Sleep-deprived Subjects Research Articles

Impact of endogenous analgesia triggered by acupuncture, stress, or noxious stimulation on REM sleep-deprived rats.

Poor sleep increases pain, at least in part, by disrupting endogenous pain modulation. However, the efficacy of endogenous analgesia in sleep-deprived subjects has never been tested. To assess this issue, we chose three different ways of triggering endogenous analgesia: (1) acupuncture, (2) acute stress, and (3) noxious stimulation, and compared their ability to decrease the pronociceptive effect induced by REM-SD (Rapid Eye Movement Sleep Deprivation) with that to decrease inflammatory hyperalgesia in the classical carrageenan model. First, we tested the ability of REM-SD to worsen carrageenan-induced hyperalgesia: Alow dose of carrageenan (30 µg) in sleep-deprived Wistar rats resulted in a potentiated hyperalgesic effectthat was more intense and longer-lasting than that induced by a higher standard dose of carrageenan (100 µg) or by REM-SD alone. Then, we found that (1) acupuncture, performed at ST36, completely reversed the pronociceptive effect induced by REM-SD or by carrageenan; (2) immobilization stress completely reversed the pronociceptive effect of REM-SD, while transiently inhibited carrageenan-induced hyperalgesia; (3) noxious stimulation of the forepaw by capsaicin also reversed the pronociceptive effect of REM-SD and persistently increased the nociceptive threshold above the baseline in carrageenan-treated animals. Therefore, acupuncture, stress, or noxious stimulation reversed the pronociceptive effect of REM-SD, while each intervention affected carrageenan-induced hyperalgesia differently. This study has shown that while sleep loss may disrupt endogenous pain modulation mechanisms, it does not prevent theactivation of these mechanisms to induce analgesia in sleep-deprived individuals.

The association between sleep deprivation and the risk of cardiovascular diseases: A systematic meta‑analysis.

Globally, sleep deprivation is a concerning health issue associated with an increased risk of cardiovascular diseases (CVDs). The present study aimed to explore the association between short-term sleep and the risk of CVDs, taking into consideration sex and age groups. A comprehensive review was conducted by assembling cohort studies that are available in the PubMed, Cochrane Library, and Embase databases. Individuals with ≤5 or ≤6 h of sleep per day were considered as sleep-deprived subjects. To minimize potential bias, two reviewers thoroughly evaluated the selected articles. Relevant data were extracted, and pooled odds ratios (ORs) or relative risks (RRs) were calculated using a random-effects model. In total, 18 cohort studies involving adult subjects were included in the present analysis. The pooled results strongly indicated that sleep deprivation was associated with a greater risk of CVDs [RR: 1.09, 95% confidence interval (CI): 1.02-1.16, P=0.009]. However, when the pooled analysis was stratified by sex and age, the following results were observed: short-term sleep women (RR: 1.06, 95% CI: 0.96-1.17, P=0.27), short-term sleep men (RR: 1.07, 95% CI: 0.97-1.17, P=0.17); ≥18 years-old sleep-deprived population (RR: 1.09, 95% CI: 1.00-1.17, P=0.04), ≥40 years-old sleep-deprived population (RR: 1.09, 95% CI: 0.98-1.22, P=0.11), and subjects with co-existing diseases, such as diabetes and hyperlipidemia (RR: 1.06, 95% CI: 0.94-1.20, P=0.32). In conclusion, short-term sleep is associated with the increased risk of CVDs. Among subjects who were aged ≥18 years-old, there was a strong association with the development of CVDs compared with those who were aged ≥40 years-old. Furthermore, men were at a higher risk of CVDs than women. Adequate sleep (7-8 h per day) may play a role in improving cardiac health. The results of the present study may provide valuable support for further research in public health, highlighting the correlation between sleep deprivation and the risk of CVDs.

Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation.

