Sleep Stages Research Articles

Effects of acoustic environment on sleep and mental health in residential regions near railways

Noise is an important environmental risk factor for physical and mental health. Furthermore, long-term noise exposure is burdensome for the mind and body and has become a serious problem. Rail transportation is one of the main methods used to transport goods in China; however, the noise and vibrations generated by freight trains have serious impacts on residents in nearby regions. To further investigate the relationship between railway noise and road noise exposure and changes in sleep duration and mental health scores, a field study in Harbin, China, was conducted and a health risk prediction model was constructed. The results showed that for every 1 dB increase in the Equivalent continuous A-weighted sound pressure level (LAeq), the percentage of deep sleep among residents living near the railway decreased by 0.2 %. Although residents near the railway reported similar sleep evaluations compared to those living farther away, they exhibited poorer mental health. Cox risk modeling indicates that the risk of mental health problems is approximately three times higher for those living near the railway. These findings potentially provide benefits in developing strategies to reduce the risk of mental illness for people residing near railways.

Overnight Sleep Staging Using Chest-Worn Accelerometry

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform “proxy” sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13–83 years, with BMI 18–47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen’s kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.

Novel murine closed-loop auditory stimulation paradigm elicits macrostructural sleep benefits in neurodegeneration.

Boosting slow-wave activity (SWA) by modulating slow waves through closed-loop auditory stimulation (CLAS) might provide a powerful non-pharmacological tool to investigate the link between sleep and neurodegeneration. Here, we established mouse CLAS (mCLAS)-mediated SWA enhancement and explored its effects on sleep deficits in neurodegeneration, by targeting the up-phase of slow waves in mouse models of Alzheimer's disease (AD, Tg2576) and Parkinson's disease (PD, M83). We found that tracking a 2 Hz component of slow waves leads to highest precision of non-rapid eye movement (NREM) sleep detection in mice, and that its combination with a 30° up-phase target produces a significant 15-30% SWA increase from baseline in wild-type (WTAD)and transgenic (TGAD) mice versus a mock stimulation group. Conversely, combining 2 Hz with a 40° phase target yields a significant increase ranging 30-35% in WTPD and TGPD mice. Interestingly, these phase-target-triggered SWA increases are not genotype dependent but strain specific. Sleep alterations that may contribute to disease progression and burden were described in AD and PD lines. Notably, pathological sleep traits were rescued by mCLAS, which elicited a 14% decrease of pathologically heightened NREM sleep fragmentation in TGAD mice, accompanied by a steep decrease in microarousal events during both light and dark periods. Overall, our results indicate that model-tailored phase targeting is key to modulate SWA through mCLAS, prompting the acute alleviation of key neurodegeneration-associated sleep phenotypes and potentiating sleep regulation and consolidation. Further experiments assessing the long-term effect of mCLAS in neurodegeneration may majorly impact the establishment of sleep-based therapies.

Agrypnia excitata: a human model to explore the derailment of sleep-wake cycle integrated control.

The commemoration of the 70th anniversary of rapid eye movement sleep discovery offers a unique possibility to reassess the peculiar organic condition of agrypnia excitata. Agrypnia excitata is characterized by a severe loss of sleep leading to a complete derangement of physiological sleep-wake cycle and body homeostasis. Agrypnia excitata is a definite clinico-neurophysiological condition characterized by: (1) slow-wave sleep loss with disruption of sleepwake cycle; (2) a 24-hr motor and autonomic overactivity; and (3) peculiar episodes of oneiric stupor. Agrypnia excitata may happen within different pathophysiologies, such as delirium tremens, Morvan's syndrome and fatal familial insomnia, suggesting some general reflections on the composition and function of the cerebral neuronal network generating wake and sleep behaviour and regulating body homeostasis, with a focus on rapid eye movement sleep.

SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging

Background and Objective:Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully explored the sleep transition rules that are essential for sleep experts to identify sleep stages. Therefore, one objective of this paper is to develop an automatic sleep staging model to capture the transition rules between sleep stages. Methods:In this paper, we propose a novel sleep staging model named SleepGCN. It utilizes the deep features of electroencephalogram (EEG) and electrooculogram (EOG) signals extracted by the sleep representation learning (SRL) module, in conjunction with the transition rules learned by the sleep transition rule learning (STRL) module to identify sleep stages. Specifically, the SRL module utilizes the residual network (ResNet) and Long Short Term Memory (LSTM) structure to capture the deep time-invariant features and temporal information of each sleep stage from the two-channel EEG-EOG, and then applies a feature enhancement block to obtain the refined features. The STRL module employs a Graph Convolutional Network (GCN) and a transition rule matrix to capture transition rules between sleep stages based on the sequence labels of the input signals. Results:We evaluate SleepGCN on five public datasets: SleepEDF-20, SleepEDF-78, SHHS, DOD-H and DOD-O. Overall, SleepGCN achieves an accuracy of 89.70%, 87.70%, 86.16%, 82.07%, and 81.20%, alongside a macro-average F1-score of 85.20%, 82.70%, 77.69%, 72.44%, and 72.93% across these datasets, respectively. Conclusions:The results achieved by our proposed model are much better than those of all other compared models. The ablation study validates the contributions of the SRL and STRL modules proposed in SleepGCN to the sleep staging tasks. Additionally, it shows that the sleep staging model using two-channel EEG-EOG outperforms the model using single-channel EEG or EOG. Overall, SleepGCN is an effective solution for sleep staging using two-channel EEG-EOG.

Sub-narrow band sleep stage analysis -eigenvalues and eigenvectors of the multi-band cross-correlation matrix-

Here we present an analysis of eigenvalues and eigenvectors of a correlation matrix that contains intra- and inter-frequency band correlations for narrow band filtered EEG signals of healthy subjects during sleep. We find that inter-band correlations of the same electrode are particularly suitable for distinguishing different sleep stages. However, largest eigenvalues and their corresponding eigenvectors show an extremely stable behavior during night sleep, while sleep stages manifest via specific deviations from an overall stable structure expressed by relative eigenvalues and deviations from average eigenvectors. Concluding that valuable information is encoded along the whole spectrum of eigenvalues and eigenvectors of the correlation matrix, we interpret our results in term of dynamical system theory.

A Lightweight Model for Physiological Signals-Based Sleep Staging With Multiclass CAM for Model Explainability

A Lightweight Model for Physiological Signals-Based Sleep Staging With Multiclass CAM for Model Explainability

Refining sleep staging accuracy: Transfer learning coupled with scorability models.

This study aimed to 1) improve sleep staging accuracy through transfer learning, to achieve or exceede human inter-expert agreement; 2) introduce a scorability model to assess the quality and trustworthiness of automated sleep staging. A deep neural network (base model) was trained on a large multi-site polysomnography (PSG) dataset from the United States. Transfer learning was used to calibrate the model to a reduced montage and limited samples from the Korean Genome and Epidemiology Study (KoGES) dataset. Model performance was compared to inter-expert reliability among three human experts. A scorability assessment was developed to predict the agreement between the model and human experts. Initial sleep staging by the base model showed lower agreement with experts (κ=0.55) compared to inter-expert agreement (κ=0.62). Calibration with 324 randomly sampled training cases matched expert agreement levels. Further targeted sampling improved performance, with models exceeding inter-expert agreement (κ=0.70). The scorability assessment, combining biosignal quality and model confidence features, predicted model-expert agreement moderately well (R²=0.42). Recordings with higher scorability scores demonstrated greater model-expert agreement than inter-expert agreement. Even with lower scorability scores, model performance was comparable to inter-expert agreement. Fine-tuning a pre-trained neural network through targeted transfer learning significantly enhances sleep staging performance for an atypical montage, achieving and surpassing human expert agreement levels. The introduction of a scorability assessment provides a robust measure of reliability, ensuring quality control and enhancing the practical application of the system before deployment. This approach marks an important advancement in automated sleep analysis, demonstrating the potential for AI to exceed human performance in clinical settings.

Propagation of sharp wave-ripple activity in the mouse hippocampal CA3 subfield in vitro.

