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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Multivariate nonlinear mixed model to analyze longitudinal image data: MRI study of early brain development
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Shun Xu, University of North Carolina at Chapel Hill, USA
Martin Styner, University of North Carolina at Chapel Hill, USA
John Gilmore, University of North Carolina at Chapel Hill, USA
Joseph Piven, University of North Carolina at Chapel Hill, USA
Guido Gerig, University of Utah, Salt Lake City, USA
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.
Citation:
Shun Xu, Martin Styner, John Gilmore, Joseph Piven, Guido Gerig, "Multivariate nonlinear mixed model to analyze longitudinal image data: MRI study of early brain development," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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