  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.
