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

> **NIH NIH K99** · DUKE UNIVERSITY · 2021 · $111,379

## 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 and monitoring the effects of interventions. I
propose developing AI-based infant neuroimaging analysis tools for studying the early human brain development
via collected data from NIH funded Baby Connectome Project (BCP). In my pilot studies, I have shown the
superior performance of AI-based approaches in neonatal and 6-month infant brain segmentation. My first aim
is to develop an automated and accurate brain segmentation pipeline with 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, and will be released as a user-friendly, web-based interface for researchers to use
in scientific community (complementary Aim 4). In Aim 2, I will delineate the growth trajectories of regional brain
volumes, 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 use the first-year longitudinal multimodal MRI scans from
BCP to predict the developmental outcomes at age 2. 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 a crucial new tool for studying developmental neuroscience and
improve our capacity to efficiently measure and identify relevant infant brain structures and connectivity and their
r...

## Key facts

- **NIH application ID:** 10554951
- **Project number:** 7K99HD103912-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** YUN WANG
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $111,379
- **Award type:** 7
- **Project period:** 2022-01-25 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10554951

## Citation

> US National Institutes of Health, RePORTER application 10554951, Leveraging artificial intelligence to develop novel tools for studying infant brain development (7K99HD103912-02). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10554951. Licensed CC0.

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