Whether you are reading, running or sleeping, your brain and its fluid environment continuously interacts to distribute nutrients and clear metabolic waste. Yet, the precise mechanisms for solute transport within the human brain have remained hard to quantify using imaging techniques alone. From multi-modal human brain MRI data sets in sleeping and sleep-deprived subjects, we identify and quantify CSF tracer transport parameters using forward and inverse subject-specific computational modelling. Our findings support the notion that extracellular diffusion alone is not sufficient as a brain-wide tracer transport mechanism. Instead, we show that human MRI observations align well with transport by either by an effective diffusion coefficent 3.5[Formula: see text] that of extracellular diffusion in combination with local clearance rates corresponding to a tracer half-life of up to 5h, or by extracellular diffusion augmented by advection with brain-wide average flow speeds on the order of 1-9 [Formula: see text]m/min. Reduced advection fully explains reduced tracer clearance after sleep-deprivation, supporting the role of sleep and sleep deprivation on human brain clearance.

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.

Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks. Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and β rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics. Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen's kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods. Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.

Psychomotor Performance after 30 h of Sleep Deprivation Combined with Exercise.

Sleep deprivation (SD) usually impairs psychomotor performance, but most experiments are usually focused on sedentary conditions. The purpose of this study was to evaluate the influence of 30 h of complete SD combined with prolonged, moderate exercise (SDE) on human psychomotor performance. Eleven endurance-trained men accustomed to overnight exertion were tested twice: in well-slept and non-fatigued conditions (Control) and immediately after 30 h of SDE. They performed a multiple-choice reaction time test (MCRT) at rest and during each workload of the graded exercise test to volitional exhaustion. At rest, the MCRT was shorter after SDE than in the Control (300 ± 13 ms vs. 339 ± 11 ms, respectively, p < 0.05). During graded exercise, there were no significant differences in MCRT between groups, but the fastest reaction was observed at lower workloads after SDE (158 ± 7 W vs. 187 ± 11 W in Control, p < 0.05). The total number of missed reactions tended to be higher after SDE (8.4 ± 0.7 vs. 6.3 ± 0.8 in Control, p = 0.06). In conclusion, SDE is different from SD alone; however, well-trained men, accustomed to overnight exertion can maintain psychomotor abilities independently of the extent of central fatigue. Exercise can be used to enhance psychomotor performance in sleep-deprived subjects in whom special caution is required in order to avoid overload.

Dissociable brainstem functional connectivity changes correlate with objective and subjective vigilance decline after total sleep deprivation in healthy male subjects.

The maintenance of vigilance relies on the activation of the cerebral cortex by the arousal system centered on the brainstem. Previous studies have suggested that both objective and subjective vigilance are susceptible to sleep deprivation. This study aims to explore the alterations in brainstem arousal system functional connectivity (FC) and its involvement in these two types of vigilance decline following total sleep deprivation (TSD). Thirty-seven healthy male subjects underwent two counterbalanced resting-state fMRI scans, once in rested wakefulness (RW) and once after 36 h of TSD. The pontine tegmental area and caudal midbrain (PTA-cMidbrain), the core regions of the brainstem arousal system, were chosen as the seeds for FC analysis. The difference in PTA-cMidbrain FC between RW and TSD conditions was then investigated, as well as its associations with objective vigilance measured by psychomotor vigilance task (PVT) and subjective vigilance measured by Stanford Sleepiness Scale. The sleep-deprived subjects showed increased PTA-cMidbrain FC with the thalamus and cerebellum and decreased PTA-cMidbrain FC with the occipital, parietal, and sensorimotor regions. TSD-induced increases in PVT reaction time were negatively correlated with altered PTA-cMidbrain FC in the dorsolateral prefrontal cortex, extrastriate visual cortex, and precuneus. TSD-induced increases in subjective sleepiness were positively correlated with altered PTA-cMidbrain FC in default mode regions including the medial prefrontal cortex and precuneus. Our results suggest that different brainstem FC patterns underlie the objective and subjective vigilance declines induced by TSD.

Efficacy of Different Concentrations of Withanolides in Ashwagandha Supplements for Insomnia and Enhancing Energy Level