Sharp wave-ripple complexes (SPW-Rs) are spontaneous oscillatory events that characterize hippocampal activity during resting periods and slow-wave sleep. SPW-Rs are related to memory consolidation - the process during which newly acquired memories are transformed into long-lasting memory traces. To test the involvement of SPW-Rs in this process, it is crucial to understand how SPW-Rs originate and propagate throughout the hippocampus. SPW-Rs can originate in CA3, and they typically spread from CA3 to CA1, but little is known about their formation within CA3. To investigate the generation and propagation of SPW-Rs in CA3, we recorded from mouse hippocampal slices using multi-electrode arrays and patch-clamp electrodes. We characterized extracellular and intracellular correlates of SPW-Rs and quantified their propagation along the pyramidal cell layer of CA3. We found that a hippocampal slice can be described by a speed and a direction of propagation of SPW-Rs. The preferred propagation direction was from CA3c (the subfield closer to the dentate gyrus) toward CA3a (the subfield at the boundary to CA2). In patch-clamp recordings from CA3 pyramidal neurons, propagation was estimated separately for excitatory and inhibitory currents associated with SPW-Rs. We found that propagation speed and direction of excitatory and inhibitory currents were correlated. The magnitude of the speed of propagation of SPW-Rs within CA3 was consistent with the speed of propagation of action potentials in axons of CA3 principal cells. KEY POINTS: Hippocampal sharp waves are considered important for memory consolidation; therefore, it is of interest to understand the mechanisms of their generation and propagation. Here, we used two different approaches to study the propagation of sharp waves in mouse CA3 in vitro: multi-electrode arrays and multiple single-cell recordings. We find a preferred direction of propagation of sharp waves from CA3c toward CA3a - both in the local field potential and in sharp wave-associated excitatory and inhibitory synaptic activity. The speed of sharp wave propagation is consistent with the speed of action potential propagation along the axons of CA3 pyramidal neurons. These new insights into the dynamics of sharp waves in the CA3 network will inform future experiments and theoretical models of sharp-wave generation mechanisms.

Maternal immune activation exerts long-term effects on activity and sleep in male offspring mice.

Exposure to infectious or non-infectious immune activation during early development is a serious risk factor for long-term behavioural dysfunctions. Mouse models of maternal immune activation (MIA) have increasingly been used to address neuronal and behavioural dysfunctions in response to prenatal infections. One commonly employed MIA model involves administering poly(I:C) (polyriboinosinic-polyribocytdilic acid), a synthetic analogue of double-stranded RNA, during gestation, which robustly induces an acute viral-like inflammatory response. Using electroencephalography (EEG) and infrared (IR) activity recordings, we explored alterations in sleep/wake, circadian and locomotor activity patterns on the adult male offspring of poly(I:C)-treated mothers. Our findings demonstrate that these offspring displayed reduced home cage activity during the (subjective) night under both light/dark or constant darkness conditions. In line with this finding, these mice exhibited an increase in non-rapid eye movement (NREM) sleep duration as well as an increase in sleep spindles density. Following sleep deprivation, poly(I:C)-exposed offspring extended NREM sleep duration and prolonged NREM sleep bouts during the dark phase as compared with non-exposed mice. Additionally, these mice exhibited a significant alteration in NREM sleep EEG spectral powerunder heightened sleep pressure. Together, our study highlights the lasting effects of infection and/or immune activation during pregnancy on circadian activity and sleep/wake patterns in the offspring.

Associations between self-reported parasomnias and psychiatric illness in 370,000 patients with sleep disorders.

To assess self-reported parasomnias in patients with sleep disorders and explore relationships with psychiatric illness, comorbidities, subjective sleep assessments, and polysomnographic study results. Results from intake questionnaires and polysomnographic assessments, collected from 240 sleep centers across 30 US states between 2004 and 2019, were analyzed retrospectively. Of 540,000 total patients, 371,889 who answered parasomnia-specific questions were included. Patients responding "often" or "always" to parasomnia-specific questions were considered "symptom-positive," whereas a "few times" or "never" were considered "symptom-negative" (controls). The study sample was 54.5% male with mean age 54 years (range, 2-107 years). Frequencies for the different parasomnias were 16.0% for any parasomnia, 8.8% for somniloquy, 6.0% for hypnagogic hallucinations, 4.8% for sleep-related eating disorder, 2.1% for sleep paralysis, and 1.7% for somnambulism. Frequent parasomnias were highly associated with diagnosed depression (odds ratio = 2.72). All parasomnias were associated with being younger and female and with symptoms of depression, anxiety, insomnia, restless legs, pain, medical conditions, fatigue, and sleepiness. Associations with objective sleep metrics showed characteristics of consolidated sleep and differentiated weakly between nonrapid eye movement sleep and rapid eye movement sleep parasomnias. Machine learning accurately classified patients with parasomnia versus controls (balanced accuracies between 71% and 79%). Benzodiazepines, antipsychotics, and opioids increased the odds of experiencing parasomnias, while antihistamines and melatonin reduced the odds. Z-drugs were found to increase the likelihood of a sleep-related eating disorder. Our findings suggest that parasomnias may be clinically relevant, yet understudied, symptoms of depression and anxiety. Further investigation is needed to quantify the nature of multimorbidity, including causality and implications for diagnosis and treatment.

