Acoustic enhancement of slow wave sleep on consecutive nights improves alertness and attention in chronically short sleepers

The glymphatic system and Amyotrophic lateral sclerosis

The glymphatic system and the meningeal lymphatic vessels provide a pathway for transport of solutes and clearance of toxic material from the brain. Of specific relevance to ALS, this is applicable for TDP-43 and glutamate, both major elements in disease pathogenesis. Flow is propelled by arterial pulsation, respiration, posture, as well as the positioning and proportion of aquaporin-4 channels (AQP4). Non-REM slow wave sleep is the is key to glymphatic drainage which discontinues during wakefulness. In Parkinson's disease and Alzheimer's disease, sleep impairment is known to predate the development of characteristic clinical features by several years and is associated with progressive accumulation of toxic proteinaceous products. While sleep issues are well described in ALS, consideration of preclinical sleep impairment or the potential of a failing glymphatic system in ALS has rarely been considered. Here we review how the glymphatic system may impact ALS. Preclinical sleep impairment as an unrecognized major risk factor for ALS is considered, while potential therapeutic options to improve glymphatic flow are explored.

Application of graph frequency attention convolutional neural networks in depression treatment response

Depression, a prevalent global mental health disorder, necessitates precise treatment response prediction for the improvement of personalized care and patient prognosis. The Graph Convolutional Neural Networks (GCNs) have emerged as a promising technique for handling intricate signals and classification tasks owing to their end-to-end neural architecture and nonlinear processing capabilities. In this context, this article proposes a model named the Graph Frequency Attention Convolutional Neural Network (GFACNN). Primarily, the model transforms the EEG signals into graphs to depict the connections between electrodes and brain regions, while integrating a frequency attention module to accentuate brain rhythm information. The proposed approach delves into the application of graph neural networks in the classification of EEG data, aiming to evaluate the response to antidepressant treatment and discern between treatment-resistant and treatment-responsive cases. Experimental results obtained from an EEG dataset at Peking University People's Hospital demonstrate the notable performance of GFACNN in distinguishing treatment responses among depression patients, surpassing deep learning methodologies including CapsuleNet and GoogLeNet. This highlights the efficacy of graph neural networks in leveraging the connections within EEG signal data. Overall, GFACNN exhibits potential for the classification of depression EEG signals, thereby potentially aiding clinical diagnosis and treatment.

Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices

Sleep is essential for our physical and mental well-being. During sleep, despite the paucity of overt behavior, our brain remains active and exhibits a wide range of coupled brain oscillations. In particular slow oscillations are characteristic for sleep, however whether they are directly involved in the functions of sleep, or are mere epiphenomena, is not yet fully understood. To disentangle the causality of these relationships, experiments utilizing techniques to detect and manipulate sleep oscillations in real-time are essential. In this review, we first overview the theoretical principles of closed-loop auditory stimulation (CLAS) as a method to study the role of slow oscillations in the functions of sleep. We then describe technical guidelines and best practices to perform CLAS and analyze results from such experiments. We further provide an overview of how CLAS has been used to investigate the causal role of slow oscillations in various sleep functions. We close by discussing important caveats, open questions, and potential topics for future research.

Effects of continuous positive airway pressure treatment on sleep architecture in adults with obstructive sleep apnea and type 2 diabetes

