Systematic evaluation of an exhaustive set of connectivity estimators in bivariate and multivariate modes for an improved virtual source connectivity analysis

Objective: Brain activity is measured using noninvasive electrophysiological techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). Data recorded from sensors outside the skull are regularly transformed into a virtual source space. Brain activity is typically parcellated into anatomical brain areas using an atlas. Then, functional connectivity (FC) is estimated between pairs of regions, with their brain activity characterized by a representative time series extracted from multiple voxel time series (multidimensional), using various techniques. Several FC estimators have been used to quantify FC between pairs of brain areas. In contrast, multivariate extensions of these estimators have been proposed, thereby eliminating the need for representative time series for each brain area. Approach: An appropriate framework for systematically evaluating FC estimators in the virtual MEG space and across multiple processing steps for brain network construction is missing. Here, we compared an exhaustive set of bivariate FC estimators with techniques for extracting representative time series, their multivariate extensions, and multivariate estimators for detecting MCI subjects versus healthy controls, using a k-NN classifier and an appropriate graph distance metric. Main Results: Our results demonstrate that the multivariate extension of bivariate FC estimators (representative-free approach), which summarizes pairwise FC strength across all voxels of two brain areas, and accurate multivariate estimators that consider pairs of region-wise voxel time series at once, clearly outperform bivariate FC estimators based on representative time series. Significance: Multivariate extension of bivariate FC estimators and multivariate FC estimators are the natural alternatives to the combination of representative time series per brain area and bivariate FC estimators.