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Multivariate Varying Coefficient Models for DTI Tract Statistics

Department of Biostatistics, Radiology, Psychiatry and Computer Science, and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA.
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2010
Publication Date:
Volume Number:
Issue Number:
Pt 1
Int Conf Med Image Comput Comput Assist Interv. 2010 Sep;13(Pt 1):690-7.
PubMed ID:
Projects:DiffusionMRI, Projects:DTIPopulationAnalysis
Appears in Collections:
P01 CA142538/CA/NCI NIH HHS/United States
P01 DA022446/DA/NIDA NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
P41 RR005959/RR/NCRR NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 EB534816/EB/NIBIB NIH HHS/United States
R01 HD053000/HD/NICHD NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
R01 MH086633/MH/NIMH NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
R01 NS055754/NS/NINDS NIH HHS/United States
R21 AG033387/AG/NIA NIH HHS/United States
R41 NS059095/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
UL1 RR025747/RR/NCRR NIH HHS/United States
Generated Citation:
Zhu H., Styner M., Li Y., Kong L., Shi Y., Lin W., Coe C., Gilmore J.H. Multivariate Varying Coefficient Models for DTI Tract Statistics. Int Conf Med Image Comput Comput Assist Interv. 2010 Sep;13(Pt 1):690-7. PMID: 20879291. PMCID: PMC2964931.
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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.

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