Genetics of Path Lengths in Brain Connectivity Networks: HARDI-Based Maps in 457 Adults
Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra’s algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer’s risk gene, CLU.
KeywordsStructural connectivity neuroimaging genetics Dijkstra’s algorithm HARDI tractography path length
Unable to display preview. Download preview PDF.
- 4.Dennis, E.L., et al.: Altered structural brain connectivity in healthy carriers of the autism risk gene, CNTNAP2. Brain Connect 1(6), 447–459 (2011)Google Scholar
- 5.Lopez, L.M., et al.: A genome-wide search for genetic influences and biological pathways related to the brain’s white matter integrity. Neurobiol Aging (in Press. 2012)Google Scholar
- 6.Jahanshad, N., et al.: Brain structure in healthy adults is related to serum transferring and the H63D polymorphism in the HFE gene. Proc. Natl. Acad. Sci. U S A 109(14), E851–E859 (2012)Google Scholar
- 7.Kochunov, P., et al.: Genome-wide association of full brain white matter integrity – from the ENIGMA DTI working group. Organization of Human Brain Mapping, Beijing, China (2012)Google Scholar
- 8.Aganj, I., et al.: A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography. Med. Image Anal. 15(4), 414–425 (2011)Google Scholar
- 12.Prados, E., et al.: Control Theory and Fast Marching Techniques for Brain Connectivity Mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
- 13.Patel, V., et al.: Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture. In: ISBI, pp. 1109–1112. IEEE, Rotterdam (2010)Google Scholar
- 18.Veale, A.M.O.: Introduction to Quantitative Genetics - Falconer, D.S. The Royal Statistical Society Series C-Applied Statistics 9(3), 202–203 (1960)Google Scholar
- 22.Dennis, E.L., et al.: Test-retest reliability of graph theory measures of structural brain connectivity. In: Medical Image Computing and Computer Assisted Intervention, Nice, France. LNCS (in press, 2012)Google Scholar
- 23.Jahanshad, N., et al.: Sex differences in the Human Connectome: 4-Tesla high angular resolution diffusion tensor imaging (HARDI) tractography in 234 young adult twins. In: ISBI, pp. 939–943. IEEE, Chicago (2011)Google Scholar