Monotonic Gaussian Process for spatio-temporal disease progression modeling in brain imaging data

• Main Authors: Clément Abi Nader, Nicholas Ayache, Philippe Robert, Marco Lorenzi
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: NeuroImage
• Subjects: Alzheimer’s disease; Disease progression modeling; Gaussian process; Bayesian modeling; Stochastic variational inference; Clinical trials
• Online Access: http://www.sciencedirect.com/science/article/pii/S1053811919308572

We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from collections of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic trajectories, the temporal sources are defined as monotonic and time-reparameterized Gaussian Processes. To account for the non-stationarity of brain images, we model the spatial sources as sparse codes convolved at multiple scales. The method was tested on synthetic data favourably comparing with standard blind source separation approaches. The application on large-scale imaging data from a clinical study allows to disentangle differential temporal progression patterns mapping brain regions key to neurodegeneration, while revealing a disease-specific time scale associated to the clinical diagnosis.