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

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

Investigation of physiological and pathological conditions using electroencephalographic connectivity metrics

Functional connectivity (FC) metrics identify statistical (undirected) associations among distinct brain areas and therefore represent a powerful tool to investigate brain inter-regional interactions in distinct behavioural states. However, the application and interpretation of FC in electrophysiological data is impacted by important confounds related to the instantaneous propagation of electric fields generated by primary current sources to many of the on-scalp sensors – the so-called phenomenon of “volume conduction”. Because of this linear mixing of different sources, common FC methods may lead to the identification of apparent couplings that do not reflect true brain inter-regional interactions. To overcome this problem, new FC metrics have been specifically designed to minimize the impact of volume conduction. Among these novel methods, the weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) attracted a growing interest during the last decade, and have been successfully applied to describe brain function in a wide range of different conditions, including states associated with altered levels of consciousness. In spite of the many promising applications and results, the two methods have never been characterized in detail, nor compared to investigate their potential similarities or differences. Given these premises, in the present thesis, my aim was to assess the properties of wPLI and wSMI in order to define their respective potential advantages and disadvantages, as well as to determine whether useful information could be gained through their combined application. To this aim I performed three distinct, complementary studies. In my first project, I simulated realistic high-density EEG data based on imposed interaction dynamics between sources of interest to test the accuracy of wPLI and wSMI at detecting different types of linear and nonlinear functional interactions. Based on the resulting finding that they provide complementary information, I applied the two methods to the study of EEG data, collected in physiological and pathological states. In my second study, I analyzed power, wPLI and wSMI changes across distinct physiological stages of vigilance, specifically wakefulness (W), NREM and REM sleep in 24 healthy participants. Specifically, I explored the role of power- and FC-based features in identifying differences between all stages of interest (W, N2, N3, REM), stages characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences and differences in sensory disconnection (REM vs. W), using a cross-participant classification paradigm. Finally, in my third study, I applied the two methods for investigating the effects of motor rehabilitation on brain functional correlates in 16 multiple sclerosis patients. Obtained results demonstrated that wPLI and wSMI provide distinct and complementary information about functional brain dynamics and indicate that the conjoint use of these two methods may represent a powerful tool to investigate brain connectivity in physiological and pathological conditions

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

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

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

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±6yrs, 13F) were analysed 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 i) the four vigilance stages, ii) W+REM vs. N2+N3, and iii) 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 (0.5-4Hz) 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 (12-16Hz), 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