Fast Optimal Transport Averaging of Neuroimaging Data

TL;DR

This paper introduces a fast, efficient algorithm for averaging neuroimaging data across subjects using optimal transport, improving handling of complex brain geometries and variability.

Contribution

It presents a novel Kantorovich-based averaging method linked to Wasserstein barycenters, enabling efficient, smooth optimization for neuroimaging data.

Findings

Algorithm demonstrates strong convergence guarantees.

Effective on various neuroimaging modalities.

Handles data on arbitrary discrete domains.

Abstract

Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest. To address the problem of variability, data are commonly smoothed before group linear averaging. In this work we build on ideas originally introduced by Kantorovich to propose a new algorithm that can average efficiently non-normalized data defined over arbitrary discrete domains using transportation metrics. We show how Kantorovich means can be linked to Wasserstein barycenters in order to take advantage of an entropic smoothing approach. It leads to a smooth convex optimization problem and an algorithm with strong convergence guarantees. We illustrate the versatility of this tool and its empirical behavior on functional neuroimaging data, functional MRI and magnetoencephalography (MEG) source estimates, defined on voxel grids and triangulations of the folded cortical surface.