# Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2022 · $388,750

## Abstract

Project Abstract
The increasing availability of large-scale longitudinal multimodal infant brain MRI datasets, e.g., the Baby
Connectome Project (BCP), provides an unprecedented opportunity to precisely chart the dynamic trajectories
of early brain development, essential for understanding normative growth and neurodevelopmental disorders. A
major barrier is the critical lack of computational tools, atlases and parcellations for cortical surface-based
analysis of the challenging infant MRI, which typically exhibits low tissue contrast and regionally-heterogeneous,
dynamic changes of cortical properties. To fill this gap, we have pioneered a comprehensive set of infant-
dedicated cortical surface analysis tools and atlases. Our tools and discoveries on early brain development
have been highlighted in NIMH’s 2015-2020 Strategic Plan. However, computational approaches are still
lacking for infant cortical parcellation based on the dynamic brain properties from longituidnal multimodal MRI.
Parcellation is a prerequsite in a wide variety of infant neuroimaging applications, e.g., region localization, inter-
individual variability investigation, inter-study comparison, statistical sensitivity boosting, node definition for
network analysis, and feature reduction for identificaiton of brain disorders. Hence, this project is focused on
creating and disseminating novel computational tools for both population-level and individualized infant
cortical parcellation utilizing developmental patterns of multiple complementary brain properties, and
applying them to better understanding of inter-individual variability and early brain development. The
motivation is that the dynamic development of multiple properties (e.g., cortical thickness, folding, diffusivity,
myelin content, surface area, structural and functional connectivity) in infants essentially reflects the rapid
changes of underlying microstructures and their connectivity, which jointly determine the functional principle of
each region. Hence, developmental patterns are ideal for deriving distinct regions in development, microstructure,
function, and connectivity for early brain development studies. To achieve this goal, we propose four specific
aims. In Aim 1, we will develop a novel method for population-level cortical parcellation based on
developmental patterns of multiple properties, by nonlinear fusion of heterogeneous multimodal information
from a large population of infants. In Aim 2, we further propose a novel approach for individualized parcellation
of each infant’s cortical surfaces based on its own multimodal developmental patterns, thus accounting for
remarkable inter-subject variability. We will leverage the population-level parcellation to guide the individualized
parcellation in an iterative manner via graph cuts, thus leading to precise individualized parcellations that are
easily comparable across individuals. In Aim 3, to understand the remarkable inter-individual variability in each
pa...

## Key facts

- **NIH application ID:** 10407000
- **Project number:** 5R01MH116225-05
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Gang Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $388,750
- **Award type:** 5
- **Project period:** 2018-07-11 → 2024-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10407000

## Citation

> US National Institutes of Health, RePORTER application 10407000, Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI (5R01MH116225-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10407000. Licensed CC0.

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