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.
The work presented in this paper was supported in part by NIH Grants R01 EB016061 and R01 EB026549 from the National Institute of Biomedical Imaging and Bioengineering and R01 MH116026 from the National Institute of Mental Health.
"Bayesian functional registration of fMRI activation maps." Ann. Appl. Stat. 16 (3) 1676 - 1699, September 2022. https://doi.org/10.1214/21-AOAS1562