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Estimating anatomical trajectories with Bayesian mixed-effects modeling
We introduce a mass-univariate framework for the analysis of whole-brain structural trajectories using longitudinal Voxel-Based Morphometry data and Bayesian inference. Our approach to developmental and aging longitudinal studies characterizes heterogeneous structural growth/decline between and with...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607727/ https://www.ncbi.nlm.nih.gov/pubmed/26190405 http://dx.doi.org/10.1016/j.neuroimage.2015.06.094 |
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author | Ziegler, G. Penny, W.D. Ridgway, G.R. Ourselin, S. Friston, K.J. |
author_facet | Ziegler, G. Penny, W.D. Ridgway, G.R. Ourselin, S. Friston, K.J. |
author_sort | Ziegler, G. |
collection | PubMed |
description | We introduce a mass-univariate framework for the analysis of whole-brain structural trajectories using longitudinal Voxel-Based Morphometry data and Bayesian inference. Our approach to developmental and aging longitudinal studies characterizes heterogeneous structural growth/decline between and within groups. In particular, we propose a probabilistic generative model that parameterizes individual and ensemble average changes in brain structure using linear mixed-effects models of age and subject-specific covariates. Model inversion uses Expectation Maximization (EM), while voxelwise (empirical) priors on the size of individual differences are estimated from the data. Bayesian inference on individual and group trajectories is realized using Posterior Probability Maps (PPM). In addition to parameter inference, the framework affords comparisons of models with varying combinations of model order for fixed and random effects using model evidence. We validate the model in simulations and real MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We further demonstrate how subject specific characteristics contribute to individual differences in longitudinal volume changes in healthy subjects, Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD). |
format | Online Article Text |
id | pubmed-4607727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Academic Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-46077272015-11-02 Estimating anatomical trajectories with Bayesian mixed-effects modeling Ziegler, G. Penny, W.D. Ridgway, G.R. Ourselin, S. Friston, K.J. Neuroimage Article We introduce a mass-univariate framework for the analysis of whole-brain structural trajectories using longitudinal Voxel-Based Morphometry data and Bayesian inference. Our approach to developmental and aging longitudinal studies characterizes heterogeneous structural growth/decline between and within groups. In particular, we propose a probabilistic generative model that parameterizes individual and ensemble average changes in brain structure using linear mixed-effects models of age and subject-specific covariates. Model inversion uses Expectation Maximization (EM), while voxelwise (empirical) priors on the size of individual differences are estimated from the data. Bayesian inference on individual and group trajectories is realized using Posterior Probability Maps (PPM). In addition to parameter inference, the framework affords comparisons of models with varying combinations of model order for fixed and random effects using model evidence. We validate the model in simulations and real MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We further demonstrate how subject specific characteristics contribute to individual differences in longitudinal volume changes in healthy subjects, Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD). Academic Press 2015-11-01 /pmc/articles/PMC4607727/ /pubmed/26190405 http://dx.doi.org/10.1016/j.neuroimage.2015.06.094 Text en © 2015 The Authors. Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ziegler, G. Penny, W.D. Ridgway, G.R. Ourselin, S. Friston, K.J. Estimating anatomical trajectories with Bayesian mixed-effects modeling |
title | Estimating anatomical trajectories with Bayesian mixed-effects modeling |
title_full | Estimating anatomical trajectories with Bayesian mixed-effects modeling |
title_fullStr | Estimating anatomical trajectories with Bayesian mixed-effects modeling |
title_full_unstemmed | Estimating anatomical trajectories with Bayesian mixed-effects modeling |
title_short | Estimating anatomical trajectories with Bayesian mixed-effects modeling |
title_sort | estimating anatomical trajectories with bayesian mixed-effects modeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607727/ https://www.ncbi.nlm.nih.gov/pubmed/26190405 http://dx.doi.org/10.1016/j.neuroimage.2015.06.094 |
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