Machine Learning Predicts Phenoconversion from Polysomnography in Isolated REM Sleep Behavior Disorder

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of alpha-synucleinopathies. This study aimed at developing a fully-automated machine learning framework for the prediction of phenoconversion in patients with iRBD by using data recorded during polysomnography (PSG). A total of 66 patients with iRBD were included, of whom 18 converted to an overt alpha-synucleinopathy within 2.7 ± 1.0 years. For each patient, a baseline PSG was available. Sleep stages were scored automatically, and time and frequency domain features were derived from electromyography (EMG) and electroencephalography (EEG) signals in REM and non-REM sleep. Random survival forest was employed to predict the time to phenoconversion, using a four-fold cross-validation scheme and by testing several combinations of features. The best test performances were obtained when considering EEG features in REM sleep only (Harrel’s C-index: 0.723 ± 0.113; Uno’s C-index: 0.741 ± 0.11; integrated Brier score: 0.174 ± 0.06). Features describing EEG slowing had high importance for the machine learning model. This is the first study employing machine learning applied to PSG to predict phenoconversion in patients with iRBD. If confirmed in larger cohorts, these findings might contribute to improving the design of clinical trials for neuroprotective treatments.

Effect of bedroom environment on sleep and physiological parameters for individuals with good sleep quality: A pilot study

Bedroom conditions affect sleep, overall health, and well-being. Field studies reporting measurements of bedroom environment and sleep parameters are almost lacking in the scientific community since the majority of research has been conducted in laboratories. To fill this knowledge gap, we have investigated the associations among bedroom environmental factors (temperature, humidity, CO2, pressure), measured sleep and physiological parameters (heart rate, heart rate variability, and breathing rate) in participants with good sleep quality. Six subjects were enrolled in the study, possessing a minimum of two years of experience in using the wrist actigraph chosen for the experimentation, and exhibiting good sleep quality. The air quality measurements were conducted using the air quality monitor, whereas advanced actigraphs were employed for the assessment of various sleep stage durations. The statistical analysis, carried out applying Linear Mixed Models, revealed that an increase of 100 ppm of CO2 concentration levels is associated with an approximate 0.29 % decline in sleep quality whereas, concerning the effect of the relative humidity, we observed a 0.1 % reduction in sleep quality associated with a 1 % rise in RH. On the other hand, a 1 °C rise in air temperature is associated with an approximate 0.16 % reduction in sleep efficiency, and, at a CO2 concentration of about 900 ppm, the autonomic nervous system (ANS) indicator remains positive for temperature values < 20 °C. These results contribute to mounting evidence underscoring the crucial role of the bedroom atmosphere in facilitating high-quality sleep.

Effects of early midlife ovarian removal on sleep: Polysomnography-measured cortical arousal, homeostatic drive, and spindle characteristics

Bilateral salpingo-oophorectomy (BSO; removal of ovaries and fallopian tubes) prior to age 48 is associated with elevated risk for both Alzheimer's disease (AD) and sleep disorders such as insomnia and sleep apnea. In early midlife, individuals with BSO show reduced hippocampal volume, function, and hippocampal-dependent verbal episodic memory performance associated with changes in sleep. It is unknown whether BSO affects fine-grained sleep measurements (sleep microarchitecture) and how these changes might relate to hippocampal-dependent memory. We recruited thirty-six early midlife participants with BSO. Seventeen of these participants were taking 17β-estradiol therapy (BSO+ET) and 19 had never taken ET (BSO). Twenty age-matched control participants with intact ovaries (AMC) were also included. Overnight at-home polysomnography recordings were collected, along with subjective sleep quality and hot flash frequency. Multivariate Partial Least Squares (PLS) analysis was used to assess how sleep varied between groups. Compared to AMC, BSO without ET was associated with significantly decreased time spent in non-rapid eye movement (NREM) stage 2 sleep as well as increased NREM stage 2 and 3 beta power, NREM stage 2 delta power, and spindle power and maximum amplitude. Increased spindle maximum amplitude was negatively correlated with verbal episodic memory performance. Decreased sleep latency, increased sleep efficiency, and increased time spent in rapid eye movement sleep were observed for BSO+ET. Findings suggest there is an association between ovarian hormone loss and sleep microarchitecture, which may contribute to poorer cognitive outcomes and be ameliorated by ET.

