Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI

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Project Abstract The increasing availability of large-scale longitudinal multimodal infant brain MRI datasets, e.g., the Baby Connectome Project (BCP), provides an unprecedented opportunity to precisely chart the dynamic trajectories of early brain development, essential for understanding normative growth and neurodevelopmental disorders. A major barrier is the critical lack of computational tools, atlases and parcellations for cortical surface-based analysis of the challenging infant MRI, which typically exhibits low tissue contrast and regionally-heterogeneous, dynamic changes of cortical properties. To fill this gap, we have pioneered a comprehensive set of infant- dedicated cortical surface analysis tools and atlases. Our tools and discoveries on early brain development have been highlighted in NIMH’s 2015-2020 Strategic Plan. However, computational approaches are still lacking for infant cortical parcellation based on the dynamic brain properties from longituidnal multimodal MRI. Parcellation is a prerequsite in a wide variety of infant neuroimaging applications, e.g., region localization, inter- individual variability investigation, inter-study comparison, statistical sensitivity boosting, node definition for network analysis, and feature reduction for identificaiton of brain disorders. Hence, this project is focused on creating and disseminating novel computational tools for both population-level and individualized infant cortical parcellation utilizing developmental patterns of multiple complementary brain properties, and applying them to better understanding of inter-individual variability and early brain development. The motivation is that the dynamic development of multiple properties (e.g., cortical thickness, folding, diffusivity, myelin content, surface area, structural and functional connectivity) in infants essentially reflects the rapid changes of underlying microstructures and their connectivity, which jointly determine the functional principle of each region. Hence, developmental patterns are ideal for deriving distinct regions in development, microstructure, function, and connectivity for early brain development studies. To achieve this goal, we propose four specific aims. In Aim 1, we will develop a novel method for population-level cortical parcellation based on developmental patterns of multiple properties, by nonlinear fusion of heterogeneous multimodal information from a large population of infants. In Aim 2, we further propose a novel approach for individualized parcellation of each infant’s cortical surfaces based on its own multimodal developmental patterns, thus accounting for remarkable inter-subject variability. We will leverage the population-level parcellation to guide the individualized parcellation in an iterative manner via graph cuts, thus leading to precise individualized parcellations that are easily comparable across individuals. In Aim 3, to understand the remarkable inter-individual variability in each parcellated region, we will discover the representative regional appearance patterns of each cortical property from a large infant population, based on multi-scale spatial-frequency characterizations of cortical property maps via spherical wavelets. In Aim 4, leveraging our tools, atlases, and parcellations, we will chart the multimodal developmental trajectories for each representative pattern of each property and investigate their relationships with behavioral/cognitive scores. Finally, we will freely release our tools, parcellations and the processed BCP data to the public.