Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China; Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States
Han Zhang
Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States
Zhengwang Wu
Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States
Dan Hu
Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States
Zhen Zhou
Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States
Jessica B Girault
Department of Psychiatry, the University of North Carolina at Chapel Hill, Chapel Hill, United States
Li Wang
Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States
Weili Lin
Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States
Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.
