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Genetics of Anisotropy Asymmetry: Registration and Sample Size Effects

  • Neda Jahanshad
  • Agatha D. Lee
  • Natasha Leporé
  • Yi-Yu Chou
  • Caroline C. Brun
  • Marina Barysheva
  • Arthur W. Toga
  • Katie L. McMahon
  • Greig I. de Zubicaray
  • Margaret J. Wright
  • Paul M. Thompson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

Brain asymmetry has been a topic of interest for neuroscientists for many years. The advent of diffusion tensor imaging (DTI) allows researchers to extend the study of asymmetry to a microscopic scale by examining fiber integrity differences across hemispheres rather than the macroscopic differences in shape or structure volumes. Even so, the power to detect these microarchitectural differences depends on the sample size and how the brain images are registered and how many subjects are studied. We fluidly registered 4 Tesla DTI scans from 180 healthy adult twins (45 identical and fraternal pairs) to a geometrically-centered population mean template. We computed voxelwise maps of significant asymmetries (left/right hemisphere differences) for common fiber anisotropy indices (FA, GA). Quantitative genetic models revealed that 47-62% of the variance in asymmetry was due to genetic differences in the population. We studied how these heritability estimates varied with the type of registration target (T1- or T2-weighted) and with sample size. All methods consistently found that genetic factors strongly determined the lateralization of fiber anisotropy, facilitating the quest for specific genes that might influence brain asymmetry and fiber integrity.

Keywords

Fractional Anisotropy Midsagittal Plane Arcuate Fasciculus Structural Asymmetry Brain Asymmetry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Toga, A., Thompson, P.: Mapping brain asymmetry. Nat. Rev. Neurosci. 4(1) (2003)Google Scholar
  2. 2.
    Thirion, J.P., Prima, S., Subsol, G., Roberts, N.: Statistical analysis of normal and abnormal dissymmetry in volumetric medical images. Med. Im. Analy. 4(2) (2000)Google Scholar
  3. 3.
    Chiang, M., Barysheva, M., Lee, A., Madsen, S., Klunder, A., Toga, A., McMahon, K., de Zubicaray, G., Wright, M., Srivastava, A., Balov, N., Thompson, P.: Genetics of brain fiber architecture and intelligence. Journal of Neuroscience (2009)Google Scholar
  4. 4.
    Westerhausen, R., Huster, R.J., Kreuder, F., Wittling, W., Schweiger, E.: Corticospinal tract asymmetries at the level of the internal capsule: Is there an association with handedness? Neuroimage 37(2), 379–386 (2007)CrossRefGoogle Scholar
  5. 5.
    de Jong, L., Kovacs, S., Bamps, S., Calenbergh, F.V., Sunaert, S., van Loon, J.: The arcuate fasciculus: a comparison between diffusion tensor tractography and anatomy using the fiber dissection technique. Surgical Neurology 71(1) (2009)Google Scholar
  6. 6.
    Rodrigo, S., Naggara, O., Oppenheim, C., Golestani, N., Poupon, C., Cointepas, Y., Mangin, J.F., Le Bihan, D., Meder, J.F.: Human subinsular asymmetry studied by diffusion tensor imaging and fiber tracking. AJNR 28(8), 1526–1531 (2007)Google Scholar
  7. 7.
    Dubois, J., Hertz-Pannier, L., Cachia, A., Le Bihan, D., Dehaene-Lambertz, G.: Structural asymmetries in the infant language and sensori-motor networks. Cerebral Cortex 19(2), 414–423 (2008)CrossRefGoogle Scholar
  8. 8.
    Barnea-Goraly, N., Menon, V., Eckert, M., Tamm, L., Bammer, R., Karchemskiy, A., Dant, C.C., Reiss, A.L.: White matter development during childhood and adolescence: A cross-sectional diffusion tensor imaging study. Cereb. Cortex 15(12), 1848–1854 (2005)CrossRefGoogle Scholar
  9. 9.
    Pfefferbaum, A., Sulluvan, E.V., Carmelli, D.: Genetic regulation of regional microstructure of the corpus callosum in late life. Neuroreport 12(8), 1677–1681 (2001)CrossRefGoogle Scholar
  10. 10.
    Wang, Q., Seghers, D., D’Agostino, E., Maes, F., Vandermeulen, D., Suetens, P., Hammers, A.: Construction and validation of mean shape atlas templates for atlas-based brain image segmentation. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 689–700. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Holmes, C.J., Hoge, R., Collins, L., Woods, R., Toga, A.W., Evans, A.C.: Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22(2), 324–333 (1998)CrossRefGoogle Scholar
  12. 12.
    Leporé, N., Brun, C., Pennec, X., Chou, Y.Y., Lopez, O., Aizenstein, H., Becker, J., Toga, A., Thompson, P.: Mean template for tensor-based morphometry using deformation tensors. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 826–833. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Leporé, N., Chou, Y.Y., Lopez, O.L., Aizenstein, H.J., Becker, J.T., Toga, A.W., Thompson, P.M.: Fast 3D fluid registration of brain magnetic resonance images, vol. 6916. SPIE, San Diego (2008)Google Scholar
  14. 14.
    Kochunov, P., Lancaster, J., Thompson, P., Toga, A., Brewer, P., Hardies, J., Fox, P.: An optimized individual target brain in the Talairach coordinate system. Neuroimage 17(2), 922–927 (2002)CrossRefGoogle Scholar
  15. 15.
    Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-Euclidean metrics for fast and simple calculus on diffusion tensors. MRM 56(2), 411–421 (2006)Google Scholar
  16. 16.
    Falconer, D., Macka, T.F.: Introduction to Quantitative Genetics, 4th edn. Addison Wesley Longman, Amsterdam (1995) (Pearson Education)Google Scholar
  17. 17.
    Rijsdijk, F.V., Sham, P.C.: Analytic approaches to twin data using structural equation models. Briefings in Bioinformatics 3(2), 119–133 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Neda Jahanshad
    • 1
    • 2
  • Agatha D. Lee
    • 1
  • Natasha Leporé
    • 1
  • Yi-Yu Chou
    • 1
  • Caroline C. Brun
    • 1
  • Marina Barysheva
    • 1
  • Arthur W. Toga
    • 1
  • Katie L. McMahon
    • 3
  • Greig I. de Zubicaray
    • 3
  • Margaret J. Wright
    • 4
  • Paul M. Thompson
    • 1
  1. 1.Laboratory of Neuro Imaging, Department of NeurologyUCLAUSA
  2. 2.Medical Imaging Informatics Group, Department of RadiologyUCLAUSA
  3. 3.fMRI Laboratory, Centre for MRUniversity of QueenslandBrisbaneAustralia
  4. 4.Queensland Institute of Medical ResearchBrisbaneAustralia

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