# Mapping the developing infant connectome

> **NIH NIH R01** · EMORY UNIVERSITY · 2022 · $445,713

## Abstract

PROJECT SUMMARY/ABSTRACT
 The overarching goal of this proposal is to develop new and innovative analytic tools for longitudinal infant
brain research and to leverage these tools to chart the development of infant brain networks during the first 6
months of life, a period of unparalleled postnatal growth and change. Describing the developmental trajectory
of brain systems during this formative period has the potential to provide groundbreaking insights into major
areas of scientific inquiry, including the identification of brain systems that underlie the development of
cognitive functions, the discovery of how structural and functional network specializations arise, and the
identification of brain networks that contribute to neuropsychiatric illness. However, despite this potential,
longitudinal studies of infant brain development are still nascent, and prevailing analytic tools—largely
designed for cross-sectional analyses of adult data—are ill-suited for fully capturing fast-pace developmental
processes during infancy. This proposal aims to 1) develop innovative analytic tools that are specifically
designed to address challenges inherent to longitudinal infant brain research; 2) leverage these tools to
examine graph theoretic measures of brain network development in typical infancy; and 3) disseminate these
tools and approaches to the broader research community. Methods development will focus on two key areas:
registration (the approach for transforming individual brain images to a common space) and statistical analysis
of longitudinal data (the approach for constructing and analyzing growth curves of brain development).
Methods development and analyses will be conducted on anatomical, diffusion tensor imaging and resting-
state functional MRI data collected from infants at three longitudinal time points between birth and 6 months of
age. Aim 1 of this proposal is to develop and validate a novel hierarchical, tensor-based registration approach,
designed to handle the challenges associated with registering highly heterogeneous images, a characteristic of
longitudinal infant data. Aim 2 will improve an already state-of-the-art approach for the analysis of longitudinal
data and pioneer its application to the case of longitudinal neuroimaging data. Finally, Aim 3 will leverage these
methods to produce a temporally-precise mapping of typical growth curves of brain network development in the
first postnatal months, providing a benchmark against which to interpret and understand how alternate
trajectories of brain development can lead to disability. These aims will help advance the frontier of studies of
brain development into early infancy, a formative, and yet relatively uncharted, period of development.

## Key facts

- **NIH application ID:** 10413004
- **Project number:** 5R01EB027147-04
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** VINCE D CALHOUN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $445,713
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10413004

## Citation

> US National Institutes of Health, RePORTER application 10413004, Mapping the developing infant connectome (5R01EB027147-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10413004. Licensed CC0.

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