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Estimating anatomical trajectories with Bayesian mixed-effects modeling.

To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: http://dx.doi.org/10.1016/j.neuroimage.2015.06.094 Byline: G. Ziegler [g.ziegler@ucl.ac.uk] (a,b,*), W.D. Penny (a), G.R. Ridgway (a,c), S. Ourselin (b,d), K.J. Friston (a) (1) Keywords Brain mo... Full description

Journal Title: NeuroImage November 1, 2015, Vol.121, pp.51-68
Main Author: Ziegler, G
Other Authors: Penny, W D , Ridgway, G R , Ourselin, S , Friston, K J , Ziegler, G
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2015.06.094
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title: Estimating anatomical trajectories with Bayesian mixed-effects modeling.
format: Article
creator:
  • Ziegler, G
  • Penny, W D
  • Ridgway, G R
  • Ourselin, S
  • Friston, K J
  • Ziegler, G
subjects:
  • Aged–Pathology
  • Aged, 80 and Over–Anatomy & Histology
  • Aging–Pathology
  • Alzheimer Disease–Pathology
  • Bayes Theorem–Physiology
  • Brain–Methods
  • Cognitive Dysfunction–Methods
  • Female–Methods
  • Human Development–Methods
  • Humans–Methods
  • Longitudinal Studies–Methods
  • Magnetic Resonance Imaging–Methods
  • Male–Methods
  • Middle Aged–Methods
  • Models, Statistical–Methods
  • Bayesian Inference
  • Brain Morphology
  • Dementia
  • Lifespan Brain Aging
ispartof: NeuroImage, November 1, 2015, Vol.121, pp.51-68
description: To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: http://dx.doi.org/10.1016/j.neuroimage.2015.06.094 Byline: G. Ziegler [g.ziegler@ucl.ac.uk] (a,b,*), W.D. Penny (a), G.R. Ridgway (a,c), S. Ourselin (b,d), K.J. Friston (a) (1) Keywords Brain morphology; Lifespan brain aging; Dementia; Longitudinal analysis; Multi-level models; Bayesian inference Highlights * 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. 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). Author Affiliation: (a) Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK (b) Dementia Research Centre, Institute of Neurology, University College London, UK (c) FMRIB, Nuffield Dept. of Clinical Neurosciences, University of Oxford, UK (d) Translational Imaging Group, Centre for Medical Image Computing, University College London, UK * Corresponding author at: Wellcome Trust Center for Neuroimaging, 12 Queen Square, WC1N 3BG London, UK. Article History: Received 24 September 2014; Accepte
language: eng
source:
identifier: E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2015.06.094
fulltext: fulltext
issn:
  • 10959572
  • 1095-9572
url: Link


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titleEstimating anatomical trajectories with Bayesian mixed-effects modeling.
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28Thomas, Ronald G
29Donohue, Michael
30Walter, Sarah
31Gessert, Devon
32Sather, Tamie
33Jiminez, Gus
34Balasubramanian, Archana B
35Mason, Jennifer
36Sim, Iris
37Harvey, Danielle
38Bernstein, Matthew
39Fox, Nick
40Thompson, Paul
41Schuff, Norbert
42Decarli, Charles
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45Senjem, Matt
46Vemuri, Prashanthi
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51Foster, Norm
52Reiman, Eric M
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titleEstimating anatomical trajectories with Bayesian mixed-effects modeling.
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15Saykin, Andrew J
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26Hefti, Franz
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30Snyder, Peter
31Schwartz, Adam
32Montine, Tom
33Thomas, Ronald G
34Donohue, Michael
35Walter, Sarah
36Gessert, Devon
37Sather, Tamie
38Jiminez, Gus
39Balasubramanian, Archana B
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41Sim, Iris
42Harvey, Danielle
43Bernstein, Matthew
44Fox, Nick
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46Schuff, Norbert
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51Vemuri, Prashanthi
52Jones, David
53Kantarci, Kejal
54Ward, Chad
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57Reiman, Eric M
58Chen, Kewei
59Mathis, Chet
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61Morris, John C
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66Potkin, Steven
67Shen, Li
68Faber, Kelley
69Kim, Sungeun
70Nho, Kwangsik
71Thal, Lean
72Thal, Leon
73Buckholtz, Neil
74Snyder, Peter J
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76Frank, Richard
77Hsiao, John
78Kaye, Jeffrey
79Quinn, Joseph
80Silbert, Lisa
81Lind, Betty
82Carter, Raina
83Dolen, Sara
84Schneider, Lon S
85Pawluczyk, Sonia
86Beccera, Mauricio
87Teodoro, Liberty
88Spann, Bryan M
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93Lord, Joanne L
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