Sleep Stages Research Articles

Abnormal Sexual Behavior During Sleep in an Adolescent: Sexsomnia and Defecation

Abstract Sexsomnia is characterized by sexual behaviors ranging from masturbation, sexual sounds, and verbalizations, to touching and full sexual intercourse during NREM sleep, with subsequent amnesia. Autonomic activation occurs with sexual arousal. Sexsomnia has been described mostly in adult males and is still an underrecognized condition. Treatment is often effective but without current consensus.In a case now reported of an adolescent male with sexsomnia, which is rarely reported in the literature, a novel feature that is now first reported is defecation accompanying sexsomnia as a co-occurring autonomic disturbance during sleep.

Parietal-specific activation reveals pain from inadequate levels of altitude acclimatization/ adaptation

ObjectivePain is acknowledged as a prominent physiological symptom of high-altitude (HA) acclimatization, yet there exists a dearth of empirical support regarding the impact of variations in HA acclimatization/ adaptation on pain perception in hypoxic environments.MethodsA total of 65 HA residents were recruited, all of whom had resided continuously in HA regions for a minimum duration of one month. The study involved an assessment of peripheral oxygen saturation (SpO2) and haematocrit (HCT) levels, administration of the Pain Frequency, Intensity and Burden Scale (P-FIBS), and the resting-state electroencephalogram (rs-EEG) signals of all frequency bands including delta, theta, alpha, beta and gamma were acquired from the frontal lobe, parietal lobe, temporal lobe and occipital lobe. Residents’ acclimatization levels were assessed using altitude acclimatization/ adaptation index (AAI), and categorized into high and low AAI groups.ResultsResidents in the high-AAI group reported lower levels of perceived pain compared to those in the low-AAI group (t = 1.61, p = 0.04). Further analysis using EEG frequency bands demonstrated that parietal delta (β = −0.35, 95%CI = [−1.18, −0.01]) and beta (β = 0.31, 95%CI =[0.01, 1.19]) powers acted as mediators in the relationship between AAI and pain perception. Brain waves of other frequencies, including theta (β = −0.02, 95%CI = [−0.46, 0.41]), alpha (β = −0.03, 95%CI = [−0.34, 0.18]), and gamma (β = −0.06, 95%CI =[−0.09, 0.03]), did not show a significant mediating effect.ConclusionsA lower level of HA adaptation indicates a higher intensity of pain perception. The enhancement of EEG activity might be regarded as a compensatory mechanism. It endeavors to maintain the normal operation of the body’s physiological functions by increasing the efficiency of neural activities, thereby helping individuals to improve their adaptability to the HA environment. The parietal lobe, as a key brain region responsible for processing sensory information throughout the body, exhibits the most significant activation state compared with other brain regions when an individual is in a HA environment and the adaptation level shows dynamic changes. Notably, once the beta waves, which are highly associated with attention, are activated, the increase in an individual’s alertness often indicates that the individual will experience a more intense and higher level of pain sensation. However, the activation of delta waves, which usually occur during the stage of deep sleep and can prompt the brain to maintain a relaxed state, generally means that the individual will perceive a relatively lower level of pain. Consequently, the rs-EEG power in the delta and beta frequency bands of the parietal lobe region serves as an important mediating factor in the connection between AAI and pain perception.

Parietal-specific activation reveals pain from inadequate levels of altitude acclimatization/ adaptation

