Parcellation of Infant Surface Atlas Using Developmental Trajectories of Multidimensional Cortical Attributes
Cortical surface atlases, equipped with anatomically and functionally defined parcellations, are of fundamental importance in neuroimaging studies. Typically, parcellations of surface atlases are derived based on the sulcal-gyral landmarks, which are extremely variable across individuals and poorly matched with microstructural and functional boundaries. Cortical developmental trajectories in infants reflect underlying changes of microstructures, which essentially determines the molecular organization and functional principles of the cortex, thus allowing better definition of developmentally, microstructurally, and functionally distinct regions, compared to conventional sulcal-gyral landmarks. Accordingly, a parcellation of infant cortical surface atlas was proposed, based on the developmental trajectories of cortical thickness in infants, revealing regional patterning of cortical growth. However, cortical anatomy is jointly characterized by biologically-distinct, multidimensional cortical attributes, i.e., cortical thickness, surface area, and local gyrification, each with its distinct genetic underpinning, cellular mechanism, and developmental trajectories. To date, the parcellations based on the development of surface area and local gyrification is still missing. To bridge this critical gap, for the first time, we parcellate an infant cortical surface atlas into distinct regions based solely on developmental trajectories of surface area and local gyrification, respectively. For each cortical attribute, we first nonlinearly fusethe subject-specific similarity matrices of vertices’ developmental trajectories of all subjects into a single matrix, which helps better capture common and complementary information of the population than the conventional method of simple averaging of all subjects’ matrices. Then, we perform spectral clustering based on this fused matrix. We have applied our method to parcellate an infant surface atlas using the developmental trajectories of surface area and local gyrification from 35 healthy infants, each with up to 7 time points in the first two postnatal years, revealing biologically more meaningful growth patterning than the conventional method.
KeywordsSurface area local gyrification infant atlas parcellation
Unable to display preview. Download preview PDF.
- 2.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
- 4.Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zollei, L., Polimeni, J.R., Fischl, B., Liu, H., Buckner, R.L.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011)CrossRefGoogle Scholar
- 5.Chen, C.H., Fiecas, M., Gutierrez, E.D., Panizzon, M.S., Eyler, L.T., Vuoksimaa, E., Thompson, W.K., Fennema-Notestine, C., Hagler, D.J., Jernigan Jr., 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
- 6.Li, G., Wang, L., Shi, F., Lin, W., Shen, D.: Constructing 4D infant cortical surface atlases based on dynamic developmental trajectories of the cortex. Med. Image Comput. Comput. Assist. Interv. 17, 89–96 (2014)Google Scholar
- 8.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 (2014)Google 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
- 13.Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. Adv. Neur. In. 14, 849–856 (2002)Google Scholar