBD). This review aims at summarising the results of studies published over the last decade regarding the use of actigraphy. Thirty-five studies proved eligible, and results were analysed separately for insomnia, narcolepsy and RBD. Actigraphy showed to consistently differentiate insomnia patients from healthy controls. Furthermore, the application of advanced analytical techniques has been shown to provide both unique insights into the physiology of insomnia and sleep misperception and to improve the specificity of actigraphy in detecting wakefulness within sleep periods. Regarding narcolepsy, several studies showed that actigraphy can detect peculiar sleep/wake disruption and the effects of pharmacological treatments. Finally, although the number of studies in RBD patients is still limited, the available evidence indicates a reduced amplitude of the activity pattern, sleep-wake rhythm dysregulation and daytime sleepiness. Therefore, the potential use of these markers as predictors of phenoconversion should be further explored. In conclusion, quantitative actigraphy presents a renewed interest when considering the possibility of using actigraphy in clinical sleep medicine to diagnose, monitor, and follow sleep disorders other than CRSWD.

Thwarting Alzheimer's Disease through Healthy Lifestyle Habits: Hope for the Future

Alzheimer's disease (AD) is a neurodegenerative disorder that slowly disintegrates memory and thinking skills. Age is known to be the major risk factor in AD, but there are several nonmodifiable and modifiable causes. The nonmodifiable risk factors such as family history, high cholesterol, head injuries, gender, pollution, and genetic aberrations are reported to expediate disease progression. The modifiable risk factors of AD that may help prevent or delay the onset of AD in liable people, which this review focuses on, includes lifestyle, diet, substance use, lack of physical and mental activity, social life, sleep, among other causes. We also discuss how mitigating underlying conditions such as hearing loss and cardiovascular complications could be beneficial in preventing cognitive decline. As the current medications can only treat the manifestations of AD and not the underlying process, healthy lifestyle choices associated with modifiable factors is the best alternative strategy to combat the disease.

Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification

The smart world under Industry 4.0 is witnessing a notable spurt in sleep disorders and sleep-related issues in patients. Artificial intelligence and IoT are taking a giant leap in connecting sleep patients remotely with healthcare providers. The contemporary single-channel-based monitoring devices play a tremendous role in predicting sleep quality and related issues. Handcrafted feature extraction is a time-consuming job in machine learning-based automatic sleep classification. The proposed single-channel work uses Tsfresh to extract features from both the EEG channels (Pz-oz and Fpz-Cz) of the SEDFEx database individually to realise a single-channel EEG. The adopted mRMR feature selection approach selected 55 features from the extracted 787 features. A stacking ensemble classifier achieved 95%, 94%, 91%, and 88% accuracy using stratified 5-fold validation in 2, 3, 4, and 5 class classification employing healthy subjects data. The outcome of the experiments indicates that Tsfresh is an excellent tool to extract standard features from EEG signals.

SSAVE: A tool for analysis and visualization of sleep periods using electroencephalography data

Human sleep architecture is structured with repeated episodes of rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep. An overnight sleep study facilitates identification of macro and micro changes in the pattern and duration of sleep stages associated with sleep disorders and other aspects of human mental and physical health. Overnight sleep studies record, in addition to electroencephalography (EEG) and other electro-physiological signals, a sequence of sleep-stage annotations. SSAVE, introduced here, is open-source software that takes sleep-stage annotations and EEG signals as input, identifies and characterizes periods of NREM and REM sleep, and produces a hypnogram and its time-matched EEG spectrogram. SSAVE fills an important gap for the rapidly growing field of sleep medicine by providing an easy-to-use tool for sleep-period identification and visualization. SSAVE can be used as a Python package, a desktop standalone tool or through a web portal. All versions of the SSAVE tool can be found on: https://manticore.niehs.nih.gov/ssave.

Depression and Anxiety Mediate the Association between Sleep Quality and Self-Rated Health in Healthcare Students

OBJECTIVES

This study aimed to investigate factors associated with sleep quality in healthcare students and to determine whether depressive and anxiety symptoms may explain some of the associations between sleep quality and self-rated health.

STUDY DESIGN

This is a cross-sectional study at wave one.

METHODS

A total of 637 healthcare students were recruited via a stratified random sampling method in Hangzhou, China. The Sleep Quality Questionnaire (SQQ) and the four-item Patient Health Questionnaire (PHQ-4) were used to assess sleep quality and depressive and anxiety symptoms, respectively. Self-rated health was assessed via a self-developed questionnaire of both physical and psychological health. Structural equation modeling was used to examine the direct and indirect effects of sleep quality on self-rated health through depressive and anxiety symptoms.

RESULTS

Students engaged in part-time employment (p = 0.022), with poor perceived employment prospects (p = 0.009), and who did not participate in recreational sports (p = 0.008) had worse sleep quality. Structural equation modeling revealed a significant total effect of sleep quality on self-rated health (b = 0.592, p < 0.001), a significant direct effect of both sleep quality and depressive and anxiety symptoms on self-rated health (b = 0.277, 95% CI: 0.032-0.522), and a significant indirect effect of sleep quality on self-rated health through depressive and anxiety symptoms (b = 0.315, 95% CI: 0.174-0.457).

CONCLUSIONS

Depressive and anxiety symptoms partially explain the association between sleep quality and self-rated health. Intervening upon sleep quality, depressive, and anxiety symptoms may bolster the self-rated health of healthcare students.

