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

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

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 prio...

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Bibliographic Details
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

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http://www.sciencedirect.com/science/article/pii/S1053811919308572

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