Genetics of Path Lengths in Brain Connectivity Networks: HARDI-Based Maps in 457 Adults

  • Neda Jahanshad
  • Gautam Prasad
  • Arthur W. Toga
  • Katie L. McMahon
  • Greig I. de Zubicaray
  • Nicholas G. Martin
  • Margaret J. Wright
  • Paul M. Thompson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7509)


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.


Structural connectivity neuroimaging genetics Dijkstra’s algorithm HARDI tractography path length 


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  1. 1.
    Hagmann, P., et al.: White matter maturation reshapes structural connectivity in the late developing human brain. Proc. Natl. Acad. Sci. U S A 107(44), 19067–19072 (2010)CrossRefGoogle Scholar
  2. 2.
    Zhou, Y., et al.: Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer’s disease and mild cognitive impairment. Alzheimers Dement 4(4), 265–270 (2008)CrossRefGoogle Scholar
  3. 3.
    Brown, J.A., et al.: Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc. Natl. Acad. Sci. U S A 108(51), 20760–20765 (2011)CrossRefGoogle Scholar
  4. 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. 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. 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. 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. 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
  9. 9.
    Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006)CrossRefGoogle Scholar
  10. 10.
    Fischl, B., et al.: Automatically parcellating the human cerebral cortex. Cereb. Cortex 14(1), 11–22 (2004)CrossRefGoogle Scholar
  11. 11.
    Parker, G.J., Wheeler-Kingshott, C.A., Barker, G.J.: Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE Trans. Med. Imaging 21(5), 505–512 (2002)CrossRefGoogle Scholar
  12. 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. 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
  14. 14.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Zalesky, A.: DT-MRI fiber tracking: a shortest paths approach. IEEE Trans. Med. Imaging 27(10), 1458–1471 (2008)CrossRefGoogle Scholar
  16. 16.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  17. 17.
    Thompson, P.M., et al.: Genetic influences on brain structure. Nat. Neurosci. 4(12), 1253–1258 (2001)CrossRefGoogle Scholar
  18. 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
  19. 19.
    Harold, D., et al.: Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat. Genet. 41(10), 1088–1093 (2009)CrossRefGoogle Scholar
  20. 20.
    Braskie, M.N., et al.: Common Alzheimer’s Disease Risk Variant Within the CLU Gene Affects White Matter Microstructure in Young Adults. J. Neurosci. 31(18), 6764–6770 (2011)CrossRefGoogle Scholar
  21. 21.
    Kang, H.M., et al.: Efficient control of population structure in model organism association mapping. Genetics 178(3), 1709–1723 (2008)CrossRefGoogle Scholar
  22. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Neda Jahanshad
    • 1
  • Gautam Prasad
    • 1
  • Arthur W. Toga
    • 1
  • Katie L. McMahon
    • 2
  • Greig I. de Zubicaray
    • 3
  • Nicholas G. Martin
    • 4
  • Margaret J. Wright
    • 4
  • Paul M. Thompson
    • 1
  1. 1.Imaging Genetics Center - Laboratory of Neuro Imaging, Department of NeurologyUCLA School of MedicineLos AngelesUSA
  2. 2.Centre for Advanced ImagingUniversity of QueenslandBrisbaneAustralia
  3. 3.School of PsychologyUniversity of QueenslandBrisbaneAustralia
  4. 4.Queensland Institute of Medical ResearchBrisbaneAustralia

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