Objective Pain is acknowledged as a prominent physiological symptom of high-altitude (HA) acclimatization, yet there exists a dearth of empirical support regarding the impact of variations in HA acclimatization/ adaptation on pain perception in hypoxic environments. Methods A total of 65 HA residents were recruited, all of whom had resided continuously in HA regions for a minimum duration of one month. The study involved an assessment of peripheral oxygen saturation (SpO2) and haematocrit (HCT) levels, administration of the Pain Frequency, Intensity and Burden Scale (P-FIBS), and the resting-state electroencephalogram (rs-EEG) signals of all frequency bands including delta, theta, alpha, beta and gamma were acquired from the frontal lobe, parietal lobe, temporal lobe and occipital lobe. Residents’ acclimatization levels were assessed using altitude acclimatization/ adaptation index (AAI), and categorized into high and low AAI groups. Results Residents in the high-AAI group reported lower levels of perceived pain compared to those in the low-AAI group (t = 1.61, p = 0.04). Further analysis using EEG frequency bands demonstrated that parietal delta (β = −0.35, 95%CI = [−1.18, −0.01]) and beta (β = 0.31, 95%CI =[0.01, 1.19]) powers acted as mediators in the relationship between AAI and pain perception. Brain waves of other frequencies, including theta (β = −0.02, 95%CI = [−0.46, 0.41]), alpha (β = −0.03, 95%CI = [−0.34, 0.18]), and gamma (β = −0.06, 95%CI =[−0.09, 0.03]), did not show a significant mediating effect. Conclusions A lower level of HA adaptation indicates a higher intensity of pain perception. The enhancement of EEG activity might be regarded as a compensatory mechanism. It endeavors to maintain the normal operation of the body’s physiological functions by increasing the efficiency of neural activities, thereby helping individuals to improve their adaptability to the HA environment. The parietal lobe, as a key brain region responsible for processing sensory information throughout the body, exhibits the most significant activation state compared with other brain regions when an individual is in a HA environment and the adaptation level shows dynamic changes. Notably, once the beta waves, which are highly associated with attention, are activated, the increase in an individual’s alertness often indicates that the individual will experience a more intense and higher level of pain sensation. However, the activation of delta waves, which usually occur during the stage of deep sleep and can prompt the brain to maintain a relaxed state, generally means that the individual will perceive a relatively lower level of pain. Consequently, the rs-EEG power in the delta and beta frequency bands of the parietal lobe region serves as an important mediating factor in the connection between AAI and pain perception.

Personalizing brain stimulation: continual learning for sleep spindle detection

Objective.Personalized stimulation, in which algorithms used to detect neural events adapt to a user's unique neural characteristics, may be crucial to enable optimized and consistent stimulation quality for both fundamental research and clinical applications. Precise stimulation of sleep spindles-transient patterns of brain activity that occur during non rapid eye movement sleep that are involved in memory consolidation-presents an exciting frontier for studying memory functions; however, this endeavour is challenged by the spindles' fleeting nature, inter-individual variability, and the necessity of real-time detection.Approach.We tackle these challenges using a novel continual learning framework. Using a pre-trained model capable of both online classification of sleep stages and spindle detection, we implement an algorithm that refines spindle detection, tailoring it to the individual throughout one or more nights without manual intervention.Main results.Our methodology achieves accurate, subject-specific targeting of sleep spindles and enables advanced closed-loop stimulation studies. While fine-tuning alone offers minimal benefits for single nights, our approach combining weight averaging demonstrates significant improvement over multiple nights, effectively mitigating catastrophic forgetting.Significance.This work represents an important step towards signal-level personalization of brain stimulation that can be applied to different brain stimulation paradigms including closed-loop brain stimulation, and to different neural events. Applications in fundamental neuroscience may enhance the investigative potential of brain stimulation to understand cognitive processes such as the role of sleep spindles in memory consolidation, and may lead to novel therapeutic applications.

Group-Delivered Mindfulness-Based Cognitive Therapy to Reduce Psychological Distress and Improve Sleep in Patients With Inflammatory Bowel Diseases: A Multicenter Randomized Controlled Trial (MindIBD).

Many patients with inflammatory bowel disease (IBD) suffer from psychological distress, sleep disturbances, fatigue, and a reduced quality of life-even during remission. Mindfulness-Based Cognitive Therapy (MBCT) has been effective in other populations, and therefore it may also benefit IBD patients. The primary objective was to evaluate the effectiveness of MBCT plus treatment as usual (TAU) in reducing psychological distress compared to TAU alone. This multicenter randomized controlled trial study included IBD patients in remission, aged 16 and older, who experienced at least mild levels of psychological distress (Hospital Anxiety and Depression Scale ≥ 11). Assessments were conducted at baseline, post-intervention (3 months), and at 6, 9, and 12 months after baseline. This trial was registered at ClinicalTrials.gov: NCT04646785. A total of 142 participants were allocated to MBCT + TAU (n = 70) or TAU alone (n = 72). Group-delivered MBCT significantly reduced psychological distress (d = -0.61) and improved well-being (d = 0.40) post-intervention, with consolidation of the effects over time. Exploratory objective sleep metrics (ie, electroencephalography) showed a reduction in total sleep time (d = 0.67) after MBCT with an increase in the proportion of deep sleep (d = 0.70). While flare occurrence showed no difference, fecal calprotectin levels reduced in the MBCT + TAU group over the follow-up period (d = -0.49). Mindfulness-Based Cognitive Therapy can be considered a valuable addition to the limited effective psychosocial interventions set for IBD patients, as it reduces psychological distress and improves well-being. This study also shows the possible impact of MBCT on biological processes, such as sleep and inflammation.

