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

Imperatori, L. S., Cataldi, J., Betta, M., Ricciardi, E., Ince, R. A.A. , Siclari, F. and Bernardi, G. (2021) Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics. Sleep, 44(5), zsaa247. (doi: 10.1093/sleep/zsaa247) (PMID:33220055)

[img] Text
227095.pdf - Accepted Version
Restricted to Repository staff only until 21 November 2021.

1MB

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.

Item Type:Articles
Additional Information:This work was supported by the Swiss National Science Foundation (Ambizione Grant [PZ00P3_173955] to F.S.), the Divesa Foundation Switzerland (to F.S.), the Pierre-Mercier Foundation for Science (to F.S.), the Bourse Pro-Femme of the University of Lausanne (to F.S.), the Foundation for the University of Lausanne (to F.S and G.B.), the Wellcome Trust [214120/Z/18/Z] (to R.A.A.I.) and an International Brain Research Organization Pan-European Regional Committee shortterm postdoctoral fellowship (to G.B.).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ince, Dr Robin and Imperatori, Ms Laura
Authors: Imperatori, L. S., Cataldi, J., Betta, M., Ricciardi, E., Ince, R. A.A., Siclari, F., and Bernardi, G.
College/School:College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
Journal Name:Sleep
Publisher:Oxford University Press
ISSN:0161-8105
ISSN (Online):1550-9109
Published Online:21 November 2020
Copyright Holders:Copyright © 2020 Sleep Research Society
First Published:First published in Sleep 44(5): zsaa237
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304240Beyond Pairwise Connectivity: developing an information theoretic hypergraph methodology for multi-modal resting state neuroimaging analysisRobin InceWellcome Trust (WELLCOTR)214120/Z/18/ZNP - Centre for Cognitive Neuroimaging (CCNi)