  Inter-subject registration of cortical areas is necessary in functional
imaging (fMRI) studies for making inferences about equivalent brain function
across a population. However, many high-level visual brain areas are defined as
peaks of functional contrasts whose cortical position is highly variable. As
such, most alignment methods fail to accurately map functional regions of
interest (ROIs) across participants. To address this problem, we propose a
locally optimized registration method that directly predicts the location of a
seed ROI on a separate target cortical sheet by maximizing the functional
correlation between their time courses, while simultaneously allowing for
non-smooth local deformations in region topology. Our method outperforms the
two most commonly used alternatives (anatomical landmark-based AFNI alignment
and cortical convexity-based FreeSurfer alignment) in overlap between predicted
region and functionally-defined LOC. Furthermore, the maps obtained using our
method are more consistent across subjects than both baseline measures.
Critically, our method represents an important step forward towards predicting
brain regions without explicit localizer scans and deciphering the poorly
understood relationship between the location of functional regions, their
anatomical extent, and the consistency of computations those regions perform
across people.
