Multimodal assessment of REM sleep dynamics and its relation to brain function in humans

Sleep is essential for health and wellbeing, but the function of rapid eye movement sleep (REMS) remains elusive.

This is partly because REMS has been viewed as a homogenous state, yet it contains highly dynamic features, requiring multimodal approaches for its characterisation.

With the goal to uncover the mechanisms underlying restoration during REMS I will assess multimodal dynamic REMS features and relate them to brain function.

I will further test causality of these relationships by using a novel technology I recently established to modulate REMS alpha oscillations: phase-locked closed- loop auditory stimulation (CLAS).

In a cross-sectional population, 1-week at- home sleep electroencephalography, functional near-infrared spectroscopy, and other physiological recordings will be combined with daily cognitive, sleepiness, and mood assessments.

This will be followed by 3 in-lab overnight visits including two stimulation nights targeting different phases of alpha oscillations expected to differentially impact brain rhythms. Effects of stimulation will be assessed on multimodal physiological features.

Finally, I will develop a machine learning algorithm to automatically detect REMS which will be key to perform extended CLAS experiments at home in the future.

This project will significantly deepen our understanding of REMS to help develop interventions for many disorders in which sleep is disturbed.