Abstract
The 1/f spectral slope of the electroencephalogram (EEG) estimated in the gamma frequency range has been proposed as an arousal marker that differentiates wake, non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Here we sought to replicate and extend these findings in a large sample, providing a comprehensive characterization of how slope changes with age, sex, and its test-retest reliability as well as potential confounds that could affect the slope estimation. We used 10,255 whole-night polysomnograms (PSGs) from the National Sleep Research Resource. All preprocessing steps were performed using an open-source Luna package and the spectral slope was estimated by fitting log-log linear regression models on the absolute power from 30 to 45 Hz separately for wake, NREM and REM stages. We confirmed that the mean spectral slope grows steeper going from wake to NREM to REM sleep. We found that the choice of mastoid referencing scheme modulated the extent to which electromyogenic, or electrocardiographic artifacts were likely to bias 30-45 Hz slope estimates, as well as other sources of technical, device-specific bias. Nonetheless, within individuals, slope estimates were relatively stable over time. Both cross-sectionally and longitudinal, slopes tended to become shallower with increasing age, particularly for REM sleep; males tended to show flatter slopes than females across all states. Our findings support that spectral slope can be a valuable arousal marker for both clinical and research endeavors but also underscore the importance of considering inter-individual variation and multiple methodological aspects related to its estimation.
Significance statement
In a large sample, we validate the EEG spectral slope as a practical and scalable neurobiological marker of cortical arousal across wake, non-rapid eye movement (NREM), and rapid eye movement (REM) sleep states. We report sources of intra- and inter-individual variability, including changes across the lifespan. The slope during REM (when it is steepest, consistent with higher cortical inhibition) showed the greatest age-related flattening, suggesting that sleep-based biomarkers may be particularly sensitive to age-related physiological change, including cognitive decline. We also highlight critical methodological and sample issues. Our findings support alternative parameterizations of the EEG as being valuable for clinical and research endeavors, but also underscore the importance of accounting for inter-individual and technical sources of variation.
Footnotes
The authors declare no conflicts of interest.
NIH/NIMH grant R03 MH108908 (S.M.P.), NIH/NHLBI grant R01 HL146339 (S.M.P.), NIH/NHLBI grant R21 HL145492 (S.M.P.), NIH/NIMHD grant R21 MD012738 (S.M.P.)
This study has been published as a preprint at bioRxiv (doi: https://doi.org/10.1101/2021.11.08.467763).
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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