# Individual Motor Outcome Prediction in Preterm Children Using Neonatal Neuroimaging

> **NIH NIH F30** · WASHINGTON UNIVERSITY · 2022 · $32,686

## Abstract

Project Summary
Each year in the United States alone, 500,000 infants are born preterm (<37 weeks gestation), putting them at
increased risk for neurodevelopmental disabilities, including cerebral palsy and other motor impairments. While
specific clinical populations are known to be at increased risk, the likelihood of disability for any individual child
cannot currently be accurately predicted based upon clinical risk factors alone, limiting our ability to effectively
target therapies and develop new interventions. Prior neuroimaging studies have linked preterm birth to
disrupted development of the motor system, encompassing the motor cortex, thalamus, basal ganglia, and
cerebellum and associated white matter tracts including the corpus callosum (CC) and corticospinal tract (CST).
While aberrant structural and functional connectivity across these regions have been associated with poorer
motor outcomes, this has not been investigated across childhood in longitudinal cohorts in a way that allows for
individualized outcome prediction. This study proposes to use multiple advanced neuroimaging modalities to
statistically model how changes in neonatal structural and functional connectivity within the motor system can
predict childhood motor outcomes in children born very preterm (VPT; <30 weeks' gestation). This
investigation will leverage a unique, highly valuable, prospective, longitudinal cohort (currently being studied
through R01 MH113570) that includes 175 VPT children, including 41 with cerebral palsy and 68 with other
motor deficits. We collected state-of-the-art neonatal neuroimaging data for these children, including high-
resolution anatomic, functional, and diffusion data. They have also undergone standardized testing of both fine
and gross motor function at ages 2, 5, and 9/10 years, with retention rates >80% across assessment waves.
Across the three aims of this study, latent growth curve models will be created and compared to determine the
individual-level predictive ability of motor system functional connectivity and CC and CST microstructure, both
individually and in combination, on motor trajectories through age 10 years. This project would both advance
our ability to predict outcomes for individual preterm children into middle childhood and build the applicant's
skills in neuroimaging, longitudinal data analysis, and scientific communication in a research environment with
clear expertise in these areas. In the process, she would become proficient in the methods necessary for
furthering our understanding of the relationships between early brain development and disability in high-risk
populations. She would also become prepared to undertake not only strong experimental work, but also care
for patients with neurodevelopmental disabilities while effectively integrating her research with disability
advocacy. This would pave the way for the applicant to become a successful physician-scientist and child
neurologist creating better outcomes...

## Key facts

- **NIH application ID:** 10474294
- **Project number:** 5F30HD105336-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Peppar Elizabeth Pei-pei Cyr
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $32,686
- **Award type:** 5
- **Project period:** 2021-05-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10474294

## Citation

> US National Institutes of Health, RePORTER application 10474294, Individual Motor Outcome Prediction in Preterm Children Using Neonatal Neuroimaging (5F30HD105336-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10474294. Licensed CC0.

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