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Semiparametric Bayesian Local Functional Models for Diffusion Tensor Tract Statistics

Institution:
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
2Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
3Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
4Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
5Department of Statistical Science, Duke University, Durham, NC, USA.
Publisher:
Elsevier Science
Publication Date:
Oct-2012
Journal:
Neuroimage
Volume Number:
63
Issue Number:
1
Pages:
460-74
Citation:
Neuroimage. 2012 Oct 15;63(1):460-74.
PubMed ID:
22732565
PMCID:
PMC3677778
Keywords:
Confidence band, Diffusion Tensor Imaging, Fiber Bundle, Infinite factor model, Local hypothesis, LPP2, Markov Chain Monte Carlo
Appears in Collections:
NA-MIC
Sponsors:
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 RR013642/RR/NCRR NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 ES017240/ES/NIEHS NIH HHS/United States
R01 HD053000/HD/NICHD NIH HHS/United States
R01 MH060352/MH/NIMH 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
R21 AG033387/AG/NIA NIH HHS/United States
T32 MH019111/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
UL1 RR025747/RR/NCRR NIH HHS/United States
Generated Citation:
Hua Z., Dunson D.B., Gilmore J.H., Styner M., Zhu H. Semiparametric Bayesian Local Functional Models for Diffusion Tensor Tract Statistics. Neuroimage. 2012 Oct 15;63(1):460-74. PMID: 22732565. PMCID: PMC3677778.
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We propose a semiparametric Bayesian local functional model (BFM) for the analysis of multiple diffusion properties (e.g., fractional anisotropy) along white matter fiber bundles with a set of covariates of interest, such as age and gender. BFM accounts for heterogeneity in the shape of the fiber bundle diffusion properties among subjects, while allowing the impact of the covariates to vary across subjects. A nonparametric Bayesian LPP2 prior facilitates global and local borrowings of information among subjects, while an infinite factor model flexibly represents low-dimensional structure. Local hypothesis testing and credible bands are developed to identify fiber segments, along which multiple diffusion properties are significantly associated with covariates of interest, while controlling for multiple comparisons. Moreover, BFM naturally group subjects into more homogeneous clusters. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFM. We apply BFM to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment in new born infants.

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HuaZ-NeuroImage2012-fig6.jpg (117.591kB)