Enhanced Accuracy in Registration of Cortex Functional Data via Large-Deformation Differomorphic Maps

Abstract

The complex folding pattern of the cerebral cortex has presented a major obstacle for functional MRI studies. The considerable variability in the folding structure of the cortex virtually prevents all low-dimensional registration methods from giving accurate normalization in this area. On the other hand, growing research on localizing the human neurological behavior on cortex, and the need for mapping the subjects into a standard coordinate space before performing statistical analysis, calls for more accurate mapping and registration methods. In this paper we present our approach of using the FreeSurfer software package together with the large deformation differomorphic metric maps (LDDMM) to first automatically segment the Cortex using the former and then compute accurate differomorphic mappings between each subject and the selected template's brain using the latter. We present a comparison of the accuracy of our approach with the mapping algorithm implemented in the SPM software package using a synthetic FMRI data-set.