# Leveraging pediatric state-specific functional brain network dynamics to predict developmental outcomes

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2024 · $621,292

## Abstract

PROJECT SUMMARY
Determining accurate models of the developing brain’s functional architecture during the first two
years of life has potential prognostic value for maturation milestones and the onset of
psychopathology. A growing body of literature suggests that infant and toddler brain functional
connectivity (FC) networks (as defined using functional magnetic resonance imaging; fMRI) are
less mature than adult networks. However, there are two distinct and potentially interacting factors
present when estimating infant and toddler FC: 1) age and 2) state. Critically, infant and toddler
fMRI data are collected during natural sleep, while fMRI data in older populations are typically
collected during resting wakefulness. Research suggests that pediatric FC estimated from
sleeping state fMRI appears more similar to adult sleeping state networks (SSN) than awake adult
resting state networks. However, even within pediatric FC studies, comparisons regarding age
are compounded by developmental differences in state as time spent in different sleep stages
changes over the first two years of life. Crucially, individual variability in time spent in each sleep
stage poses a challenge for reproducibility and prediction accuracy of extant pediatric, sleeping
state fMRI studies such as the Early Life Adversity, Biological Embedding (eLABE) study, Baby
Connectome Project (BCP), Developing Human Connectome Project, and the Healthy Brain and
Child Development Study. The overarching goals of this Award are to 1) disentangle the relative
contributions of sleep stage and age in early brain network development and 2) decode sleep
stages in extant infant and toddler sleeping state fMRI. Completion of these goals will enable age
and state-specific FC prediction of mental health and clinical outcomes. Towards these goals,
infant and toddler sleep stages and FC networks will be characterized using concurrent
electroencephalogram (EEG) - fMRI. Aim 1 will optimize pediatric EEG-fMRI acquisition and
analysis. Aim 2 will determine pediatric FC SSN development by collecting cross-sectional EEG-
fMRI data from 50 children at birth and 24 months of age. Aim 3 will decode sleep stages in out
of sample fMRI data (eLABE & BCP). While EEG-fMRI in infants and toddlers is an ambitious and
new research direction for the PI, our investigative team has the necessary expertise for success,
including FC brain network analysis (PI: Wheelock), pediatric fMRI acquisition and analysis
(Smyser), concurrent EEG-fMRI acquisition and analysis (Zempel, Palanca), pediatric EEG sleep
staging (Rudock), and machine learning (Lahiri). The knowledge generated from this R01 will be
transformative, providing a state-based understanding of early developmental brain networks and
a framework for accurate developmental outcome predictions.

## Key facts

- **NIH application ID:** 10936120
- **Project number:** 1R01HD115540-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Muriah D Wheelock
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $621,292
- **Award type:** 1
- **Project period:** 2024-08-14 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10936120

## Citation

> US National Institutes of Health, RePORTER application 10936120, Leveraging pediatric state-specific functional brain network dynamics to predict developmental outcomes (1R01HD115540-01A1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10936120. Licensed CC0.

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