Autonomic Correlates of Large Muscle Group Movements During NREM Sleep in Restless Legs Syndrome: A Comparative Analysis with Periodic and Non-Periodic Leg Movements.

Large muscle group movements during sleep (LMMS) have recently been recognized as a prevalent feature in patients with Restless Legs Syndrome (RLS), yet their autonomic profile remains insufficiently characterized. This study aimed to compare heart rate (HR) changes associated with LMMS to those accompanying short-interval (SILMS), periodic (PLMS), and isolated leg movements (ISOLMS) during non-REM sleep in RLS. Thirty drug-free RLS patients (20 women, mean age 57.6 ± 12.73years) underwent full-night polysomnography. For each subject, five arousal-associated events per movement type were selected, provided they were isolated by at least 30seconds of motor/arousal-free sleep. HR changes were analyzed by computing R-R intervals and expressing them as a percentage of baseline, synchronized to movement onset. The area under the curve (AUC, -10 to +20s), HR change peak, and movement durations were statistically compared using non-parametric tests. LMMS were significantly longer than other movement types (mean duration: 9.3s vs. <3.0s for others) and induced the highest HR response (peak: 129.6%, AUC: 369.3%), followed by SILMS (peak: 125.4%, 266.3%), ISOLMS (peak: 118.2%, 173.4%), and PLMS (peak: 118.5%, 166.9%). SILMS and LMMS were associated with rapid and sustained HR increases, without post-peak bradycardia, while PLMS and ISOLMS showed a modest transient bradycardia following the peak. LMMS are associated with strong autonomic activation indicating parasympathetic withdrawal and/or sympathetic activation, distinguishing them from other sleep-related leg movements in RLS. The absence of post-peak bradycardia suggests reduced parasympathetic buffering, potentially reflecting more sustained arousal mechanisms.

Development and research of an AI-assisted decision-making platform in treatment of insomnia with acupuncture of Tongdu Yangxin acupoint prescription

To construct and validate a predictive model for the therapeutic effect of acupuncture at Tongdu Yangxin prescription (acupoint prescription for promoting the circulation of the governor vessel and nourishing the heart) on insomnia, so as to develop an open-access interactive artificial intelligence (AI)-assisted decision-making platform. Clinical data of 139 insomnia patients treated with Tongdu Yangxin acupuncture therapy were included. All the patients had received acupuncture at Baihui (GV20), Yintang (GV24+), bilateral Shenmen (HT7), and bilateral Sanyinjiao (SP6); and electric stimulation was attached to Baihui (GV20) and Yintang (GV24+), using a continuous wave and a frequency of 2 Hz. The treatment was delivered once every other day, 3 treatments a week, and for 2 consecutive weeks. Patients with Pittsburgh sleep quality index (PSQI) score reduction rate <50% were classified as the "no response group", and those with ≥50% were as the "response group". Outliers were addressed using the 1.5×IQR rule, and missing values were imputed via predictive mean matching. Key features were selected by intersecting the feature importance results from eXtreme Gradient Boosting (XGBoost) and random forest algorithms. After balancing class distribution using the Synthetic Minority Over-sampling Technique (SMOTE), 20% of the data was reserved as a validation set. The remained data underwent the stratified sampling iterations to generate 200 pairs of 3∶1 training-test sets, which was employed for training and internal validation of 8 machine learning algorithms. The optimal algorithm and data partitioning strategy were selected to construct the final model, followed by external validation. The best-performing model was deployed online via Streamlit to create an interactive AI platform. Key predictive features for model construction included insomnia duration, the total PSQI score, PSQI sleep efficiency subscore, the proportion of N1 and N2 sleep stages in total sleep duration, and the maximum pulse rate during sleep. The CatBoost-based model achieved an AUC of 0.92, the average precision of 0.77, and accuracy, average recall, and average F1-score of 0.75 on the test set. On the validation set, it attained an AUC of 0.84, with accuracy, average precision, average recall, and average F1-score all at 0.72, demonstrating robust predictive performance. An interactive AI platform was subsequently developed (https://tdyx-catboost.streamlit.app/). This study successfully establishes and validates a CatBoost-based efficacy prediction model for Tongdu Yangxin acupuncture therapy in treatment of insomnia. The developed AI platform provides data-driven decision support for acupuncture-based insomnia management.

