Title | Semiparametric Bayesian local functional models for diffusion tensor tract statistics. |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Hua, Zhaowei, David B. Dunson, John H. Gilmore, Martin A. Styner, and Hongtu Zhu |
Journal | Neuroimage |
Volume | 63 |
Issue | 1 |
Pagination | 460-74 |
Date Published | 2012 Oct 15 |
ISSN | 1095-9572 |
Keywords | Algorithms, Artificial Intelligence, Bayes Theorem, Brain, Data Interpretation, Statistical, Diffusion Tensor Imaging, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Infant, Infant, Newborn, Male, Nerve Fibers, Myelinated, Pattern Recognition, Automated |
Abstract | 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. |
DOI | 10.1016/j.neuroimage.2012.06.027 |
Alternate Journal | Neuroimage |
Original Publication | Semiparametric Bayesian local functional models for diffusion tensor tract statistics. |
PubMed ID | 22732565 |
PubMed Central ID | PMC3677778 |
Grant List | HD053000 / HD / NICHD NIH HHS / United States P01CA142538-01 / CA / NCI NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States P50 MH064065 / MH / NIMH NIH HHS / United States RR025747-01 / RR / NCRR NIH HHS / United States R01 ES017240 / ES / NIEHS NIH HHS / United States P30 HD003110 / HD / NICHD NIH HHS / United States P41 RR013642 / RR / NCRR NIH HHS / United States R01 MH070890 / MH / NIMH NIH HHS / United States R01 HD053000 / HD / NICHD NIH HHS / United States U01 MH070890 / MH / NIMH NIH HHS / United States T32 CA106209 / CA / NCI NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States R21 AG033387 / AG / NIA NIH HHS / United States T32 MH019111 / MH / NIMH NIH HHS / United States AG033387 / AG / NIA NIH HHS / United States R01 MH060352 / MH / NIMH NIH HHS / United States MH070890 / MH / NIMH NIH HHS / United States R01ES17240 / ES / NIEHS NIH HHS / United States AS1499 / AS / Autism Speaks / United States P01 DA022446 / DA / NIDA NIH HHS / United States U54 EB005149 / EB / NIBIB NIH HHS / United States P30 HD03110 / HD / NICHD NIH HHS / United States MH086633 / MH / NIMH NIH HHS / United States R01 MH091645 / MH / NIMH NIH HHS / United States UL1 RR025747 / RR / NCRR NIH HHS / United States MH091645 / MH / NIMH NIH HHS / United States MH064065 / MH / NIMH NIH HHS / United States |
Semiparametric Bayesian local functional models for diffusion tensor tract statistics.
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