Vigilance States Research Articles

The impacts of sex and the 5xFAD model of Alzheimer's disease on the sleep and spatial learning responses to feeding time.

The relationships between the feeding rhythm, sleep and cognition in Alzheimer's disease (AD) are incompletely understood, but meal time could provide an easy-to-implement method of curtailing disease-associated disruptions in sleep and cognition. Furthermore, known sex differences in AD incidence could relate to sex differences in circadian rhythm/sleep/cognition interactions. The 5xFAD transgenic mouse model of AD and non-transgenic wild-type controls were studied. Both female and male mice were used. Food access was restricted each day to either the 12-h light phase (light-fed groups) or the 12-h dark phase (dark-fed groups). Sleep (electroencephalographic/electromyographic) recording and cognitive behavior measures were collected. The 5xFAD genotype reduces NREM and REM as well as the number of sleep spindles. In wild-type mice, light-fed groups had disrupted vigilance state amounts, characteristics, and rhythms relative to dark-fed groups. These feeding time differences were reduced in 5xFAD mice. Sex modulates these effects. 5xFAD mice display poorer spatial memory that, in female mice, is curtailed by dark phase feeding. Similarly, female 5xFAD mice have decreased anxiety-associated behavior. These emotional and cognitive measures are correlated with REM amount. Our study demonstrates that the timing of feeding can alter many aspects of wake, NREM and REM. Unexpectedly, 5xFAD mice are less sensitive to these feeding time effects. 5xFAD mice demonstrate deficits in cognition which are correlated with REM, suggesting that this circadian-timed aspect of sleep may link feeding time and cognition. Sex plays an important role in regulating the impact of feeding time on sleep and cognition in both wild-type and 5xFAD mice, with females showing a greater cognitive response to feeding time than males.

Environmental context sculpts spatial and temporal visual processing in thalamus.

Behavioral state modulates neural activity throughout the visual system; this is largely due to changes in arousal that alter internal brain state. However, behaviors are constrained by the external environmental context, so it remains unclear if this context itself dictates the regime of visual processing, apart from ongoing changes in arousal. Here, we addressed this question in awake head-fixed mice while they passively viewed visual stimuli in two different environmental contexts: either a cylindrical tube, or a circular running wheel. We targeted high-density silicon probe recordings to the dorsal lateral geniculate nucleus (dLGN) and simultaneously measured several electrophysiological and behavioral correlates of arousal changes, and thus controlled for them across contexts. We found surprising differences in spatial and temporal processing in dLGN across contexts, even in identical states of alertness and stillness. The wheel context (versus tube) showed elevated baseline activity, faster visual responses, and smaller but less selective spatial receptive fields. Further, arousal caused similar changes to visual responsiveness across all conditions, but the environmental context mainly changed the overall set-point for this relationship. Together, our results reveal an unexpected influence of the physical environmental context on fundamental aspects of visual processing in the early visual system.

Astrocytic metabolic control of orexinergic activity in the lateral hypothalamus regulates sleep and wake architecture

Neuronal activity undergoes significant changes during vigilance states, accompanied by an accommodation of energy demands. While the astrocyte-neuron lactate shuttle has shown that lactate is the primary energy substrate for sustaining neuronal activity in multiple brain regions, its role in regulating sleep/wake architecture is not fully understood. Here we investigated the involvement of astrocytic lactate supply in maintaining consolidated wakefulness by downregulating, in a cell-specific manner, the expression of monocarboxylate transporters (MCTs) in the lateral hypothalamus of transgenic mice. Our results demonstrate that reduced expression of MCT4 in astrocytes disrupts lactate supply to wake-promoting orexin neurons, impairing wakefulness stability. Additionally, we show that MCT2-mediated lactate uptake is necessary for maintaining tonic firing of orexin neurons and stabilizing wakefulness. Our findings provide both in vivo and in vitro evidence supporting the role of astrocyte-to-orexinergic neuron lactate shuttle in regulating proper sleep/wake stability.

