Discovering cortical sulcal folding patterns in neonates using large-scale dataset.

The folding of the human cerebral cortex is highly complex and variable across individuals, but certain common major patterns of cortical folding do exist. Mining such common patterns of cortical folding is of great importance in understanding the inter-individual variability of cortical folding and their relationship with cognitive functions and brain disorders. As primary cortical folds are mainly genetically influenced and are well established at term birth, neonates with minimal exposure to the complicated postnatal environmental influences are ideal candidates for mining the major patterns of cortical folding. In this paper, we propose a sulcal-pit-based method to discover the major sulcal patterns of cortical folding. In our method, first, the sulcal pattern is characterized by the spatial distribution of sulcal pits, which are the locally deepest points in cortical sulci. Since deep sulcal pits are genetically related, relatively consistent across individuals, and also stable during brain development, they are well suited for representing and characterizing the sulcal patterns. Then, the similarity between the distributions of sulcal pits is measured from the spatial, geometrical, and topological points of view. Next, a comprehensive similarity matrix is constructed for the whole dataset by adaptively fusing these measurements together, thus capturing both their common and complementary information. Finally, leveraging the similarity matrix, a hierarchical affinity propagation algorithm is used to group similar sulcal folding patterns together. The proposed method has been applied to 677 neonatal brains, and revealed multiple distinct and meaningful sulcal patterns in the central sulcus, superior temporal sulcus, and cingulate sulcus.