Comprehensive Visualisation of Ventilation Curve and Arousal Surrounding Respiratory Events: A Novel Endotyping Approach in OSA.

Obstructive sleep apnea (OSA) diagnosis, whilst primarily reliant on the apnea-hypopnea index (AHI), inadequately reflects the complex underlying mechanisms driving respiratory events. Endotyping, by identifying key physiological traits, enables personalised treatment. In this study, we propose visualising the respiratory and arousal dynamics surrounding respiratory events, providing an overview of patient-specific patterns. We analysed 20 polysomnography recordings from patients with OSA. We calculated ventilation and ventilatory drive curves for each recording during NREM sleep. In the first method, we extracted the known endotype parameters-passive ventilation (Vpassive), active ventilation (Vactive), arousal threshold (AT) and loop gain (LG)-for each respiratory event. In a second method, apnoeas and hypopneas were time-aligned, and a global representation of ventilation and ventilatory drive curves was obtained by averaging curves across events. Using the averaged curves, the same endotype parameters were extracted for comparison. The study involved 12 females and 8 males aged 22-72 with moderate obesity (median BMI 27.5 kg/m2) and a median AHI of 37.5 events/h. According to Method 1, patients exhibited relatively high collapsibility (45.6 [36.8-56.0]% eupnoea), good muscle compensation (22.7 [2.9-36.7]% eupnoea), moderate AT (144.4 [131.0-153.5]% eupnoea) and relatively low LG (0.58 [0.54-0.74]). Physiological traits derived from Method 2 differed for Vpassive (higher Vpassive) and LG (lower LG), compared to Method 1. The correlation coefficients between the two methods are rho = 0.2, 0.9, 0.6 and 0.4 for Vpassive, Vactive, AT and LG, respectively. Whilst differing in some aspects, the visualisation provided by these two methods streamlines endotype identification and facilitates successful targeted treatment.

Ultraslow serotonin oscillations in the hippocampus delineate substates across NREM and waking.

Beyond the vast array of functional roles attributed to serotonin (5-HT) in the brain, changes in 5-HT levels have been shown to accompany changes in behavioral states, including WAKE, NREM, and REM sleep. Whether 5-HT dynamics at shorter time scales can be seen to delineate substates within these larger brain states remains an open question. Here, we performed simultaneous recordings of extracellular 5-HT using a recently developed G-Protein-Coupled Receptor-Activation-Based 5-HT sensor (GRAB5-HT3.0) and local field potential in the hippocampal CA1 of mice, which revealed the presence of prominent ultraslow (<0.05 Hz) 5-HT oscillations both during NREM and WAKE states. Interestingly, the phase of these ultraslow 5-HT oscillations was found to distinguish substates both within and across larger behavioral states. Hippocampal ripples occurred preferentially on the falling phase of ultraslow 5-HT oscillations during both NREM and WAKE, with higher power ripples concentrating near the peak specifically during NREM. By contrast, hippocampal-cortical coherence was strongest, and microarousals and intracranial EMG peaks were most prevalent during the rising phase in both wake and NREM. Overall, ultraslow 5-HT oscillations delineate substates within the larger behavioral states of NREM and WAKE, thus potentially temporally segregating internal memory consolidation processes from arousal-related functions.

