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We introduce a probabilistic generative model for disentanglingspatio-temporal disease trajectories from series of high-dimensional brainimages. The model is based on spatio-temporal matrix factorization, whereinference on the sources is constrained by anatomically plausible statisticalpriors. To model realistic trajectories, the temporal sources are defined asmonotonic and time-reparametrized Gaussian Processes. To account for thenon-stationarity of brain images, we model the spatial sources as sparse codesconvolved at multiple scales. The method was tested on synthetic datafavourably comparing with standard blind source separation approaches. Theapplication on large-scale imaging data from a clinical study allows todisentangle differential temporal progression patterns mapping brain regionskey to neurodegeneration, while revealing a disease-specific time scaleassociated to the clinical diagnosis.

Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data