An Electrophysiological Marker of Arousal Level in Humans
Janna D. LendneriD, Randolph F. HelfrichiD, Bryce A. ManderiD, Luis Georg RomundstadiD, Jack J. LiniD, Matthew P. WalkeriD, Pål G. LarssoniD, Robert Thomas KnightiD
Abstract
Deep non-rapid eye movement sleep (NREM) - also called slow wave sleep (SWS) - and general anesthesia are prominent states of reduced arousal linked to the occurrence of slow oscillations in the electroencephalogram (EEG). Rapid eye movement (REM) sleep, however, is also associated with a diminished arousal level, but is characterized by a desynchronized, wake-like EEG. This observation challenges the notion of oscillations as the main physiological mediator of reduced arousal. Using intracranial and surface EEG recordings in four independent data sets, we establish the 1/f spectral slope as an electrophysiological marker that accurately delineates wakefulness from anesthesia, SWS and REM sleep. The spectral slope reflects the non-oscillatory, scale-free measure of neural activity and has been proposed to index the local balance between excitation and inhibition. Taken together, these findings reconcile the long-standing paradox of reduced arousal in both REM and NREM sleep and provide a common unifying physiological principle -- a shift in local Excitation/ Inhibition balance -- to explain states of reduced arousal such as sleep and anesthesia in humans.\n\nSignificance StatementThe clinical assessment of arousal levels in humans depends on subjective measures such as responsiveness to verbal commands. While non-rapid eye movement (NREM) sleep and general anesthesia share some electrophysiological markers, rapid eye movement sleep (REM) is characterized by a wake-like electroencephalogram. Here, we demonstrate that non-oscillatory, scale-free electrical brain activity -- recorded from both scalp electroencephalogram and intracranial recordings in humans -- reliably tracks arousal levels during both NREM and REM sleep as well as under general anesthesia with propofol. Our findings suggest that non-oscillatory brain activity can be used effectively to monitor vigilance states.
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