EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions.
Laura Sophie Imperatori, Monica Betta, Luca Cecchetti, Andrés Canales-Johnson, Emiliano Ricciardi, Francesca Siclari, Pietro Pietrini, Srivas Chennu, Giulio Bernardi
Scientific Reports June 20, 2019 DOI: 10.1038/s41598-019-45289-7 (opens in new tab) via PubMed
Summary
AI-generated from the abstractTwo methods for measuring brain connectivity, wPLI and wSMI, detect different types of neural interactions. Using simulated EEG data, wPLI is sensitive to couplings that mix linear and nonlinear dependencies, while only wSMI detects purely nonlinear interactions. In real EEG recordings from 12 healthy adults during wakefulness and deep sleep, both methods showed different sensitivity to changes in brain connectivity across these states. The findings suggest that using both methods together provides a more complete picture of the functional basis of consciousness in both healthy and altered states.
Study at a glance
| Characteristics | Simulation and experimental study Peer reviewed |
|---|---|
| Sample size | 12 |
| Population | Healthy adults |
| Key finding | wPLI detects mixed linear and nonlinear interactions, while wSMI detects purely nonlinear interactions, and both methods show different sensitivity to changes in brain connectivity across wakefulness and deep sleep. |
Abstract
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 methods 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 anaesthesia. 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, characterised 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.