Advertisement

Investigating Cortical Variability Using a Generic Gyral Model

  • Gabriele Lohmann
  • D. Yves von Cramon
  • Alan C. F. Colchester
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

In this paper, we present a systematic investigation of the variability of the human cortical folding using a generic gyral model (GGM). The GGM consists of a fixed number of vertices that can be registered non-linearly to an individual anatomy so that for each individual we have a clearly defined set of landmarks that is spread across the cortex. This allows us to obtain a regionalized estimation of inter-subject variability. Since the GGM is stratified into different levels of depth, it also allows us to estimate variability as a function of depth. As another application of a polygonal line representation underlying the generic gyral model, we present a cortical parcellation scheme that can be used to regionalize cortical measurements.

Keywords

Anterior Commissure Polygonal Line Cortical Gyrus Cortical Measurement Major Gyrus 
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.
    Lohmann, G., von Cramon, D.Y., Colchester, A.C.F.: A construction of an averaged representation of human cortical gyri using non-linear principal component analysis. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, Springer, Heidelberg (2005)Google Scholar
  2. 2.
    Essen, D.V.: A population-average, landmark- and surface-based (pals) atlas of the human cerebral cortex. Neuroimage 28, 635–662 (2005)CrossRefGoogle Scholar
  3. 3.
    Talairach, P., Tournoux, J.: Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System - an Approach to Cerebral Imaging. Thieme Medical Publishers, New York (1988)Google Scholar
  4. 4.
    Gold, A., Rangarajan, S.: A graduated assignment algorithm for graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 18 (1996)Google Scholar
  5. 5.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, 2nd edn. Cambridge University Press, Cambridge (1992)MATHGoogle Scholar
  6. 6.
    Bookstein, F.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 567–585 (1989)MATHCrossRefGoogle Scholar
  7. 7.
    Bookstein, F.: Morphometric tools for landmark data. Cambridge University Press, Cambridge (1991)MATHGoogle Scholar
  8. 8.
    Goualher, E., Procyk, D.L., Collins, R., Venugopal, C., Barillot, A.C., Evans, G.L.: Automated extraction and variability analysis of sulcal neuroanatomy. IEEE Transactions on Medical Imaging 18, 206–217 (1999)CrossRefGoogle Scholar
  9. 9.
    Cachia, J.F., Mangin, D., Riviere, F., Kherif, N., Boddaert, A., Andrade, D., Papadopoulos-Orfanos, J.B., Poline, I., Bloch, M., Zilbovicius, P., Sonigo, F., Brunelle, J., Regis, A.: A primal sketch of the cortex mean curvature: A morphogenesis based approach to study the variability of the folding patterns. IEEE Transactions on Medical Imaging 22, 754–765 (2003)CrossRefGoogle Scholar
  10. 10.
    Lohmann, G., Cramon, D.Y.v., Steinmetz, H.: Sulcal variability of twins. Cerebral Cortex 9, 754–763 (1999)CrossRefGoogle Scholar
  11. 11.
    Goualher, A.M., Argenti, M., Duyme, W.F.C., Baar, H.E., Hulshoff Pol, D.I., Boomsma, A., Zouaoui, C., Barillot, A.C., Evans, G.L.: Statistical sulcal shape comparisons: application to the detection of the genetic encoding of the central sulcus shape. Neuroimage 11, 564–574 (2000)CrossRefGoogle Scholar
  12. 12.
    Thompson, T.D., Cannon, A.W., Toga, P.: Mapping genetic influences on human brain structure. Annals of Medicine 34, 523–536 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gabriele Lohmann
    • 1
  • D. Yves von Cramon
    • 1
  • Alan C. F. Colchester
    • 2
  1. 1.Max-Planck-Institute for Human Cognitive and Brain SciencesLeipzigGermany
  2. 2.University of Kent at CanterburyUK

Personalised recommendations