Hypersomnolence (HYP), or excessive daytime sleepiness, is the most common symptom encountered in
sleep medicine, and can present as linked to other medical disorders or independently. Discriminating
among the multiple possible causes of HYP is a complex process, and the underlying cause is often
unknown. Furthermore, there are currently no reliable electrophysiological parameters or biomarkers for
HYP, which is a severe limitation to the diagnostic and therapeutic process. Understanding the
biophysical presentation of HYP in sleep brain dynamics is essential to both the identification of reliable
electrophysiological biomarkers and to building a mechanistic understanding of the physiological
manifestations of HYP.
Most studies of sleep EEG dynamics focus on rhythms uniformly grouped by their dominant frequency,
sometimes addressing their spatial presentation, but overall ignoring the articulation of sleep rhythms in
space-time organized events. In recent work on typical adult populations the PI has introduced data-
driven techniques that reveal the space-time patterns of slow oscillations (SOs) and spindles, both sleep
rhythms cardinal to sleep homeostasis, with SOs explicitly tied to the restorative-ness of a night of sleep.
This research line has also shown that differentiation of sleep rhythms in space-time patterns is a powerful
approach to revealing biophysical differentiation among events classified as the same “rhythm”
suggesting their potential differential contribution to sleep functions. Here, we propose to apply these
data-driven approaches to describe in detail the space-time presentation of HYP in sleep brain dynamics,
in order to determine HYP biomarkers and to advance our understanding of the manifestations of HYP in
brain activity important to health and cognition.
This study will re-analyze a well-characterized dataset including the sleep studies of persons with HYP
and controls, with both groups also articulated based on presence/absence of major depressive disorder.
Specifically, we will describe the space-time patterns of SOs and spindles on the scalp and their main
biophysical properties, comparing them among the HYP and control group (aim 1). We will then use
machine learning classification to determine for each individual the estimated cortical-subcortical currents
that most differentiate SOs space-time types, compare the results in HYP and controls (aim 2). Finally,
we will statistically evaluate the link between these biophysical quantifiers of space-time sleep patterns
and clinical/behavioral assessments of HYP symptoms, depression, and anxiety. This research will lead to
new insights into potential brain mechanisms that underlie HYP, as well as refined diagnostic and future
therapies for the multitude suffering with HYP disorder.