ObjectivesAshwagandha was found to have promising results in treating insomnia and bringing forward circadian rhythms. However, anyone with insomnia responded differently to various concentrations of withanolides in Ashwagandha supplements. In this study, the concentration of withanolides in Ashwagandha and total sleep time improvement, sleep circadian rhythms, and energy level elevation were investigated in sleep deprived subjects.MethodsA total of 132 participants with insomnia aged between 18 and 50 were selected for inclusion in the study. Unstandardized Ashwagandha supplements were distributed to group A (n=39). Ashwagandha with 5% of withanolides was assigned to group B (n=53), and group C (n=40) received ashwagandha contained 2.5% of withanolides. The data were collected from self-reported questionnaires and analyzed using the Shapiro-Wilk test and the Wilcoxon signed-rank test with R Studio 4.1.2.ResultsGroup B showed the greatest improvement in sleep quality. In the beginning, 38% of participants slept less than 6 hours, decreasing to 26% after the trial (p=0.007197). 42% of the participants slept before 12am at baseline, increasing to 64% at the end (p=0.0004883). More participants in group C were able to sleep more than 7 hours (p-value=3.426e-05) and those with high energy levels increased dramatically from 13% to 40% (p-value=0.0001221). However, some people have reported acne and oiliness as side effects.ConclusionsAshwagandha with higher levels of withanolides has been shown to be more effective in treating insomnia. Moderate levels may improve both energy levels and total sleep time, while unstandardized ashwagandha may only be effective in increasing energy levels.Funding SourcesThe company Nutripacks sponsored this testing trial.

Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective.

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting‐state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep‐deprived subjects showed increased regional‐level temporal variability in large‐scale brain regions, and decreased regional‐level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra‐network temporal variability in the default mode network (DMN) and increased inter‐network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter‐network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = −.42, p = 5.57 × 10−4) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep‐deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.

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

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

A State-of-the-Art Review on the Use of Modafinil as A Performance-enhancing Drug in the Context of Military Operationality.

Modafinil is an eugeroic drug that has been examined to maintain or recover wakefulness, alertness, and cognitive performance when sleep deprived. In a nonmilitary context, the use of modafinil as a nootropic or smart drug, i.e., to improve cognitive performance without being sleep deprived, increases. Although cognitive performance is receiving more explicit attention in a military context, research into the impact of modafinil as a smart drug in function of operationality is lacking. Therefore, the current review aimed at presenting a current state-of-the-art and research agenda on modafinil as a smart drug. Beside the question whether modafinil has an effect or not on cognitive performance, we examined four research questions based on the knowledge on modafinil in sleep-deprived subjects: (1) Is there a difference between the effect of modafinil as a smart drug when administered in repeated doses versus one single dose?; (2) Is the effect of modafinil as a smart drug dose-dependent?; (3) Are there individual-related and/or task-related impact factors?; and (4) What are the reported mental and/or somatic side effects of modafinil as a smart drug? We conducted a systematic search of the literature in the databases PubMed, Web of Science, and Scopus, using the search terms "Modafinil" and "Cognitive enhance*" in combination with specific terms related to the research questions. The inclusion criteria were studies on healthy human subjects with quantifiable cognitive outcome based on cognitive tasks. We found no literature on the impact of a repeated intake of modafinil as a smart drug, although, in users, intake occurs on a regular basis. Moreover, although modafinil was initially said to comprise no risk for abuse, there are now indications that modafinil works on the same neurobiological mechanisms as other addictive stimulants. There is also no thorough research into a potential risk for overconfidence, whereas this risk was identified in sleep-deprived subjects. Furthermore, eventual enhancing effects were beneficial only in persons with an initial lower performance level and/or performing more difficult tasks and modafinil has an adverse effect when used under time pressure and may negatively impact physical performance. Finally, time-on-task may interact with the dose taken. The use of modafinil as a smart drug should be examined in function of different military profiles considering their individual performance level and the task characteristics in terms of cognitive demands, physical demands, and sleep availability. It is not yet clear to what extent an improvement in one component (e.g., cognitive performance) may negatively affect another component (e.g., physical performance). Moreover, potential risks for abuse and overconfidence in both regular and occasional intake should be thoroughly investigated to depict the trade-off between user benefits and unwanted side effects. We identified that there is a current risk to the field, as this trade-off has been deemed acceptable for sleep-deprived subjects (considering the risk of sleep deprivation to performance) but this reasoning cannot and should not be readily transposed to non-sleep-deprived individuals. We thus conclude against the use of modafinil as a cognitive enhancer in military contexts that do not involve sleep deprivation.

Total sleep deprivation reduces top-down regulation of emotion without altering bottom-up affective processing.

