Constructing 4D Infant Cortical Surface Atlases Based on Dynamic Developmental Trajectories of the Cortex
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.
KeywordsInfant cortical surface atlas construction surface parcellation
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- 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
- 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.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
- 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