Bayesian covariate selection in mixed-effects models for longitudinal shape analysis

Prasanna Muralidharan, James Fishbaugh, Eun Young Kim, Hans J. Johnson, Jane S. Paulsen, Guido Gerig, P. Thomas Fletcher

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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.

    Original languageEnglish (US)
    Title of host publication2016 IEEE International Symposium on Biomedical Imaging
    Subtitle of host publicationFrom Nano to Macro, ISBI 2016 - Proceedings
    PublisherIEEE Computer Society
    Pages656-659
    Number of pages4
    ISBN (Electronic)9781479923502
    DOIs
    StatePublished - Jun 15 2016
    Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
    Duration: Apr 13 2016Apr 16 2016

    Publication series

    NameProceedings - International Symposium on Biomedical Imaging
    Volume2016-June
    ISSN (Print)1945-7928
    ISSN (Electronic)1945-8452

    Other

    Other2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
    CountryCzech Republic
    CityPrague
    Period4/13/164/16/16

    Keywords

    • Bayesian analysis
    • Huntington's disease
    • Longitudinal shape analysis
    • model selection

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Radiology Nuclear Medicine and imaging

    Fingerprint Dive into the research topics of 'Bayesian covariate selection in mixed-effects models for longitudinal shape analysis'. Together they form a unique fingerprint.

  • Cite this

    Muralidharan, P., Fishbaugh, J., Kim, E. Y., Johnson, H. J., Paulsen, J. S., Gerig, G., & Fletcher, P. T. (2016). Bayesian covariate selection in mixed-effects models for longitudinal shape analysis. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings (pp. 656-659). [7493352] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2016-June). IEEE Computer Society. https://doi.org/10.1109/ISBI.2016.7493352