Sleep loss is reported to influence affective processing, causing changes in overall mood and altering emotion regulation. These aspects of affective processing are seldom investigated together, making it difficult to determine whether total sleep deprivation has a global effect on how affective stimuli and emotions are processed, or whether specific components of affective processing are affected selectively. Sixty healthy adults were recruited for an in-laboratory study and, after a monitored night of sleep and laboratory acclimation, randomly assigned to either a total sleep deprivation condition (n = 40) or a rested control condition (n = 20). Measurements of mood, vigilant attention to affective stimuli, affective working memory, affective categorization, and emotion regulation were taken for both groups. With one exception, measures of interest were administered twice: once at baseline and again 24 hours later, after the sleep deprived group had spent a night awake (working memory was assessed only after total sleep deprivation). Sleep deprived individuals experienced an overall reduction in positive affect with no significant change in negative affect. Despite the substantial decline in positive affect, there was no evidence that processing affectively valenced information was biased under total sleep deprivation. Sleep deprived subjects did not rate affective stimuli differently from rested subjects, nor did they show sleep deprivation-specific effects of affect type on vigilant attention, working memory, and categorization tasks. However, sleep deprived subjects showed less effective regulation of negative emotion. Overall, we found no evidence that total sleep deprivation biased the processing of affective stimuli in general. By contrast, total sleep deprivation appeared to reduce controlled processing required for emotion regulation.

Inverse correlation of fluctuations of cerebral blood and water concentrations in humans

Near-infrared spectroscopy (fNIRS) measures concentrations of oxygenated (HbO) and deoxygenated (HbR) hemoglobin in the brain. Recently, we demonstrated its potential also for measuring concentrations of cerebral water (hbox {cH}_{2}hbox {O}). We performed fNIRS measurements during rest to study fluctuations in concentrations of hbox {cH}_{2}hbox {O}, HbO and HbR in 33 well-rested healthy control subjects (HC) and 18 acutely sleep-deprived HC. Resting-state fNIRS signal was filtered in full-band, cardiac, respiratory, low-, and very-low-frequency bands. The sum of HbO and HbR constitutes the regional cerebral blood volume (CBV). CBV and hbox {cH}_{2}hbox {O} concentrations were analyzed via temporal correlation and phase synchrony. Fluctuation in concentrations of hbox {cH}_{2}hbox {O} and CBV was strongly anti-correlated across all frequency bands in both frontal and parietal cortices. Fluctuation in concentrations of {hbox {cH}}_{2}{hbox {O}} and CBV showed neither a completely synchronous nor a random phase relationship in both frontal and parietal cortices. Acutely sleep-deprived subjects did not show significant differences in temporal correlation or phase synchrony between fluctuations in {hbox {cH}}_{2}{hbox {O}} and CBV concentrations compared with well-rested HC. The reciprocal interrelation between fluctuations in CBV and {hbox {cH}}_{2}{hbox {O}} concentrations is consistent with the Munro–Kellie doctrine of constant intracranial volume. This coupling may constitute a functional mechanism underlying glymphatic circulation, which persists despite acutely disturbed sleep patterns.

Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface.

A passive brain–computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects’ data. Trajectory slopes of CORE vector magnitude and angle: m(|R|) and m(∠R) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.

Alteration in Resting-State EEG Microstates Following 24 Hours of Total Sleep Deprivation in Healthy Young Male Subjects.

Purpose: The cognitive effects of total sleep deprivation (TSD) on the brain remain poorly understood. Electroencephalography (EEG) is a very useful tool for detecting spontaneous brain activity in the resting state. Quasi-stable electrical distributions, known as microstates, carry useful information about the dynamics of large-scale brain networks. In this study, microstate analysis was used to study changes in brain activity after 24 h of total sleep deprivation.Participants and Methods: Twenty-seven healthy volunteers were recruited and underwent EEG scans before and after 24 h of TSD. Microstate analysis was applied, and six microstate classes (A–F) were identified. Topographies and temporal parameters of the microstates were compared between the rested wakefulness (RW) and TSD conditions.Results: Microstate class A (a right-anterior to left-posterior orientation of the mapped field) showed lower global explained variance (GEV), frequency of occurrence, and time coverage in TSD than RW, whereas microstate class D (a fronto-central extreme location of the mapped field) displayed higher GEV, frequency of occurrence, and time coverage in TSD compared to RW. Moreover, subjective sleepiness was significantly negatively correlated with the microstate parameters of class A and positively correlated with the microstate parameters of class D. Transition analysis revealed that class B exhibited a higher probability of transition than did classes D and F in TSD compared to RW.Conclusion: The observation suggests alterations of the dynamic brain-state properties of TSD in healthy young male subjects, which may serve as system-level neural underpinnings for cognitive declines in sleep-deprived subjects.