Metabotropic glutamate receptor function and regulation of sleep-wake cycles

Metabotropic glutamate (mGlu) receptors are the most abundant family of G-protein coupled receptors and are widely expressed throughout the central nervous system (CNS). Alterations in glutamate homeostasis, including dysregulations in mGlu receptor function, have been indicated as key contributors to multiple CNS disorders. Fluctuations in mGlu receptor expression and function also occur across diurnal sleep-wake cycles. Sleep disturbances including insomnia are frequently comorbid with neuropsychiatric, neurodevelopmental, and neurodegenerative conditions. These often precede behavioral symptoms and/or correlate with symptom severity and relapse. Chronic sleep disturbances may also be a consequence of primary symptom progression and can exacerbate neurodegeneration in disorders including Alzheimer's disease (AD). Thus, there is a bidirectional relationship between sleep disturbances and CNS disorders; disrupted sleep may serve as both a cause and a consequence of the disorder. Importantly, comorbid sleep disturbances are rarely a direct target of primary pharmacological treatments for neuropsychiatric disorders even though improving sleep can positively impact other symptom clusters. This chapter details known roles of mGlu receptor subtypes in both sleep-wake regulation and CNS disorders focusing on schizophrenia, major depressive disorder, post-traumatic stress disorder, AD, and substance use disorder (cocaine and opioid). In this chapter, preclinical electrophysiological, genetic, and pharmacological studies are described, and, when possible, human genetic, imaging, and post-mortem studies are also discussed. In addition to reviewing the important relationships between sleep, mGlu receptors, and CNS disorders, this chapter highlights the development of selective mGlu receptor ligands that hold promise for improving both primary symptoms and sleep disturbances.

A short-term follow-up study on the relationship between early adolescent proactive/reactive aggression and sleep quality

OBJECTIVE/BACKGROUND

Sleep quality is closely related to aggressive behavior. Previous studies rarely focused on the relationship between proactive/reactive aggression and sleep quality through a person-centered approach.

METHODS

The Pittsburgh Sleep Quality Index Scale and the Reactive-Proactive Aggression Questionnaire were used to assess 553 elementary and middle school students twice at 6-month intervals to better understand the relationship between proactive/reactive aggression and sleep quality in early adolescence.

RESULTS

Findings revealed that (1) Sleep quality was positive longitudinally related to both proactive aggression and reactive aggression; Proactive aggression negatively influenced sleep quality, and reactive aggression did not influence sleep quality longitudinally. (2) The sleep quality of persistent non-aggressors and stopped aggressors was significantly better than that of persistent aggressors and new aggressors.

CONCLUSION

In early adolescence, proactive aggression was mutually related to sleep quality. Therefore, future research should focus on the bidirectional association between aggression and sleep quality. In addition, we should improve the sleep quality for different types of aggressors and transformers, especially for new aggressors.

CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19 Using Biobehavioral Rhythms Derived From Wearable Physiological Data

Goal: To investigate whether a deep learning model can detect Covid-19 from disruptions in the human body's physiological (heart rate) and rest-activity rhythms (rhythmic dysregulation) caused by the SARS-CoV-2 virus. Methods: We propose CovidRhythm, a novel Gated Recurrent Unit (GRU) Network with Multi-Head Self-Attention (MHSA) that combines sensor and rhythmic features extracted from heart rate and activity (steps) data gathered passively using consumer-grade smart wearable to predict Covid-19. A total of 39 features were extracted (standard deviation, mean, min/max/avg length of sedentary and active bouts) from wearable sensor data. Biobehavioral rhythms were modeled using nine parameters (mesor, amplitude, acrophase, and intra-daily variability). These features were then input to CovidRhythm for predicting Covid-19 in the incubation phase (one day before biological symptoms manifest). Results: A combination of sensor and biobehavioral rhythm features achieved the highest AUC-ROC of 0.79 [Sensitivity = 0.69, Specificity = 0.89, F[Formula: see text] = 0.76], outperforming prior approaches in discriminating Covid-positive patients from healthy controls using 24 hours of historical wearable physiological. Rhythmic features were the most predictive of Covid-19 infection when utilized either alone or in conjunction with sensor features. Sensor features predicted healthy subjects best. Circadian rest-activity rhythms that combine 24 h activity and sleep information were the most disrupted. Conclusions: CovidRhythm demonstrates that biobehavioral rhythms derived from consumer-grade wearable data can facilitate timely Covid-19 detection. To the best of our knowledge, our work is the first to detect Covid-19 using deep learning and biobehavioral rhythms features derived from consumer-grade wearable data.

Benzodiazepines and Sleep Architecture: A Systematic Review

BACKGROUND

Insomnia, defined as a difficulty in initiating or maintaining sleep, is a relevant medical issue. Benzodiazepines (BZDs) are commonly prescribed to treat insomnia. Two phases characterize human sleep structure: sleep with Non-Rapid Eye Movement (NREM) and sleep with Rapid Eye Movement (REM). Physiological sleep includes NREM and REM phases in a continuous cycle known as "Sleep Architecture."

OBJECTIVE

This systematic review summarizes the studies that have investigated effects of BZDs on Sleep Architecture.

METHODS

The articles selection included human clinical trials (in English, Portuguese, or Spanish) only, specifically focused on BZDs effects on sleep architecture. PubMed, BVS, and Google Scholar databases were searched.

RESULTS

Findings on BZDs effects on sleep architecture confirm an increase in stage 2 of NREM sleep and a decrease in time of stages 3 and 4 of NREM sleep with a reduction in time of REM sleep during the nocturnal sleep.

CONCLUSION

Variations in NREM and REM sleep may lead to deficits in concentration and working memory and weight gain. The increase in stage 2 of NREM sleep may lead to a subjective improvement of sleep quality with no awakenings. BZDz should be prescribed with zeal and professional judgment. These patients should be closely monitored for possible long-term side effects.

