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).
Highlights
Magnetic Resonance Imaging (MRI) and computational morphometry have become important tools for in-vivo analysis of changes in healthy and pathological brain development and aging (Frisoni et al, 2010; Fjell and Walhovd, 2010)
To demonstrate the potential of this method, we focused on explaining variability of local rates of atrophy based on the E4 allele of the Apolipoprotein gene, an established risk factor for increased lifetime prevalence of AD
In addition to the above group differences of change, these results demonstrate the sensitivity of our method for analysis of additional withingroup heterogeneity of change
Summary
Magnetic Resonance Imaging (MRI) and computational morphometry have become important tools for in-vivo analysis of changes in healthy and pathological brain development and aging (Frisoni et al, 2010; Fjell and Walhovd, 2010). A longitudinal study is more powerful for a fixed number of subjects It permits separation of within- and between-subject variability, and helps to ameliorate confounds. Another important advantage is that in addition to providing estimates of population average brain changes it enables a characterization of systematic differences in longitudinal trajectories among individuals. This allows researchers to identify adverse as well as protective factors that may influence healthy and pathological changes in brain anatomy and function over time Individual subjects' trajectories are promising biomarkers for early stage diagnosis (Chetelat and Baron, 2003), tracking of disease progression (Fonteijn et al, 2012; Jedynak et al, 2012; Sabuncu et al, 2014; Donohue et al, 2014; Young et al, 2014) and monitoring of potential treatments (Douaud et al, 2013)
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