Heart Rate Variability: Influence of Pre-processing Methods in Identifying Single-Night Sleep-Deprived Subjects

Autonomic nervous system (ANS) activity can be non-invasively estimated from the heart rate variability (HRV) signal obtained from the electrocardiogram (ECG). The aim of this work is to find useful parameters that allow to establish the presence of a single night sleep deprivation in healthy subjects. The study included two experimental groups of subjects: Non-sleep-deprived (sleep ≥ 4 h, N = 13) and sleep-deprived (sleep <4 h, N = 10). The RR series extracted from 5 min resting ECG signals were pre-processed using four different algorithms to detect and edit artifacts. The RR series were analyzed in terms of time-domain, frequency-domain and using the Poincare plot, in order to determine differences in HRV indexes across domains and subject groups. Statistical analyses were performed with the Friedman and Mann–Whitney tests, along, with correlation analysis. Pre-processing methods showed a moderate level of agreement. In subjects with 4 h of single-night sleep deprivation, results differed according to the selected method. SD21 index derived from the Poincare plot was the only HRV index showing differences between sleep-deprived and non-deprived subjects.

Effect of Partial Sleep Deprivation on Cognition in Young Adults

Background: Sleep deprivation (SD) is an emerging pandemic. Young adults voluntarily sleep deprive dueto increased social demands and to achieve better in academics. The present study is therefore intended toobserve if partial sleep deprivation improves performance of cognitive functions.Objective: To compare cognitive performance in partially sleep deprived young adults and subjects withadequate sleep duration.Materials and Method: A total of 56 young adults 18-25 years of age were recruited based on inclusion andexclusion criteria. By recall method they were allotted into partially sleep deprived group (n=30) (<6 hr/dayfor 5 consecutive days) and group with normal sleep duration (7-9 hours/day) (n=26). Epworthsleepscale(ESS) was used to measure day time sleepiness. Cognitive domain like sustained attention, executive function,learning and memory were tested for all subjects in both the groups by neuropsychological battery from “Acompendium of neuropsychological test” by Strauss and Spreen. Descriptive statistics and Independent “t”test were used for statistical analysis.Results: Daytime sleepiness was significantly higher in partially sleep deprived group (P=0.0235).Sustainedattention score was significantly lower (p=0.026) in partially sleep deprivation group than the other group.There was no significant difference between the groups in parameters like executive functions, learning andmemory.Conclusion: Partially sleep deprived group showed impaired attention as attention being associated withfrontal regions of the brain that are the first to be affected by sleep deprivation. Executive functions, learningand memory were not statistically different between the 2 groups as other regions of brain involved wouldremain active until the sleep deprivation becomes more severe.

The Effects of Caffeine Intake on Cardiovascular Parameters in Sleep Deprived Medical Residents

Physicians and medical residents are particularly affected by sleep deprivation are, especially in East European countries. The aim of our study is to analyze the effect of caffeine intake on cardiovascular functions in sleep deprived residents (clinicians in-training) after continuous 24h on-call duty. 26 medical residents aged between 22-33 years old, 12 men and 14 women, who began their activity at 2 pm were included. Each subject consumed coffee or caffeinated drinks such as Coca cola during this period, after 2 am, expressed in caffeine units. We have evaluated their cardiovascular function using impedance cardiography (ICG-M501) and blood pressure measurement using the manometric method, before (at 7 pm) and after caffeine consumption (at 7 am), during one night of on-call duty. Surprisingly, after caffeine consumption, all subjects have had a decrease of the heart rate after one night of sleep deprivation (from mean: 83 b/min before to 69.73 b/min after, p = 0.000), also the mean arterial blood pressure is lower after the overnight call (from mean: 95.3 mmHg before to 88.9 mmHg after). Moreover, cardiac output, stroke volume and cardiac index decreases along with an increase of peripheral vascular resistance. Caffeine intake exerts a paradoxical effect on sleep deprived subjects; acute sleep loss, due to continuously, intense on-call work, modifies several cardiovascular parameters, such as heart rate, blood pressures, stroke volume and cardiac output.

