Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture

Patel, V, Chiang, MC, Thompson, PM, McMahon, KL, de Zubicaray, GI, Martin, NG, Wright, MJ and Toga, AW (2010). Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture. In: 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Proceedings. ISBI 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, Netherlands, (1109-1112). 14-17 April 2010. doi:10.1109/ISBI.2010.5490187

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Author Patel, V
Chiang, MC
Thompson, PM
McMahon, KL
de Zubicaray, GI
Martin, NG
Wright, MJ
Toga, AW
Title of paper Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture
Conference name ISBI 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Conference location Rotterdam, Netherlands
Conference dates 14-17 April 2010
Proceedings title 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Proceedings   Check publisher's open access policy
Journal name International Symposium on Biomedical Imaging. Proceedings   Check publisher's open access policy
Place of Publication Piscataway, NJ, U.S.A.
Publisher Institute of Electrical and Electronic Engineers (IEEE)
Publication Year 2010
Sub-type Fully published paper
DOI 10.1109/ISBI.2010.5490187
ISBN 9781424441266
1424441269
ISSN 1945-7928
1945-8452
1945-7936
Volume 2010
Start page 1109
End page 1112
Total pages 4
Collection year 2011
Language eng
Formatted Abstract/Summary
Recent advances in diffusion-weighted MRI (DWI) have enabled studies of complex white matter tissue architecture in vivo. To date, the underlying influence of genetic and environmental factors in determining central nervous system connectivity has not been widely studied. In this work, we introduce new scalar connectivity measures based on a computationally-efficient fast-marching algorithm for quantitative tractography. We then calculate connectivity maps for a DTI dataset from 92 healthy adult twins and decompose the genetic and environmental contributions to the variance in these metrics using structural equation models. By combining these techniques, we generate the first maps to directly examine genetic and environmental contributions to brain connectivity in humans. Our approach is capable of extracting statistically significant measures of genetic and environmental contributions to neural connectivity.
© 2010 IEEE.
Keyword Algorithms
Brain
Genetics
Magnetic resonance imaging
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Session FR-OS8: Diffusion MRI and Analysis I

 
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Created: Wed, 09 Mar 2011, 15:52:40 EST by Sandrine Ducrot on behalf of Centre for Advanced Imaging