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Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow 1 , but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording 2 . Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as −1.51 ± 4.34 cm s −1 , −0.84 ± 3.06 cm s −1 and −0.50 ± 2.55 cm s −1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording. A conformal ultrasound patch can be used for hands-free volumetric imaging and continuous monitoring of cerebral blood flow

Drowsiness is a leading cause of accidents on the road as it negatively affects the driver’s ability to safely operate a vehicle. Neural activity recorded by EEG electrodes is a widely used physiological correlate of driver drowsiness. This paper presents a novel dynamical modeling solution to estimate the instantaneous level of the driver drowsiness using EEG signals, where the PERcentage of eyelid CLOSure (PERCLOS) is employed as the ground truth of driver drowsiness. Applying our proposed modeling framework, we find neural features present in EEG data that encode PERCLOS. In the decoding phase, we use a Bayesian filtering solution to estimate the PERCLOS level over time. A data set that comprises 18 driving tests, conducted by 13 drivers, has been used to investigate the performance of the proposed framework. The modeling performance in estimation of PERCLOS provides robust and repeatable results in tests with manual and automated driving modes by an average RMSE of 0.117 (at a PERCLOS range of 0 to 1) and average High Probability Density percentage of 62.5%. We further hypothesized that there are biomarkers that encode the PERCLOS across different driving tests and participants. Using this solution, we identified possible biomarkers such as Theta and Delta powers. Results show that about 73% and 66% of the Theta and Delta powers which are selected as biomarkers are increasing as PERCLOS grows during the driving test. We argue that the proposed method is a robust and reliable solution to estimate drowsiness in real-time which opens the door in utilizing EEG-based measures in driver drowsiness detection systems.

Sleepiness at the wheel is an important contributor to road traffic accidents. Slow eye movement (SEM) serves as a reliable physiological indicator for the sleep onset period (SOP). To detect SEM for recognizing drivers’ SOP, a Parallel Multimodal CNN-Transformer (PMMCT) model is proposed. The model employs two parallel feature extraction modules to process bimodal signals, each comprising convolutional layers and Transformer encoder layers. The extracted features are fused and then classified using fully connected layers. The model is evaluated on two bimodal signal combinations HEOG + O2 and HEOG + HSUM, where HSUM is the sum of two single-channel horizontal electrooculogram (HEOG) signals and captures electroencephalograph (EEG) features similar to those in the conventional O2 channel. Experimental results indicate that using the PMMCT model, the HEOG + HSUM combination performs comparably to the HEOG + O2 combination and outperforms unimodal HEOG by 2.73% in F1-score, with average classification accuracy and F1-score of 99.89% and 99.35%, outperforming CNN, CNN-LSTM, and CNN-LSTM-Attention models. The model exhibits minimal false positives and false negatives, with average values of 5.2 and 0.8. By combining CNNs’ local feature extraction with Transformers’ global temporal modeling, and using only two HEOG electrodes, the system offers superior performance while enhancing wearable device comfort for real-world applications.

PurposeOptimal cognitive performance might prevent vehicle accidents. Identifying time-related circadian and homeostatic parameters having an impact on cognitive performance of drivers may be crucial to optimize drivers’ performance.MethodsIn this prospective study conducted on bus drivers, two drivers alternated driving during a 24-h round trip and were accompanied by an interviewer. Each driver was tested using Karolinska Sleepiness Scale (KSS) and the reversed digit span Wechsler Working Memory test before the start of his shift and then every 6 h during a “work/driving” day. Psychomotor Vigilance Task (PVT) was assessed before and after the journey. Linear mixed model was used to explore the factors affecting cognitive performance and sleepiness in univariate and multivariate analysis.ResultsAmong 35 bus drivers, the effect of time of day on working memories was statistically significant (p = 0.001), with the lowest working memory scores at 04:00 am (± 1). The highest score of subjective sleepiness was also at 04:00 am (± 1). The time on task parameter affected sleepiness significantly (p = 0.024) and sleepiness was significantly associated with decreased working memory. Psychomotor Vigilance Task reaction time mean and the number of minor lapses were significantly increased after the journey, which suggested decreased vigilance. In multivariable analysis, a longer interval between the beginning of working hours and testing time (B (95% CI) = 15.25 (0.49 to 30), p = 0.043) was associated with higher (i.e., slower) PVT reaction time mean.ConclusionsThese results suggest that optimizing bus drivers’ working schedules may improve drivers’ sleepiness and cognitive performance and thus increase road safety.

