Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics.
Laura Sophie Imperatori, Jacinthe Cataldi, Monica Betta, Emiliano Ricciardi, Robin A A Ince, Francesca Siclari, Giulio Bernardi
Sleep May 14, 2021 Peer reviewed DOI: 10.1093/sleep/zsaa247 via PubMed 30 citations
Summary
Functional connectivity metrics, which describe how brain regions interact, can reveal differences across stages of sleep and wakefulness that power-based analyses alone may miss. Analyzing overnight sleep and resting-state wakefulness recordings from 24 healthy adults, the study found that combining power features with two connectivity measures—weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI)—improved the accuracy of classifying four vigilance stages (wakefulness, NREM-N2, NREM-N3, and REM sleep) compared to using any single feature type. Delta-band connectivity (0.5–4 Hz) was most important across all classifications, suggesting slow waves play a role in consciousness and sensory disconnection.
Study at a glance
| Design | observational study |
|---|---|
| Sample size | 24 |
| Population | healthy participants aged 27 ± 6 years, 13 female |
| Key finding | Combining power and functional connectivity features improves classification of sleep and wakefulness stages, with delta-band connectivity most important overall and sigma- and alpha-band connectivity specifically distinguishing states of consciousness and sensory disconnection. |
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.