Sleep Staging Framework with Physiologically Harmonized Sub-Networks

Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming gold-standard manual scoring procedure. Recently there have seen promising results on automatic stage scoring by extracting spatio-temporal features via deep neural networks from electroencephalogram (EEG). However, such methods fail to consistently yield good performance due to a missing piece in data representation: the medical criterion of the sleep scoring task on top of EEG features. We argue that capturing stage-specific features that satisfy the criterion of sleep medicine is non-trivial for automatic sleep scoring. This paper considers two criteria: Transient stage marker and Overall profile of EEG features, then we propose a physiologically meaningful framework for sleep stage scoring via mixed deep neural networks. The framework consists of two sub-networks: feature extraction networks, constructed in consideration of the physiological characteristics of sleep, and an attention-based scoring decision network. Moreover, we quantize the framework for potential use under an IoT setting. For proof-of-concept, the performance of the proposed framework is demonstrated by introducing multiple sleep datasets with the largest comprising 42,560 h recorded from 5,793 subjects. From the experiment results, the proposed method achieves a competitive stage scoring performance, especially for Wake, N2, and N3, with higher F1 scores of 0.92, 0.86, and 0.88, respectively. Moreover, the feasibility analysis of framework quantization provides a potential for future implementation in the edge computing field and clinical settings.

The Management of Sleep Disturbances in Patients with Schizophrenia: A Case Series

Patients with schizophrenia commonly encounter a variety of sleep disorders. Disturbed sleep can be found in 30-80% of patients, depending on the degree of psychotic symptomatology. Difficulty falling asleep, maintaining, or achieving restful sleep is associated with symptom severity and has been reported as a prodromal symptom of psychotic relapse. Although some sleep disorders improve with antipsychotic treatment, in many cases, even during disease remission, sleep continues to be fragmented, or even different pathophysiological mechanism is causing sleep disruption. Moreover, it may be complicated if the patient needs specific treatment, such as positive airway pressure (PAP) therapy, due to sleep-disordered breathing. The article presents case reports of patients with schizophrenia with sleep disturbances. As presented in our case reports, cognitive behavioral therapy seems effective in treating comorbid insomnia, even in patients with schizophrenia. The second and third case reports emphasise the need for broader clinical considerations, a cross-diagnostic approach, and cooperation in care for patients with severe mental disorders.

A hidden Markov modeling approach combining objective measure of activity and subjective measure of self-reported sleep to estimate the sleep-wake cycle

Characterizing the sleep-wake cycle in adolescents is an important prerequisite to better understand the association of abnormal sleep patterns with subsequent clinical and behavioral outcomes. The aim of this research was to develop hidden Markov models (HMM) that incorporate both objective (actigraphy) and subjective (sleep log) measures to estimate the sleep-wake cycle using data from the NEXT longitudinal study, a large population-based cohort study. The model was estimated with a negative binomial distribution for the activity counts (1-minute epochs) to account for overdispersion relative to a Poisson process. Furthermore, self-reported measures were dichotomized (for each one-minute interval) and subject to misclassification. We assumed that the unobserved sleep-wake cycle follows a two-state Markov chain with transitional probabilities varying according to a circadian rhythm. Maximum-likelihood estimation using a backward-forward algorithm was applied to fit the longitudinal data on a subject by subject basis. The algorithm was used to reconstruct the sleep-wake cycle from sequences of self-reported sleep and activity data. Furthermore, we conduct simulations to examine the properties of this approach under different observational patterns including both complete and partially observed measurements on each individual.

Delayed circadian rhythms and insomnia symptoms in obsessive-compulsive disorder

BACKGROUND

Accumulating evidence implicates sleep and circadian rhythm disturbance in obsessive-compulsive disorder (OCD). However, a multimethod characterization of sleep and circadian rhythms in OCD, their association with symptom severity, and the functional relationship between these variables is lacking.

METHODS

The present study measured multiple indicators of sleep and circadian rhythms in a sample of adults with OCD, adults without OCD, and healthy controls (n = 74). Participants completed measures of morningness-eveningness, delayed sleep-wake phase disorder (DSWPD), insomnia symptoms, and OCD symptoms, as well as one week of sleep monitoring via a sleep diary and actigraphy.

RESULTS

Delayed circadian rhythms (higher eveningness, later mid-sleep timing, and higher rates of DSWPD) and higher insomnia symptoms were observed in those with OCD compared to healthy controls, as well as associations between delayed circadian rhythms and insomnia symptoms and OCD symptom severity across the full sample. Further, insomnia symptoms mediated the relationship between delayed circadian rhythms and OCD symptoms. In contrast, there were no links between total sleep time or sleep quality and OCD.

LIMITATIONS

Data collection during COVID-19 pandemic, correlational data, no physiological measure of circadian rhythms.

CONCLUSIONS

These findings highlight a robust association between delayed circadian rhythms and OCD and suggest insomnia symptoms may be one mechanism in this relationship. Sleep and circadian rhythm disturbance may be novel targets for OCD treatment.

A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals

The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep stage scoring rely on hand-engineered features that require prior knowledge of sleep analysis. This paper presents an end-to-end deep transfer learning framework for automatic feature extraction and sleep stage scoring based on a single-channel EEG. The proposed framework was evaluated over the three primary signals recommended by the American Academy of Sleep Medicine (C4-M1, F4-M1, O2-M1) from two data sets that have different properties and are recorded with different hardware. Different Time-Frequency (TF) imaging approaches were evaluated to generate TF representations for the 30 s EEG sleep epochs, eliminating the need for complex EEG signal pre-processing or manual feature extraction. Several training and detection scenarios were investigated using transfer learning of convolutional neural networks (CNN) and combined with recurrent neural networks. Generating TF images from continuous wavelet transform along with a deep transfer architecture composed of a pre-trained GoogLeNet CNN followed by a bidirectional long short-term memory (BiLSTM) network showed the best scoring performance among all tested scenarios. Using 20-fold cross-validation applied on the C4-M1 channel, the proposed framework achieved an average per-class accuracy of 91.2%, sensitivity of 77%, specificity of 94.1%, and precision of 75.9%. Our results demonstrate that without changing the model architecture and the training algorithm, our model could be applied to different single-channel EEGs from different data sets. Most importantly, the proposed system receives a single EEG epoch as an input at a time and produces a single corresponding output label, making it suitable for real time monitoring outside sleep labs as well as to help sleep lab specialists arrive at a more accurate diagnoses.

