Changes in sleep EEG with aging in humans and rodents

Effect of Aging and a Dual Orexin Receptor Antagonist on Sleep Architecture and Non-REM Oscillations Including an REM Behavior Disorder Phenotype in the PS19 Mouse Model of Tauopathy

The impact of tau pathology on sleep microarchitecture features, including slow oscillations, spindles, and their coupling, has been understudied, despite the proposed importance of these electrophysiological features toward learning and memory. Dual orexin receptor antagonists (DORAs) are known to promote sleep, but whether and how they affect sleep microarchitecture in the setting of tauopathy is unknown. In the PS19 mouse model of tauopathy MAPT (microtubule-associated protein tau) P301S (both male and female), young PS19 mice 2-3 months old show a sleep electrophysiology signature with markedly reduced spindle duration and power and elevated slow oscillation (SO) density compared with littermate controls, although there is no significant tau hyperphosphorylation, tangle formation, or neurodegeneration at this age. With aging, there is evidence for sleep disruption in PS19 mice, characterized by reduced REM duration, increased non-REM and REM fragmentation, and more frequent brief arousals at the macrolevel and reduced spindle density, SO density, and spindle-SO coupling at the microlevel. In ∼33% of aged PS19 mice, we unexpectedly observed abnormal goal-directed behaviors in REM, including mastication, paw grasp, and forelimb/hindlimb extension, seemingly consistent with REM behavior disorder (RBD). Oral administration of DORA-12 in aged PS19 mice increased non-REM and REM duration, albeit with shorter bout lengths, and increased spindle density, spindle duration, and SO density without change to spindle-SO coupling, power in either the SO or spindle bands, or the arousal index. We observed a significant effect of DORA-12 on objective measures of RBD, thereby encouraging future exploration of DORA effects on sleep-mediated cognition and RBD treatment.SIGNIFICANCE STATEMENT The specific effect of tauopathy on sleep macroarchitecture and microarchitecture throughout aging remains unknown. Our key findings include the following: (1) the identification of a sleep EEG signature constituting an early biomarker of impending tauopathy; (2) sleep physiology deteriorates with aging that are also markers of off-line cognitive processing; (3) the novel observation that dream enactment behaviors reminiscent of RBD occur, likely the first such observation in a tauopathy model; and (4) a dual orexin receptor antagonist is capable of restoring several of the sleep macroarchitecture and microarchitecture abnormalities.

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening

Introduction

Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease.

Methods

The first crucial step in evaluating individuals' quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance.

Results

The performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures.

Discussion

The current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals' sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

Sleep-wake patterns are altered with age, Prdm13 signaling in the DMH, and diet restriction in mice

Old animals display significant alterations in sleep-wake patterns such as increases in sleep fragmentation and sleep propensity. Here, we demonstrated that PR-domain containing protein 13 (Prdm13)+ neurons in the dorsomedial hypothalamus (DMH) are activated during sleep deprivation (SD) in young mice but not in old mice. Chemogenetic inhibition of Prdm13+ neurons in the DMH in young mice promotes increase in sleep attempts during SD, suggesting its involvement in sleep control. Furthermore, DMH-specific Prdm13-knockout (DMH-Prdm13-KO) mice recapitulated age-associated sleep alterations such as sleep fragmentation and increased sleep attempts during SD. These phenotypes were further exacerbated during aging, with increased adiposity and decreased physical activity, resulting in shortened lifespan. Dietary restriction (DR), a well-known anti-aging intervention in diverse organisms, ameliorated age-associated sleep fragmentation and increased sleep attempts during SD, whereas these effects of DR were abrogated in DMH-Prdm13-KO mice. Moreover, overexpression of Prdm13 in the DMH ameliorated increased sleep attempts during SD in old mice. Therefore, maintaining Prdm13 signaling in the DMH might play an important role to control sleep-wake patterns during aging.

