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.

Neurophysiological Correlates of a Single Session of Prefrontal tDCS in Patients with Prolonged Disorders of Consciousness: A Pilot Double-Blind Randomized Controlled Study

“…To explore connectivity, we used wSMI and wPLI, which have been shown to be robust metrics against volume conduction ( [18,19], respectively). These two metrics, however, account for different types of functional interactions [28]. Indeed, wPLI would be sensitive to a mixture of linear and non-linear interdependencies (mainly simple linear connectivity), while purely non-linear and complex inter-areal coupling dynamics of the brain can be captured by wSMI.…”

Motor-system dynamics during naturalistic reading of action narratives in first and second language

“…Likewise, the number of data segments was similar [ F (3,75) = 1.45, p = .25] across the four texts (L1-AT: M = 62.65, SD = 3.93; L1-NT: M = 69.57, SD = 3.93; L2-AT: M = 68.46, SD = 3.93; L2-NT: M = 63.96, SD = 3.93). As done in previous EEG studies employing the wSMI method to Complex/complex-compound sentences examine temporally variable cognitive states ( Imperatori et al, 2019 ), we selected 1-s segments from continuous data. These processing steps were implemented using custom MATLAB scripts based on EEGLAB toolbox ( Delorme and Makeig, 2004 ) and custom-made scripts for further processing.…”

“…Moreover, although an EEG setting poses major challenges to typical word-by-word analyses over long written passages (due to the tendency to look back at previous chunks of discourse, the difficulties of ascribing differential signatures between texts to any specific fine-grained variable, and the impossibility of tracking global neural states cutting across the reading act) ( Picton et al, 2000 ), these limitations can be overcome by (i) using texts that are carefully controlled over multiple relevant dimensions and (ii) analyzing neural activity spread over the whole of each reading session. In particular, the latter requisite can be met via functional connectivity metrics, such as the weighted symbolic mutual information (wSMI) method, capable of discriminating between cognitive macro-states over extended time periods ( Imperatori et al, 2019 ).…”

Brain-scale cortico-cortical functional connectivity in the delta-theta band is a robust signature of conscious states: an intracranial and scalp EEG study

“…Functional connectivity of scalp EEG, of magnetoencephalography (MEG) and of iEEG signals can be estimated and quantified through different mathematical measures. Among them, spectral and information theory methods have been developed [20][21][22][23][24][25][26][27][28] to overcome the instantaneous propagation of electric fields generated by a primary current source to multiple sensors, which induce couplings that do not reflect true brain inter-regional interactions [29][30][31] . Of special interest, weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) recently emerged as functional connectivity methods robust to artefactual coupling.…”