NeurIPS 2022accept46 citations

Aligning individual brains with fused unbalanced Gromov Wasserstein

Alexis Thual, Quang Huy TRAN, Tatiana Zemskova, Nicolas Courty, Rémi Flamary, Stanislas Dehaene, Bertrand Thirion

Abstract

Individual brains vary in both anatomy and functional organization, even within a given species. Inter-individual variability is a major impediment when trying to draw generalizable conclusions from neuroimaging data collected on groups of subjects. Current co-registration procedures rely on limited data, and thus lead to very coarse inter-subject alignments. In this work, we present a novel method for inter-subject alignment based on Optimal Transport, denoted as Fused Unbalanced Gromov Wasserstein (FUGW). The method aligns two cortical surfaces based on the similarity of their functional signatures in response to a variety of stimuli, while penalizing large deformations of individual topographic organization. We demonstrate that FUGW is suited for whole-brain landmark-free alignment. The unbalanced feature allows to deal with the fact that functional areas vary in size across subjects. Results show that FUGW alignment significantly increases between-subject correlation of activity during new independent fMRI tasks and runs, and leads to more precise maps of fMRI results at the group level.

Brain imagingOptimal transportfMRIregistration
BibTeX
@inproceedings{
thual2022aligning,
title={Aligning individual brains with fused unbalanced Gromov Wasserstein},
author={Alexis Thual and Quang Huy TRAN and Tatiana Zemskova and Nicolas Courty and R{\'e}mi Flamary and Stanislas Dehaene and Bertrand Thirion},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=vy7B8z0-4D}
}