Ascorbate insufficiency disrupts glutamatergic signaling and alters electroencephalogram phenotypes in a mouse model of Alzheimer's disease

Clinical studies have reported that increased epileptiform and subclinical epileptiform activity can be detected in many patients with an Alzheimer's disease (AD) diagnosis using electroencephalogram (EEG) and this may correlate with poorer cognition. Ascorbate may have a specific role as a neuromodulator in AD as it is released concomitantly with glutamate reuptake following excitatory neurotransmission. Insufficiency may therefore result in an exacerbated excitatory/inhibitory imbalance in neuronal signaling. Using a mouse model of AD that requires dietary ascorbate (Gulo−/-APPswe/PSEN1dE9), EEG was recorded at baseline and during 4 weeks of ascorbate depletion in young (5-month-old) and aged (20-month-old) animals. Data were scored for changes in quantity of spike trains, individual spikes, sleep-wake rhythms, sleep fragmentation, and brainwave power bands during light periods each week. We found an early increase in neuronal spike discharges with age and following ascorbate depletion in AD model mice and not controls, which did not correlate with brain amyloid load. Our data also show more sleep fragmentation with age and with ascorbate depletion. Additionally, changes in brain wave activity were observed within different vigilance states in both young and aged mice, where Gulo−/-APPswe/PSEN1dE9 mice had shifts towards higher frequency bands (alpha, beta, and gamma) and ascorbate depletion resulted in shifts towards lower frequency bands (delta and theta). Microarray data supported ascorbate insufficiency altering glutamatergic transmission through the decreased expression of glutamate related genes, however no changes in protein expression of glutamate reuptake transporters were observed. These data suggest that maintaining optimal brain ascorbate levels may support normal brain electrical activity and sleep patterns, particularly in AD patient populations where disruptions are observed.

The timing of sleep spindles is modulated by the respiratory cycle in humans

ObjectiveCoupling of sleep spindles with cortical slow waves and hippocampus sharp-waves ripples is crucial for sleep-related memory consolidation. Recent literature evidenced that nasal respiration modulates neural activity in large-scale brain networks. In rodents, this respiratory drive strongly varies according to vigilance states. Whether sleep oscillations are also respiration-modulated in humans remains open. In this work, we investigated the influence of breathing on sleep spindles during non-rapid-eye-movement sleep in humans. MethodsFull night polysomnography of twenty healthy participants were analysed. Spindles and slow waves were automatically detected during N2 and N3 stages. Spindle-related sigma power as well as spindle and slow wave events were analysed according to the respiratory phase. ResultsWe found a significant coupling between both slow and fast spindles and the respiration cycle, with enhanced sigma activity and occurrence probability of spindles during the middle part of the expiration phase. A different coupling was observed for slow waves negative peaks which were rather distributed around the two respiration phase transitions. ConclusionOur findings suggest that breathing cycle influences the dynamics of brain activity during non-rapid-eye-movement sleep. SignificanceThis coupling may enable sleep spindles to synchronize with other sleep oscillations and facilitate information transfer between distributed brain networks.

Restoration of sleep-wake behavior following short photoperiod exposure in ventral subicular lesioned male Wistar rats: A 24-h sleep-wake electroencephalographical study.

The ventral subiculum regulates emotion, stress responses, and spatial and social cognition. In our previous studies, we have demonstrated anxiety- and depression-like symptoms, deficits in spatial and social cognition in ventral subicular lesioned (VSL) rats, and restoration of affective and cognitive behaviors following photoperiod manipulation (short photoperiod regime, SPR; 6:18 LD cycle). In the present study, we have studied the impact of VSL on sleep-wake behavioral patterns and the effect of SPR on sleep-wakefulness behavior. Adult male Wistar rats subjected to VSL demonstrated decreased wake duration and enhanced total sleep time due to increased non-rapid eye movement sleep (NREMS) and rapid eye movement sleep (REMS). Power spectral analysis indicated increased delta activity during NREMS and decreased sigma band power during all vigilance states. Light is one of the strongest entrainers of the circadian rhythm, and its manipulation may have various physiological and functional consequences. We investigated the effect of 21-day exposure to SPR on sleep-wakefulness (S-W) behavior in VSL rats. We observed that SPR exposure restored S-W behavior in VSL rats, resulting in an increase in wake duration and a significant increase in theta power during wake and REMS. This study highlights the crucial role of the ventral subiculum in maintaining normal sleep-wakefulness patterns and highlights the effectiveness of photoperiod manipulation as a non-pharmacological treatment for reversing sleep disturbances reported in mood and neuropsychiatric disorders like Alzheimer's disease, bipolar disorder, and major depressive disorder, which also involve alterations in circadian rhythm.

Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an electroencephalography (EEG)-based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM), the K nearest neighbor (KNN), the Naive Bayes (NB), the Decision Tree (DT), and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake and drowsy). Segmentation into 10 s intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

Distal Immunization and Systemic Cytokines Establish a Transient Immune Alert State in the Intestine.

Conventionally, immune responses are studied in the context of inflamed tissues and their corresponding draining lymph nodes (LNs). However, little is known about the effects of systemic inflammatory signals generated during local inflammation on distal tissues and nondraining LNs. Using a mouse model of cutaneous immunization, we found that systemic inflammatory stimuli triggered a rapid and selective distal response in the small intestine and the mesenteric LN (mesLN). This consisted of increased permeability of intestinal blood vessels and lymphatic drainage of bloodborne solutes into the mesLN, enhanced activation and migration of intestinal dendritic cells, as well as amplified T cell responses in the mesLNs to systemic but not orally derived Ags. Mechanistically, we found that the small intestine endothelial cells preferentially expressed molecules involved in TNF-α signaling and that TNF-α blockade markedly diminished distal intestinal responses to cutaneous immunization. Together, these findings reveal that the intestinal immune system is rapidly and selectively activated in response to inflammatory cues regardless of their origin, thus identifying an additional layer of defense and enhanced surveillance of a key barrier organ at constant risk of pathogen encounter.

Prediction maintenance based on vibration analysis and deep learning — A case study of a drying press supported on a Hidden Markov Model

The main objective of this paper is to describe a methodology that was developed to support maintenance decision-making methods based on equipment condition. Condition-Based Maintenance allows to increase equipment availability and maximize investments. This is mainly due to the prevention of unexpected equipment downtime. By avoiding turning on/off industrial equipment, production flows are more efficient, allowing manufacturers to improve the quality of the end-product. The industry aims more and more to correspond satisfying customer expectations. We argue that the methodology developed in this paper adds value to the existing literature, namely because the fact that it is possible to anticipate the state of an equipment without a large amount of support data. In other words, although one could find information gaps regarding the occurrence of failures, it was possible to accurately assess the state of the equipment. This approach is robust, as it can be used in distinct equipment with different sensors, making this methodology generalizable for Condition-Based Maintenance. The paper presents the validation of the preceding through a case study on drying presses in the paper industry. To do so, three states were adopted, namely: “Proper function”; “Alert state”; and “Equipment failure”. The methodology follows a series of steps, going through the collection of values from vibration sensors, imputation of values using Deep Artificial Neural Networks through on-line sensors, until reaching the last stage of classification carried out by the Hidden Markov Model. Through optimized observations from the previous steps, it was possible to define the hidden states through the Viterbi algorithm, which corresponds to the health states of the equipment. Additionally, it was possible to demonstrate that the proposed methodology can accurately characterize the condition states of the equipment based on the data obtained and can be generalized to other types of equipment.

The neurotransmitters of sleep and arousal

The sleep and arousal, or the vigilance state, is controlled by a highly dynamic brain system that involves numerous interconnected brain circuits. However, while many reviews presented the anatomical bases of how different components of the brain area contribute to the vigilance states, less attention is paid from the perspective of different neurotransmitters. As multiple different neuronal populations could exist and express in the same brain area, attributing the effect of different brain areas on sleep and arousal would not be precise without consideration of the act of neurotransmitters. This review summarizes the recent progress on how different neurotransmitters affect vigilance states. By doing so, this review aims to provide an alternative angle in understanding how different vigilance state is regulated by the brain.

Germicidal lamps using UV-C radiation may pose health safety issues: a biomolecular analysis of their effects on apoptosis and senescence.

