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Spatiotemporal Morphometry of Adjacent Tissue Layers with Application to the Study of Sulcal Formation

  • Vidya Rajagopalan
  • Julia Scott
  • Piotr A. Habas
  • Kio Kim
  • François Rousseau
  • Orit A. Glenn
  • A. James Barkovich
  • Colin Studholme
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

The process of brain growth involves the expansion of tissue at different rates at different points within the brain. As the layers within the developing brain evolve they can thicken or increase in area as the brain surface begins to fold. In this work we propose a new spatiotemporal formulation of tensor based volume morphometry that is derived in relation to tissue boundaries. This allows the study of the directional properties of tissue growth by separately characterizing the changes in area and thickness of the adjacent layers. The approach uses temporally weighted, local regression across a population of anatomies with different ages to model changes in components of the growth radial and tangential to the boundary between tissue layers. The formulation is applied to the study of sulcal formation from in-utero MR imaging of human fetal brain anatomy. Results show that the method detects differential growth of tissue layers adjacent to the cortical surface, particularly at sulcal locations, as early as 22 gestational weeks.

Keywords

Fetal Brain Brain Growth Cortical Plate Tissue Boundary March Cube Algorithm 
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.
    Van Essen, D.C.: A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 385, 313–318 (1997)CrossRefGoogle Scholar
  2. 2.
    Welker, W.: Why does cerebral cortex fissure and fold? a review of determinants of gyri and sulci. Cerebral Cortex 8, 3–136 (1990)CrossRefGoogle Scholar
  3. 3.
    Armstrong, E., Schleicher, A., Omran, H., Curtis, M., Zilles, K.: The ontogeny of human gyrification. Cerebral Cortex 5(1), 56–63 (1995)CrossRefGoogle Scholar
  4. 4.
    Rakic, P.: Genetic control of cortical convolutions. Science 303(5666), 1983–1984 (2004)CrossRefGoogle Scholar
  5. 5.
    Toro, R., Burnod, Y.: A morphogenetic model for the development of cortical convolutions. Cereb. Cortex 15, 1900–1913 (2005)CrossRefGoogle Scholar
  6. 6.
    Lefevre, J., Mangin, J.F.: A reaction-diffusion model of human brain development. PLoS Comput. Biol. 6, e1000749 (2010)Google Scholar
  7. 7.
    Rajagopalan, V., Scott, J.A., Habas, P.A., Corbett-Detig, J.M., Kim, K., Rousseau, F., Barkovich, A.J., Glenn, O.A., Studholme, C.: Local tissue growth patterns underlying normal fetal human brain gyrification quantified in utero. Journal of Neuroscience 31(8), 2878–2887 (2011)CrossRefGoogle Scholar
  8. 8.
    Dubois, J., Benders, M., Cachia, A., Lazeyras, F., Ha-Vinh Leuchter, R., Sizonenko, S., Borradori-Tolsa, C., Mangin, J., Huppi, P.: Mapping the early cortical folding process in the preterm newborn brain. Cereb. Cortex 18(6), 1444–1454 (2008)CrossRefGoogle Scholar
  9. 9.
    Lenroot, R.K., Gogtay, N., Greenstein, D.K., Wells, E.M., Wallace, G.L., Clasen, L.S., Blumenthal, J.D., Lerch, J., Zijdenbos, A.P., Evans, A.C., Thompson, P.M., Giedd, J.N.: Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage 36(4), 1065–1073 (2007)CrossRefGoogle Scholar
  10. 10.
    Lee, J., Fonov, V., Evans, A.: Mapping brain growth of early childhood using deformation based morphometry. NeuroImage 47(supplement 1), S153–S153 (2009)Google Scholar
  11. 11.
    Aljabar, P., Bhatia, K.K., Murgasova, M., Hajnal, J.V., Boardman, J.P., Srinivasan, L., Rutherford, M.A., Dyet, L.E., Edwards, A.D., Rueckert, D.: Assessment of brain growth in early childhood using deformation-based morphometry. Neuroimage 39(1), 348–358 (2008)CrossRefGoogle Scholar
  12. 12.
    Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Cleveland, W.S., Devlin, S.J.: Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association 83, 596–610 (1988)CrossRefzbMATHGoogle Scholar
  14. 14.
    Nichols, T.E., Holmes, A.P.: Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum. Brain Mapp. 15(1), 1–25 (2002)CrossRefGoogle Scholar
  15. 15.
    Kim, K., Habas, P.A., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C.: Intersection based motion correction of multislice MRI for 3-D in utero fetal brain image formation. IEEE Trans. Med. Imaging 29(1), 146–158 (2010)CrossRefGoogle Scholar
  16. 16.
    Habas, P.A., Kim, K., Corbett-Detig, J.M., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C.: A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation. Neuroimage 53(2), 460–470 (2010)CrossRefGoogle Scholar
  17. 17.
    Lopes, A., Brodlie, K.: Improving the robustness and accuracy of the marching cubes algorithm for isosurfacing. IEEE Trans. Viz. and Comput. Graph. 9(1), 16–29 (2003)CrossRefGoogle Scholar
  18. 18.
    Rakic, P., Ayoub, A.E., Breunig, J.J., Dominguez, M.H.: Decision by division: Making cortical maps. Trends Neurosci. 32(5), 291–301 (2009)CrossRefGoogle Scholar
  19. 19.
    Smart, I.H., McSherry, G.M.: Gyrus formation in the cerebral cortex in the ferret. I. Description of the external changes. J. Anat, 146, 141–152 (1986)Google Scholar
  20. 20.
    Kostovic, I., Rakic, P.: Developmental history of the transient subplate zone in the visual and somatosensory cortex of the macaque monkey and human brain. J. Comp. Neurol. 297(3), 441–470 (1990)CrossRefGoogle Scholar
  21. 21.
    Hilgetag, C.C., Barbas, H.: Role of mechanical factors in the morphology of the primate cerebral cortex. PLoS Comput. Biol. 2(3), e22 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vidya Rajagopalan
    • 1
    • 2
  • Julia Scott
    • 1
    • 2
  • Piotr A. Habas
    • 1
    • 2
  • Kio Kim
    • 1
    • 2
  • François Rousseau
    • 3
  • Orit A. Glenn
    • 4
  • A. James Barkovich
    • 4
  • Colin Studholme
    • 1
    • 2
  1. 1.Biomedical Image Computing GroupUniversity of WashingtonSeattleUSA
  2. 2.Department of PediatricsUniversity of WashingtonSeattleUSA
  3. 3.LSIIT, UMR 7005 CNRS/University of StrasbourgIllkirchFrance
  4. 4.Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoUSA

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