Estimating anatomical trajectories with Bayesian mixed-effects modeling

Ziegler, G, Penny, W D, Ridgway, G R, Ourselin, S, Friston, K J and , Alzheimer's Disease Neuroimaging Initiative (2015) Estimating anatomical trajectories with Bayesian mixed-effects modeling. NeuroImage, 121. pp. 51-68. ISSN 1053-8119

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Abstract

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).

Item Type: Article
Additional Information: Copyright © 2015. Published by Elsevier Inc.
Uncontrolled Keywords: aged,aged 80 and over,aging,alzheimer disease,bayes theorem,brain,cognitive dysfunction,female,human development,humans,longitudinal studies,magnetic resonance imaging,male,middle aged,statistical models
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 19 Aug 2017 05:06
Last Modified: 22 Apr 2020 05:22
URI: https://ueaeprints.uea.ac.uk/id/eprint/64579
DOI: 10.1016/j.neuroimage.2015.06.094

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