Monica Betta

2 papers in the library · 30 citations · publishing 2019-2021

Papers

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

Sleep May 14, 2021 Laura Sophie Imperatori, Jacinthe Cataldi, Monica Betta et al. 30 citations

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.

DOI: 10.1093/sleep/zsaa247 Full text EEG Nrem Classification Consciousness +3

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

Scientific Reports June 20, 2019 Laura Sophie Imperatori, Monica Betta, Luca Cecchetti et al.

Two 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.

DOI: 10.1038/s41598-019-45289-7 Full text