Protocol of the Budapest sleep, experiences, and traits study: An accessible resource for understanding associations between daily experiences, individual differences, and objectively measured sleep.

Sleep is both a neurophysiological state and a biologically necessary behavior that performs a variety of indispensable roles for human health, development, and cognitive functioning. Despite this, comparatively little is known about the relationships between daily experiences and sleep features. Importantly, these relationships are bidirectional in nature, may be differently associated with subjectively and objectively assessed sleep, and may also be modulated by individual differences To address this challenge, we created the Budapest Sleep, Experiences, and Traits Study (BSETS), a multidisciplinary observational sleep study utilizing novel remote EEG devices. BSETS was designed to establish a dataset for future use in investigating the relationships between sleep features and daily experiences. In this paper we describe the protocol of the currently ongoing BSETS, which examines a community-dwelling sample of over 250 healthy participants who are studied in a naturalistic setting using a large questionnaire assessing psychological, demographic, and anthropometric information, as well as evening/morning diaries of sleep and daily experiences, and mobile EEG recordings over a period of 7 days. This dataset will become an accessible resource to the wider scientific community and can be utilized to investigate the complex multidirectional relationships between objectively and subjectively measured sleep, daily experiences, and individual differences, bestowing it with significant value for sleep researchers as well as practitioners working in clinical settings with patients suffering from disordered sleep.

Predicting Severity of Huntington's Disease With Wearable Sensors.

The Unified Huntington's Disease Rating Scale (UHDRS) is the primary clinical assessment tool for rating motor function in patients with Huntington's disease (HD). However, the UHDRS and similar rating scales (e.g., UPDRS) are both subjective and limited to in-office assessments that must be administered by a trained and experienced rater. An objective, automated method of quantifying disease severity would facilitate superior patient care and could be used to better track severity over time. We conducted the present study to evaluate the feasibility of using wearable sensors, coupled with machine learning algorithms, to rate motor function in patients with HD. Fourteen participants with symptomatic HD and 14 healthy controls participated in the study. Each participant wore five adhesive biometric sensors applied to the trunk and each limb while completing brief walking, sitting, and standing tasks during a single office visit. A two-stage machine learning method was employed to classify participants by HD status and to predict UHDRS motor subscores. Linear discriminant analysis correctly classified all participants' HD status except for one control subject with abnormal gait (96.4% accuracy, 92.9% sensitivity, and 100% specificity in leave-one-out cross-validation). Two regression models accurately predicted individual UHDRS subscores for gait, and dystonia within a 10% margin of error. Our regression models also predicted a composite UHDRS score-a sum of left and right arm rigidity, total chorea, total dystonia, bradykinesia, gait, and tandem gait subscores-with an average error below 15%. Machine learning classifiers trained on brief in-office datasets discriminated between controls and participants with HD, and could accurately predict selected motor UHDRS subscores. Our results could enable the future use of biosensors for objective HD assessment in the clinic or remotely and could inform future studies for the use of this technology as a potential endpoint in clinical trials.

Family aggregation of upper airway soft tissue structures in normal subjects and patients with sleep apnea.

RATIONALE: Sleep apnea is believed to be a genetic disorder. Thus, we hypothesized that anatomic risk factors for sleep apnea would demonstrate family aggregation. OBJECTIVES: We used volumetric magnetic resonance imaging in a sib pair "quad" design to study the family aggregation of the size of upper airway soft tissue structures that are associated with increased risk for obstructive sleep apnea. METHODS: We examined 55 sleep apnea probands (apnea-hypopnea index [AHI]: 43.2 +/- 26.3 events/h), 55 proband siblings (AHI: 11.8 +/- 16.6 events/h), 55 control subjects (AHI: 2.1 +/- 1.7 events/h), and 55 control siblings (AHI: 4.2 +/- 4.0 events/h). The study design used exact matching on ethnicity and sex, frequency matching on age, and statistical control for visceral neck fat and craniofacial dimensions. MEASUREMENTS AND MAIN RESULTS: The data support our a priori hypothesis that the volume of the important upper airway soft tissue structures is heritable. The volume of the lateral pharyngeal walls (h(2) = 36.8%; p = 0.001), tongue (h(2) = 36.5%; p = 0.0001), and total soft tissue (h(2) = 37.5%; p = 0.0001) demonstrated significant levels of heritability after adjusting for sex, ethnicity, age, visceral neck fat, and craniofacial dimensions. In addition, our data indicate that heritability of the upper airway soft tissue structures is found in normal subjects and patients with apnea. Thus, it is not simply a consequence of the prevalence of apnea. CONCLUSIONS: This is the first time family aggregation of size of the upper airway soft tissue structures has been demonstrated.

Identification of upper airway anatomic risk factors for obstructive sleep apnea with volumetric magnetic resonance imaging.

We used sophisticated volumetric analysis techniques with magnetic resonance imaging in a case-control design to study the upper airway soft tissue structures in 48 control subjects (apnea-hypopnea index, 2.0 +/- 1.6 events/hour) and 48 patients with sleep apnea (apnea-hypopnea index, 43.8 +/- 25.4 events/hour). Our design used exact matching on sex and ethnicity, frequency matching on age, and statistical control for craniofacial size and visceral neck fat. The data support our a priori hypotheses that the volume of the soft tissue structures surrounding the upper airway is enlarged in patients with sleep apnea and that this enlargement is a significant risk factor for sleep apnea. After covariate adjustments the volume of the lateral pharyngeal walls (p < 0.0001), tongue (p < 0.0001), and total soft tissue (p < 0.0001) was significantly larger in subjects with sleep apnea than in normal subjects. These data also demonstrated, after covariate adjustments, significantly increased risk of sleep apnea the larger the volume of the tongue, lateral pharyngeal walls, and total soft tissue: (1) total lateral pharyngeal wall (odds ratio [OR], 6.01; 95% confidence interval [CI], 2.62-17.14); (2) total tongue (OR, 4.66; 95% CI, 2.31-10.95); and (3) total soft tissue (OR, 6.95; 95% CI, 3.08-19.11). In a multivariable logistic regression analysis the volume of the tongue and lateral walls was shown to independently increase the risk of sleep apnea.