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


Autoria(s): 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.
Data(s)

2013

Resumo

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.

Identificador

http://eprints.qut.edu.au/85859/

Publicador

IEEE

Relação

DOI:10.1109/ISBI.2013.6556679

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.

Direitos

Copyright 2013 IEEE

Fonte

Faculty of Health; Institute of Health and Biomedical Innovation

Palavras-Chave #Anatomical connectivity #brain #diffusion imaging #efficiency #networks #random effects analysis #tractography
Tipo

Conference Paper