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FRATS: Functional Regression Analysis of DTI Tract Statistics

1Department of Biostatistics, and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA.
2Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
3Department of Statistics, Yunnan University, Kunming, 650091 Yunnan, China.
4Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
IEEE Engineering in Medicine and Biology Society
Publication Date:
IEEE Trans Med Imaging
Volume Number:
Issue Number:
IEEE Trans Med Imaging. 2010 Apr;29(4):1039-49.
PubMed ID:
Diffusion tensor imaging, Fiber Bundle, functional regression, global test statistic, registration, Projects:DiffusionMRI, Projects:DTIPopulationAnalysis
Appears in Collections:
U54 EB005149/EB/NIBIB NIH HHS/United States
UL1 RR025747/RR/NCRR NIH HHS/United States
R01 MH086633/MH/NIMH NIH HHS/United States
R21 AG033387/AG/NIA NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 HD053000/HD/NICHD NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
R01 NS055754/NS/NINDS NIH HHS/United States
NSF Grant BCS-08-26844
Generated Citation:
Zhu H., Styner M., Tang N., Liu Z., Lin W., Gilmore J.H. FRATS: Functional Regression Analysis of DTI Tract Statistics. IEEE Trans Med Imaging. 2010 Apr;29(4):1039-49. PMID: 20335089. PMCID: PMC2896997.
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Diffusion tensor imaging (DTI) provides important information on the structure of white matter fiber bundles as well as detailed tissue properties along these fiber bundles in vivo. This paper presents a functional regression framework, called FRATS, for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The functional regression framework consists of four integrated components: the local polynomial kernel method for smoothing multiple diffusion properties along individual fiber bundles, a functional linear model for characterizing the association between fiber bundle diffusion properties and a set of covariates, a global test statistic for testing hypotheses of interest, and a resampling method for approximating the p-value of the global test statistic. The proposed methodology is applied to characterizing the development of five diffusion properties including fractional anisotropy, mean diffusivity, and the three eigenvalues of diffusion tensor along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. Significant age and gestational age effects on the five diffusion properties were found in both tracts. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles.

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