Self-perceived rhythmicity in affective and cognitive functions is related to psychiatric symptoms in adolescents

The objective of this study was to evaluate the relationship between self-perceived rhythms measured using the Mood Rhythm Instrument for adolescents (MRhI-Y) and depressive and psychiatric symptoms measured with the Children's Depressive Instrument (CDI) and the Strengths and Difficulties Questionnaire (SDQ). In this study, 186 adolescents were recruited in Rio Grande do Sul, Brazil. We performed a Spearman correlation analysis to evaluate the relationships between quantitative variables. All variables that had a statistically significant correlation were included in ANOVA multiple regression models. The dependent variables in the multiple regression analyses were CDI score and total and emotional scores on the SDQ. We found that only Cognitive self-perceived rhythmicity contributed significantly to the first multiple regression with CDI as the outcome variable. The second regression with SDQ Emotional score as the outcome variable showed that female sex, age, and self-perceived affective rhythmicity contributed significantly to the model. The third regression with SDQ total score as the outcome variable showed that chronotype, self-perceived cognitive symptoms, and affective rhythmicity contributed significantly to the model. In conclusion, we found that lower self-perceived rhythmicity in cognitive factors and higher self-perceived rhythmicity in affective factors were related to presence and intensity of psychiatric and depressive symptoms.

Development of an age-dependent cognitive index: relationship between impaired learning and disturbances in circadian timekeeping

Preclinical quantitative models of cognitive performance are necessary for translation from basic research to clinical studies. In rodents, non-cognitive factors are a potential influence on testing outcome and high variability in behavior requires multiple time point testing for better assessment of performance in more sophisticated tests. Thus, these models have limited translational value as most human cognitive tests characterize cognition using single digit scales to distinguish between impaired and unimpaired function. To address these limitations, we developed a cognitive index for learning based on previously described scores for strategies used by mice to escape the Barnes maze. We compared the cognitive index and circadian patterns of light-dark entrainment in young (4-6 months), middle-aged (13-14 months), and aged (18-24 months) mice as cognitive changes during aging are often accompanied by pronounced changes in sleep-wake cycle. Following continuous analysis of circadian wheel-running activity (30-40 days), the same cohorts of mice were tested in the Barnes maze. Aged mice showed significant deficits in the learning and memory portions of the Barnes maze relative to young and middle-aged animals, and the cognitive index was positively correlated to the memory portion of the task (probe) in all groups. Significant age-related alterations in circadian entrainment of the activity rhythm were observed in the middle-aged and aged cohorts. In middle-aged mice, the delayed phase angle of entrainment and increased variability in the daily onsets of activity preceded learning and memory deficits observed in aged animals. Interestingly, learning-impaired mice were distinguished by a positive relationship between the extent of Barnes-related cognitive impairment and variability in daily onsets of circadian activity. While it is unclear whether changes in the sleep-wake cycle or other circadian rhythms play a role in cognitive impairment during aging, our results suggest that circadian rhythm perturbations or misalignment may nevertheless provide an early predictor of age-related cognitive decline.

Association of perceptions of artificial light-at-night, light-emitting device usage and environmental noise appraisal with psychological distress, sleep quality and chronotype: A cross sectional study

Exposure to artificial light-at-night (ALAN) is increasing globally, and there are concerns around how ALAN may impact sleep, psychological and physical health. However, there is a lack of evidence in the literature on how individuals perceive ALAN relative to their sleeping environment and habits, and how such perceptions correspond to objectively assessed night-time illuminance at the level of the residence. This cross-sectional study examined how such perceptions associate with sleep quality, sleep timing, psychological distress and cognitive failures. Further we examined the association between illuminance levels calculated as the biologically-relevant melatonin-suppression index (MSI) and the self-report of perception of ALAN. Five hundred and fifty two adult participants completed a survey addressing perception of ALAN in sleep environment along with the Pittsburgh Sleep Quality Index, Munich Chronotype Questionnaire, Cognitive Failure Questionnaire and the General Health Questionnaire. We report that perception of external ALAN in the sleeping environment was associated with poorer sleep quality, more cognitive failures and greater psychological distress, when controlling for age, sex, house location and MSI. No associations were found between the perception of external ALAN and MSI scores, and MSI scores were not associated with scores on any of the self-report measures. Internal lighting passing into the sleeping environment was associated with poorer sleep quality but not with psychological wellbeing. Habitual use of light-emitting devices was associated with poorer psychological wellbeing but not with sleep quality and sleep timing. Perception of environmental noise annoyance at night was associated with higher psychological distress and poorer quality sleep, and the perception of noise annoyance was associated with perception of ALAN. These results may suggest heightened attentional bias towards ALAN associated with poor sleep quality and higher levels of psychological distress, and highlight the need for more granular approaches in the study of ALAN and sleep and psychological health in terms of levels individual ALAN exposure, and an interpretation that seeks to integrate biological and psychological perspectives.

