A framework for using diffusion weighted imaging to improve cortical parcellation

Abstract

Cortical parcellation refers to anatomical labelling of every point in the cortex. An accurate parcellation is useful in many analysis techniques including the study of regional changes in cortical thickness or volume in ageing and neurodegeneration. Parcellation is also key to anatomic apportioning of functional imaging changes. We present preliminary work on a novel algorithm that takes an entire cortical parcellation and iteratively updates it to better match connectivity information derived from diffusion weighted imaging. We demonstrate the algorithm on a cohort of 17 healthy controls. Initial results show the algorithm recovering artificially induced mis-registrations of the parcellation and also converging to a group-wise average. This work introduces a framework to investigate the relationship between structure and function, with no a-priori knowledge of specific regions of interest.