TY - GEN
T1 - Multivariate varying coefficient models for DTI tract statistics
AU - Zhu, Hongtu
AU - Styner, Martin
AU - Li, Yimei
AU - Kong, Linglong
AU - Shi, Yundi
AU - Lin, Weili
AU - Coe, Christopher
AU - Gilmore, John H.
N1 - Funding Information:
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.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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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U2 - 10.1007/978-3-642-15705-9_84
DO - 10.1007/978-3-642-15705-9_84
M3 - Conference contribution
C2 - 20879291
AN - SCOPUS:84861328527
SN - 3642157041
SN - 9783642157042
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 690
EP - 697
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
T2 - 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Y2 - 20 September 2010 through 24 September 2010
ER -