Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects

Zhan, L., Jahanshad, N., Jin, Y., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Wright, M. J., & Thompson, P. M. (2013) Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects. In ISBI 2013 : 2013 10th IEEE International Symposium on Biomedical Imaging : From Nano to Macro : April 7-11, 2013, The Westin San Francisco Market Street, San Francisco, CA, IEEE, San Francisco, USA, pp. 1134-1137.

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As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how results depend on tractography methods used to compute fiber networks. We applied 11 tractography methods to high angular resolution diffusion images of the brain (4-Tesla 105-gradient HARDI) from 536 healthy young adults. We parcellated 70 cortical regions, yielding 70×70 connectivity matrices, encoding fiber density. We computed popular graph theory metrics, including network efficiency, and characteristic path lengths. Both metrics were robust to the number of spherical harmonics used to model diffusion (4th-8th order). Age effects were detected only for networks computed with the probabilistic Hough transform method, which excludes smaller fibers. Sex and total brain volume affected networks measured with deterministic, tensor-based fiber tracking but not with the Hough method. Each tractography method includes different fibers, which affects inferences made about the reconstructed networks.

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ID Code: 85859
Item Type: Conference Paper
Refereed: Yes
Keywords: Anatomical connectivity, brain, diffusion imaging, efficiency, networks, random effects analysis, tractography
DOI: 10.1109/ISBI.2013.6556679
ISBN: 9781467364560
ISSN: 1945-7928
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2013 IEEE
Deposited On: 01 Sep 2015 02:53
Last Modified: 03 Sep 2015 05:11

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