Two-step registration method boosts sensitivity in longitudinal fixel-based analyses

TL;DR

This paper introduces a two-step registration method for longitudinal fixel-based analysis that improves sensitivity by reducing measurement variability, especially in studies of white matter changes in Alzheimer's disease.

Contribution

It adapts a 2-step intra-subject registration approach to longitudinal FBA, demonstrating enhanced statistical power over direct registration methods.

Findings

Reduced variability of measurements with the 2-step method

Enhanced statistical power in fixelwise and tract-based analyses

Improved detection of white matter changes in Alzheimer's disease

Abstract

Longitudinal analyses are increasingly used in clinical studies as they allow the study of subtle changes over time within the same subjects. In most of these studies, it is necessary to align all the images studied to a common reference by registering them to a template. In the study of white matter using the recently developed fixel-based analysis (FBA) method, this registration is important, in particular because the fiber bundle cross-section metric is a direct measure of this registration. In the vast majority of longitudinal FBA studies described in the literature, sessions acquired for a same subject are directly independently registered to the template. However, it has been shown in T1-based morphometry that a 2-step registration through an intra-subject average can be advantageous in longitudinal analyses. In this work, we propose an implementation of this 2-step registration method in a typical longitudinal FBA aimed at investigating the evolution of white matter changes in Alzheimer's disease (AD). We compared at the fixel level the mean absolute effect and standard deviation yielded by this registration method and by a direct registration, as well as the results obtained with each registration method for the study of AD in both fixelwise and tract-based analyses. We found that the 2-step method reduced the variability of the measurements and thus enhanced statistical power in both types of analyses.