# Predicting the early childhood outcomes of preterm brain shape abnormalities

> **NIH NIH R01** · CHILDREN'S HOSPITAL OF LOS ANGELES · 2020 · $448,068

## Abstract

PROJECT SUMMARY / ABSTRACT
The last months of pregnancy are particularly important for the development of the child's brain, and the
consequences of premature birth on its development can be substantial. Prematurely born children are at
higher risk of various cognitive impairments and exhibits more behavioral disorders than full-term born children.
Thus early detection and management of at risk children are essential. There is growing evidence of significant
volumetric abnormalities in subcortical structures of premature neonates, which may be associated to negative
long-term neurodevelopmental outcomes. Understanding these abnormalities could help elucidate the
underlying pathophysiology and enable early determination of at-risk patients, both of which would inform the
design of novel treatment strategies. However, to date there is still a lack of sensitive, reliable, and accessible
algorithms capable of characterizing the influence of prematurity on the anatomy of neonatal brain subcortical
structures. In addition, few studies have looked directly at the long-term neurodevelopmental implications of
these neonatal subcortical structures abnormalities. Predicting long-term neurodevelopmental outcomes early
on – and preferably at neonatal ages – is likely to have a transformative effect on their outcome. Our
preliminary data indicate significant morphological differences in the putamen, ventricles, corpus callosum,
and thalamus between preterm and term neonates. We propose to develop biomarkers of prematurity by
statistically comparing the morphological and diffusion properties of subcortical structures between preterm
and term neonates using brain MRI. These results will further be used in a sparse learning framework to
predict long-term neurodevelopmental outcomes of prematurity. Hypotheses: By combining subcortical
morphological and diffusion properties, we will be able to: (1) delineate specific correlative relationships
between structures regionally and differentially affected by normal maturation and different patterns of white
matter injury, and (2) improve the specificity of neuroimaging to predict neurodevelopmental outcomes earlier.
Aim 1: Build a new toolbox for neonatal subcortical structures analyses that combine 1) a group lasso-based
analysis of significant regions of shape changes, 2) a structural correlation network analysis, 3) a neonatal
tractography, and 4) tensor-based analysis on tracts. Aim 2: Ascertain biomarkers of prematurity in neonates
with different patterns of abnormalities. Aim 3: Assess the predictive potential of imaging and clinical features
on neurodevelopmental outcomes among premature children at 12 and 18 months and 6-8 years of age.
Impact: This application will provide the first complete subcortical network analysis in both term and preterm
neonates. In the first study of its kind for prematurity, we will use sparse and multi-task learning to determine
which of the biomarkers of prematurity at birth are the ...

## Key facts

- **NIH application ID:** 9994891
- **Project number:** 5R01EB025032-04
- **Recipient organization:** CHILDREN'S HOSPITAL OF LOS ANGELES
- **Principal Investigator:** Natasha Lepore
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $448,068
- **Award type:** 5
- **Project period:** 2017-09-22 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994891

## Citation

> US National Institutes of Health, RePORTER application 9994891, Predicting the early childhood outcomes of preterm brain shape abnormalities (5R01EB025032-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9994891. Licensed CC0.

---

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