An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia

INTRODUCTION

Schizophrenia and Major Depressive Disorder (MDD) are highly burdensome mental disorders, with significant cost to both individuals and society. Despite these disorders representing distinct clinical categories, they are each heterogenous in their symptom profiles, with considerable transdiagnostic features. Although movement and sleep abnormalities exist in both disorders, little is known of the precise nature of these changes longitudinally. Passively-collected longitudinal data from wearable sensors is well suited to characterize naturalistic features which may cross traditional diagnostic categories (e.g., highlighting behavioral markers not captured by self-report information).

METHODS

The present analyses utilized raw minute-level actigraphy data from three diagnostic groups: individuals with schizophrenia (N = 23), individuals with depression (N = 22), and controls (N = 32), respectively, to interrogate naturalistic behavioral differences between groups. Subjects' week-long actigraphy data was processed without diagnostic labels via unsupervised machine learning clustering methods, in order to investigate the natural bounds of psychopathology. Further, actigraphic data was analyzed across time to determine timepoints influential in model outcomes.

RESULTS

We find distinct actigraphic phenotypes, which differ between diagnostic groups, suggesting that unsupervised clustering of naturalistic data aligns with existing diagnostic constructs. Further, we found statistically significant inter-group differences, with depressed persons showing the highest behavioral variability.

LIMITATIONS

However, diagnostic group differences only consider biobehavioral trends captured by raw actigraphy information.

CONCLUSIONS

Passively-collected movement information combined with unsupervised deep learning algorithms shows promise in identifying naturalistic phenotypes in individuals with mental health disorders, specifically in discriminating between MDD and schizophrenia.

Automatic sleep stages classification using multi-level fusion

Sleep efficiency is a factor that can determine a person's healthy life. Sleep efficiency can be calculated by analyzing the results of the sleep stage classification. There have been many studies to classify sleep stages automatically using multiple signals to improve the accuracy of the sleep stage classification. The fusion method is used to process multi-signal data. Fusion methods include data-level fusion, feature-level fusion, and decision-level fusion methods. We propose a multi-level fusion method to increase the accuracy of the sleep stage classification when using multi-signal data consisting of electroencephalography and electromyography signals. First, we used feature-level fusion to fuse the extracted features using a convolutional neural network for multi-signal data. Then, after obtaining each classified result using the fused feature data, the sleep stage was derived using a decision-level fusion method that fused classified results. We used public datasets, Sleep-EDF, to measure performance; we confirmed that the proposed multi-level fusion method yielded a higher accuracy of 87.2%, respectively, compared to single-level fusion method and more existing methods. The proposed multi-level fusion method showed the most improved performance in classifying N1 stage, where existing methods had the lowest performance.

Deficiency of the circadian clock gene Rev-erbα induces mood disorder-like behaviours and dysregulation of the serotonergic system in mice

Mood disorders such as depression, anxiety, and bipolar disorder are highly associated with disrupted daily rhythms of activity, which are often observed in shift work and sleep disturbance in humans. Recent studies have proposed the REV-ERBα protein as a key circadian nuclear receptor that links behavioural rhythms to mood regulation. However, how the Rev-erbα gene participates in the regulation of mood remains poorly understood. Here, we show that the regulation of the serotonergic (5-HTergic) system, which plays a central role in stress-induced mood behaviours, is markedly disrupted in Rev-erbα^-/- mice. Rev-erbα^-/- mice exhibit both negative and positive behavioural phenotypes, including anxiety-like and mania-like behaviours, when subjected to a stressful environment. Importantly, Rev-erbα^-/- mice show a significant decrease in the expression of a gene that encodes the rate-limiting enzyme of serotonin (5-HT) synthesis in the raphe nuclei (RN). In addition, 5-HT levels in Rev-erbα^-/- mice are significantly reduced in the prefrontal cortex, which receives strong inputs from the RN and controls stress-related behaviours. Our findings indicate that Rev-erbα plays an important role in controlling the 5-HTergic system and thus regulates mood and behaviour.

Exploring the longitudinal clustering of lifestyle behaviors, social determinants of health, and depression

Lifestyle behaviors such as exercise, sleep, smoking, diet, and social interaction are associated with depression. This study aimed to model the complex relationships between lifestyle behaviors and depression and among the lifestyle behaviors. Data from three waves of the Midlife in the United States study were used, involving 6898 adults. Network models revealed associations between the lifestyle behaviors and depression, with smoker status being strongly associated with depression. Depression, smoker status, age, time, and exercise were some of the most central components of the networks. Future lifestyle intervention research might prioritize specific behaviors based on these associations and centrality indices.

Sleep problems in infancy and early school age in very preterm infants

BACKGROUND

Sleep plays an important role for psychological and physical health, especially in infants at high risk for long-term neurodevelopmental impairment such as preterm infants.

OBJECTIVE

Our study aimed at determining risk factors for long-term sleep impairment in very-preterm (VPT; <32 weeks of gestation) infants.

METHODS

Sleep problems were analyzed in an observational study in infants of the German Neonatal Network born between January 1st 2009 and December 31st 2014. Parental questionnaires of n = 2928 VPT children were evaluated regarding the child's sleep behavior at five years of age. Univariate and logistic regression analyses were used to identify risk factors for delayed sleep onset and hyperactivity/inattention (Strength and Difficulties Questionnaire). In a second cohort of n = 342 VPT infants, sleep habits were evaluated at toddlers age via the Infant Sleep Questionnaire.

RESULTS

In our cohorts, 424/2928 (14.5 %) preterm children were diagnosed with delayed sleep onset at early school age while 57/342 (16.7 %) had sleep impairment in early infancy. Gestational age was not independently associated with sleep problems (i.e., early school age: OR 0.97, 95 % CI 0.9-1.1, p = 0.15). Notably, in both our cohorts, neonatal exposure to analgesics and sedatives was associated with a higher risk for sleep problems, i.e., early school age: exposure to sedatives: OR 1.31, 95%CI 1.02-1.7, p = 0.03. Sleep problems and drug exposure were both associated with hyperactivity/inattention.

