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.

翻译：个体大脑在解剖结构和功能组织上均存在差异，即使在同一物种内部亦是如此。当试图从群体受试者的神经影像数据中得出可推广的结论时，个体间变异性是一个主要障碍。当前的共配准方法依赖于有限数据，因此导致受试者间的对齐非常粗糙。在本研究中，我们提出了一种基于最优传输的受试者间对齐新方法，称为融合非平衡格罗莫夫-瓦瑟斯坦（FUGW）。该方法根据个体皮层面对多种刺激设置时功能特征的相似性进行对齐，同时惩罚个体地形组织的大变形。我们证明FUGW非常适用于全脑无标记点对齐。其非平衡特性允许处理不同受试者功能区域大小不一的问题。我们的结果表明，FUGW对齐显著提高了独立功能数据中受试者间活动的相关性，并在群体水平上实现了更精确的映射。