Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline

TL;DR

This paper introduces a novel method to predict the progression of subcortical brain structures in Alzheimer's disease using baseline MRI data, modeling disease trajectories on shape manifolds and personalizing predictions with cognitive measurements.

Contribution

The study presents a new predictive framework combining shape analysis and longitudinal data to forecast brain structure changes in Alzheimer's disease from baseline MRI.

Findings

The proposed method outperforms geodesic regression extrapolation.

Personalization with cognitive data improves prediction accuracy.

Successful prediction of follow-up visits demonstrates clinical relevance.

Abstract

We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data. The disease progression is modeled as a trajectory on a group of diffeomorphisms in the context of large deformation diffeomorphic metric mapping (LDDMM). We first exhibit the limited predictive abilities of geodesic regression extrapolation on this group. Building on the recent concept of parallel curves in shape manifolds, we then introduce a second predictive protocol which personalizes previously learned trajectories to new subjects, and investigate the relative performances of two parallel shifting paradigms. This design only requires the baseline imaging data. Finally, coefficients encoding the disease dynamics are obtained from longitudinal cognitive measurements for each subject, and exploited to refine our methodology which is demonstrated to successfully predict the follow-up visits.