The battle against the COVID-19 pandemic has spurred a heightened state of vigilance in global healthcare, leading to the proliferation of diverse sanitization methods. Among these approaches, germicidal lamps utilizing ultraviolet (UV) rays, particularly UV-C (wavelength ranging from 280 to 100 nm), have gained prominence for domestic use. These light-emitting diode (LED) lamps are designed to sanitize the air, objects, and surfaces. However, the prevailing concern is that these UV lamps are often introduced into the market without adequate accompanying information to ensure their safe utilization. Importantly, exposure to absorbed UV light can potentially trigger adverse biological responses, encompassing cell death and senescence. Our research encompassed a series of investigations aimed at comprehending the biological repercussions of UV-C radiation exposure from readily available domestic lamps. Our focus centered on epithelial retinal cells, keratinocytes, and fibroblasts, components of the skin and ocular targets frequently exposed to UV irradiation. Our findings underscore the potential harm associated with even brief exposure to UV, leading to irreversible and detrimental alterations in both skin cells and retinal cells of the eye. Notably, epithelial retinal cells exhibited heightened sensitivity, marked by substantial apoptosis. In contrast, keratinocytes demonstrated resilience to apoptosis even at elevated UV doses, though they were prone to senescence. Meanwhile, fibroblasts displayed a gradual amplification of both senescence and apoptosis as radiation doses escalated. In summary, despite the potential benefits offered by UV-C in deactivating pathogens like SARS-CoV-2, it remains evident that the concurrent risks posed by UV-C to human health cannot be ignored.

Automating Polysomnography in an Under-Explored Animal Model: Preliminary Results from Hibernating Bears

Animal hibernation is a hypometabolic state that may inform translational research to improve clinical outcomes for hypoxic patients whose oxygen supply does not match their demand. Bears are an excellent animal model, as they decrease their body temperature to only 30-35°C during hibernation and suppress their metabolism down to 25% of normal resting levels while having a more comparable physiology to humans than deep hibernators such as small rodents. As little is known about their sleep patterns during hibernation, we continuously monitored for more than 3500 days via biotelemetry a variety of physiological parameters from 16 captive American black bears in and out of hibernation. We recorded EEG, EOG and EMG signals that are commonly used to determine wake, REM sleep and NREM sleep vigilance states in conventional animal models (polysomnography). Such a data set is too large to fully annotate manually, so we compared two automated approaches. We first manually annotated two one-day recordings at body temperature extremes from each of 6 bears during hibernation when body temperatures were oscillating widely in multiday cycles with intermittent bouts of shivering (Tøien et al. 2011). We also manually scored a one-day, non-hibernating recording at normal (summer) body temperature from these bears. Based on this reference data set, we evaluated two automated scoring applications based on different machine learning classifiers: open source Somnotate (author Paul Brodersen), which is trained on multiple files, and proprietary Somnivore (author Giancarlo Allocca), which is trained on a subset of epochs from each individual recording. Somnotate gave best results during hold-one-out testing when using separate models for hibernating and non-hibernating data, and then performed comparably for both conditions. Somnivore was by design trained on a small subset (100 epochs of each kind) of each file to be tested. Both applications provided typical F-measures against manual reference scores in the 0.90-0.97 range. Outliers in the lower 0.72-0.88 range were highly correlated between the two applications, indicating that specific files are more challenging to annotate — either manually, automatically, or both. We conclude that both applications have accuracies on par with manual scorers when trained on high quality data. Supported by NIH COBRE under grant number [P20GM130443]. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Smart Driver Monitoring System using Deep Learning

This research introduces a monitoring system that leverages deep learning methodologies, to analyze and interpret facial features to assess the driver’s alertness. With the rapid advancement of technology especially in automobiles this type of monitoring can be employed in all types of vehicles. The proposed system employs deep learning models like YOLOv8 to detect the state of alertness of the driver. This project aims to achieve this by training the deep learning model on a custom dataset with varying background noise to train the model as accurately as possible. Various performance metrics and evaluations will be done to evaluate the accuracy of the model to integrate it into automobiles. This holds great promise for revolutionizing intelligent transportation systems, automotive technologies, regulatory frameworks, public policies, and societal initiatives aimed at improving road safety, driver well-being, and operational excellence on a global scale, ushering in a safer, more efficient, and sustainable transportation ecosystem.

