Abstract
Diffusion-weighted magnetic resonance imaging (MRI) provides a unique approach to understand the geometric structure of brain fiber bundles and to delineate the diffusion properties across subjects and time. It can be used to identify structural connectivity abnormalities and helps to diagnose brain-related disorders. The aim of this paper is to develop a novel, robust, and efficient dimensional reduction and regression framework, called hierarchical functional principal regression model (HFPRM), to effectively correlate high-dimensional fiber bundle statistics with a set of predictors of interest, such as age, diagnosis status, and genetic markers. The three key novelties of HFPRM include the simultaneous analysis of a large number of fiber bundles, the disentanglement of global and individual latent factors that characterizes between-tract correlation patterns, and a bi-level analysis on the predictor effects. Simulations are conducted to evaluate the finite sample performance of HFPRM. We have also applied HFPRM to a genome-wide association study to explore important genetic variants in neonatal white matter development.
Knickmeyer was partially supported by the National Institutes of Health grant MH083045.
Gilmore was partially supported by the National Institutes of Health grants MH064065, MH070890, and HD053000.
Styner was partially supported by the National Institutes of Health grant EB005149-01.
Zhu was partially supported by the National Institutes of Health grant MH086633, the National Science Foundation grants SES-1357666 and DMS-1407655, as well as a senior investigator grant from the Cancer Prevention Research Institute of Texas.
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Zhang, J. et al. (2017). HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_38
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