Nursing intervention in older adults with insomnia

Introduction. Aging is loosely related to the chronological age of the person and in turn to the loss of physical, functional, psychological and social capacities. Sleep is a physiological process of fascination and of vital importance for the comprehensive health of the human being, the aging process alters the stages of sleep, which in the long term has side effects and represents economic costs for those older adults who are prescribed with pharmacological treatments. The amount of sleep remains constant throughout adult life, which is why doctors recommend that older adults sleep 7 to 8 hours a day to meet their sleep needs. There are sleep problems in older adults and one of them is insomnia, which is reflected in their daily lives, which is why music therapy appears as a non-pharmacological treatment to help reduce insomnia. Objetive. To evaluate the effect of nursing intervention in older adults with insomnia. Methodology. This research is quantitative, quasi-experimental in design, longitudinal and descriptive in scope. It was carried out in a period between April 2024-June 2024, in the province of Tungurahua, Canton Ambato, Parroquia La Matriz, Neighborhood Ciudadela España. The sample was 21 older adults from the Hogar de Ancianos Sagrado Corazón de Jesús, who met the inclusion and exclusion criteria. Results. The results obtained in this research demonstrate that music therapy as a nursing intervention has positive effects on older adults with insomnia. Conclusion. With the application of the Athens Test before and after the music therapy sessions, a significant improvement was shown in various aspects of sleep, such as difficulty falling asleep, nocturnal awakenings and total sleep duration.

From Infancy to Childhood: A Comprehensive Review of Event- and Task-Related Brain Oscillations.

Brain development from infancy through childhood involves complex structural and functional changes influenced by both internal and external factors. This review provides a comprehensive analysis of event and task-related brain oscillations, focusing on developmental changes across different frequency bands, including delta, theta, alpha, beta, and gamma. Electroencephalography (EEG) studies highlight that these oscillations serve as functional building blocks for sensory and cognitive processes, with significant variations observed across different developmental stages. Delta oscillations, primarily associated with deep sleep and early cognitive demands, gradually diminish as children age. Theta rhythms, crucial for attention and memory, display a distinct pattern in early childhood, evolving with cognitive maturation. Alpha oscillations, reflecting thalamocortical interactions and cognitive performance, increase in complexity with age. Beta rhythms, linked to active thinking and problem-solving, show developmental differences in motor and cognitive tasks. Gamma oscillations, associated with higher cognitive functions, exhibit notable changes in response to sensory stimuli and cognitive tasks. This review underscores the importance of understanding oscillatory dynamics to elucidate brain development and its implications for sensory and cognitive processing in childhood. The findings provide a foundation for future research on developmental neuroscience and potential clinical applications.

The Association of Depression with Obstructive Sleep Apnea in Patients with Cystic and Non-Cystic Fibrosis Bronchiectasis.

Obstructive sleep apnea (OSA) and cystic fibrosis (CF) are chronic conditions that profoundly impact quality of life. OSA, characterized by repeated episodes of upper airway collapse, can exacerbate CF symptoms due to nocturnal airway obstruction. Recent studies highlight the prevalence of OSA in CF patients, especially in adults, and its detrimental effects on health and quality of life. From April 2019 to December 2021, we conducted a study with 104 bronchiectasis patients at Marmara University Pendik Training and Research Hospital. After exclusions, 70 participants (35 CF and 35 non-CF) were included. Sleep parameters were assessed with polysomnography, and depressive mood was evaluated using the Zung Self-Rating Depression Scale (SDS). Daytime sleepiness was measured using the Epworth Sleepiness Scale (ESS). The statistical analyses included t-tests, chi-square tests, and logistic regression. Among the CF patients, depressive mood was significantly associated with female sex (OR: 4.28, 95% CI: 1.27-12.04) and anemia (OR: 7.87, 95% CI: 1.50-41.27). Higher ESS scores indicated greater daytime sleepiness in the depressive groups (p = 0.051). Depressive CF patients also had a significantly longer disease duration and more frequent annual exacerbations. No significant differences were found in total sleep time, sleep efficiency, or sleep stages between the depressive and non-depressive groups. A lower forced vital capacity (FVC) was observed in the depressive CF patients, although not significantly. Depression is prevalent among adult CF patients with OSA, with significant associations with female sex and anemia. These findings underscore the need for integrated care addressing both physical and mental health aspects, including interventions for respiratory symptoms, anemia management, and sleep quality enhancement to improve overall quality of life.

