Data-driven mapping between functional connectomes using optimal transport

TL;DR

This paper introduces a method using optimal transport to map and transform functional connectomes between different brain atlases, enhancing cross-study comparability and analysis consistency.

Contribution

It presents a novel application of optimal transport to align connectomes across atlases without extra pre-processing, improving interpretability and generalization.

Findings

Transformed connectomes closely match gold-standard connectomes.

Connectomes maintain individual differences in brain-behavior relationships.

Method improves cross-atlas connectome comparisons.

Abstract

Functional connectomes derived from functional magnetic resonance imaging have long been used to understand the functional organization of the brain. Nevertheless, a connectome is intrinsically linked to the atlas used to create it. In other words, a connectome generated from one atlas is different in scale and resolution compared to a connectome generated from another atlas. Being able to map connectomes and derived results between different atlases without additional pre-processing is a crucial step in improving interpretation and generalization between studies that use different atlases. Here, we use optimal transport, a powerful mathematical technique, to find an optimum mapping between two atlases. This mapping is then used to transform time series from one atlas to another in order to reconstruct a connectome. We validate our approach by comparing transformed connectomes against their "gold-standard" counterparts (i.e., connectomes generated directly from an atlas) and demonstrate the utility of transformed connectomes by applying these connectomes to predictive models based on a different atlas. We show that these transformed connectomes are significantly similar to their "gold-standard" counterparts and maintain individual differences in brain-behavior associations, demonstrating both the validity of our approach and its utility in downstream analyses. Overall, our approach is a promising avenue to increase the generalization of connectome-based results across different atlases.