PurposeSince disagreement has been found between an objective sleep propensity measured by sleep onset latency (SOL) and subjective sleepiness assessment measured by the Epworth sleepiness scale (ESS) score, distinct underlying causes and consequences were suggested for these two sleepiness measures. We addressed the issue of validation of the ESS against objective sleepiness and sleep indexes by examining the hypothesis that these two sleepiness measures are disconnected due to their differential relationship with the antagonistic drives for sleep and wake.MethodsThe polysomnographic records of 50-min napping attempts were collected from 27 university students on three occasions. Scores on the first and second principal components of the electroencephalographic (EEG) spectrum were calculated to measure the sleep and wake drives, respectively. Self-assessments of subjective sleepiness and sleep were additionally collected in online survey of 633 students at the same university.ResultsAn ESS score was disconnected with the polysomnographic and self-assessed SOL in the nap study and online survey, respectively. An ESS score but not SOL was significantly linked to the spectral EEG measure of the sleep drive, while SOL but not ESS showed a significant association with the spectral EEG measure of the opposing wake drive.ConclusionsEach of two sleepiness measures was validated against objective indicators of the opposing sleep-wake regulating processes, but different underlying causes were identified for two distinct aspects of sleepiness. A stronger sleep drive and a weaker opposing drive for wake seem to contribute to a higher ESS score and to a shorter SOL, respectively.

ObjectiveContinuous positive airway pressure (CPAP) therapy reduces circulating intercellular adhesion molecule 1 (ICAM-1) in adults with obstructive sleep apnea (OSA). ICAM-1 levels may affect the daytime sleepiness and elevated blood pressure associated with OSA. We evaluated the association of changes from baseline in ICAM-1 with changes of objective and subjective measures of sleepiness, as well as 24-h ambulatory blood pressure monitoring (ABPM) measures, following 4 months of CPAP treatment.MethodsThe study sample included adults with newly diagnosed OSA. Plasma ICAM-1, 24-h ABPM, Epworth Sleepiness Scale (ESS), and psychomotor vigilance task (PVT) were obtained at baseline and following adequate CPAP treatment. The associations between changes in natural log ICAM-1 and changes in the number of lapses on PVT, ESS score, and 24-h mean arterial blood pressure (MAP) were assessed using multivariate regression models, controlling for a priori baseline covariates of age, sex, BMI, race, site, smoking status, physical activity, anti-hypertensive medications, AHI, and daily hours of CPAP use.ResultsAmong 140 adults (83% men), mean (± SD) body mass index (BMI) was 31.5 ± 4.2 kg/m2, and apnea-hyopnea index (AHI) was 36.8 ± 15.3 events/h. Sleepiness measures, although not ICAM-1 or ABPM measures, improved significantly following CPAP treatment. We observed no statistically significant associations between the change in ICAM-1 and changes in sleepiness, MAP, or other ABPM measures.ConclusionChanges in ICAM-1 levels were not related to changes in sleepiness or ABPM following CPAP treatment of adults with OSA. Future work should explore whether or not other biomarkers may have a role in mediating these treatment outcomes in adults with OSA.

PurposeThe current meta-analysis aimed to obtain a more stable estimate of the effect size of Ramadan diurnal intermittent fasting (RDF) on sleep duration and daytime sleepiness.MethodsDatabases (Scopus, ScienceDirect, ProQuest Medical, PubMed/MEDLINE, Web of Science, EBSCOhost, Cochrane, CINAHL, and Google Scholar) were searched from database inception to the end of June 2019. The sleep quality measures analyzed were excessive daytime sleepiness (EDS) measured by the Epworth sleepiness scale (ESS) and total sleep time (TST). Subgroup analyses for age, sex, and levels of physical activity were conducted.ResultsWe identified 24 studies (involving 646 participants, median age 23.7 years, 73% men) conducted in 12 countries from 2001 to 2019. The results revealed that TST decreased from 7.2 h per night [95% confidence interval (CI) 6.7–7.8] before Ramadan to 6.4 h (95% CI 5.3–7.5) during Ramadan, while the ESS score increased slightly from 6.1 (95% CI 4.5–7.7) before Ramadan to 7.0 (95% CI 5.2–8.8) during Ramadan. Effect sizes on sleep quality measures during RDF demonstrated a moderate reduction in TST (number of studies, K = 22; number of subjects, N = 571, Hedges’ g value of −0.43, 95% CI − 0.64 to −0.22, Q = 90, τ2 = 0.15, I2 = 78%, P < 0.001), while ESS score showed negligible effect on EDS (K = 9, N = 362, Hedges’ g value of −0.06, 95% CI −0.43 to 0.28, Q = 21, τ2 = 0.13, I2 = 76%, P value = 0.001).ConclusionDuring the month of Ramadan, there is approximately a 1 hour reduction in TST and nearly a 1 point increase in the ESS score.

