# Early Prediction of Cerebral Palsy in Premature Infants using Advanced MRI Biomarkers

> **NIH NIH R01** · CINCINNATI CHILDRENS HOSP MED CTR · 2020 · $331,820

## Abstract

Project Summary/Abstract
 
Cerebral palsy (CP) is the most common physical disability in children. Almost half of all new CP diagnoses are
made in children who were born preterm. Although CP results from abnormal development or injury of the
brain during the fetal or neonatal period, children with CP typically do not receive a diagnosis until 2 years of
age. These first 2 years are critical for neuroplasticity, when proven habilitative interventions could restore
motor function. Our strong preliminary data suggest that early and accurate individualized prediction of CP is
possible by using a combination of advanced brain MRI biomarkers at term corrected age (CA). We have
developed reliable methods for measuring structural and functional connectivity in neonates using diffusion
MRI (dMRI) and functional connectivity MRI (fcMRI), respectively. These methods can sensitively diagnose
reduced neuronal connectivity, even in infants with a normal-appearing structural MRI (sMRI) that later develop
CP. We have found that a combination of 3 sensorimotor network biomarkers correctly classified 98% of
preterm infants with or without CP. The overall objective of this proposal is to determine the value of brain
connectivity biomarkers, individually and in combination, to accurately diagnose CP within 3 months of birth.
We propose a large multicenter prospective cohort study in very preterm infants (≤31 weeks gestational age),
using advanced MRI at term CA and developmental testing at 1 and 2 years CA. Our central hypothesis is
that CP is a disorder of reduced sensorimotor network connectivity, and sensitive diagnosis of this reduced
connectivity using advanced MRI at term CA will result in early and accurate prediction of CP. Diagnosis of CP
soon after birth will guide the prescription and refinement of early, evidence-based sensorimotor interventions
and novel neuroprotective therapies to enable improved outcomes in children with CP. The two specific aims
to test the central hypothesis are: (1) To differentiate regional and global structural and functional connectivity
at term CA in infants with a normal sMRI who develop CP, compared to infants who do not; (2) To define the
prognostic test properties of structural connectivity biomarkers at term CA, independently and in combined
multivariable models, and identify the model that most accurately enables personalized prediction of CP in very
preterm infants. Under the first aim, we will perform dMRI tractography of 6 sensorimotor tracts and evaluate
regional and global brain connectivity using graph theory measures. For the second aim, we will evaluate
promising connectivity biomarkers to identify the most significant multivariable model for individualized
prediction of CP. The approach is innovative because it will integrate advances in neuroimaging with
established epidemiologic principles to elucidate pathophysiology and accurately predict CP within 3 months of
birth in a large population of very preterm infa...

## Key facts

- **NIH application ID:** 9925842
- **Project number:** 5R01NS096037-05
- **Recipient organization:** CINCINNATI CHILDRENS HOSP MED CTR
- **Principal Investigator:** NEHAL A. PARIKH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $331,820
- **Award type:** 5
- **Project period:** 2016-09-30 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9925842

## Citation

> US National Institutes of Health, RePORTER application 9925842, Early Prediction of Cerebral Palsy in Premature Infants using Advanced MRI Biomarkers (5R01NS096037-05). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/9925842. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*