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Constructing 4D Infant Cortical Surface Atlases Based on Dynamic Developmental Trajectories of the Cortex

  • Gang Li
  • Li Wang
  • Feng Shi
  • Weili Lin
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Cortical surface atlases play an increasingly important role for analysis, visualization, and comparison of results across different neuroimaging studies. As the first two years of life is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex, longitudinal (4D) cortical surface atlases for the infant brains during this period is highly desired yet still lacking for early brain development studies. In this paper, we construct the first longitudinal (4D) cortical surface atlases for the dynamic developing infant cortical structures at 1, 3, 6, 9, 12, 18 and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. To ensure longitudinal consistency and unbiasedness of the 4D infant cortical surface atlases, we first compute the within-subject mean cortical folding geometries by groupwise registration of longitudinal surfaces of each infant. Then we establish intersubject cortical correspondences by groupwise registration of the within-subject mean cortical folding geometries of all infants. More importantly, for the first time, we further parcellate the 4D infant surface atlases into developmentally and functionally distinctive regions based solely on the dynamic developmental trajectories of the cortical thickness, by using the spectral clustering method. Specifically, to deal with the problem that each infant has different number of scans, we first compute the within-subject affinity matrix of vertices’ cortical thickness trajectories of each infant, and then we use the averaged affinity matrix of all infants for parcellation. Our constructed 4D infant cortical surface atlases with developmental trajectories based parcellation will greatly facilitate the surface-based analysis of dynamic brain development in infants.

Keywords

Infant cortical surface atlas construction surface parcellation 

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References

  1. 1.
    Van Essen, D.C., Dierker, D.L.: Surface-based and probabilistic atlases of primate cerebral cortex. Neuron 56, 209–225 (2007)CrossRefGoogle Scholar
  2. 2.
    Fischl, B., Sereno, M.I., Tootell, R.H., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping 8, 272–284 (1999)CrossRefGoogle Scholar
  3. 3.
    Hill, J., Dierker, D., Neil, J., Inder, T., Knutsen, A., Harwell, J., Coalson, T., Van Essen, D.: A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants. J. Neurosci. 30, 2268–2276 (2010)CrossRefGoogle Scholar
  4. 4.
    Li, G., Nie, J., Wang, L., Shi, F., Lin, W., Gilmore, J.H., Shen, D.: Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age. Cereb. Cortex 23, 2724–2733 (2013)CrossRefGoogle Scholar
  5. 5.
    Rodriguez-Carranza, C.E., Mukherjee, P., Vigneron, D., Barkovich, J., Studholme, C.: A framework for in vivo quantification of regional brain folding in premature neonates. Neuroimage 41, 462–478 (2008)CrossRefGoogle Scholar
  6. 6.
    Xue, H., Srinivasan, L., Jiang, S., Rutherford, M., Edwards, A.D., Rueckert, D., Hajnal, J.V.: Automatic segmentation and reconstruction of the cortex from neonatal MRI. Neuroimage 38, 461–477 (2007)CrossRefGoogle Scholar
  7. 7.
    Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M.S., Killiany, R.J.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006)CrossRefGoogle Scholar
  8. 8.
    Zilles, K., Amunts, K.: TIMELINE Centenary of Brodmann’s map - conception and fate. Nature Reviews Neuroscience 11, 139–145 (2010)CrossRefGoogle Scholar
  9. 9.
    Chen, C.H., Fiecas, M., Gutierrez, E.D., Panizzon, M.S., Eyler, L.T., Vuoksimaa, E., Thompson, W.K., Fennema-Notestine, C., Hagler Jr., D.J., Jernigan, T.L., Neale, M.C., Franz, C.E., Lyons, M.J., Fischl, B., Tsuang, M.T., Dale, A.M., Kremen, W.S.: Genetic topography of brain morphology. Proc. Natl. Acad. Sci. U S A 110, 17089–17094 (2013)CrossRefGoogle Scholar
  10. 10.
    Wang, L., Shi, F., Yap, P.T., Gilmore, J.H., Lin, W., Shen, D.: 4D multi-modality tissue segmentation of serial infant images. PLoS One 7, e44596 (2012)Google Scholar
  11. 11.
    Yeo, B.T., Sabuncu, M.R., Vercauteren, T., Ayache, N., Fischl, B., Golland, P.: Spherical demons: fast diffeomorphic landmark-free surface registration. IEEE Trans. Med. Imaging 29, 650–668 (2010)CrossRefGoogle Scholar
  12. 12.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. Adv. Neur. In. 14, 849–856 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gang Li
    • 1
  • Li Wang
    • 1
  • Feng Shi
    • 1
  • Weili Lin
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA

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