Transcranial volumetric imaging using a conformal ultrasound patch.

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 flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. 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.

Identification of Fatigue and Sleepiness in Immune and Neurodegenerative Disorders from Measures of Real-World Gait Variability.

Current assessments of fatigue and sleepiness rely on patient reported outcomes (PROs), which are subjective and prone to recall bias. The current study investigated the use of gait variability in the "real world" to identify patient fatigue and daytime sleepiness. Inertial measurement units were worn on the lower backs of 159 participants (117 with six different immune and neurodegenerative disorders and 42 healthy controls) for up to 20 days, whom completed regular PROs. To address walking bouts that were short and sparse, four feature groups were considered: sequence-independent variability (SIV), sequence-dependant variability (SDV), padded SDV (PSDV), and typical gait variability (TGV) measures. These gait variability measures were extracted from step, stride, stance, and swing time, step length, and step velocity. These different approaches were compared using correlations and four machine learning classifiers to separate low/high fatigue and sleepiness.Most balanced accuracies were above 50%, the highest was 57.04% from TGV measures. The strongest correlation was 0.262 from an SDV feature against sleepiness. Overall, TGV measures had lower correlations and classification accuracies.Identifying fatigue or sleepiness from gait variability is extremely complex and requires more investigation with a larger data set, but these measures have shown performances that could contribute to a larger feature set.Clinical relevance- Gait variability has been repeatedly used to assess fatigue in the lab. The current study, however, explores gait variability for fatigue and daytime sleepiness in real-world scenarios with multiple gait-impacted disorders.

Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework.

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.

Time of day, time of sleep, and time on task effects on sleepiness and cognitive performance of bus drivers.

PURPOSE: Optimal 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. METHODS: In 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. RESULTS: Among 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. CONCLUSIONS: These results suggest that optimizing bus drivers' working schedules may improve drivers' sleepiness and cognitive performance and thus increase road safety.

Obstructive Sleep Apnea Syndrome Phenotyping by Cluster Analysis: Typical Sleepy, Obese Middle-aged Men with Desaturating Events are A Minority of Patients in A Multi-ethnic Cohort of 33% Women.

OBJECTIVE: Several clinical obstructive sleep apnea syndrome (OSAS) phenotypes associated with heterogeneous cardiovascular risk profiles have been recently identified. The purpose of this study was to identify clusters amongst these profiles that allow for the differentiation of patients. METHODS: This retrospective study included all moderate-to-severe OSAS patients referred to the sleep unit over a 5-year period. Demographic, symptom, comorbidity, polysomnographic, and continuous positive airway pressure (CPAP) adherence data were collected. Statistical analyses were performed to identify clusters of patients. RESULTS: A total of 567 patients were included (67% men, 54±13 years, body mass index: 32±7 kg/m2, 65% Caucasian, 32% European African). Five clusters were identified: less severe OSAS (n=172); healthier severe OSAS (n=160); poorly sleeping OSAS patients with cardiometabolic comorbidities (n=87); younger obese men with sleepiness at the wheel (n=94); sleepy obese men with very severe desaturating OSAS and cardiometabolic comorbidities (n=54). Patients in clusters 3 and 5 were older than those in clusters 2 and 4 (P=0.034). Patients in clusters 4 and 5 were significantly more obese than those in the other clusters (P=0.04). No significant differences were detected in terms of symptoms and comorbidities. Polysomnographic profiles were very discriminating between clusters. CPAP adherence was similar in all clusters but, among adherent patients, daily usage was more important in cluster 1 (less severe patients) than in cluster 5. CONCLUSION: This study highlights that the typical sleepy obese middle-aged men with desaturating events represent only a minority of patients in our multi-ethnic moderate-to-severe OSAS cohort of 33% females.

Sleep/wake cycle alterations as a cause of neurodegenerative diseases: A Mendelian randomization study.