Obstructive sleep apnea (OSA) severely impacts sleep and has long-term health consequences. Treating sleep apnea with continuous positive airway pressure (CPAP) not only relieves obstructed breathing, but also improves sleep. CPAP improves sleep by reducing apnea-induced awakenings. CPAP may also improve sleep by enhancing features of sleep architecture assessed with electroencephalography (EEG) that maximize sleep depth and neuronal homeostasis, such as the slow oscillation and spindle EEG activity, and by reducing neurophysiological arousal during sleep (i.e., beta EEG activity). We examined cross-sectional differences in quantitative EEG characteristics of sleep, assessed with power spectral analysis, in 29 adults with type 2 diabetes treated with CPAP and 24 adults undergoing SHAM CPAP treatment (total n = 53). We then examined changes in spectral characteristics of sleep as the SHAM group crossed over to active CPAP treatment (n = 19). Polysomnography (PSG) from the CPAP titration night was used for the current analyses. Analyses focused on EEG frequencies associated with sleep maintenance and arousal. These included the slow oscillation (0.5–1 Hz), sigma activity (12–16 Hz, spindle activity), and beta activity (16–20 Hz) in F3, F4, C3, and C4 EEG channels. Whole night non-rapid eye movement (NREM) sleep and the first period of NREM spectral activity were examined. Age and sex were included as covariates. There were no group differences between CPAP and SHAM in spectral characteristics of sleep architecture. However, SHAM cross-over to active CPAP was associated with an increase in relative 12–16 Hz sigma activity across the whole night and a decrease in average beta activity across the whole night. Relative slow oscillation power within the first NREM period decreased with CPAP, particularly for frontal channels. Sigma and beta activity effects did not differ by channel. These findings suggest that CPAP may preferentially enhance spindle activity and mitigate neurophysiological arousal. These findings inform the neurophysiological mechanisms of improved sleep with CPAP and the utility of quantitative EEG measures of sleep as a treatment probe of improvements in neurological and physical health with CPAP.

Brassica rapa L. (Tibetan turnip) prevents sleep-deprivation induced cognitive deficits via the inhibition of neuroinflammation and mitochondrial depolarization

Brassica rapa L., an edible, feeding and medicinal plant cultivated on the Tibetan plateau with altitudes above 3800 m, has several pharmacological effects. However, its therapeutic effects against memory impairment and central fatigue have yet to be conclusively established. In this study, the Y-maze and Morris water maze tasks revealed that Brassica rapa L. aqueous extract (BE) significantly ameliorated cognitive deficits of sleep deprivation (SD)-treated mice. Moreover, BE treatment partially alleviated SD-induced reductions in the levels of peripheral energy metabolism, and significantly decreased inflammatory factor levels in serum and hippocampus. In addition, BE treatment significantly relieved central fatigue and stabilized the excitability as well as activities of neurons by regulating the levels of hypothalamus tryptophan metabolites and striatum neurotransmitters. The neuroprotective effects of BE were also confirmed using glutamate-treated HT22 cells, whereby BE pretreatment significantly attenuated intracellular ROS production and mitochondrial depolarization via adenosine 5'-monophosphate activated protein kinase/peroxisome proliferators-activated receptors (AMPK/PPAR-γ) signaling pathways. Thus, BE might probably prevent SD-induced learning and memory deficits by inhibiting neuroinflammation and restoring mitochondrial energy metabolism in the hippocampus. These findings imply that BE is a potential complementary therapy for those suffering from deficient sleep or neurometabolic disorders, although this needs verification by prospective clinical studies.

Brain Stimulation for Improving Sleep and Memory

Over the past few decades, the importance of sleep has become increasingly recognized for many physiologic functions, including cognition. Many studies have reported the deleterious effect of sleep loss or sleep disruption on cognitive performance. Beyond ensuring adequate sleep quality and duration, discovering methods to enhance sleep to augment its restorative effects is important to improve learning in many populations, such as the military, students, age-related cognitive decline, and cognitive disorders.

Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview

Sleep is crucial for human health from metabolic, mental, emotional, and social points of view; obtaining good sleep in terms of quality and duration is fundamental for maintaining a good life quality. Over the years, several systems have been proposed in the scientific literature and on the market to derive metrics used to quantify sleep quality as well as detect sleep disturbances and disorders. In this field, wearable systems have an important role in the discreet, accurate, and long-term detection of biophysical markers useful to determine sleep quality. This paper presents the current state-of-the-art wearable systems and software tools for sleep staging and detecting sleep disorders and dysfunctions. At first, the paper discusses sleep's functions and the importance of monitoring sleep to detect eventual sleep disturbance and disorders. Afterward, an overview of prototype and commercial headband-like wearable devices to monitor sleep is presented, both reported in the scientific literature and on the market, allowing unobtrusive and accurate detection of sleep quality markers. Furthermore, a survey of scientific works related the effect of the COVID-19 pandemic on sleep functions, attributable to both infection and lifestyle changes. In addition, a survey of algorithms for sleep staging and detecting sleep disorders is introduced based on an analysis of single or multiple biosignals (EEG-electroencephalography, ECG-electrocardiography, EMG-electromyography, EOG-electrooculography, etc.). Lastly, comparative analyses and insights are provided to determine the future trends related to sleep monitoring systems.

Auditory deep sleep stimulation in older adults at home: a randomized crossover trial

Background

Auditory stimulation has emerged as a promising tool to enhance non-invasively sleep slow waves, deep sleep brain oscillations that are tightly linked to sleep restoration and are diminished with age. While auditory stimulation showed a beneficial effect in lab-based studies, it remains unclear whether this stimulation approach could translate to real-life settings.

Methods

We present a fully remote, randomized, cross-over trial in healthy adults aged 62-78 years (clinicaltrials.gov: NCT03420677). We assessed slow wave activity as the primary outcome and sleep architecture and daily functions, e.g., vigilance and mood as secondary outcomes, after a two-week mobile auditory slow wave stimulation period and a two-week Sham period, interleaved with a two-week washout period. Participants were randomized in terms of which intervention condition will take place first using a blocked design to guarantee balance. Participants and experimenters performing the assessments were blinded to the condition.

Results

Out of 33 enrolled and screened participants, we report data of 16 participants that received identical intervention. We demonstrate a robust and significant enhancement of slow wave activity on the group-level based on two different auditory stimulation approaches with minor effects on sleep architecture and daily functions. We further highlight the existence of pronounced inter- and intra-individual differences in the slow wave response to auditory stimulation and establish predictions thereof.

Conclusions

While slow wave enhancement in healthy older adults is possible in fully remote settings, pronounced inter-individual differences in the response to auditory stimulation exist. Novel personalization solutions are needed to address these differences and our findings will guide future designs to effectively deliver auditory sleep stimulations using wearable technology.

Strategies to Limit Cognitive Impairments under Sleep Restriction: Relationship to Stress Biomarkers

Adding relaxation techniques during nap or auditory stimulation of EEG slow oscillation (SO) during nighttime sleep may limit cognitive impairments in sleep-deprived subjects, potentially through alleviating stress-releasing effects. We compared daytime sleepiness, cognitive performances, and salivary stress biomarker responses in 11 volunteers (aged 18–36) who underwent 5 days of sleep restriction (SR, 3 h per night, with 30 min of daily nap) under three successive conditions: control (SR-CT), relaxation techniques added to daily nap (SR-RT), and auditory stimulation of sleep slow oscillations (SO) during nighttime sleep (SR-NS). Test evaluation was performed at baseline (BASE), the fifth day of chronic SR (SR5), and the third and fifth days after sleep recovery (REC3, REC5, respectively). At SR5, less degradation was observed for percentage of commission errors in the executive Go–noGo inhibition task in SR-RT condition compared to SR-CT, and for sleepiness score in SR-NS condition compared both to SR-CT and SR-RT. Beneficial effects of SR-RT and SR-NS were additionally observed on these two parameters and on salivary α-amylase (sAA) at REC3 and REC5. Adding relaxation techniques to naps may help performance in inhibition response, and adding nocturnal auditory stimulation of SO sleep may benefit daytime sleepiness during sleep restriction with persistent effects during recovery. The two strategies activated the autonomic nervous system, as shown by the sAA response.

Sleep Stage Classification Based on Multi-Centers: Comparison Between Different Ages, Mental Health Conditions and Acquisition Devices

PURPOSE

To investigate the general sleep stage classification performance of deep learning networks, three datasets, across different age groups, mental health conditions, and acquisition devices, comprising adults (SHHS) and children without mental health conditions (CCSHS), and subjects with mental health conditions (XJ), were included in this study.

