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Spatiotemporal Modeling of Discrete-Time Distribution-Valued Data Applied to DTI Tract Evolution in Infant Neurodevelopment

Institution:
1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
3Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA,
Publisher:
ISBI 2013
Publication Date:
Dec-2013
Journal:
Proc IEEE Int Symp Biomed Imaging.
Volume Number:
2013
Pages:
684-7
Citation:
Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:684-7.
PubMed ID:
24443688
PMCID:
PMC3892706
Keywords:
spatiotemporal growth trajectory, distribution valued data, diffusion tensor imaging, early neurodevelopment, Mallow’s distance
Appears in Collections:
NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 NS055754/NS/NINDS NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
R01 NS061965/NS/NINDS NIH HHS/United States
DANA Foundation
Generated Citation:
Sharma A., Fletcher P.T., Gilmore J.H., Escolar M.L., Vardhan A., Gupta A., Styner M., Gerig G. Spatiotemporal Modeling of Discrete-Time Distribution-Valued Data Applied to DTI Tract Evolution in Infant Neurodevelopment. Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:684-7. PMID: 24443688. PMCID: PMC3892706.
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This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of ’distance’ between distributions and an ’average’ of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe’s disease in comparison with a normative trend estimated from 15 healthy controls.

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