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:个体大脑在解剖和功能组织上,甚至在特定物种内部,都有差异。在试图从所收集的各组主题的神经成像数据中得出可概括性结论时,个人之间的变异性是一个主要障碍。当前的共同登记程序依赖于有限的数据,从而导致非常粗略的主体间对齐。在这项工作中,我们提出了一个基于最佳运输的跨主体对齐新颖方法,称为Fused Unclance Gromov Vasserstein(FUGW)。这种方法根据不同刺激环境的功能特征相似性对皮质表面进行对齐,同时惩罚个别地形组织的大规模畸形。我们证明FUGW非常适合整体的无标志性对齐。这种不平衡性特征可以处理不同主体之间功能领域大小不同这一事实。我们的结果显示,FOGW的对齐明显增加了独立功能数据活动的主体间关联性,并导致在集团一级进行更精确的绘图。