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Most approaches to the estimation of brain functional connectivity from thefunctional magnetic resonance imaging (fMRI) data rely on computing somemeasure of statistical dependence, or more generally, a distance betweenunivariate representative time series of regions of interest (ROIs) consistingof multiple voxels. However, summarizing a ROI's multiple time series with itsmean or the first principal component (1PC) may result to the loss ofinformation as, for example, 1PC explains only a small fraction of variance ofthe multivariate signal of the neuronal activity.  We propose to compare ROIs directly, without the use of representative timeseries, defining a new measure of multivariate connectivity between ROIs, notnecessarily consisting of the same number of voxels, based on the Wassersteindistance. We assess the proposed Wasserstein functional connectivity measure onthe autism screening task, demonstrating its superiority over commonly usedunivariate and multivariate functional connectivity measures.

Multivariate Wasserstein Functional Connectivity for Autism Screening