Multiple intrinsic timescales govern distinct brain states in human sleep

Abstract

Human sleep exhibits multiple, recurrent temporal regularities, ranging from circadian rhythms to sleep stage cycles and neuronal oscillations during non-rapid eye movement (non-REM) sleep. Moreover, recent evidence revealed a functional role of aperiodic activity, which reliably discriminates different sleep stages. Aperiodic activity is commonly defined as the spectral slope χ of the 1/frequency (1/f χ ) decay function of the electrophysiological power spectrum. However, several lines of inquiry now indicate that the aperiodic component of the power spectrum might be better characterized by a superposition of several decay processes with associated timescales. Here, we determined multiple timescales, which jointly shape aperiodic activity using human intracranial encephalography (iEEG). Across three independent studies (47 participants, 23 female), our results reveal that aperiodic activity reliably dissociated sleep stage-dependent dynamics in a regionally-specific manner. A principled approach to parametrize aperiodic activity delineated several, spatially- and state-specific timescales. Lastly, we employed pharmacological modulation by means of propofol anesthesia to disentangle state-invariant timescales that may reflect physical properties of the underlying neural population from state-specific timescales that likely constitute functional interactions. Collectively, these results establish the presence of multiple intrinsic timescales that define the electrophysiological power spectrum during distinct brain states. Significance Statement Sleep is characterized by prominent temporal regularities. In this study, we unveil a previously unrecognized principle that governs neural activity during human sleep. Our results shed light on the existence of a set of intrinsic timescales that fundamentally define the current state of the sleeping brain. These timescales serve as indicators of both physiological and functional interactions within the underlying neural population. Through pharmacological modulation, we differentiated state-specific functional interactions from state-invariant timescales, suggesting that the latter may reflect the inherent physical properties of the neural population at play.