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A mixed-effects model with time reparametrization for longitudinal univariate manifold-valued data

Jean-Baptiste Schiratti 1, 2 Stéphanie Allassonniere 1 Alexandre Routier 2 Olivier Colliot 2 Stanley Durrleman 2
2 ARAMIS - Algorithms, models and methods for images and signals of the human brain
Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : Mixed-effects models provide a rich theoretical framework for the analysis of longitudinal data. However , when used to analyze or predict the progression of a neurodegenerative disease such as Alzheimer ' s disease , these models usually do not take into account the fact that subjects may be at different stages of disease progression and the interpretation of the model may depend on some implicit reference time. In this paper , we propose a generative statistical model for longitudinal data , described in a univariate Riemannian manifold setting , which estimates an average disease progression model , subject-specific time shifts and acceleration factors. The time shifts account for variability in age at disease-onset time. The acceleration factors account for variability in speed of disease progression. For a given individual , the estimated time shift and acceleration factor define an affine reparametrization of the average disease progression model. This statistical model has been used to analyze neuropsychological assessments scores and cortical thickness measurements from the Alzheimer ' s Disease Neuroimaging Initiative database. The numerical results showed that we can distinguish between slow versus fast progressing and early versus late-onset individuals .
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Contributor : Jean-Baptiste Schiratti <>
Submitted on : Friday, June 12, 2015 - 12:45:02 PM
Last modification on : Monday, December 14, 2020 - 5:20:39 PM
Long-term archiving on: : Tuesday, April 25, 2017 - 7:19:29 AM


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  • HAL Id : hal-01163213, version 1
  • PUBMED : 26221703


Jean-Baptiste Schiratti, Stéphanie Allassonniere, Alexandre Routier, Olivier Colliot, Stanley Durrleman. A mixed-effects model with time reparametrization for longitudinal univariate manifold-valued data. IPMI - Information Processing in Medical Imaging , Sebastien Ourselin, Daniel C. Alexander, Carl-Fredrik Westin, M. Jorge Cardoso, Jun 2015, Sleat, Isle of Skye, United Kingdom. pp.564-575. ⟨hal-01163213⟩



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