Cognitive Deficits in Aging Related to Changes in Basal Forebrain Neuronal Activity

Aging is a physiological process accompanied by a decline in cognitive performance. The cholinergic neurons of the basal forebrain provide projections to the cortex that are directly engaged in many cognitive processes in mammals. In addition, basal forebrain neurons contribute to the generation of different rhythms in the EEG along the sleep/wakefulness cycle. The aim of this review is to provide an overview of recent advances grouped around the changes in basal forebrain activity during healthy aging. Elucidating the underlying mechanisms of brain function and their decline is especially relevant in today's society as an increasingly aged population faces higher risks of developing neurodegenerative diseases such as Alzheimer's disease. The profound age-related cognitive deficits and neurodegenerative diseases associated with basal forebrain dysfunction highlight the importance of investigating the aging of this brain region.

Spinal cord injury leads to more neurodegeneration in the hippocampus of aged male rats compared to young rats

Although the disruptive effects of spinal cord injury (SCI) on the hippocampus have been confirmed in some animal studies, no study has investigated its retrograde manifestations in the hippocampus of aged subjects. Herein, we compared the aged rats with young ones 3 weeks after the induction of SCI (Groups: Sham.Young, SCI.Young, Sham.Aged, SCI.Aged). The locomotion, hippocampal apoptosis, hippocampal rhythms (Delta, Theta, Beta, Gamma) max frequency (Max.rf) and power, hippocampal neurogenesis, and hippocampal receptors (NMDA, GABA A, Muscarinic1/M1), which are important in the generation of rhythms and neurogenesis, were compared in aged rats in contrast to young rats. At the end of the third week, the number of apoptotic (Tunel⁺) cells in the hippocampus (CA1, DG) of SCI animals was significantly higher compared to the sham animals, and also, it was significantly higher in the SCI.Aged group compared to SCI.Young group. Moreover, the rate of neurogenesis (DCX⁺, BrdU⁺ cells) and expression of M1 and NMDA receptors were significantly lower in the SCI.Aged group compared to SCI.Young group. The power and Max.fr of all rhythms were significantly lower in SCI groups compared to sham groups. Despite the decrease in the power of rhythms in the SCI.Aged group compared to SCI.Young group, there was no significant difference between them, and in terms of Max.fr index, only the Max.fr of theta and beta rhythms were significantly lower in the SCI.Aged group compared to SCI.Young group. This study showed that SCI could cause more neurodegeneration in the hippocampus of aged animals compared to young animals.

To sleep or not to sleep - Effects on memory in normal aging and disease

Sleep behavior undergoes significant changes across the lifespan, and aging is associated with marked alterations in sleep amounts and quality. The primary sleep changes in healthy older adults include a shift in sleep timing, reduced slow-wave sleep, and impaired sleep maintenance. However, neurodegenerative and psychiatric disorders are more common among the elderly, which further worsen their sleep health. Irrespective of the cause, insufficient sleep adversely affects various bodily functions including energy metabolism, mood, and cognition. In this review, we will focus on the cognitive changes associated with inadequate sleep during normal aging and the underlying neural mechanisms.

Changes in the cortical network during sleep stage transitions

Sleep state transitions are closely related to insomnia, drowsiness, and sleep maintenance. However, how the cortical network varies during such a transition process remains unclear. Changes in the cortical interaction during the short-term process of sleep stage transitions were investigated. In all, 40 healthy young participants underwent overnight polysomnography. The phase transfer entropy of six frequency bands was obtained from 16 electroencephalography channels to assess the strength and direction of information flow between the cortical regions. Differences in the cortical network between the first and the last 10 s in a 40-s transition period across wakefulness, N1, N2, N3, and rapid eye movement were, respectively, studied. Various frequency bands exhibited different patterns during the sleep stage transitions. It was found that the mutual transitions between the sleep stages were not necessarily the opposite. More significant changes were observed in the sleep deepening process than in the process of sleep awakening. During sleep stage transitions, changes in the inflow and outflow strength of various cortical regions led to regional differences, but for the entire sleep progress, such an imbalance did not intensify, and a dynamic balance was instead observed. The detailed findings of variations in cortical interactions during sleep stage transition promote understanding of sleep mechanism, sleep process, and sleep function. Additionally, it is expected to provide helpful clues for sleep improvement, like reducing the time required to fall asleep and maintaining sleep depth.

