Structure-specific statistical mapping of white matter tracts

Paul A. Yushkevich, Hui Zhang, Tony J Simon, James C. Gee

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

We present a new model-based framework for the statistical analysis of diffusion imaging data associated with specific white matter tracts. The framework takes advantage of the fact that several of the major white matter tracts are thin sheet-like structures that can be effectively modeled by medial representations. The approach involves segmenting major tracts and fitting them with deformable geometric medial models. The medial representation makes it possible to average and combine tensor-based features along directions locally perpendicular to the tracts, thus reducing data dimensionality and accounting for errors in normalization. The framework enables the analysis of individual white matter structures, and provides a range of possibilities for computing statistics and visualizing differences between cohorts. The framework is demonstrated in a study of white matter differences in pediatric chromosome 22q11.2 deletion syndrome.

Original languageEnglish (US)
Title of host publicationMathematics and Visualization
PublisherSpringer Heidelberg
Pages83-112
Number of pages30
Edition9783540883777
DOIs
StatePublished - 2009

Publication series

NameMathematics and Visualization
Number9783540883777
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X

ASJC Scopus subject areas

  • Modeling and Simulation
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Structure-specific statistical mapping of white matter tracts'. Together they form a unique fingerprint.

  • Cite this

    Yushkevich, P. A., Zhang, H., Simon, T. J., & Gee, J. C. (2009). Structure-specific statistical mapping of white matter tracts. In Mathematics and Visualization (9783540883777 ed., pp. 83-112). (Mathematics and Visualization; No. 9783540883777). Springer Heidelberg. https://doi.org/10.1007/978-3-540-88378-4_5