Leveraging artificial intelligence to develop novel tools for studying infant brain development - 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. This proposal aims to capitalize developed artificial intelligence methods and platform in the K99 phase by utilizing extensive infant MRI datasets from both the Baby Connectome Project (BCP) and the Developing Human Connectome Project (dHCP), to delineate growth trajectories of brain structure/function and measure the relationship between those trajectories and neuropsychological functions (Aim 1), and then to predict the developmental outcomes (Aim 2). In Aim 1, 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 2, 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. We will also validate our AI model with multimodal MRI scans from dHCP, an independent dataset. The successful execution of this project is expected to employ innovative data-driven methods to investigate the nuances of early brain development, filling critical knowledge gaps of early development, and advancing efforts toward early identification of developmental delays.
