Aligning individual brains with fused unbalanced Gromov Wasserstein
Alexis Thual, Quang Huy Tran, Tatiana Zemskova, Nicolas Courty, Rémi Flamary, Stanislas Dehaene, Bertrand Thirion
摘要
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 cortical surfaces based on the similarity of their functional signatures in response to a variety of stimulation settings, while penalizing large deformations of individual topographic organization. We demonstrate that FUGW is well-suited for whole-brain landmark-free alignment. The unbalanced feature allows to deal with the fact that functional areas vary in size across subjects. Our results show that FUGW alignment significantly increases between-subject correlation of activity for independent functional data, and leads to more precise mapping at the group level.
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引用它的顶会 Paper10
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- Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport LossPaul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc 等NeurIPS 2024 · 被引用 7 次
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它引用的顶会 Paper3
- The Unbalanced Gromov Wasserstein Distance: Conic Formulation and RelaxationThibault Séjourné, François-Xavier Vialard, Gabriel PeyréNeurIPS 2021 · 被引用 106 次
- Unbalanced Optimal Transport through Non-negative Penalized Linear RegressionLaetitia Chapel, Rémi Flamary, Haoran Wu, Cédric Févotte 等NeurIPS 2021 · 被引用 67 次
- Modeling Shared responses in Neuroimaging Studies through MultiView ICAHugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion 等NeurIPS 2020 · 被引用 29 次
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