An Electroencephalogram Signature of Melanin-Concentrating Hormone Neuron Activities Predicts Cocaine Seeking

BackgroundIdentifying biomarkers that predict substance use disorder propensity may better strategize antiaddiction treatment. Melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus critically mediate interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers. MethodsSurface EEG signals and real-time calcium (Ca2+) activities of lateral hypothalamus MCH neurons (Ca2+MCH) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca2+MCH using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca2+MCH either before or after cocaine experience were used to predict future drug-seeking behaviors. ResultsAn EEG waveform derivative—a modified theta-delta-theta peak ratio (EEGTDT ratio)—accurately tracked real-time Ca2+MCH in rats. The prediction was robust during rapid eye movement sleep (REMS), persisted through vigilance states, sleep manipulations, and circadian phases, and was consistent across sex. Moreover, cocaine self-administration and long-term withdrawal altered EEGTDT ratio, suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEGTDT ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEGTDT ratio also exhibited advantages over conventional REMS measures for the predictions. ConclusionsThe identified EEGTDT ratio may serve as a noninvasive measure for assessing MCH neuron activities in vivo and evaluating REMS; it may also serve as a potential biomarker for predicting drug use propensity.

JAK-STAT signaling maintains homeostasis in T cells and macrophages

Immune cells need to sustain a state of constant alertness over a lifetime. Yet, little is known about the regulatory processes that control the fluent and fragile balance that is called homeostasis. Here we demonstrate that JAK-STAT signaling, beyond its role in immune responses, is a major regulator of immune cell homeostasis. We investigated JAK-STAT-mediated transcription and chromatin accessibility across 12 mouse models, including knockouts of all STAT transcription factors and of the TYK2 kinase. Baseline JAK-STAT signaling was detected in CD8+ T cells and macrophages of unperturbed mice—but abrogated in the knockouts and in unstimulated immune cells deprived of their normal tissue context. We observed diverse gene-regulatory programs, including effects of STAT2 and IRF9 that were independent of STAT1. In summary, our large-scale dataset and integrative analysis of JAK-STAT mutant and wild-type mice uncovered a crucial role of JAK-STAT signaling in unstimulated immune cells, where it contributes to a poised epigenetic and transcriptional state and helps prepare these cells for rapid response to immune stimuli.

IoT- Based Drowsiness Monitoring and Crash Detection System to Enhance Road Safety

This project is a solution designed to address the critical issue of driver drowsiness and enhance road safety. The system leverages the power of the Internet of Things (IoT) to continuously monitor driver alertness and promptly detect potential accidents. It comprises a network of sensors strategically placed within the vehicle, including facial recognition cameras and accelerometers. These sensors work together to capture and analyze a driver's physiological and behavioural data in real-time. Facial recognition technology detects signs of drowsiness such as drooping eyelids and yawning. Through a secure IoT connection, this data is transmitted to a central processing unit within the vehicle. Advanced algorithms process this information, assessing the driver's state of alertness. In the event of detected drowsiness, the system initiates a series of proactive interventions, such as sounding alarms, vibrating the driver's seat, or even communicating with an external monitoring centre for further assistance. Moreover, the system's crash detection capabilities are enabled by the accelerometers, which monitor sudden changes in the vehicle's motion. If a collision or accident is detected, the system promptly sends an alert, including the vehicle's GPS coordinates, to emergency services or designated contacts, facilitating rapid response and potentially saving lives. Keywords:- Drowsiness, Crash Detection, Raspberry Pi, IoT

