Leveraging artificial intelligence to develop novel tools for studying infant brain development

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PROJECT SUMMARY. The first 24-months of human life are dynamic, characterized by rapid growth, and increasingly recognized as crucial for establishing cognitive abilities and behaviors that last a lifetime. However, little is known about trajectories of structural and functional brain development during this sensitive period in typically developing infants, and even less is known about how deviations in these trajectories relate to emerging cognition and behavior or predict later developmental outcomes. This is partially due to current technical limitations on quantification of brain structure and function in infants via magnetic resonance imaging (MRI) – an important, non-invasive approach to the study of developmental neuroscience. Currently there are insufficient methods to analyze infant MRI scans across the first 24 months of life, especially for brain segmentation – the first and critical step for virtually all quantitative analyses across MRI modalities. Without accurate and automated segmentation, infant MRI analysis is prone to systematic errors and is labor-intensive, limiting the rigor and reproducibility of infant MRI research. This limitation curtails and delays the utility of large-scale infant MRI datasets in the foreseeable future. Addressing these research gaps would significantly advance efforts toward early identification of developmental delays and/or disorders. I propose developing AI-based infant neuroimaging analysis tools for studying the early human brain development via two large-scale datasets: the NIH funded Baby Connectome Project and a centralized MRI data repository from Environmental Influence on Child Health Outcomes. In my pilot studies, I have shown the show good-to-excellent agreement with ground-truth labels from two different sources, and superior performance compared to other commonly used segmentation methods. My first aim is to develop an automated and generalizable brain segmentation pipeline with 3D convolutional neural networks – an AI approach. This segmentation tool can accommodate and process infant brain scans spanning each month over the first 2 years of life. The final AI-based pipeline will be rigorously validated internally, and tested externally. We will release the pipeline as a user-friendly, web-based interface for researchers to use in scientific community. In Aim 2, I will delineate the growth trajectories of regional brain morphometrics, major functional networks, and measure their relationships to neuropsychological functions during the first 24months of life via data from BCP. In Aim 3, I will leverage two different approaches (AI and LPCA) to predict the developmental outcomes assessed up to 3 years old. with the first-year longitudinal multimodal MRI scans from BCP. The interdisciplinary training phase of the award, conducted in the laboratory of Dr. Jonathan Posner at Columbia University, includes a comprehensive plan for the acquisition of technical and professional skills that will enable my transition to research independence. The successful completion of this project will yield reliable tools and novel data-driven methods for studying early brain developmental, fill critical knowledge gaps of early development, and advance efforts toward early identification of developmental delays and disorders.