Sleep Is Compromised in −12° Head Down Tilt Position

Recent studies are elucidating the interrelation between sleep, cranial perfusion, and cerebrospinal fluid (CSF) circulation. Head down tilt (HDT) as a simulation of microgravity reduces cranial perfusion. Therefore, our aim was to assess whether HDT is affecting sleep (clinicaltrials.gov; identifier NCT 02976168). 11 male subjects were recruited for a cross-over designed study. Each subject participated in two campaigns each comprising 3 days and 2 nights. Intervention started on the second campaign day and consisted of maintenance of horizontal position or −12° HDT for 21 h. Ultrasound measurements were performed before, at the beginning and the end of intervention. Polysomnographic measurements were assessed in the second night which was either spent in horizontal posture or at −12° HDT. Endpoints were sleep efficiency, sleep onset latency, number of sleep state changes and arousals, percentages of N3, REM, light sleep stages and subjective sleep parameters. N3 and REM sleep reduced by 25.6 and 19.1 min, respectively (P = 0.002, g = -0.898; P = 0.035, g = -0.634) during −12° HDT. Light sleep (N1/2) increased by 33.0 min at −12° HDT (P = 0.002, g = 1.078). On a scale from 1 to 9 subjective sleep quality deteriorated by 1.3 points during −12° HDT (P = 0.047, g = -0.968). Ultrasonic measurement of the venous system showed a significant increase of the minimum (P = 0.009, P < 0.001) and maximum (P = 0.004, P = 0.002) cross-sectional area of the internal jugular vein at −12° HDT. The minimum cross-sectional area of the external jugular vein differed significantly between conditions over time (P = 0.001) whereas frontal skin tissue thickness was not significantly different between conditions (P = 0.077, P = 0.811). Data suggests venous congestion at −12° HDT. Since subjects felt comfortable with lying in −12° HDT under our experimental conditions, this posture only moderately deteriorates sleep. Obviously, the human body can almost compensate the several fold effects of gravity in HDT posture like an affected CSF circulation, airway obstruction, unusual patterns of propioception and effects on the cardiovascular system

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging

How varenicline affects sleep quality and functional connectivity? A polysomnographic evaluation

The SmokeFreeBrain (SFB), which is an EU, H2020-funded project aims to compare several antismoking approaches. Among them, the varenicline intervention seems to be extremely robust in terms of nicotine abstinence rate. However, there are some reports of side effects during sleep associated with insomnia and negatively aroused dreaming. However, these symptoms have never been objectively quantified. This study presents preliminary results from 17 participants who underwent entire polysomnographic (PSG) recordings before and 21 days after the intervention initiation. Our aim was to investigate how both smoking abstinence and varenicline treatment affect sleep quality. We employed both visual sleep scoring and functional connectivity analysis. The purpose of visual sleep scoring analysis, performed according to the guidelines of the American Association of Sleep Medicine (AASM) was to investigate sleep macro-architecture, defined as the sleep cycles during night. We calculated various sleep parameters like efficiency, onset, stage and latency duration, sleep fragmentation and the number of arousals during sleep. Additionally, we also estimated the co-operative degree among electroencephalographic time series as well as the interactions among brain and heart. The latter analysis aimed to quantify neuroplasticity changes associated both with smoking cessation and varenicline treatment. Early results demonstrated beneficial effects from nicotine abstinence (increased oxygen saturation level, facilitated sleep onset). However, there were neurophysiological patterns of increased arousal both on autonomic (heart rate variability features) and on cortical level (increased connectivity within beta band). These patterns observed even during deep sleep stages indicating poor sleep quality

Microgravity induced resting state networks and metabolic alterations during sleep onset

There is concrete evidence that weightlessness and microgravity may affect sleep quality. However, most of the studies failed to provide an integrative understanding of sleep disorders. This study proposes a novel, multi-modal, data-driven model for identifying the detrimental factors that are crucial for sleep quality. Its aim is to quantify the impact of weightlessness on cortical functional connectivity and metabolic blood biomarkers. It also investigates the efficacy of the Reactive Sledge Jump as a countermeasure.The study involves healthy volunteers assigned either to a control or to a sledge group. The data include polysomnographic recordings and blood biomarkers. Reconstruction of the cortical resting-state networks through the sLORETA methodology is performed. Then, functional connectivity is obtained, and regression models are developed to explain the variance of the sleep macro-architecture characteristics.The study results indicate that neither the bed rest nor the countermeasure affect sleep macro-architecture or the biomarkers under consideration. There are statistically significant functional connectivity alterations within the alpha band. There are significant correlations among all the three biomarkers and sleep quality characteristics. Glucose and prolactin values can predict the sleep onset latency, whereas insulin and group (countermeasure or control) predict the number of awakenings. Finally, the biomarker values are significantly correlated with functional connectivity interactions.Our findings provide evidence that sleep disorders occur firstly at a cortical level following a non-uniform pattern. These disorders are evident as cortical connectivity disruptions before their clinical manifestation as biomarkers’ alterations or deterioration in terms of sleep characteristics. The proposed methodology highlights the significance of a personalized, multi-parametric evaluation of sleep quality which is able to identify sleep disorders prior to their clinical onset