A novel helmet design and implementation for drowsiness and fall detection of workers on-site using EEG and Random-Forest Classifier

This paper proposes a low-cost novel EEG based BCI prototype to detect if an on-site worker is sleep-deprived or not elegantly. The worker is required to wear a modified safety helmet with an innocuously placed signal acquisition device and it’s paraphernalia that does not hinder the worker’s activities. A few time and frequency domain features have been derived from the collected data to recognize sleep deprivation of workers. The smart helmet communicates with a local server within radio range. The server runs a random forest classifier algorithm to classify if the worker is sleep deprived or not and alerts the supervisor if necessary. A single Inertial Measurement Unit (IMU) sensor is utilized to detect if the worker has fallen down. The entire setup is supported by an android application that keeps the supervisor up-to-date on the statuses of the workers. A classification accuracy as high as 98% for the helmet based EEG setup was obtained through in-house live experiments upon sleep-deprived subjects.

Regional cerebral hypoperfusion after acute sleep deprivation: A STROBE-compliant study of arterial spin labeling fMRI.

Previous neuroimaging studies have shown that functional changes occur after acute sleep deprivation, which suggest detrimental effects of a lack of sleep on the intrinsic functional architecture of the brain. We aimed to identify regional resting perfusion changes in subjects with acute sleep deprivation.Thirty-three healthy subjects with habitual good sleep participated in 36 hours (2 days and 1 night) of sleep deprivation and then underwent the attention network test and pseudo-continuous arterial spin labeling scanning. Regional cerebral blood flow was used to compare cerebral perfusion before and after sleep deprivation. Correlation analyses of regional perfusion changes and scores on the attention network test were performed.Compared with the baseline (n = 20) scans, the scans of subjects after sleep deprivation (n = 26) revealed a slower response time (549.99 milliseconds vs 603.36 milliseconds; t = −2.301; P = .028) and a significantly higher lapse rate (0.88% vs 22.85%; t = −2.977; P = .006). The sleep deprivation subjects showed lower cerebral blood flow (CBF) in the left parahippocampal gyrus/fusiform cortex (pHipp/Fus), right pHipp/Fus, and right prefrontal cortex (PFC) relative to the baseline subjects (Gaussian random field correction, voxel level P < .01, and cluster level P < .05). Although no significant relationships were observed between the altered regional CBF (rCBF) values and the attention network test scores, the receiver-operating characteristic and leave-one-out cross-validation analyses revealed that significant decreases in rCBF in the bilateral pHipp/Fus and right PFC could discriminate between sleep deprivation and good sleep status.We observed that rCBF was reduced after 36 hours (2 days and 1 night) of sleep deprivation. Our preliminary findings suggest an acute vulnerability to hypoperfusion due to lack of sleep.

Abnormal functional connectivity density in sleep-deprived subjects.

Sleep deprivation (SD) can alter the intrinsic brain functional organization. However, its effects on intrinsic low-frequency connectivity in the whole brain have not been well characterized. In this study, we used voxel-based functional connectivity density (FCD) analysis to investigate the effects of SD on the spontaneous functional organization of the brain. Thirty-seven healthy participants underwent this within-subject crossover functional magnetic resonance imaging (fMRI) study during rested wakefulness (RW) and after 36h of total sleep deprivation (TSD). Decreased long-/short-range FCDs were observed in the posterior cingulate cortex, precuneus, inferior parietal lobule, dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, and ventromedial prefrontal cortex. Increased long-/short-range FCDs were found in the sensory integration and arousal regulating areas, including the postcentral gyrus, thalamus, superior temporal gyrus, and occipital-temporal cortex. Moreover, a significant negative correlation was found between the short-range FCD of the PCC and the reaction time of Psychomotor Vigilance Task. In the present study, spontaneous functional organization with significant group-wise differences between RW and TSD sessions was identified. Our findings extend our understanding of the neural mechanism of how brain activity is altered in sleep-deprived individuals.