Bayesian Functional Registration of fMRI Activation Maps

TL;DR

This paper introduces a Bayesian method for aligning fMRI activation maps across individuals, improving group analysis by reducing inter-subject variability and enabling better inference of brain function differences.

Contribution

It presents a novel Bayesian functional registration technique that integrates intensity and feature information for more accurate alignment of fMRI data.

Findings

Enhanced sensitivity in group-level fMRI analysis.

Effective reduction of inter-individual variability.

Validated on simulation and thermal pain data.

Abstract

Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population-level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common reference map. Our proposed Bayesian functional registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. It combines intensity-based and feature-based information into an integrated framework and allows inference to be performed on the transformation via the posterior samples. We evaluate the method in a simulation study and apply it to data from a study of thermal pain. We find that the proposed approach provides increased sensitivity for group-level inference.