Locally-Optimized Inter-Subject Alignment of Functional Cortical Regions
This work addresses the challenge of making reliable inferences about brain function across populations in neuroscience, representing an incremental improvement in inter-subject alignment methods.
The paper tackled the problem of accurately aligning functional brain regions across individuals in fMRI studies, where existing methods often fail due to high variability in cortical positions. The proposed locally optimized registration method outperformed two common alternatives by improving overlap with functionally-defined regions and increasing consistency across subjects.
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.