Share Email Print

Proceedings Paper

Multivariate longitudinal statistics for neonatal-pediatric brain tissue development
Author(s): Shun Xu; Martin Styner; John Gilmore; Guido Gerig
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The topic of studying the growth of human brain development has become of increasing interest in the neuroimaging community. Cross-sectional studies may allow comparisons between means of different age groups, but they do not provide a growth model that integrates the continuum of time, nor do they present any information about how individuals/population change over time. Longitudinal data analysis method arises as a strong tool to address these questions. In this paper, we use longitudinal analysis methods to study tissue development in early brain growth. A novel approach of multivariate longitudinal analysis is applied to study the associations between the growth of different brain tissues. In this paper, we present the methodologies to statistically study scalar (univariate) and vector (multivariate) longitudinal data, and demonstrate exploratory results in a neuroimaging study of early brain tissue development. We obtained growth curves as a quadratic function of time for all three tissues. The quadratic terms were tested to be statistically significant, showing that there was indeed a quadratic growth of tissues in early brain development. Moreover, our result shows that there is a positive correlation between repeated measurements of any single tissue, and among those of different tissues. Our approach is generic in natural and thus can be applied to any longitudinal data with multiple outcomes, even brain structures. Also, our joint mixed model is flexible enough to allow incomplete and unbalanced data, i.e. subjects do not need to have the same number of measurements, or be measured at the exact time points.

Paper Details

Date Published: 26 March 2008
PDF: 11 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69140C (26 March 2008); doi: 10.1117/12.773966
Show Author Affiliations
Shun Xu, Univ. of North Carolina, Chapel Hill (United States)
Martin Styner, Univ. of North Carolina, Chapel Hill (United States)
John Gilmore, Univ. of North Carolina, Chapel Hill (United States)
Guido Gerig, Univ. of Utah (United States)

Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

© SPIE. Terms of Use
Back to Top