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

> **NIH NIH K99** · DUKE UNIVERSITY · 2022 · $123,367

## 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. 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...

## Key facts

- **NIH application ID:** 10465176
- **Project number:** 5K99HD103912-03
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** YUN WANG
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $123,367
- **Award type:** 5
- **Project period:** 2022-01-25 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10465176

## Citation

> US National Institutes of Health, RePORTER application 10465176, Leveraging artificial intelligence to develop novel tools for studying infant brain development (5K99HD103912-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10465176. Licensed CC0.

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