Sleep and/or wake cycle alterations are common in neurodegenerative diseases (ND). Our aim was to determine whether there is a causal relationship between sleep and/or wake cycle patterns and ND (Parkinson's disease (PD) age at onset (AAO), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS)) using two-sample Mendelian Randomization (MR). We selected 12 sleep traits with available Genome-Wide Association Study (GWAS) to evaluate their causal relationship with the ND risk through Inverse-Variance Weighted regression as main analysis. We used as outcome the latest ND GWAS with available summary-statistics: PD-AAO (N = 17,996), AD (N = 21,235) and ALS (N = 40,136). MR results pointed to a causal effect of subjective and objective-measured morning chronotype on later PD-AAO (95%CI:0.33-1.81, p = 8.47×10-09 and 95%CI:-7.28 to -4.44, p = 5.87×10-16, respectively). Sleep efficiency was causally associated with a decreased AD risk (95%CI:-20.408 to -0.66, p = 0.04) and daytime sleepiness with an increased ALS risk (95%CI:0.15 to 1.61, p = 0.01). Our study suggests that sleep and/or wake patterns have causal relationship with ND. Given that sleep and/or wake patterns are modifiable risk factors, sleep interventions should be investigated as a potential treatment in PD-AAO, AD and ALS.

Awareness of sleepiness: Temporal dynamics of subjective and objective sleepiness.

We systematically examined the temporal relationships between subjective sleepiness and both physiological drowsiness and performance impairment in a controlled laboratory setting. Eighteen healthy young adults (8 women; MAGE  = 21.44 ± 3.24 years) underwent 40 hr of extended wakefulness, completing a bihourly Karolinska Sleepiness Scale (KSS) and 10-min Psychomotor Vigilance Task (PVT). Microsleeps and slow eye movements (SEMs) were scored during the PVT. KSS scores increased 3 hr prior to performance impairment (p < .001) and 4-6 hr prior to physiological sleepiness (p < .001). There were strong within-subject correlations between KSS and PVT lapses (r = 0.75, p < .001) and physiological drowsiness (r > 0.60, p < .001). Between-subjects product-moment correlations were more modest but showed a significant positive increase across time awake, suggesting that subjective sleepiness and objective outcomes were more tightly correlated after sleep loss. Cross-correlations showed significant positive correlations at 0-lag (p < .034); however, a high proportion of participants showed maximal correlations at positive lags, suggesting KSS was associated with future objective impairment. Within individuals, subjective sleepiness was highly correlated with objective impairment, between-subject correlations were more modest, possibly due to interindividual vulnerability to sleep loss. These results suggest that subjective sleepiness represents an inbuilt early warning system for subsequent drowsiness and performance impairment.

Differential relationship of two measures of sleepiness with the drives for sleep and wake.

PURPOSE: Since 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. METHODS: The 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. RESULTS: An 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. CONCLUSIONS: Each 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.

Changes in sleepiness and 24-h blood pressure following 4 months of CPAP treatment are not mediated by ICAM-1.

OBJECTIVE: Continuous 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. METHODS: The 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. RESULTS: Among 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. CONCLUSION: Changes 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.

Predictors of fatigue severity in early, de novo Parkinson disease patients: A 1-year longitudinal study.

INTRODUCTION: Fatigue is one of the most common and disabling nonmotor symptom in Parkinson's disease (PD). The aim of the present study was to investigate the 1-year course of fatigue in a consecutive sample of de novo drug-naïve patients with PD, and at systematically searching for baseline motor and nonmotor predictors associated with fatigue severity over time. METHODS: Fifty-five consecutive de novo PD patients (age: 64.71 ± 7.74 years) underwent a comprehensive examination, including Parkinson Fatigue Scale, Epworth Sleepiness Scale, Parkinson's Disease Sleep Scale, Beck Depression Inventory, Parkinson's Anxiety Scale, Apathy Evaluation Scale, and an extensive neuropsychological evaluation. Bivariate and multiple regression analyses were performed to identify baseline predictors independently related to fatigue severity at 1-year follow-up. RESULTS: Prevalence rate of fatigue (defined by PFS cut-off) increased from 22% at baseline to 38% at 1-year follow-up. A similar increase in prevalence was observed for excessive daytime sleepiness, and apathy. Among patients with fatigue at baseline, 91% had fatigue at follow-up too (i.e., persistent fatigue). Multivariate regression analysis identified fatigue (p < 0.01), daytime sleepiness (p < 0.01), and emotional apathy (p < 0.01) as the main baseline variables significantly predicting fatigue severity at 1-year follow-up. CONCLUSION: In early PD, fatigue increases and persists over time, and its severity is related to higher baseline levels of fatigue, excessive daytime sleepiness, and emotional apathy. These results warrant to monitor fatigue since the early stage of disease, and suggest that treating excessive daytime sleepiness and emotional apathy might prevent its worsening.