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...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2015
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| _version_ | 1797075868353298432 |
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| author | Ziegler, G Penny, W Ridgway, G Ourselin, S Friston, K |
| author_facet | Ziegler, G Penny, W Ridgway, G Ourselin, S Friston, K |
| author_sort | Ziegler, G |
| collection | OXFORD |
| 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). |
| first_indexed | 2024-03-06T23:56:18Z |
| format | Journal article |
| id | oxford-uuid:7457191d-713a-4e9b-90b1-15b1fffb8012 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:56:18Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:7457191d-713a-4e9b-90b1-15b1fffb80122022-03-26T20:02:10ZEstimating anatomical trajectories with Bayesian mixed-effects modelingJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7457191d-713a-4e9b-90b1-15b1fffb8012EnglishSymplectic Elements at OxfordElsevier2015Ziegler, GPenny, WRidgway, GOurselin, SFriston, KWe 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). |
| spellingShingle | Ziegler, G Penny, W Ridgway, G Ourselin, S Friston, K 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 |
| work_keys_str_mv | AT zieglerg estimatinganatomicaltrajectorieswithbayesianmixedeffectsmodeling AT pennyw estimatinganatomicaltrajectorieswithbayesianmixedeffectsmodeling AT ridgwayg estimatinganatomicaltrajectorieswithbayesianmixedeffectsmodeling AT ourselins estimatinganatomicaltrajectorieswithbayesianmixedeffectsmodeling AT fristonk estimatinganatomicaltrajectorieswithbayesianmixedeffectsmodeling |