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EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions

Imperatori, Laura Sophie, Betta, Monica, Cecchetti, Luca, Canales-Johnson, Andrés, Ricciardi, Emiliano, Pietrini, Pietro, Siclari, Francesca, Chennu, Srivas, Bernardi, Giulio (2019) EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions. Scientific Reports, 9 . Article Number 8894. E-ISSN 2045-2322. (doi:10.1038/s41598-019-45289-7) (KAR id:74303)

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The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent two robust and widely used methods for MEG/EEG functional connectivity estimation. Interestingly, both ethods have been shown to detect relative alterations of brain functional connectivity in conditions associated with changes in the level of consciousness, such as following severe brain injury or under naesthesia. Despite these promising findings, it was unclear whether wPLI and wSMI may account for distinct or similar types of functional interactions. Using simulated high-density (hd-)EEG data, we demonstrate that while wPLI has high sensitivity for couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI can detect purely nonlinear interaction dynamics. Moreover, we evaluated the potential impact of these differences on real experimental data by computing wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults during wakefulness and deep (N3-)sleep, characterized by different levels of consciousness. In line with the simulation-based findings, this analysis revealed that both methods have different sensitivity for changes in brain connectivity across the two vigilance states. Our results indicate that the conjoint use of wPLI and wSMI may represent a powerful tool to study the functional bases of consciousness in physiological and pathological conditions.

Item Type: Article
DOI/Identification number: 10.1038/s41598-019-45289-7
Subjects: Q Science
Divisions: Faculties > Sciences > School of Computing
Depositing User: Srivas Chennu
Date Deposited: 07 Jun 2019 10:07 UTC
Last Modified: 19 Oct 2020 14:08 UTC
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Chennu, Srivas:
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