Understanding Sleep Regulation in Normal and Pathological Conditions, and Why It Matters

Sleep occupies a peculiar place in our lives and in science, being both eminently familiar and profoundly enigmatic. Historically, philosophers, scientists and artists questioned the meaning and purpose of sleep. If Shakespeare's verses from MacBeth depicting "Sleep that soothes away all our worries" and "relieves the weary laborer and heals hurt minds" perfectly epitomize the alleviating benefits of sleep, it is only during the last two decades that the growing understanding of the sophisticated sleep regulatory mechanisms allows us to glimpse putative biological functions of sleep. Sleep control brings into play various brain-wide processes occurring at the molecular, cellular, circuit, and system levels, some of them overlapping with a number of disease-signaling pathways. Pathogenic processes, including mood disorders (e.g., major depression) and neurodegenerative illnesses such Huntington's or Alzheimer's diseases, can therefore affect sleep-modulating networks which disrupt the sleep-wake architecture, whereas sleep disturbances may also trigger various brain disorders. In this review, we describe the mechanisms underlying sleep regulation and the main hypotheses drawn about its functions. Comprehending sleep physiological orchestration and functions could ultimately help deliver better treatments for people living with neurodegenerative diseases.

Tick-borne encephalitis affects sleep–wake behavior and locomotion in infant rats

Background/Aims

Tick-borne encephalitis (TBE) is a disease affecting the central nervous system. Over the last decade, the incidence of TBE has steadily increased in Europe and Asia despite the availably of effective vaccines. Up to 50% of patients after TBE suffer from post-encephalitic syndrome that may develop into long-lasting morbidity. Altered sleep–wake functions have been reported by patients after TBE. The mechanisms causing these disorders in TBE are largely unknown to date. As a first step toward a better understanding of the pathology of TBEV-inducing sleep dysfunctions, we assessed parameters of sleep structure in an established infant rat model of TBE.

Methods

13-day old Wistar rats were infected with 1 × 10⁶ FFU Langat virus (LGTV). On day 4, 9, and 21 post infection, Rotarod (balance and motor coordination) and open field tests (general locomotor activity) were performed and brains from representative animals were collected in each subgroup. On day 28 the animals were implanted with a telemetric EEG/EMG system. Sleep recording was continuously performed for 24 consecutive hours starting at day 38 post infection and visually scored for Wake, NREM, and REM in 4 s epochs.

Results

As a novelty of this study, infected animals showed a significant larger percentage of time spend awake during the dark phase and less NREM and REM compared to the control animals (p < 0.01 for all comparisons). Furthermore, it was seen, that during the dark phase the wake bout length in infected animals was prolonged (p = 0.043) and the fragmentation index decreased (p = 0.0085) in comparison to the control animals. LGTV-infected animals additionally showed a reduced rotarod performance ability at day 4 (p = 0.0011) and day 9 (p = 0.0055) and day 21 (p = 0.0037). A lower locomotor activity was also seen at day 4 (p = 0.0196) and day 9 (p = 0.0473).

Conclusion

Our data show that experimental TBE in infant rats affects sleep–wake behavior, leads to decreased spontaneous locomotor activity, and impaired moto-coordinative function.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13578-022-00859-7.

Intervention Effects of Okra Extract on Brain-Gut Peptides and Intestinal Microorganisms in Sleep Deprivation Rats

Objective

Okra, possessing various bioactive components, is used to treat different diseases. This study sought to estimate the intervention effects of okra extract (OE) on brain-gut peptides (BGPs) and intestinal microorganisms in sleep deprivation (SD) rats.

Methods

SD rat models were established using the modified multiple platform method and then treated with normal saline, diazepam tablets, or different doses of OE. Body weight and average daily water consumption of rats were recorded. Depressive behaviors of rats were assessed by the open field test and sucrose preference test. Serum levels of noradrenaline, melatonin, inflammatory factors (IL-1β/IL-6/TNF-α/IL-4/IL-10), and BGP indexes, including gastrin (GAS), motilin (MTL), 5-hydroxytryptamine (5-HT), cholecystokinin (CCK), and vasoactive intestinal peptide (VIP) were measured by ELISA. Additionally, the DNA relative contents of representative intestinal microorganisms in the collected rat feces were determined using RT-qPCR.

