Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics. Issue 5 (21st November 2020)

• Record Type: Journal Article
• Authors: Imperatori, Laura Sophie; Cataldi, Jacinthe; Betta, Monica; Ricciardi, Emiliano; Ince, Robin A A; Siclari, Francesca; Bernardi, Giulio
• Is Part Of: Sleep. Volume 44:Issue 5(2021)
• Publication Date: 2020-11-21
• Subjects: EEG -- functional connectivity -- sleep -- classification -- NREM -- REM -- consciousness -- disconnection; Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsaa247
• Languages: English
• ISSNs: 0161-8105

Abstract

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 ± 6 years, 13F) were analyzed 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 (1) the four vigilance stages, (2) W+REM vs. N2+N3, and (3) 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 (0.5–4 Hz) 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 (12–16 Hz), 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.