Enhancing EEG-based sleep staging efficiency with minimal channels through adversarial domain adaptation and active deep learning.

Accurate sleep-stage classification is crucial for advancing both sleep research and healthcare applications. Traditional Deep Learning (DL) and Domain Adaptation (DA) methods often struggle due to the limited availability of labeled data in the target domain and their inability to capture the subtle distinctions between sleep-stage classes, which hampers classification accuracy. To address these limitations, we introduce a novel framework, Adversarial Domain Adaptation with Active Deep Learning (ADAADL). This framework combines adversarial learning with active learning strategies to improve feature alignment and effectively leverage unlabeled data. ADAADL employs two separate sleep-stage classifiers as discriminators, allowing for a more refined consideration of class boundaries during the feature alignment process. Moreover, it incorporates entropy measures alongside cross-entropy loss during training to make better use of the information from unlabeled data. The active learning component (ADL) further enhances performance by iteratively selecting and labeling the most informative data points, thereby reducing annotation efforts and improving generalization to unseen data. Experimental evaluations on three benchmark EEG datasets demonstrate that ADAADL produces robust, transferable features, significantly outperforming existing DA methods in classification accuracy. This research advances sleep-stage classification techniques, offering improved accuracy for real-world applications and contributing to a deeper understanding of sleep dynamics.

Sleep Indicators and staging: A Functional Near-Infrared Spectroscopy Study in Healthy Young Adults.

Sleep Indicators and staging: A Functional Near-Infrared Spectroscopy Study in Healthy Young Adults.

A deep learning software tool for automated sleep staging in rats via single channel EEG

Poor quality and poor duration of sleep have been associated with cognitive decline, diseases, and disorders. Therefore, sleep studies are imperative to recapitulate phenotypes associated with poor sleep quality and uncover mechanisms contributing to psychopathology. Classification of sleep stages, vigilance state bout durations, and number of transitions amongst vigilance states serves as a proxy for evaluating sleep quality in preclinical studies. Currently, the gold standard for sleep staging is expert human inspection of polysomnography (PSG) obtained from preclinical rodent models and this approach is immensely time consuming. To accelerate the analysis, we developed a deep-learning-based software tool for automated sleep stage classification in rats. This study aimed to develop an automated method for classifying three sleep stages in rats (REM/paradoxical sleep, NREM/slow-wave sleep, and wakefulness) using a deep learning approach based on single-channel EEG data. Single-channel EEG data were acquired from 16 rats, each undergoing two 24 h recording sessions. The data were labeled by human experts in 10 s epochs corresponding to three stages: REM/paradoxical sleep, NREM/slow-wave sleep, and wakefulness. A deep neural network (DNN) model was designed and trained to classify these stages using the raw temporal data from the EEG. The DNN achieved strong performance in predicting the three sleep stages, with an average F1 score of 87.6% over a cross-validated test set. The algorithm was able to predict key parameters of sleep architecture, including total bout duration, average bout duration, and number of bouts, with significant accuracy. Our deep learning model effectively automates the classification of sleep stages using single-channel EEG data in rats, reducing the need for labor-intensive manual annotation. This tool enables high-throughput sleep studies and may accelerate research into sleep-related pathologies. Furthermore, we provide over 700 h of expert-scored sleep data, available for public use in future research studies.

The recognition of behaviorally distinct sleep stages in Drosophila melanogaster uncovers previously obscured homeostatic and circadian control of sleep.

