Enhanced hyperalignment via spatial prior information

TL;DR

This paper introduces ProMises, a statistical model for hyperalignment of fMRI data that incorporates spatial prior information, resulting in more interpretable, unique, and efficient whole-brain functional alignment across subjects.

Contribution

The paper proposes the ProMises model, reformulating hyperalignment as a statistical framework with a spatial prior, addressing interpretability, uniqueness, and whole-brain analysis limitations of existing methods.

Findings

ProMises improves inter-subject classification accuracy.

The method enhances interpretability of functional alignments.

ProMises ensures unique transformations and efficient whole-brain analysis.

Abstract

Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group-level analysis. However, it is often violated in practice, even after alignment to a standard anatomical template. Hyperalignment, based on sequential Procrustes orthogonal transformations, has been proposed as a method of aligning shared functional information into a common high-dimensional space and thereby improving inter-subject analysis. Though successful, current hyperalignment algorithms have a number of shortcomings, including difficulties interpreting the transformations, a lack of uniqueness of the procedure, and difficulties performing whole-brain analysis. To resolve these issues, we propose the ProMises (Procrustes von Mises-Fisher) model. We reformulate functional alignment as a statistical model and impose a prior distribution on the orthogonal parameters (the von Mises-Fisher distribution). This allows for the embedding of anatomical information into the estimation procedure by penalizing the contribution of spatially distant voxels when creating the shared functional high-dimensional space. Importantly, the transformations, aligned images, and related results are all unique. In addition, the proposed method allows for efficient whole-brain functional alignment. In simulations and application to data from four fMRI studies we find that ProMises improves inter-subject classification in terms of between-subject accuracy and interpretability compared to standard hyperalignment algorithms.