Conference proceeding
Bayesian covariate selection in mixed-effects models for longitudinal shape analysis
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Vol.2016-, pp.656-659
International Symposium on Biomedical Imaging (ISBI), 13 (2016)
04/2016
DOI: 10.1109/ISBI.2016.7493352
PMCID: PMC5225990
PMID: 28090246
Abstract
The goal of longitudinal shape analysis is to understand how anatomical shape changes over time, in response to biological processes, including growth, aging, or disease. In many imaging studies, it is also critical to understand how these shape changes are affected by other factors, such as sex, disease diagnosis, IQ, etc. Current approaches to longitudinal shape analysis have focused on modeling age-related shape changes, but have not included the ability to handle covariates. In this paper, we present a novel Bayesian mixed-effects shape model that incorporates simultaneous relationships between longitudinal shape data and multiple predictors or covariates to the model. Moreover, we place an Automatic Relevance Determination (ARD) prior on the parameters, that lets us automatically select which covariates are most relevant to the model based on observed data. We evaluate our proposed model and inference procedure on a longitudinal study of Huntington's disease from PREDICT-HD. We first show the utility of the ARD prior for model selection in a univariate modeling of striatal volume, and next we apply the full high-dimensional longitudinal shape model to putamen shapes.
Details
- Title: Subtitle
- Bayesian covariate selection in mixed-effects models for longitudinal shape analysis
- Creators
- Prasanna Muralidharan - University of UtahJames Fishbaugh - York UniversityEun Young Kim - University of IowaHans J Johnson - University of IowaJane S Paulsen - University of IowaGuido Gerig - York UniversityP. Thomas Fletcher - University of Utah
- Resource Type
- Conference proceeding
- Publication Details
- 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Vol.2016-, pp.656-659
- Conference
- International Symposium on Biomedical Imaging (ISBI), 13 (2016)
- DOI
- 10.1109/ISBI.2016.7493352
- PMID
- 28090246
- PMCID
- PMC5225990
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2016
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Psychological and Brain Sciences
- Record Identifier
- 9984185369902771
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