Sleep episodes in humans and mammalian models are composed of distinct stages - a fundamental discovery in sleep science. This recognition enabled researchers to study the physiological underpinnings of sleep stages and examine the comorbidities and consequences associated with disrupted sleep. The mechanisms underlying sleep homeostasis remain an open problem, and the fruit fly Drosophila melanogaster continues to be a powerful genetic and neurobiological model to study sleep regulation. Despite recent convergent evidence for distinct sleep stages in the fly, sleep continues to be treated as a unitary state consisting of any inactivity lasting five minutes or longer. This likely obscures important aspects of sleep regulation in the fly, limiting its impact on our understanding of sleep regulation. Inspired by recently developed wearable technologies, which score sleep stages based on motion detection and accelerometry, we asked if distinct fly sleep stages can be identified based on durations of inactivity bouts, and, if so, whether such stages are differentially regulated by the two major processes governing sleep: sleep homeostasis and circadian timekeeping. We show that discrete sleep stages can indeed be identified, and that they show distinct interactions amongst each other, and distinct relationships to the homeostat and the circadian clock. Importantly, we find that sleep deprivation produces increases in longer (presumably deeper) sleep stages while producing decreases in shorter sleep stages. These opposing responses to deprivation obscure homeostatic sleep responses when the traditional, unitary definition of sleep is used, a reality that has likely hampered the identification of homeostatic regulatory mechanisms in the fly. We concluded that using the simple behavioral criterion we describe here to differentiate sleep stages will be critical for understanding homeostatic and circadian sleep regulation in Drosophila , which remains an important model organism for the discovery of mechanisms of sleep regulation.

Association of tinnitus with obstructive sleep apnea and rapid eye movement-related obstructive sleep apnea in similar hearing threshold groups.

Association of tinnitus with obstructive sleep apnea and rapid eye movement-related obstructive sleep apnea in similar hearing threshold groups.

PP2Acα regulates sleep amount and sleep homeostasis in mice

Genetic studies in mice have identified multiple sleep-regulating protein kinases, but the sleep functions of protein phosphatases remain largely unclear. Here, we performed adeno-associated virus-mediated somatic genetics analysis of PP2A catalytic subunits–PP2Acα and PP2Acβ–in sleep regulation in male mice. Adult brain chimeric (ABC) knockout of PP2Acα, but not PP2Acβ, across mouse brain neurons reduces daily amount of rapid eye movement (REM) and non-REM (NREM) sleep, but elevates NREM delta power. Additionally, ABC-PP2AcαKO diminishes and delays homeostatic recovery NREM sleep after sleep deprivation. ABC-expression of wild-type PP2Acα or PP2Acβ, but not methylation deficient mutant PP2Acα, rescues the sleep phenotypes of ABC-PP2AcαΚΟ mice. Moreover, selective knockout of PP2Acα in CaMKII+ or Vglut2+ neurons, but not in mDlx+ or Vgat+ neurons, recapitulates ABC-PP2AcαKO sleep phenotypes. These results identify PP2Acα as a key regulator of sleep amount and sleep homeostasis mainly in the excitatory neurons, and suggest that methylation of PP2Acα is critical for its sleep functions.

Sleep Architecture, Muscle Function, and Daily Life Activities in Patients with Sarcopenia

Abstract To examine associations between polysomnography, muscle mass and strength, and daily life activity index (DLAI) in patients with sarcopenia.We measured polysomnography, muscle mass and strength, and DLAI in 16 patients with sarcopenia and 26 controls &gt; 60 years old and then compared variables and correlations in the patients with sarcopenia.We found no differences in polysomnography between patients with sarcopenia and controls. Among patients with sarcopenia, latency to rapid eye-movement (REM) sleep was positively correlated with weight, REM %, and total sleep time was positively correlated with grip strength. Latency of REM sleep was negatively correlated with body mass index, NREM sleep, and apnea index was negatively correlated with grip strength. Daily life activity index correlated positively with grip strength.Patients with sarcopenia showed significant correlations between polysomnography and weight, body mass index, and grip strength, suggesting a complex relationship involving sleep architecture, muscle function, and DLAI that deserves more research.

Sleep state influences early sound encoding at cortical but not subcortical levels

