Multivariate Wasserstein Functional Connectivity for Autism Screening
This work addresses a domain-specific problem for researchers in neuroscience and medical imaging by offering an incremental improvement in connectivity estimation methods.
The paper tackled the problem of information loss in brain functional connectivity estimation by proposing a multivariate Wasserstein distance measure that directly compares regions of interest without summarizing them into univariate time series. The result demonstrated superiority over existing methods in autism screening tasks, though no specific numbers were provided.
Most approaches to the estimation of brain functional connectivity from the functional magnetic resonance imaging (fMRI) data rely on computing some measure of statistical dependence, or more generally, a distance between univariate representative time series of regions of interest (ROIs) consisting of multiple voxels. However, summarizing a ROI's multiple time series with its mean or the first principal component (1PC) may result to the loss of information as, for example, 1PC explains only a small fraction of variance of the multivariate signal of the neuronal activity. We propose to compare ROIs directly, without the use of representative time series, defining a new measure of multivariate connectivity between ROIs, not necessarily consisting of the same number of voxels, based on the Wasserstein distance. We assess the proposed Wasserstein functional connectivity measure on the autism screening task, demonstrating its superiority over commonly used univariate and multivariate functional connectivity measures.