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1 CRC - Centre de Recherche des Cordeliers 2 ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute 3 ARAMIS - Algorithms, models and methods for images and signals of the human brain UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute, Inria de Paris 4 Service d-Explorations Fonctionnelles Neurologie CHU Pitié-Salpêtrière

Abstract : We introduce a mixed-effects model to learn spatiotempo-ral patterns on a network by considering longitudinal measures distributed on a fixed graph. The data come from repeated observations of subjects at different time points which take the form of measurement maps distributed on a graph such as an image or a mesh. The model learns a typical group-average trajectory characterizing the propagation of measurement changes across the graph nodes. The subject-specific trajectories are defined via spatial and temporal transformations of the group-average scenario, thus estimating the variability of spatiotemporal patterns within the group. To estimate population and individual model parameters, we adapted a stochastic version of the Expectation-Maximization algorithm, the MCMC-SAEM. The model is used to describe the propagation of cortical atrophy during the course of Alzheimer-s Disease. Model parameters show the variability of this average pattern of atrophy in terms of trajectories across brain regions, age at disease onset and pace of propagation. We show that the personaliza-tion of this model yields accurate prediction of maps of cortical thickness in patients.





Author: Igor Koval - Jean-Baptiste Schiratti - Alexandre Routier - Michael Bacci - Olivier Colliot - Stéphanie Allassonnière - Stanley

Source: https://hal.archives-ouvertes.fr/



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