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

> **NIH NIH R00** · EMORY UNIVERSITY · 2024 · $248,999

## Abstract

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.

## Key facts

- **NIH application ID:** 11043038
- **Project number:** 4R00HD103912-04
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** YUN WANG
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $248,999
- **Award type:** 4N
- **Project period:** 2024-04-01 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11043038

## Citation

> US National Institutes of Health, RePORTER application 11043038, Leveraging artificial intelligence to develop novel tools for studying infant brain development (4R00HD103912-04). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/11043038. Licensed CC0.

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