Anatomically informed metrics for connectivity-based cortical parcellation from diffusion MRI.

Abstract

Connectivity information derived from diffusion MRI can be used to parcellate the cerebral cortex into anatomically and functionally meaningful subdivisions. Acquisition and processing parameters can significantly affect parcellation results, and there is no consensus on best practice protocols. We propose a novel approach for evaluating parcellation based on measuring the degree to which parcellation conforms to known principles of brain organization, specifically cortical field homogeneity and interhemispheric homology. The proposed metrics are well behaved on morphologically generated whole-brain parcels, where they correctly identify contralateral homologies and give higher scores to anatomically versus arbitrarily generated parcellations. The measures show that individual cortical fields have characteristic connectivity profiles that are compact and separable, and that the topological arrangement of such fields is strongly conserved between hemispheres and individuals. The proposed metrics can be used to evaluate the quality of parcellations at the subject and group levels and to improve acquisition and data processing for connectivity-based cortical parcellation.