Abstract
The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP.
Highlights
Scientists have long noted that the sleep onset process (SOP), the gradual transition between wakefulness and sleep, is marked by a dynamic continuum of behavioral and physiological changes [1]
The ability to understand and provide a principled characterization of SOP dynamics in both healthy and pathological subjects is of fundamental importance for sleep medicine and basic neuroscience alike
How can we tell when someone has fallen asleep? Understanding the way we fall asleep is an important problem in sleep medicine, since sleep disorders can disrupt the process of falling asleep
Summary
Scientists have long noted that the sleep onset process (SOP), the gradual transition between wakefulness and sleep, is marked by a dynamic continuum of behavioral and physiological changes [1]. The ability to understand and provide a principled characterization of SOP dynamics in both healthy and pathological subjects is of fundamental importance for sleep medicine and basic neuroscience alike. In sleep disorders such as insomnia, which has been associated with increased morbidity and mortality [2], the time course of the wake/sleep transition may be pathologically protracted, resulting in difficulty falling asleep. While there is increasing recognition of the importance of objective sleep testing, there currently exist no quantitative metrics for clinically diagnosing insomnia, which is currently defined exclusively by patient self-report [3]. Given the importance of sleep onset dynamics, the ability to track the continuous dynamical properties of the SOP in a principled, automated manner could provide critical insight into the pathophysiology of these populations, aiding in both diagnosis and in treatment
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