Exploring directed functional connectivity based on electroencephalography source signals using a global cortex factor-based multivariate autoregressive model

Background: Partial directed coherence (PDC) computed from multivariate autoregressive (MVAR) model coefficient is increasingly being used to study directed functional connectivity between brain regions in the frequency domain based on electroencephalography (EEG) source signals. However, directly fitting MVAR model to the high-dimensional source signals is difficult. Besides, although PDC measurement often shows good results for simulated data, it is not clear to what extent the results for real data are physiologically plausible. New method: We propose a new method termed global cortex factor-based MVAR (GCF-MVAR) to study directed functional connectivity based on EEG source signals. It avoids directly fitting MVAR model to high-dimensional EEG sources, instead using low-dimensional global cortex factor signals derived from the source signals by principle component analysis (PCA). To validate its physiological efficacy, we weight the PDC with source spectral power (SP) which reflects the true frequency activity in a source region. Comparison with existing method(s): The performance of GCF-MVAR is compared with FMVAR, ROI-MVAR, and MVAR by applying to both simulated and resting-state EEG data. Results: The simulation results show that GCF-MVAR has the lowest estimation error. By using the source SP to weight the PDC, GCF-MVAR improves the physiological interpretation of the source connectivity for both simulated and resting-state EEG data. Conclusions: The new method is proved to outperform than the state-of-the-art methods, and can be feasible not only for resting state studies, but also task-related connectivity and neurological disorder analysis.