Estimating anatomical trajectories with Bayesian mixed-effects modeling

• Published in: NeuroImage (Orlando, Fla.) Vol. 121; pp. 51 - 68
• Main Authors: Ziegler, G., Penny, W.D., Ridgway, G.R., Ourselin, S., Friston, K.J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2015; Elsevier Limited; Academic Press
• Subjects: Aged; Aged, 80 and over; Aging; Alzheimer Disease - pathology; Bayes Theorem; Bayesian analysis; Bayesian inference; Biomarkers; Brain - anatomy & histology; Brain - pathology; Brain morphology; Brain research; Cognitive Dysfunction - pathology; Dementia; Female; Human Development - physiology; Humans; Lifespan brain aging; Longitudinal analysis; Longitudinal Studies; Magnetic Resonance Imaging - methods; Male; Medical imaging; Middle Aged; Models, Statistical; Multi-level models; NMR; Nuclear magnetic resonance; Pharmaceutical industry; R&D; Registration; Research & development; Scanners; Studies; Longitudinal analysis; Multi-level models; Lifespan brain aging; Bayesian inference; Brain morphology; Dementia
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.06.094

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). •We introduce a framework for structural trajectories using Longitudinal MRI.•Bayesian inference on trajectories is realized using Posterior Probability Maps (PPM).•We validate the model in simulations and real MRI data from the ADNI project.