Microstate functional connectivity in EEG cognitive tasks revealed by a multivariate Gaussian hidden Markov model with phase locking value.

Abstract

Tracking the spatiotemporal fast (~100 ms) transient networks remains challenging due to a limited understanding of neural activity dynamics as well as a lack of relevant sophisticated methodologies. In this study, we introduce a novel approach to identify simultaneously distinct EEG microstates and their corresponding microstate functional connectivity (µFC) networks in which each µFC network is associated with a distinguished connectivity pattern of recurrent neuronal activity. The introduced approach is based on a multivariate Gaussian hidden Markov model (MGHMM) to decompose the sensor-space stochastic multi-subject event-related potential (ERP) into quasi-stable EEG microstates. Raw trial segments whose time windows belong to a corresponding segmented EEG microstate are then concatenated for measuring their µFC using the time-averaged phase-locking value. Illustration of this method is evaluated with synthetic data for which ground-truth microstate dynamics are known. Furthermore, we apply the method to identify EEG microstates and corresponding µFC networks in publicly available EEG data measured from visual cognitive tasks. Finally, we compare the MGHMM method with conventional dynamic FC (dFC) approaches using clustering-based K-means and time sliding windows, which conversely segregate the macrostate FC matrices across times into 'FC-states'. By using the MGHMM approach, we reveal: (1) EEG microstates, (2) µFC networks, (3) the associations of EEG microstate networks and their corresponding µFC networks dynamically modulated in publicly available EEG cognitive tasks, and (4) compared dFC performances between our proposed µFC approaches and 'FC-states' segmented by clustering-based K-means and time sliding windows. Evidence of significant improvements of microstate correlations (p -value  <  0.05) and improved tendency of FC distinction (p -value  =  0.064) over reported methods with simulated and realistic data will make this approach a preferred methodology to study dynamic brain networks and guarantee its use for further clinical applications.