Locally-Optimized Inter-Subject Alignment of Functional Cortical Regions

Abstract

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 and it is usually difficult to identify clear anatomical landmarks and boundaries for these areas, due to large variability in their cortical position. As a consequence, previous methods usually fail to accurately map such 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 regions and simultaneously constraining the global structure of the mapping, while allowing for non-local deformations in its topology. Our registration method outperformed two canonical baselines (anatomical landmark-based AFNI alignment and cortical curvature-based FreeSurfer alignment) in the percentage of overlap between predicted region and ground truth LOC. Furthermore, the maps obtained using our method are more consistent across subjects than both baseline measures. Consequently, our method has the ability to directly and immediately improve the quality of group maps for high-level visual areas in countless fMRI studies. This would dramatically increase the statistical power of such studies, as a more accurate mapping to a common space implies less smoothing and larger effect sizes.