title: Bayesian Covariate Selection in Mixed-Effects Models For Longitudinal Shape Analysis.
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.
authors.affiliation: School of Computing & SCI Institute, University of Utah.
authors.forename: Prasanna
authors.initials: P
authors.lastname: Muralidharan
authors.affiliation: CSE, New York University.
authors.forename: James
authors.initials: J
authors.lastname: Fishbaugh
authors.affiliation: Department of Psychiatry, Carver College of Medicine, University of Iowa.
authors.forename: Eun Young
authors.initials: EY
authors.lastname: Kim
authors.affiliation: Department of Psychiatry, Carver College of Medicine, University of Iowa.
authors.forename: Hans J
authors.initials: HJ
authors.lastname: Johnson
authors.affiliation: Department of Psychiatry, Carver College of Medicine, University of Iowa.
authors.forename: Jane S
authors.initials: JS
authors.lastname: Paulsen
authors.affiliation: CSE, New York University.
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
authors.affiliation: School of Computing & SCI Institute, University of Utah.
authors.forename: P Thomas
authors.initials: PT
authors.lastname: Fletcher
keywords: Bayesian analysis; Huntington's disease; Longitudinal shape analysis; model selection