Detection of atypical attentional behaviors in young subjects

BackgroundVigilance ability refers to the accuracy and speed with which a person performs a cognitive-motor task, either voluntarily (endogenous mode) or following a warning stimulus (exogenous mode). In the context of a force production task, our study focuses on the impact of the states of vigilance by proposing an original approach that allows distinguishing between good (inlier) and poor (outlier) participants. We assume that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. Our objective is particularly challenging in the context of a limited dataset with a high level of noise. New methodOur original methodological approach consists of coupling the RANSAC (RANdom SAmple Consensus) algorithm with a statistical machine learning algorithm to handle noise. Comparison with existing methodsOur clustering approach, based on the coupling of RANSAC methodology with ensemble classifiers, overcomes the limitations of conventional supervised algorithms that are either not robust to outliers (such as K-Nearest Neighbors) and/or not adapted to few-shot learning (such as Support Vector Machines and Artificial Neural Networks). ResultsThe clustering results were validated in terms of reaction time distributions and force error distributions with respect to participant groups. We show that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. ConclusionOur study has allowed us to detect atypical attentional patterns and succeeds in separating the inliers from the outliers.

Neuroligin-2 shapes individual slow waves during slow-wave sleep and the response to sleep deprivation in mice

BackgroundSleep disturbances are a common comorbidity to most neurodevelopmental disorders and tend to worsen disease symptomatology. It is thus crucial to understand mechanisms underlying sleep disturbances to improve patients’ quality of life. Neuroligin-2 (NLGN2) is a synaptic adhesion protein regulating GABAergic transmission. It has been linked to autism spectrum disorders and schizophrenia in humans, and deregulations of its expression were shown to cause epileptic-like hypersynchronized cerebral activity in rodents. Importantly, the absence of Nlgn2 (knockout: KO) was previously shown to alter sleep-wake duration and quality in mice, notably increasing slow-wave sleep (SWS) delta activity (1–4 Hz) and altering its 24-h dynamics. This type of brain oscillation is involved in memory consolidation, and is also a marker of homeostatic sleep pressure. Sleep deprivation (SD) is notably known to impair cognition and the physiological response to sleep loss involves GABAergic transmission.MethodsUsing electrocorticographic (ECoG) recordings, we here first aimed to verify how individual slow wave (SW; 0.5-4 Hz) density and properties (e.g., amplitude, slope, frequency) contribute to the higher SWS delta activity and altered 24-h dynamics observed in Nlgn2 KO mice. We further investigated the response of these animals to SD. Finally, we tested whether sleep loss affects the gene expression of Nlgn2 and related GABAergic transcripts in the cerebral cortex of wild-type mice using RNA sequencing.ResultsOur results show that Nlgn2 KO mice have both greater SW amplitude and density, and that SW density is the main property contributing to the altered 24-h dynamics. We also found the absence of Nlgn2 to accelerate paradoxical sleep recovery following SD, together with profound alterations in ECoG activity across vigilance states. Sleep loss, however, did not modify the 24-h distribution of the hypersynchronized ECoG events observed in these mice. Finally, RNA sequencing confirmed an overall decrease in cortical expression of Nlgn2 and related GABAergic transcripts following SD in wild-type mice.ConclusionsThis work brings further insight into potential mechanisms of sleep duration and quality deregulation in neurodevelopmental disorders, notably involving NLGN2 and GABAergic neurotransmission.

Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine.

Sleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time-consuming and labor-intensive. In this study, we performed an automatic sleep-wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats. The study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep-wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD. Features extracted from occipital ECoG signals were more suitable for constructing sleep-wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep-wake states of rats (average Cohen's kappa: 0.79). This study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep-wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.

Serotonergic modulation of vigilance states in zebrafish and mice

Vigilance refers to being alertly watchful or paying sustained attention to avoid potential threats. Animals in vigilance states reduce locomotion and have an enhanced sensitivity to aversive stimuli so as to react quickly to dangers. Here we report that an unconventional 5-HT driven mechanism operating at neural circuit level which shapes the internal state underlying vigilance behavior in zebrafish and male mice. The neural signature of internal vigilance state was characterized by persistent low-frequency high-amplitude neuronal synchrony in zebrafish dorsal pallium and mice prefrontal cortex. The neuronal synchronization underlying vigilance was dependent on intense release of 5-HT induced by persistent activation of either DRN 5-HT neuron or local 5-HT axon terminals in related brain regions via activation of 5-HTR7. Thus, we identify a mechanism of vigilance behavior across species that illustrates the interplay between neuromodulators and neural circuits necessary to shape behavior states.