Novel Electrophysiological Signatures of Learning and Forgetting in Human REM Sleep.

Abstract

Despite the known behavioral benefits of rapid eye movement (REM) sleep, discrete neural oscillatory events in human scalp electroencephalography (EEG) linked with behavior have not been discovered. This knowledge gap hinders mechanistic understanding of the function of sleep, as well as the development of biophysical models and REM-based causal interventions. We designed a detection algorithm to identify bursts of activity in high-density, scalp EEG within theta (4-8 Hz) and alpha (8-13 Hz) bands during REM sleep. Across 38 nights of sleep, we characterized the burst events (i.e., count, duration, density, peak frequency, amplitude) in healthy, young male and female human participants (38; 21F) and investigated burst activity in relation to sleep-dependent memory tasks: hippocampal-dependent episodic verbal memory and non-hippocampal visual perceptual learning. We found greater burst count during the more REM-intensive second half of the night (p < .05), longer burst duration during the first half of the night (p < .05), but no differences across the night in density or power (p > .05). Moreover, increased alpha burst power was associated with increased overnight forgetting for episodic memory (p < .05). Furthermore, we show that increased REM theta burst activity in retinotopically specific regions was associated with better visual perceptual performance. Our work provides a critical bridge between discrete REM sleep events in human scalp EEG that support cognitive processes, and the identification of similar activity patterns in animals models that allow for further mechanistic characterization.Significance Statement Current understanding of sleep and its role cognitive processes is incomplete due to a lack of discrete electrophysiological events in human rapid eye movement (REM) sleep detectable via scalp EEG. Our work remedies this gap in knowledge by designing an open-source, computational approach to identify electrophysiological alpha and theta burst events in REM sleep. Additionally, we provide evidence that these burst events are functionally important for learning and memory. Defining burst events in human REM will contribute to the development of a comprehensive mechanistic model of how sleep as a whole, and REM specifically, facilitate cognitive processes, and provide a deeper understanding of the fundamental electrophysiological properties of REM sleep that are distinct from non-REM sleep.