An electrophysiological marker of arousal level in humans

Abstract

Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, ‘wake-like’ EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans.

A set of composite, non-redundant EEG measures of NREM sleep based on the power law scaling of the Fourier spectrum

“…As a consequence, the spectral profiles are inherently statistical in nature. In coherence with former reports focusing on sleep stages and arousal 15 , 16 our current approach provides a characterization of the NREM sleep EEG Fourier spectra by taking into account their inherent electrophysiological and statistical regularities based on its power law scaling properties. Our focus is on individual differences in NREM sleep and we assume that the approach we follow results in an integrated characterization of NREM sleep EEG, which is superior in terms of construct validity and accuracy.…”

“…In addition to the spectral intercepts, the power law functions describing the sleep EEG spectra appropriately address the issue of the ratio of EEG power at different frequencies, providing a single measure ( α ), instead of several ones scattered redundantly in all frequency bins and bands. This approach was found to be effective in deriving measures of consciousness 24 , sleep stages 15 and arousal 16 from sleep EEG records, as well as to index aging as a function of scale free wake EEG features 22 . Here we complete these studies with the individual differences approach of NREM sleep EEG, which was suggested to be a seminal perspective of all sleep studies 45 .…”

“…Most notably, if reliable, this function suggests that classical bandwise or binwise spectral analyses are not considering the frequency-determined nature of power values when applying statistical tests focusing on specific oscillatory phenomena. Similar views were expressed and successfully implemented for the analysis of various time series characterizing the power law scaling of brain activity in humans, some of them emphasizing the conceptual and methodological aspects 12 – 14 , 19 – 23 , while others focusing on sleep cycle effects, arousal and consciousness 15 , 16 , 24 , but none of them specifically targeting the issue of interindividual differences. In addition, available reports do not consider the constant ( C ) or the intercept as a variable of interest in describing sleep–wake EEG features.…”

“…As a proof of concept, we apply these measures on a large sleep EEG dataset with previously demonstrated effects of age, sex, and general intelligence. The issue of individual differences in NREM sleep was not explicitly addressed in former reports on spectral parameters of power law scaled sleep EEG 15 , 16 , with the exception of a report focusing exclusively on whitened spectral peak sizes in the spindle range 27 . Our intention is to fill this gap and broaden the validity of the power law scaling-type of spectral EEG parameters, as well as to provide a set of non-redundant measures of individual differences in NREM sleep EEG.…”

Shifts in broadband power and alpha peak frequency observed during long-term isolation

“…A vast body of literature has demonstrated the existence of several oscillatory frequency bands, ranging from approximately 0.05 Hz to 150 Hz (up to 500 Hz in animals) 4 that have been associated with a diversity of complex cognitive operations. Furthermore, these narrowband oscillations, commonly referred to as delta (1-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (> 30 Hz) 5 , have widely been studied during resting-state settings to better understand network dynamics. Multiple studies have also been employed in patient populations 6,7 as resting-state narrowband oscillations have been shown to correlate with cognitive functions 8,9 .…”