In sleep, the brain balances protecting processes like memory consolidation with preserving responsiveness to significant external stimuli. Although reductions in higher-level auditory processes during deeper sleep have been described, the sleep-dependent changes across levels of auditory hierarchy, particularly as regards early sound representations, remain undefined. The frequency-following response (FFR) is an evoked auditory response that indexes neural encoding of sound periodicity. It is generated by neural populations in the brainstem, thalamus, and auditory cortex that phase-lock to periodic auditory stimuli and encode pitch information. The FFR’s neural sources, which can be resolved using magnetoencephalography (MEG), allow evaluation of neural representation strength throughout the auditory neuraxis as a function of sleep state, as well as neural events like slow waves and sleep spindles that are hypothesized to attenuate acoustic processing as a means of preserving the sleep state. We recorded FFRs during a 2.5 hour nap from fourteen healthy male and female human adults to investigate how sleep depth and microarchitecture affect auditory encoding. We show that FFR strength is maintained across non-rapid eye movement sleep stages in subcortical nuclei, yet decreases in deeper sleep in the auditory cortex. FFR strength was not influenced by slow wave or spindle activity, but rather by reduced communication between the thalamus and cortex. This differentiation in sound representation across the auditory hierarchy suggests ameans by which the brainmight balance environmental monitoring with preserving critical restorative processes. Significance statement Sleep balances memory consolidation with responsiveness to important external sounds, yet how auditory processing changes across sleep stages remains unclear. The frequency-following response (FFR) reflects neural encoding of sound periodicity and allows assessment of auditory processing from the brainstem to the cortex. Using magnetoencephalography (MEG), we show that while subcortical FFR strength remains stable across non-rapid eye movement sleep, cortical responses weaken in deeper sleep due to reduced thalamocortical communication. Notably, FFR strength is unaffected by sleep spindles or slow waves. These findings document how the brain selectively dampens cortical auditory processing during sleep.

The long-lasting impact of male-based research on female physiology- also true for slow wave sleep.

The long-lasting impact of male-based research on female physiology- also true for slow wave sleep.

Vagal nerve signals are modulated by spontaneous seizures in Genetic Absence Epilepsy Rats from Strasbourg

IntroductionOne-third of epileptic patients are resistant to conventional treatments. Vagus nerve stimulation is a promising therapy, especially when applied early during seizure onset. This study explores vagus nerve activity (VNA) during seizures in Genetic Absence Epilepsy Rat from Strasbourg (GAERS) model and explores how VNA changes with epilepsy duration.MethodsEleven rats (4, 6, and 10 months old, n = 4, 4, 3, respectively) were continuously recorded with electroencephalography, VNA recordings, and video for 24 h. Ictal VNA root mean square (RMS) values preceded by NREM sleep extracted from 11 rats were studied in a total of 620 seizures.ResultsOverall, VNA RMS increased during seizures, with a median rise of 60%. However, this modulation decreased with age, despite stable seizure severity. Significant differences in VNA activity and inter-quartile range were observed between age groups.DiscussionThese results support seizure severity-dependent changes in ictal VNA modulation and point toward the potential of VNA as a biomarker for seizure detection and autonomic dysfunction.

Independent Association of Sleep Apnea-Specific Hypoxic Burden and Sleep Breathing Impairment Index with Thyroid Function in Obstructive Sleep Apnea: A Retrospective Study

PurposeThis study aimed to explore thyroid function changes in patients with obstructive sleep apnea (OSA) and analyze the relationships among the sleep apnea-specific hypoxic burden (SASHB), the sleep breathing impairment index (SBII), and the function during different sleep stages.MethodsThis retrospective study included 452 patients with OSA who visited the Second Affiliated Hospital of Xi’an Jiaotong University between August 2017 and March 2024. The severity of OSA was evaluated, grouping patients by their apnea–hypopnea index (AHI), SASHB, SBII, and both SASHB and SBII during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. Multiple linear regression analyzed the independent relationships between the AHI, SASHB, SBII, and thyroid function indicators while adjusting for potential confounders.ResultsSignificant intergroup differences were observed in thyroid function indicators under various grouping methods, with different trends. After adjusting for confounding factors, SASHB, SASHB during NREM sleep (NREM-SASHB), SASHB during REM sleep (REM-SASHB), SBII, SBII during NREM sleep (NREM-SBII), and SBII during REM sleep (REM-SBII) were independently associated with elevated serum free triiodothyronine (FT3) levels (p < 0.05). Similar results were noted in the male patients, whereas no significant associations were observed in the female patients.ConclusionThere is an association between OSA and thyroid function, with SASHB and SBII independently linked to elevated FT3 levels across different sleep stages and sex subgroups. Future research should further explore these mechanisms to optimize clinical management and treatment strategies for patients with OSA.