Longitudinal deformation based morphometry pipeline to study neuroanatomical differences in structural MRI based on SyN unbiased templates

Abstract

Morphometric measures in humans derived from magnetic resonance imaging (MRI) have provided important insights into brain differences and changes associated with development and disease in vivo. Deformation-based morphometry (DBM) is a registration-based technique that has been shown to be useful in detecting local volume differences and longitudinal brain changes while not requiring a priori segmentation or tissue classification. Typically, DBM measures are derived from registration to common template brain space (one-level DBM). Here, we present a two-level DBM technique: first, the Jacobian determinants are calculated for each individual input MRI at the subject level to capture longitudinal individual brain changes; then, in a second step, an unbiased common group space is created, and the Jacobians co-registered to enable the comparison of individual morphological changes across subjects or groups. This two-level DBM is particularly suitable for capturing longitudinal intra-individual changes in vivo, as calculating the Jacobians within-subject space leads to superior accuracy. Using artificially induced volume differences, we demonstrate that this two-level DBM pipeline is 4.5x more sensitive in detecting longitudinal within-subject volume changes compared to a typical one-level DBM approach. It also captures the magnitude of the induced volume change much more accurately. Using 150 subjects from the OASIS-2 dataset, we demonstrate that the two-level DBM is superior in capturing cortical volume changes associated with cognitive decline across patients with dementia and cognitively healthy individuals. This pipeline provides researchers with a powerful tool to study longitudinal brain changes with superior accuracy and sensitivity. It is publicly available and has already been used successfully, proving its utility.