METHODS

A long short-term memory (LSTM) network was used to evaluate the effect of different ages, mental health conditions, and acquisition devices on the sleep stage classification performance and the general performance.

RESULTS

Results showed that the age and different mental health conditions may affect the sleep stage classification performance of the network. The same acquisition device using different parameters may not have an obvious effect on the classification performance. When using a single dataset and two datasets for training, the network performed better only on the training dataset. When training was conducted with three datasets, the network performed well for all datasets with a Cohen's Kappa of 0.8192 and 0.8472 for the SHHS and CCSHS, respectively, but performed relatively worse (0.6491) for the XJ, which indicated the complexity effect of different mental health conditions on the sleep stage classification task. Moreover, the performance of the network trained using three datasets was similar for each dataset to that of the network trained using a single dataset and tested on the same dataset.

CONCLUSION

These results suggested that when more datasets across different age groups, mental health conditions, and acquisition devices (ie, more datasets with different feature distributions for each sleep stage) are used for training, the general performance of a deep learning network will be superior for sleep stage classification tasks with varied conditions.

Challenges and Opportunities for Applying Wearable Technology to Sleep

Wearable technology has a history in sleep research dating back to the 1970s. Because modern wearable technology is relatively cheap and widely used by the general population, this represents an opportunity to leverage wearable devices to advance sleep medicine and research. However, there is a lack of published validation studies designed to quantify device performance against accepted gold standards, especially across different populations. Recommendations for conducting performance assessments and using wearable devices are now published with the goal of standardizing wearable device implementation and advancing the field.

Sounding It Out: Auditory Stimulation and Overnight Memory Processing

Purpose of Review

Auditory stimulation is a technique that can enhance neural oscillations linked to overnight memory consolidation. In this review, we evaluate the impacts of auditory stimulation on the neural oscillations of sleep and associated memory processes in a variety of populations.

Recent Findings

Cortical EEG recordings of slow-wave sleep (SWS) are characterised by two cardinal oscillations: slow oscillations (SOs) and sleep spindles. Auditory stimulation delivered in SWS enhances SOs and phase-coupled spindle activity in healthy children and adults, children with ADHD, adults with mild cognitive impairment and patients with major depression. Under certain conditions, auditory stimulation bolsters the benefits of SWS for memory consolidation, although further work is required to fully understand the factors affecting stimulation-related memory gains. Recent work has turned to rapid eye movement (REM) sleep, demonstrating that auditory stimulation can be used to manipulate REM sleep theta oscillations.

Summary

Auditory stimulation enhances oscillations linked to overnight memory processing and shows promise as a technique for enhancing the memory benefits of sleep.

Updated Review of the Acoustic Modulation of Sleep: Current Perspectives and Emerging Concepts

With growing interest in the use of acoustic stimuli in sleep research and acoustic interventions used therapeutically for sleep enhancement, there is a need for an overview of the current lines of research. This paper summarizes the various ways to use acoustic input before sleep or stimulation during sleep. It thereby focuses on the respective methodological requirements, advantages, disadvantages, potentials and difficulties of acoustic sleep modulation. It highlights differences in subjective and objective outcome measures, immediate and whole night effects and short versus long term effects. This recognizes the fact that not all outcome parameters are relevant in every research field. The same applies to conclusions drawn from other outcome dimensions, consideration of mediating factors, levels of stimulation processing and the impact of inter-individual differences. In addition to the deliberate influences of acoustic input on sleep, one paragraph describes adverse environmental acoustic influences. Finally, the possibilities for clinical and basic research-related applications are discussed, and emerging opportunities are presented. This overview is not a systematic review but aims to present the current perspective and hence summarizes the most up-to-date research results and reviews. This is the first review providing a summary of the broad spectrum of possibilities to acoustically influence sleep.