Results

SD decreased body weight and average daily water consumption and induced depressive behaviors as well as stress and inflammatory responses in rats. SD rats exhibited lowered GAS, MTL, 5-HT, and VIP but elevated CCK and showed diminished DNA relative contents of Bacteroidetes and probiotics (Bifidobacteria and Lactobacilli) but increased Clostridium perfringens. OE at different doses ameliorated the depressive behaviors and mitigated the stress and inflammatory responses in SD rats, raised the serum contents of GAS, MTL, 5-HT, and VIP, reduced CCK level, elevated the DNA relative contents of Bacteroidetes and probiotics, but diminished Clostridium perfringens. OE exhibited similar intervention effects to diazepam tablets (positive control).

Conclusion

OE exerts intervention effects on BGPs and intestinal microorganisms in SD rats.

Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression

Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.

Weak representation of awake/sleep states by local field potentials in aged mice

Senescence affects various aspects of sleep, and it remains unclear how sleep-related neuronal network activity is altered by senescence. Here, we recorded local field potential signals from multiple brain regions covering the forebrain in young (10-week-old) and aged (2-year-old) mice. Interregional LFP correlations across these brain regions could not detect pronounced differences between awake and sleep states in both young and aged mice. Multivariate analyses with machine learning algorithms with uniform manifold approximation and projection and robust continuous clustering demonstrated that LFP correlational patterns at multiple frequency bands, ranging from delta to high gamma bands, in aged mice less represented awake/sleep states than those in young mice. By housing aged mice in an enriched environment, the LFP patterns were changed to more precisely represent awake/sleep states. Our results demonstrate senescence-induced changes in neuronal activity at the network level and provide insight into the prevention of pathological symptoms associated with sleep disturbance in senescence.

Evaluation of electroencephalography biomarkers for Angelman syndrome during overnight sleep

Angelman syndrome (AS) is a neurodevelopmental disorder caused by loss‐of‐function mutations in the maternal copy of the UBE3A gene. AS is characterized by intellectual disability, impaired speech and motor skills, epilepsy, and sleep disruptions. Multiple treatment strategies to re‐express functional neuronal UBE3A from the dormant paternal allele were successful in rodent models of AS and have now moved to early phase clinical trials in children. Developing reliable and objective AS biomarkers is essential to guide the design and execution of current and future clinical trials. Our prior work quantified short daytime electroencephalograms (EEGs) to define promising biomarkers for AS. Here, we asked whether overnight sleep is better suited to detect AS EEG biomarkers. We retrospectively analyzed EEGs from 12 overnight sleep studies from individuals with AS with age and sex‐matched Down syndrome and neurotypical controls, focusing on low frequency (2–4 Hz) delta rhythms and sleep spindles. Delta EEG rhythms were increased in individuals with AS during all stages of overnight sleep, but overnight sleep did not provide additional benefit over wake in the ability to detect increased delta. Abnormal sleep spindles were not reliably detected in EEGs from individuals with AS during overnight sleep, suggesting that delta rhythms represent a more reliable biomarker. Overall, we conclude that periods of wakefulness are sufficient, and perhaps ideal, to quantify delta EEG rhythms for use as AS biomarkers.

A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique

Sleep stage scoring is important to determine sleep structure in preclinical and clinical research. The aim of this study was to develop an automatic sleep stage classification system for mice with a new deep neural network algorithm. For the purpose of base feature extraction, wake-sleep and rapid eye movement (REM) and non- rapid eye movement (NREM) models were developed by extracting defining features from mouse-derived electromyogram (EMG) and electroencephalogram (EEG) signals, respectively. The wake-sleep model and REM-NREM sleep model were integrated into three different algorithms including a rule-based integration approach, an ensemble stacking approach, and a multimodal with fine-tuning approach. The deep learning algorithm assessing sleep stages in animal experiments by the multimodal with fine-tuning approach showed high potential for increasing accuracy in sleep stage scoring in mice and promoting sleep research.