The Publication Database hosted by SPL
|
All Publications
Upload
Advanced Search
Gallery View
Download Statistics
Help
Import
Log in
Parametric Regression Scheme for Distributions: Analysis of DTI Fiber Tract Diffusion Changes in Early Brain Development
|
|
Institution: |
1School of Computing, SCI 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. 4Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA. |
Publisher: |
ISBI 2014 |
Publication Date: |
Apr-2014 |
Journal: |
Proc IEEE Int Symp Biomed Imaging |
Volume Number: |
2014 |
Pages: |
559-62 |
Citation: |
Proc IEEE Int Symp Biomed Imaging. 2014 Apr;2014:559-62. |
PubMed ID: |
25356194 |
PMCID: |
PMC4209698 |
Keywords: |
DTI, distribution-valued data, early neurodevelopment, linear regression, spatiotemporal growth trajectory |
Appears in Collections: |
NA-MIC |
Sponsors: |
P50 MH064065/MH/NIMH NIH HHS/United States R01 HD055741/HD/NICHD NIH HHS/United States R01 MH070890/MH/NIMH NIH HHS/United States R01 NS055754/NS/NINDS NIH HHS/United States U54 EB005149/EB/NIBIB NIH HHS/United States |
Generated Citation: |
Sharma A., Fletcher P.T., Gilmore J.H., Escolar M.L., Gupta A., Styner M., Gerig G. Parametric Regression Scheme for Distributions: Analysis of DTI Fiber Tract Diffusion Changes in Early Brain Development. Proc IEEE Int Symp Biomed Imaging. 2014 Apr;2014:559-62. PMID: 25356194. PMCID: PMC4209698. |
| Downloaded: | 774 times. [view map] |
| Paper: | Download, View online |
| Export citation: | |
| Google Scholar: | link |
Temporal modeling frameworks often operate on scalar variables by summarizing data at initial stages as statistical summaries of the underlying distributions. For instance, DTI analysis often employs summary statistics, like mean, for regions of interest and properties along fiber tracts for population studies and hypothesis testing. This reduction via discarding of variability information may introduce significant errors which propagate through the procedures. We propose a novel framework which uses distribution-valued variables to retain and utilize the local variability information. Classic linear regression is adapted to employ these variables for model estimation. The increased stability and reliability of our proposed method when compared with regression using single-valued statistical summaries, is demonstrated in a validation experiment with synthetic data. Our driving application is the modeling of age-related changes along DTI white matter tracts. Results are shown for the spatiotemporal population trajectory of genu tract estimated from 45 healthy infants and compared with a Krabbe's patient.
Additional Material
1 File (221.302kB)
Sharma-ISBI2014-fig2.jpg (221.302kB)