CONCLUSION

Sleep problems of VPT children are unrelated to gestational age which suggests rather individual risk factors. The significant neonatal exposure to analgesics and sedatives may contribute to long-term sleep impairment.

Non-rapid eye movement sleep determines resilience to social stress

Resilience, the ability to overcome stressful conditions, is found in most mammals and varies significantly among individuals. A lack of resilience can lead to the development of neuropsychiatric and sleep disorders, often within the same individual. Despite extensive research into the brain mechanisms causing maladaptive behavioral-responses to stress, it is not clear why some individuals exhibit resilience. To examine if sleep has a determinative role in maladaptive behavioral-response to social stress, we investigated individual variations in resilience using a social-defeat model for male mice. Our results reveal a direct, causal relationship between sleep amount and resilience-demonstrating that sleep increases after social-defeat stress only occur in resilient mice. Further, we found that within the prefrontal cortex, a regulator of maladaptive responses to stress, pre-existing differences in sleep regulation predict resilience. Overall, these results demonstrate that increased NREM sleep, mediated cortically, is an active response to social-defeat stress that plays a determinative role in promoting resilience. They also show that differences in resilience are strongly correlated with inter-individual variability in sleep regulation.

A Multilevel Temporal Context Network for Sleep Stage Classification

Sleep stage classification is essential in diagnosing and treating sleep disorders. Many deep learning models have been proposed to classify sleep stages by automatic learning features and temporal context information. These temporal context features come from the intra-epoch temporal features, which represent the overall morphology of an epoch, and temporal features of adjacent epochs and long epochs, which represent the influence between epochs. However, most existing methods do not fully use the complementarity of the three-level temporal features, resulting in incomplete extracted temporal features. To solve this problem, we propose a multilevel temporal context network (MLTCN) to learn the temporal features from intra-epoch, adjacent epochs, and long epochs, which utilizes the complete temporal features to improve classification accuracy. We evaluate the performance of the proposed model on the Sleep-EDF datasets published in 2013 and 2018. The experimental results show that our MLTCN can achieve an overall accuracy of 84.2% and a kappa coefficient of 0.78 on the Sleep-EDF-2013 dataset. On the larger Sleep-EDF-2018 dataset, the overall accuracy is 81.0%, and a kappa coefficient is 0.74. Our model can better assist sleep experts in diagnosing sleep disorders.

Circadian Rhythm Disruption Increases Tumor Growth Rate and Accumulation of Myeloid-Derived Suppressor Cells

Circadian rhythm disruption is implicated in the initiation and progression of many diseases, including cancer. External stimuli, such as sunlight, serve to synchronize physiological processes and cellular functions to a 24-h cycle. The immune system is controlled by circadian rhythms, and perturbation of these rhythms can potentially alter the immune response to infections and tumors. The effect of circadian rhythm disruption on the immune response to tumors remains unclear. Specifically, the effects of circadian disruption (CD) on immunosuppressive cell types within the tumor, such as myeloid-derived suppressor cells (MDSCs), are unknown. In this study, a shifting lighting schedule is used to disrupt the circadian rhythm of mice. After acclimation to lighting schedules, mice are inoculated with 4T1 or B16-F10 tumors. Tumor growth is increased in mice housed under circadian disrupting lighting conditions compared to standard lighting conditions. Analysis of immune populations within the spleen and tumor shows an increased accumulation of MDSCs within these tissues, suggesting that MDSC mediated immunosuppression plays a role in the enhanced tumor growth caused by circadian disruption. This paves the way for future studies of the effects of CD on immunosuppression in cancer.

Transcriptomic analysis in the striatum reveals the involvement of Nurr1 in the social behavior of prenatally valproic acid-exposed male mice

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that exhibits neurobehavioral deficits characterized by abnormalities in social interactions, deficits in communication as well as restricted interests, and repetitive behaviors. The basal ganglia is one of the brain regions implicated as dysfunctional in ASD. In particular, the defects in corticostriatal function have been reported to be involved in the pathogenesis of ASD. Surface deformation of the striatum in the brains of patients with ASD and their correlation with behavioral symptoms was reported in magnetic resonance imaging (MRI) studies. We demonstrated that prenatal valproic acid (VPA) exposure induced synaptic and molecular changes and decreased neuronal activity in the striatum. Using RNA sequencing (RNA-Seq), we analyzed transcriptome alterations in striatal tissues from 10-week-old prenatally VPA-exposed BALB/c male mice. Among the upregulated genes, Nurr1 was significantly upregulated in striatal tissues from prenatally VPA-exposed mice. Viral knockdown of Nurr1 by shRNA significantly rescued the reduction in dendritic spine density and the number of mature dendritic spines in the striatum and markedly improved social deficits in prenatally VPA-exposed mice. In addition, treatment with amodiaquine, which is a known ligand for Nurr1, mimicked the social deficits and synaptic abnormalities in saline-exposed mice as observed in prenatally VPA-exposed mice. Furthermore, PatDp+/- mice, a commonly used ASD genetic mouse model, also showed increased levels of Nurr1 in the striatum. Taken together, these results suggest that the increase in Nurr1 expression in the striatum is a mechanism related to the changes in synaptic deficits and behavioral phenotypes of the VPA-induced ASD mouse model.

