Multivariate Wasserstein Functional Connectivity for Autism Screening

TL;DR

This paper introduces a novel multivariate Wasserstein distance-based measure for brain functional connectivity from fMRI data, improving autism screening accuracy by directly comparing ROIs without reducing their multivariate signals.

Contribution

It proposes a new multivariate connectivity measure using Wasserstein distance that preserves more information than traditional univariate methods.

Findings

Wasserstein functional connectivity outperforms traditional measures in autism screening.

The method captures complex multivariate relationships between ROIs.

Results demonstrate improved sensitivity and specificity in classification tasks.

Abstract

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.