The Department would like to recognize Gang Li, PhD, for receiving five-year R01 funding (7/11/2018 – 4/30/2023), renewed annually ($388K+/year), for his project entitled, “Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI.”
Development in recent years of infant-dedicated cortical surface analysis tools and atlases has significantly advanced the processing of infant brain MRI datasets. Li’s study seeks to develop and disseminate novel computational tools for infant cortical parcellation to support large-scale, cortical surface-based infant MRI data analysis. Such tools will be most notably applied in the short-term to the 2016-funded Baby Connectome Project (BCP) led by the Department’s Vice Chair of Basic Research Dr. Weili Lin.
The BCP provides unprecedented opportunities for precise charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, computational tools for infant cortical parcellation based on the dynamic cortical properties from longitudinal multi-modal MRI are still lacking. Li’s study uniquely addresses this challenge through developing new computational approaches for both population-level and individualized infant cortical parcellation. Over a five-year project, the novel computational tools developed will leverage the infant cortical parcellation needed to chart the multi-modal developmental trajectories of the representative patterns found in cortical properties, as well as to investigate their relationships with gender and behavioral/cognitive scores.
This five-year R01 research is aligned with the goals of another new UNC study also led by Li (Co-PIs: Drs. Dinggang Shen & Li Wang) producing computational tools for processing BCP infant MRI data, e.g., brain tissue segmentation and volumetric atlas building. However, this research uniquely focuses on creating infant-dedicated cortical parcellation maps and investigating their imaging measurements in relation to developmental cognitive scores to produce a better understanding of inter-individual variability and early brain development.