Dendrobium officinale polysaccharide attenuates cognitive impairment in circadian rhythm disruption mice model by modulating gut microbiota

Dendrobium officinale polysaccharide (DOP) has received an increasing amount of attention as it could alleviate AD-related cognitive impairment via the regulation of microglial activation. However, the modulatory mechanism of DOP on circadian rhythm disruption (CRD) and related cognitive impairment needs further investigation. In our study, the circadian rhythm disruption mice showed a deficit in recognition and spatial memory. DOP treatment reshaped the perturbation of gut microbiota caused by CRD, including up-regulated the abundance of Akkermansia and Alistipes, down-regulated the abundance of Clostridia. In addition, DOP restored histopathological changes, reduced inflammatory cells infiltration and strengthened mucosal integrity. Mechanistically, DOP ameliorated intestinal barrier dysfunction by up-regulating tight junction protein expression, which in turn improved the invasion of lipopolysaccharide to blood and brain. The change of these contributes to inhibiting the NF-κB activation and neuroinflammation, and thus attenuating hippocampus neuronal damage and the deposition of Aβ. Meanwhile, our results revealed that DOP could reverse the levels of metabolites derived related to cognitive function improvement, and these metabolites were closely associated with the key microbiota. Therefore, we speculated that DOP has the potential to provide neuroprotection against cognitive impairment by modulating the gut microbiota.

Timing is everything: Circadian rhythms and their role in the control of sleep

Sleep and the circadian clock are intertwined and have persisted throughout history. The suprachiasmatic nucleus (SCN) orchestrates sleep by controlling circadian (Process C) and homeostatic (Process S) activities. As a "hand" on the endogenous circadian clock, melatonin is critical for sleep regulation. Light serves as a cue for sleep/wake control by activating retino-recipient cells in the SCN and subsequently suppressing melatonin. Clock genes are the molecular timekeepers that keep the 24 h cycle in place. Two main sleep and behavioural disorder diagnostic manuals have now officially recognised the importance of these processes for human health and well-being. The body's ability to respond to daily demands with the least amount of effort is maximised by carefully timing and integrating all components of sleep and waking. In the brain, the organization of timing is essential for optimal brain physiology.

Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning

In this work we introduce a novel meta-learning method for sleep scoring based on self-supervised learning. Our approach aims at building models for sleep scoring that can generalize across different patients and recording facilities, but do not require a further adaptation step to the target data. Towards this goal, we build our method on top of the Model Agnostic Meta-Learning (MAML) framework by incorporating a self-supervised learning (SSL) stage, and call it S2MAML. We show that S2MAML can significantly outperform MAML. The gain in performance comes from the SSL stage, which we base on a general purpose pseudo-task that limits the overfitting to the subject-specific patterns present in the training dataset. We show that S2MAML outperforms standard supervised learning and MAML on the SC, ST, ISRUC, UCD and CAP datasets. Clinical relevance- Our work tackles the generalization problem of automatic sleep scoring models. This is one of the main hurdles that limits the adoption of such models for clinical and research sleep studies.

Post-traumatic stress disorder prevalence and sleep quality in fire victims and rescue workers in southern Brazil: a cross-sectional study

OBJECTIVES

This survey was conducted to evaluate the prevalence of post-traumatic stress disorder (PTSD) and the sleep quality in victims and rescue team of the third deadliest nightclub fire in the world.

STUDY DESIGN

A cross-sectional study.

METHODS

Participants were victims and rescue workers exposed to a fire at a nightclub, which occurred in January 2013 in Southern Brazil. The Pittsburgh Sleep Quality Index (PSQI), composed of seven subjective sleep variables (including daytime dysfunction), and PTSD Checklist - Civilian version (PCL-C) were applied to all people who sought medical attention at the local reference center in the first year after the event. Comprehensive information was obtained concerning sociodemographic factors, health status, and sleep complaints.

RESULTS

A total of 370 individuals, 190 victims and 180 rescue workers, were included. Participants were 70% male, with an average age of 29 years. The prevalence of PTSD was 31.9%, ranging from 24.4% for rescue workers to 38.9% for victims. The prevalence of poor sleep quality was 65.9%, ranging from 56.1% for rescue workers to 75.3% for victims. Most of the participants with PTSD (91.5%) had PSQI scores >5 (poor sleepers), against 54.0% of the non-PTSD individuals. All seven PSQI subscores showed significant differences between PTSD and non-PTSD individuals, especially daytime dysfunction. Sex, shift work, previous psychiatric disease, and sleep quality remained associated with PTSD in adjusted models, with a prevalence ratio (95% CI) of 1.76 (1.28-2.43) in females, 1.73 (1.17-2.55) in shift workers, 1.36 (1.03-1.80) in individuals with psychiatric disease history, and 5.42 (2.55-11.52) in poor sleepers.

CONCLUSIONS

The presence of daytime dysfunction increased by at least tenfold the prevalence of PTSD in this sample. Considering that daytime dysfunction was shown to be strongly associated with PTSD, sleep-related issues should be addressed in the assessment of individuals exposed to traumatic events, both victims and rescuers. Factors like shift work and female sex were also associated with PTSD, especially among victims.

Predictors of rapid eye movement sleep behavior disorder in patients with Parkinson’s disease based on random forest and decision tree

Background and objectives

Sleep disorders related to Parkinson’s disease (PD) have recently attracted increasing attention, but there are few clinical reports on the correlation of Parkinson’s disease patients with rapid eye movement (REM) sleep behavior disorder (RBD). Therefore, this study conducted a cognitive function examination for Parkinson’s disease patients and discussed the application effect of three algorithms in the screening of influencing factors and risk prediction effects.

Methods

Three algorithms (logistic regression, machine learning-based regression trees and random forest) were used to establish a prediction model for PD-RBD patients, and the application effects of the three algorithms in the screening of influencing factors and the risk prediction of PD-RBD were discussed.

