Microgravity induced resting state networks and metabolic alterations during sleep onset

Abstract

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.