Bayesian functional registration of fMRI activation maps
Guoqing Wang, Abhirup Datta, Martin A. Lindquist
The Annals of Applied Statistics · 2022 · Volume 16 · Issue 3
Abstract
Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large interindividual 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.
Keywords
Bayesian methods, functional magnetic resonance imaging, group-level analysis, interindividual differences, pain, registration
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