Results

The subjects included 169 patients with Parkinson’s disease (Parkinson’s disease with RBD [PD-RBD] = 69 subjects; Parkinson’s disease without RBD [PD-nRBD] = 100 subjects). This study compared the predictive performance of RF, decision tree and logistic regression, selected a final model with the best model performance and proposed the importance of variables in the final model. After the analysis, the accuracy of RF (83.05%) was better than that of the other models (decision tree = 75.10%, logistic regression = 71.62%). PQSI, Scopa-AUT score, MoCA score, MMSE score, AGE, LEDD, PD-course, UPDRS total score, ESS score, NMSQ, disease type, RLSRS, HAMD, UPDRS III and PDOnsetage are the main variables for predicting RBD, along with increased weight. Among them, PQSI is the most important factor. The prediction model of Parkinson’s disease RBD that was established in this study will help in screening out predictive factors and in providing a reference for the prognosis and preventive treatment of PD-RBD patients.

Conclusions

The random forest model had good performance in the prediction and evaluation of PD-RBD influencing factors and was superior to decision tree and traditional logistic regression models in many aspects, which can provide a reference for the prognosis and preventive treatment of PD-RBD patients.

Multi-scale ResNet and BiGRU automatic sleep staging based on attention mechanism

Sleep staging is the basis of sleep evaluation and a key step in the diagnosis of sleep-related diseases. Despite being useful, the existing sleep staging methods have several disadvantages, such as relying on artificial feature extraction, failing to recognize temporal sequence patterns in the long-term associated data, and reaching the accuracy upper limit of sleep staging. Hence, this paper proposes an automatic Electroencephalogram (EEG) sleep signal staging model, which based on Multi-scale Attention Residual Nets (MAResnet) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed model is based on the residual neural network in deep learning. Compared with the traditional residual learning module, the proposed model additionally uses the improved channel and spatial feature attention units and convolution kernels of different sizes in parallel at the same position. Thus, multiscale feature extraction of the EEG sleep signals and residual learning of the neural networks is performed to avoid network degradation. Finally, BiGRU is used to determine the dependence between the sleep stages and to realize the automatic learning of sleep data staging features and sleep cycle extraction. According to the experiment, the classification accuracy and kappa coefficient of the proposed method on sleep-EDF data set are 84.24% and 0.78, which are respectively 0.24% and 0.21 higher than the traditional residual net. At the same time, this paper also verified the proposed method on UCD and SHHS data sets, and the figure of classification accuracy is 79.34% and 81.6%, respectively. Compared to related existing studies, the recognition accuracy is significantly improved, which validates the effectiveness and generalization performance of the proposed method.

A novel feature relearning method for automatic sleep staging based on single-channel EEG

Correctly identifying sleep stages is essential for assessing sleep quality and treating sleep disorders. However, the current sleep staging methods have the following problems: (1) Manual or semi-automatic extraction of features requires professional knowledge, which is time-consuming and laborious. (2) Due to the similarity of stage features, it is necessary to strengthen the learning of features. (3) Acquisition of a variety of data has high requirements on equipment. Therefore, this paper proposes a novel feature relearning method for automatic sleep staging based on single-channel electroencephalography (EEG) to solve these three problems. Specifically, we design a bottom–up and top–down network and use the attention mechanism to learn EEG information fully. The cascading step with an imbalanced strategy is used to further improve the overall classification performance and realize automatic sleep classification. The experimental results on the public dataset Sleep-EDF show that the proposed method is advanced. The results show that the proposed method outperforms the state-of-the-art methods. The code and supplementary materials are available at GitHub: https://github.com/raintyj/A-novel-feature-relearning-method.

Associations between Chinese college students’ anxiety and depression: A chain mediation analysis

Objective

Anxiety and depression are great public health concerns among college students. The purpose of this study was to explore whether sleep quality and quality of life (QoL) play mediating roles in anxiety and depression among Chinese college students.

Method

A total of 2757 college students (mean age = 19.07; SD = 1.14) completed the questionnaires, including a brief demographic survey. The 2-item General Anxiety Disorder (GAD-2) and the 2-item Patient Health Questionnaire (PHQ-2) were used to assess the symptoms of anxiety and depression, respectively. And the Pittsburgh Sleep Quality Index (PSQI) and the Short-Form 36 Health Survey (SF-36) were used to evaluate college students’ sleep quality and QoL, respectively. Mediation analyses were conducted by using PROCESS macro in the SPSS software.

Result

Anxiety had both direct and indirect effects on depression. Sleep quality and QoL were not only independent mediators in the relationship between anxiety and depression but also chain mediators.

Conclusion

The results of the current study highlight the crucial role of early intervention for depression with a focus on college students with anxiety, more especially, on those with poorer sleep quality and lower QoL.

The Modulatory Effect of Cyclocarya paliurus Flavonoids on Intestinal Microbiota and Hypothalamus Clock Genes in a Circadian Rhythm Disorder Mouse Model

Circadian rhythm disruption is detrimental and results in adverse health consequences. We used a multi-omics profiling approach to investigate the effects of Cyclocarya paliurus flavonoid (CPF)-enriched diets on gut microbiota, metabolites, and hypothalamus clock genes in mice with induced circadian rhythm disruption. It was observed that CPF supplementation altered the specific composition and function of gut microbiota and metabolites induced by circadian rhythm disruption. Analysis showed that the abundance of Akkermansia increased, while the abundance of Clostridiales and Ruminiclostridium displayed a significant downward trend after the CPF intervention. Correlation analysis also revealed that these gut microbes had certain correlations with the metabolites, suggesting that CPFs help the intestinal microbiota to repair the intestinal environment and modulate the release of some beneficial metabolites. Notably, single-cell RNA-seq revealed that CPF supplementation significantly regulated the expression of genes associated with circadian rhythm, myelination, and neurodegenerative diseases. Altogether, these findings highlight that CPFs may represent a promising dietary therapeutic strategy for treating circadian rhythm disruption.