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Estimating anatomical trajectories with Bayesian mixed-effects modeling
Journal article   Open access   Peer reviewed

Estimating anatomical trajectories with Bayesian mixed-effects modeling

G Ziegler, W D Penny, G R Ridgway, S Ourselin, K J Friston and Alzheimer's Disease Neuroimaging Initiative
NeuroImage, Vol.121, pp.51-68
11/01/2015
DOI: 10.1016/j.neuroimage.2015.06.094
PMCID: PMC4607727
PMID: 26190405
url
https://doi.org/10.1016/j.neuroimage.2015.06.094View
Published (Version of record) Open Access

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).
Dementia Neurology Lifespan brain aging Article Cognitive Neuroscience Longitudinal analysis Multi-level models Bayesian inference Brain morphology

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