TY - GEN
T1 - Multivariate longitudinal statistics for neonatal-pediatric brain tissue development
AU - Xu, Shun
AU - Styner, Martin
AU - Gilmore, John
AU - Gerig, Guido
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Early brain development
KW - Mixed model
KW - Multivariate longitudinal analysis
KW - Statistical analysis
UR - http://www.scopus.com/inward/record.url?scp=43749104241&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=43749104241&partnerID=8YFLogxK
U2 - 10.1117/12.773966
DO - 10.1117/12.773966
M3 - Conference contribution
AN - SCOPUS:43749104241
SN - 9780819470980
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2008
T2 - Medical Imaging 2008: Image Processing
Y2 - 17 February 2008 through 19 February 2008
ER -