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

Alexandre Bône (1) , Maxime Louis (1) , Alexandre Routier (1) , Jorge Samper (1) , Michael Bacci (1) , Benjamin Charlier (2, 1) , Olivier Colliot (1) , Stanley Durrleman (1)

1 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
2 UM - Université de Montpellier

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.

Domaines: Méthodologie [stat.ME]

Alexandre Bône, Maxime Louis, Alexandre Routier, Jorge Samper, Michael Bacci, et al.. Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline. 2017. ⟨hal-01563587v1⟩