Abstract
Diffusion tensor imaging (DTI) is important for characterizing the structure of white matter fiber bundles as well as detailed tissue properties along these fiber bundles in vivo. There has been extensive interest in the analysis of diffusion properties measured along fiber tracts as a function of age, diagnostic status, and gender, while controlling for other clinical variables. However, the existing methods have several limitations including the independent analysis of diffusion properties, a lack of method for accounting for multiple covariates, and a lack of formal statistical inference, such as estimation theory and hypothesis testing. This paper presents a statistical framework, called VCMTS, to specifically address these limitations. The VCMTS framework consists of four integrated components: a varying coefficient model for characterizing the association between fiber bundle diffusion properties and a set of covariates, the local polynomial kernel method for estimating smoothed multiple diffusion properties along individual fiber bundles, global and local test statistics for testing hypotheses of interest along fiber tracts, and a resampling method for approximating the p-value of the global test statistic. The proposed methodology is applied to characterizing the development of four diffusion properties along the splenium and genu of the corpus callosum tract in a study of neurodevelopment in healthy rhesus monkeys. Significant time effects on the four diffusion properties were found.
This work was supported in part by NSF grant BCS-08-26844 and NIH grants UL1-RR025747-01, MH086633, P01CA142538-01 and AG033387 to Dr. Zhu, NIH grants MH064065, HD053000, and MH070890 to Dr. Gilmore, NIH grants R01NS055754 and R01EB5-34816 to Dr. Lin, Lilly Research Laboratories, the UNC NDRC HD 03110, Eli Lilly grant F1D-MC-X252, and NIH Roadmap Grant U54 EB005149-01, NAMIC to Dr. Styner.
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Keywords
- Fractional Anisotropy
- Functional Principal Component Analysis
- Functional Principal Component
- Vary Coefficient Model
- Global Test Statistic
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zhu, H. et al. (2010). Multivariate Varying Coefficient Models for DTI Tract Statistics. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_84
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