The sleep EEG envelope is a novel, neuronal firing‑based human biomarker
Scientific Reports, Volume 12, Article number: 18836 (2022)
DOI: 10.1038/s41598-022-22255-4
Péter P. Ujma1,2*, Martin Dresler3, Péter Simor4,5, Dániel Fabó2, István Ulbert6,7, Loránd Erőss2, Róbert Bódizs1,2
1Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.
2National Institute of Clinical Neuroscience, Budapest, Hungary.
3Radboud University Medical Center, Donders Institute, Nijmegen,
The Netherlands.
4Institute of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary.
5UR2NF, Neuropsychology and Functional Neuroimaging Research Unit at CRCN ‑ Center for Research in Cognition and Neurosciences and UNI ‑ ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.
6Department of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
7Research Centre for Natural Sciences, Institute for Cognitive Neuroscience and Psychology, Budapest, Hungary.
Abstract
Sleep EEG reflects voltage differences relative to a reference, while its spectrum reflects its composition of various frequencies. In contrast, the envelope of the sleep EEG reflects the instantaneous amplitude of oscillations, while its spectrum reflects the rhythmicity of the occurrence of these oscillations. The sleep EEG spectrum is known to relate to demographic, psychological and clinical characteristics, but the envelope spectrum has been rarely studied. In study 1, we demonstrate in human invasive data from cortex-penetrating microelectrodes and subdural grids that the sleep EEG envelope spectrum reflects neuronal firing. In study 2, we demonstrate that the scalp EEG envelope spectrum is stable within individuals. A multivariate learning algorithm could predict age (r = 0.6) and sex (r = 0.5) from the EEG envelope spectrum. With age, oscillations shifted from a 4–5 s rhythm to faster rhythms. Our results demonstrate that the sleep envelope spectrum is a promising biomarker of demographic and disease-related phenotypes.
Keywords: Circadian rhythms and sleep; Human behaviour; Neurology