Functional Templates in fMRI: Building Accurate and Interpretable Group-Level Decoders

Abstract

Inter-individual variability poses a significant challenge in decoding brain activity across subjects. While standard anatomical registration procedures reduce morphological differences, they fail to capture functional variability between subjects. Functional alignment methods address this issue by establishing voxel-to-voxel correspondences between pairs of individuals, thereby constructing a shared functional space. This shared space can be extended at the group level by generating a functional template. However, despite the availability of toolboxes, functional templates remain underused in fMRI analysis. Adopting this approach is currently difficult due to the diversity of existing methods and the lack of clear guidelines. Comprehensive evaluations of functional templates are limited to movie-watching paradigms. Here, we extensively compare functional alignment methods (Optimal Transport, Procrustes, Ridge regression, and Shared Response Model) and template construction strategies (in-sample, out-of-sample, pairwise) within the more general framework of task decoding. In this framework, decoding accuracy measures how well individual activation patterns align. Across multiple tasks and datasets, we demonstrate that population templates built using Optimal Transport (a) yield the highest decoding accuracy, (b) are not significantly biased by individual subjects, which facilitates generalization to new subjects, and (c) preserve the cortical signal topography.