Notes:
Add code of Varying Coefficient Model For Modeling Diffusion
Tensors Along White Matter Tracts
Diffusion tensor imaging provides important information on tis-sue
structure and orientation of fiber tracts in brain white matter in
vivo. It results in diffusion tensors, which are 3*3 symmetric
positive definite (SPD) matrices, along fiber bundles. This paper
develops a functional data analysis framework to model diffusion
tensors along fiber tracts as functional data in a Riemannian
manifold with a set of covariates of interest, such as age and
gender. We propose a statistical model with varying coefficient
functions to characterize the dynamic association between
functional SPD matrix-valued responses and covariates. We calculate
weighted least squares estimators of the varying coefficient
functions for the Log-Euclidean metric in the space of SPD
matrices. We also develop a global test statistic to test specific
hypotheses about these coefficient functions and construct their
simultaneous confidence bands. Simulated data are further used to
examine the finite sample performance of the estimated varying
coefficient functions. We apply our model to study potential gender
differences and find a statistically significant aspect of the
development of diffusion tensors along the right internal capsule
tract in a clinical study of neurodevelopment.
Reference
1. Ying Yuan, Hongtu Zhu, Martin Styner, John H. Gilmore and J. S.
Marron. "Varying Coefficient Model For Modeling Diffusion Tensors
Along White Matter Tracts", Annals of Applied Statistics,
Accepted.
Changes:
Add code of Varying Coefficient Model For Modeling Diffusion
Tensors Along White Matter Tracts
Reference
1. Ying Yuan, Hongtu Zhu, Martin Styner, John H. Gilmore and J.
S. Marron. "Varying Coefficient Model For Modeling Diffusion
Tensors Along White Matter Tracts", Annals of Applied
Statistics, Accepted.
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