Sleepiness at the wheel is widely believed to be a cause of motor vehicle accidents. Nevertheless, a systematic review of studies investigating this relationship has not yet been published. The objective of this study was to quantify the relationship between sleepiness at the wheel and motor vehicle accidents. A systematic review was performed using Medline, Scopus, and ISI Web of Science. The outcome measure of interest was motor vehicle accident defined as involving four- or two-wheeled vehicles in road traffic, professional and nonprofessional drivers, with or without objective consequences. The exposure was sleepiness at the wheel defined as self-reported sleepiness at the wheel. Studies were included if they provided adjusted risk estimates of motor vehicle accidents related to sleepiness at the wheel. Risk estimates and 95% confidence intervals (95% CIs) were extracted and pooled as odds ratios (ORs) using a random-effect model. Heterogeneity was quantified using Q statistics and the I2 index. The potential causes of heterogeneity were investigated using meta-regressions. Ten cross-sectional studies (51,520 participants), six case-control studies (4904 participants), and one cohort study (13,674 participants) were included. Sleepiness at the wheel was associated with an increased risk of motor vehicle accidents (pooled OR 2.51 [95% CI 1.87; 3.39]). A significant heterogeneity was found between the individual risk estimates (Q = 93.21; I2 = 83%). Sleepiness at the wheel increases the risk of motor vehicle accidents and should be considered when investigating fitness to drive. Further studies are required to explore the nature of this relationship. PROSPERO 2015 CRD42015024805.

•This study identifies the individual differences in the accumulation of subjective daytime sleepiness by mathematical modeling.•This study reveals the crucial role of hypothalamus-dorsal striatum connectivity in predicting individual variations in daytime sleepiness, as indicated by resting-state fMRI analysis and behavioral assessments using the Karolinska Sleepiness Scale.•The findings highlight the distinct contributions of hypothalamic connections to different parts of the striatum, with the hypothalamus-dorsal striatum circuit identified as a potential target for interventions aimed at reducing excessive sleepiness and enhancing daytime alertness. While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness.

Abstract The Maintenance of Wakefulness Test (MWT) is a widely accepted objective test used to evaluate daytime somnolence and is commonly used in clinical studies evaluating novel therapeutics for excessive daytime sleepiness. In the latter, sleep onset latency (SOL) is typically the sole MWT endpoint. Here, we explored microsleeps, sleep probability measures derived from automated sleep scoring, and quantitative electroencephalography (qEEG) features as additional MWT biomarkers of daytime sleepiness, using data from a phase 1B trial of the selective orexin receptor 2 agonist danavorexton (TAK-925) in people with narcolepsy type 1 (NT1) or type 2 (NT2). Danavorexton treatment reduced the rate and duration of microsleeps during the MWT in NT1 (days 1 and 7; p ≤ .005) and microsleep rate in NT2 (days 1 and 7; p < .0001). The use of an EEG-sleep-staging − derived measure to determine the probability of wakefulness for each minute revealed a novel metric to track changes in daytime sleepiness, which were consistent with the θ/α ratio, a known biomarker of drowsiness. The slopes of line-fits to both the log-transformed sleepiness score or log-transformed θ/α ratio correlated well to (inverse) MWT SOL for NT1 (R = 0.93 and R = 0.83, respectively) and NT2 (R = 0.97 and R = 0.84, respectively), suggesting that individuals with narcolepsy have increased sleepiness immediately after lights-off. These analyses demonstrate that novel EEG-based biomarkers can augment SOL as predictors of sleepiness and its response to treatment and provide a novel framework for the analysis of wake EEG in hypersomnia disorders. Graphical Abstract Graphical Abstract