Structure-specific statistical mapping of white matter tracts

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

Research output: Contribution to journalArticle

125 Citations (Scopus)

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)
Pages (from-to)448-461
Number of pages14
JournalNeuroImage
Volume41
Issue number2
DOIs
StatePublished - Jun 2008

Fingerprint

DiGeorge Syndrome
Chromosome Deletion
Pediatrics
White Matter
Direction compound

Keywords

  • Deformable models
  • Diffusion tensor imaging
  • DS22q11.2
  • Medial representation
  • Skeletons
  • Statistical mapping
  • White matter

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Structure-specific statistical mapping of white matter tracts. / Yushkevich, Paul A.; Zhang, Hui; Simon, Tony J; Gee, James C.

In: NeuroImage, Vol. 41, No. 2, 06.2008, p. 448-461.

Research output: Contribution to journalArticle

Yushkevich, Paul A. ; Zhang, Hui ; Simon, Tony J ; Gee, James C. / Structure-specific statistical mapping of white matter tracts. In: NeuroImage. 2008 ; Vol. 41, No. 2. pp. 448-461.
@article{437ace1751664a7f9f2cfac117f2affe,
title = "Structure-specific statistical mapping of white matter tracts",
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.",
keywords = "Deformable models, Diffusion tensor imaging, DS22q11.2, Medial representation, Skeletons, Statistical mapping, White matter",
author = "Yushkevich, {Paul A.} and Hui Zhang and Simon, {Tony J} and Gee, {James C.}",
year = "2008",
month = "6",
doi = "10.1016/j.neuroimage.2008.01.013",
language = "English (US)",
volume = "41",
pages = "448--461",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "2",

}

TY - JOUR

T1 - Structure-specific statistical mapping of white matter tracts

AU - Yushkevich, Paul A.

AU - Zhang, Hui

AU - Simon, Tony J

AU - Gee, James C.

PY - 2008/6

Y1 - 2008/6

N2 - 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.

AB - 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.

KW - Deformable models

KW - Diffusion tensor imaging

KW - DS22q11.2

KW - Medial representation

KW - Skeletons

KW - Statistical mapping

KW - White matter

UR - http://www.scopus.com/inward/record.url?scp=44149096448&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=44149096448&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2008.01.013

DO - 10.1016/j.neuroimage.2008.01.013

M3 - Article

VL - 41

SP - 448

EP - 461

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 2

ER -