Citation

Kalamangalam GP, Long S, Chelaru MI. Clin. Neurophysiol. 2021; 132(7): 1550-1563.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.clinph.2021.03.014

PMID

unavailable

Abstract

OBJECTIVE: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to include data from non-rapid eye movement (NREM) and rapid eye movement (REM) sleep (von Ellenrieder et al., 2020), and the present work extends our methods to those data.

METHODS: Normalized amplitude spectra on semi-logarithmic axes from all four arousal states (wake, N2, N3 and REM) were averaged region-wise and fitted to a multi-component Gaussian distribution. A reduced model comprising five key parameters per brain region was color-coded on to cortical surface models.

RESULTS: The lognormal Gaussian mixture model described the iEEG accurately from all brain regions, in all sleep-wake states. There was smooth variation in model parameters as sleep and wake states yielded to each other. Specific observations unrelated to the model were that the primary cortical areas of vision, motor function and audition, in addition to the hippocampus, did not participate in the 'awakening' of the cortex during REM sleep.

CONCLUSIONS: Despite the significant differences in the appearance of the time-domain EEG in wakefulness and sleep, the iEEG in all arousal states was successfully described by a parametric spectral model of low dimension. SIGNIFICANCE: Spectral variation in the iEEG is continuous in space (across different cortical regions) and time (stage of circadian cycle), arguing for a 'continuum' hypothesis in the generative processes of sleep and wakefulness in human brain.

Language: en

Keywords

Epilepsy; Gaussian mixture model; Intracranial EEG; Lognormal distribution; Spectral analysis