Multivariate nonlinear mixed model to analyze longitudinal image data

MRI study of early brain development

Shun Xu, Martin Styner, John Gilmore, Joseph Piven, Guido Gerig

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

    Abstract

    With great potential in studying neuro-development, neuro-degeneration, and the aging process, longitudinal image data is gaining increasing interest and attention in the neuroimaging community. In this paper, we present a parametric nonlinear model to statistically study multivariate longitudinal data with asymptotic properties. We demonstrate our preliminary results in a combined study of two longitudinal neuroimaging data sets of early brain development to cover a wider time span and to gain a larger sample size. Such combined analysis of multiple longitudinal image data sets has not been conducted before and presents a challenge for traditional analysis methods. To our knowledge, this is the first multivariate nonlinear longitudinal analysis to study early brain development. Our methodology is generic in nature and can be applied to any longitudinal data with nonlinear growth patterns that can not easily be modeled by linear methods.

    Original languageEnglish (US)
    Title of host publication2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
    DOIs
    StatePublished - 2008
    Event2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops - Anchorage, AK, United States
    Duration: Jun 23 2008Jun 28 2008

    Other

    Other2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
    CountryUnited States
    CityAnchorage, AK
    Period6/23/086/28/08

    Fingerprint

    Neuroimaging
    Magnetic resonance imaging
    Brain
    Aging of materials

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering

    Cite this

    Xu, S., Styner, M., Gilmore, J., Piven, J., & Gerig, G. (2008). Multivariate nonlinear mixed model to analyze longitudinal image data: MRI study of early brain development. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops [4563011] https://doi.org/10.1109/CVPRW.2008.4563011

    Multivariate nonlinear mixed model to analyze longitudinal image data : MRI study of early brain development. / Xu, Shun; Styner, Martin; Gilmore, John; Piven, Joseph; Gerig, Guido.

    2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops. 2008. 4563011.

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

    Xu, S, Styner, M, Gilmore, J, Piven, J & Gerig, G 2008, Multivariate nonlinear mixed model to analyze longitudinal image data: MRI study of early brain development. in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops., 4563011, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, Anchorage, AK, United States, 6/23/08. https://doi.org/10.1109/CVPRW.2008.4563011
    Xu S, Styner M, Gilmore J, Piven J, Gerig G. Multivariate nonlinear mixed model to analyze longitudinal image data: MRI study of early brain development. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops. 2008. 4563011 https://doi.org/10.1109/CVPRW.2008.4563011
    Xu, Shun ; Styner, Martin ; Gilmore, John ; Piven, Joseph ; Gerig, Guido. / Multivariate nonlinear mixed model to analyze longitudinal image data : MRI study of early brain development. 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops. 2008.
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