Developmental Patterns Based Individualized Parcellation of Infant Cortical Surface

  • Gang LiEmail author
  • Li Wang
  • Weili Lin
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


The human cerebral cortex develops dynamically during the early postnatal stage, reflecting the underlying rapid changes of cortical microstructures and their connections, which jointly determine the functional principles of cortical regions. Hence, the dynamic cortical developmental patterns are ideal for defining the distinct cortical regions in microstructure and function for neurodevelopmental studies. Moreover, given the remarkable inter-subject variability in terms of cortical structure/function and their developmental patterns, the individualized cortical parcellation based on each infant’s own developmental patterns is critical for precisely localizing personalized distinct cortical regions and also understanding inter-subject variability. To this end, we propose a novel method for individualized parcellation of the infant cortical surface into distinct and meaningful regions based on each individual’s cortical developmental patterns. Specifically, to alleviate the effects of cortical measurement errors and also make the individualized cortical parcellation comparable across subjects, we first create a population-based cortical parcellation to capture the general developmental landscape of the cortex in an infant population. Then, this population-based parcellation is leveraged to guide the individualized parcellation based on each infant’s own cortical developmental patterns in an iterative manner. At each iteration, the individualized parcellation is gradually updated based on (1) the prior information of the population-based parcellation, (2) the individualized parcellation at the previous iteration, and also (3) the developmental patterns of all vertices. Experiments on fifteen healthy infants, each with longitudinal MRI scans acquired at six time points (i.e., 1, 3, 6, 9, 12 and 18 months of age), show that our method generates a reliable and meaningful individualized cortical parcellation based on each infant’s own developmental patterns.



This work was supported in part by NIH grants (MH100217, MH107815, MH108914, MH109773, and MH110274).


  1. 1.
    Lyall, A.E., Shi, F., Geng, X., Woolson, S., Li, G., Wang, L., Hamer, R.M., Shen, D., Gilmore, J.H.: Dynamic development of regional cortical thickness and surface area in early childhood. Cereb. Cortex 25, 2204–2212 (2015)CrossRefGoogle Scholar
  2. 2.
    Li, G., Lin, W., Gilmore, J.H., Shen, D.: Spatial patterns, longitudinal development, and hemispheric asymmetries of cortical thickness in infants from birth to 2 years of age. J. Neurosci. 35, 9150–9162 (2015)CrossRefGoogle Scholar
  3. 3.
    Zilles, K., Amunts, K.: Centenary of Brodmann’s map—conception and fate. Nat. Rev. Neurosci. 11, 139–145 (2010)CrossRefGoogle Scholar
  4. 4.
    Mueller, S., Wang, D., Fox, M.D., Yeo, B.T., Sepulcre, J., Sabuncu, M.R., Shafee, R., Lu, J., Liu, H.: Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586–595 (2013)CrossRefGoogle Scholar
  5. 5.
    Wang, D., Buckner, R.L., Fox, M.D., Holt, D.J., Holmes, A.J., Stoecklein, S., Langs, G., Pan, R., Qian, T., Li, K.: Parcellating cortical functional networks in individuals. Nat. Neurosci. 18, 1853–1860 (2015)CrossRefGoogle Scholar
  6. 6.
    Laumann, T.O., Gordon, E.M., Adeyemo, B., Snyder, A.Z., Joo, S.J., Chen, M.-Y., Gilmore, A.W., McDermott, K.B., Nelson, S.M., Dosenbach, N.U.: Functional system and areal organization of a highly sampled individual human brain. Neuron 87, 657–670 (2015)CrossRefGoogle Scholar
  7. 7.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. PAMI 26, 1124–1137 (2004)CrossRefGoogle Scholar
  8. 8.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. Adv. Neural. Inf. Process. Syst. 2, 849–856 (2002)Google Scholar
  9. 9.
    Li, G., Wang, L., Shi, F., Lin, W., Shen, D.: Constructing 4D infant cortical surface atlases based on dynamic developmental trajectories of the cortex. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 89–96. Springer, Cham (2014). doi: 10.1007/978-3-319-10443-0_12CrossRefGoogle Scholar
  10. 10.
    Chen, C.-H., Fiecas, M., Gutierrez, E., Panizzon, M.S., Eyler, L.T., Vuoksimaa, E., Thompson, W.K., Fennema-Notestine, C., Hagler, D.J., Jernigan, T.L.: Genetic topography of brain morphology. PNAS 110, 17089–17094 (2013)CrossRefGoogle Scholar
  11. 11.
    Li, G., Wang, L., Shi, F., Lin, W., Shen, D.: Simultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants. Med. Image Anal. 18, 1274–1289 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations