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

• Published in: 2013 IEEE 10th International Symposium on Biomedical Imaging pp. 1134 - 1137
• Main Authors: Liang Zhan, Jahanshad, Neda, Yan Jin, Toga, Arthur W., McMahon, Katie L., de Zubicaray, Greig I., Martin, Nicholas G., Wright, Margaret J., Thompson, Paul M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2013
• Subjects: Anatomical connectivity; brain; diffusion imaging; efficiency; Graph theory; Magnetic resonance imaging; networks; Optical fiber networks; Optical fiber theory; Probabilistic logic; random effects analysis; tractography; Transforms
• ISBN: 1467364568; 9781467364560
• ISSN: 1945-7928
• DOI: 10.1109/ISBI.2013.6556679

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 (4 th -8 th 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.