Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics.

Details

Serval ID
serval:BIB_1407770F4BC2
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics.
Journal
Sleep
Author(s)
Imperatori L.S., Cataldi J., Betta M., Ricciardi E., Ince RAA, Siclari F., Bernardi G.
ISSN
1550-9109 (Electronic)
ISSN-L
0161-8105
Publication state
In Press
Peer-reviewed
Oui
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
Abstract
Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance - wakefulness (W), NREM-N2, NREM-N3 and REM sleep - with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27±6yrs, 13F) were analysed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate i) the four vigilance stages, ii) W+REM vs. N2+N3, and iii) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI or wSMI features. Delta-power and connectivity (0.5-4Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12-16Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.
Keywords
Classification, Consciousness, Disconnection, EEG, Functional Connectivity, NREM, REM, Sleep
Pubmed
Create date
28/11/2020 9:41
Last modification date
04/12/2020 6:26
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