Magnesium-L-threonate improves sleep quality and daytime functioning in adults with self-reported sleep problems: A randomized controlled trial

Objective/BackgroundSleep problems challenge overall wellbeing. Magnesium has been implicated to benefit sleep, although the clinical evidences varied based on the magnesium source used. Magnesium L-threonate (MgT) is a promising intervention due to its brain bioavailability and effects on cognition, memory and mood. We investigated MgT supplementation on sleep quality and daily function. Patients/methodsEighty 35–55-year-olds with self-assessed sleep problems participated in a randomized, double-blind, placebo-controlled, parallel-arm study, taking 1 g/day of MgT or placebo for 21 days. Sleep and daily behaviors were measured subjectively using standardized questionnaires including the Insomnia Severity Index, Leeds Sleep Evaluation Questionnaire, and Restorative Sleep Questionnaire, and objectively using an Oura ring. The Profile of Mood States questionnaire and a daily diary were used to evaluate mood, energy and productivity, and record any safety concerns. ResultsThe MgT group maintained good sleep quality and daytime functioning, while placebo declined. From objective Oura ring measurements, MgT significantly (p < 0.05) improved vs placebo deep sleep score, REM sleep score, light sleep time, and activity and readiness parameters activity score, activity daily movement score, readiness score, readiness activity balance, and readiness sleep balance. From subjective questionnaires, MgT significantly (p < 0.05) improved vs placebo behavior upon awakening, energy and daytime productivity, grouchiness, mood and mental alertness. MgT was safe and well tolerated. ConclusionsThis showed MgT improved sleep quality, especially deep/REM sleep stages, improved mood, energy, alertness, and daily activity and productivity. These are consistent with how MgT works in neuron cells and animal models, suggesting broader positive impacts on overall brain health.

A hippocampal circuit mechanism to balance memory reactivation during sleep.

Memory consolidation involves the synchronous reactivation of hippocampal cells active during recent experience in sleep sharp-wave ripples (SWRs). How this increase in firing rates and synchrony after learning is counterbalanced to preserve network stability is not understood. We discovered a network event generated by an intrahippocampal circuit formed by a subset of CA2 pyramidal cells to cholecystokinin-expressing (CCK+) basket cells, which fire a barrage of action potentials ("BARR") during non-rapid eye movement sleep. CA1 neurons and assemblies that increased their activity during learning were reactivated during SWRs but inhibited during BARRs. The initial increase in reactivation during SWRs returned to baseline through sleep. This trend was abolished by silencing CCK+ basket cells during BARRs, resulting in higher synchrony of CA1 assemblies and impaired memory consolidation.

High-Frequency Imaging Reveals Synchronised Delta- and Theta-Band Ca2+ Oscillations in the Astrocytic Soma In Vivo.

One of the major breakthroughs of neurobiology was the identification of distinct ranges of oscillatory activity in the neuronal network that were found to be responsible for specific biological functions, both physiological and pathological in nature. Astrocytes, physically coupled by gap junctions and possessing the ability to simultaneously modulate the functions of a large number of surrounding synapses, are perfectly positioned to introduce synchronised oscillatory activity into the neural network. However, astrocytic somatic calcium signalling has not been investigated to date in the frequency ranges of common neuronal oscillations, since astrocytes are generally considered to be slow responders in terms of Ca2+ signalling. Using high-frequency two-photon imaging, we reveal fast Ca2+ oscillations in the soma of astrocytes in the delta (0.5-4 Hz) and theta (4-8 Hz) frequency bands in vivo in the rat cortex under ketamine-xylazine anaesthesia, which is known to induce permanent slow-wave sleep. The high-frequency astrocytic Ca2+ signals were not observed under fentanyl anaesthesia, excluding the possibility that the signals were introduced by motion artefacts. We also demonstrate that these fast astrocytic Ca2+ signals, previously considered to be exclusive to neurons, are present in a large number of astrocytes and are phase synchronised at the astrocytic network level. We foresee that the disclosure of these high-frequency astrocytic signals may help with understanding the appearance of synchronised oscillatory signals and may open up new avenues of treatment for neurological conditions characterised by altered neuronal oscillations.