# Predicting long-term outcomes in preterm infants using multimodal neuroimaging techniques and environmental factors

> **NIH NIH K99** · STANFORD UNIVERSITY · 2022 · $132,057

## Abstract

Project Summary/Abstract
The outlined proposal expands the previous research in preterm infant brains by integrating multiple advanced
neuroimaging methods, including novel quantitative magnetic resonance imaging (MRI) techniques and
environmental factors to predict long-term neurodevelopmental issues. Preterm infants have a high rate of
long-term neurodevelopmental impairments that often require additional health care and intervention. Recent
advances in neuroimaging have provided great insight into the patterns of specific alterations in preterm
infants. However, the studies are limited to demonstrate how brain function and structure and their interaction
relate to or explain later neurodevelopmental outcomes in preterm infants. Further, while recent studies
showed that microstructural tissue properties using recently developed quantitative MRI techniques (multi-shell
diffusion tensor imaging, quantitative T1, qT1) are more sensitive and reliable to define underlying neurological
diseases and, thus, it is crucial to investigate the microstructural tissue properties during the neonatal period to
fully understand how these measures are related to preterm birth and predict later cognitive problems in
preterm infants. The overarching aims of this project are designed to use neonatal multimodal neuroimaging
techniques, including qT1, for the first time, combining with environmental factors using advanced
computational approaches to define early biomarkers of later preterm neurodevelopmental outcomes. We
hypothesize that the links between structural-functional brain networks are significantly altered, and when
combined, will provide an exclusive prediction on later neurodevelopmental outcomes. Similarly, we will test
the working hypothesis preterm infants will have abnormal white and grey matter microstructures, and these
patterns will be correlated with the neurodevelopmental problems. Furthermore, we will explore the
environmental factors contributing to later cognitive issues that will play an essential role in predicting later
neurodevelopmental problems in preterm infants. The proposed research results will provide an early
diagnostic tool that could inform the treatments and implementation of preventative interventions before any
cognitive problem emerges. It also has an important impact on identifying behavioral targets to improve the life
course outcomes in preterm infants.

## Key facts

- **NIH application ID:** 10507663
- **Project number:** 1K99HD109507-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Elveda Gozdas
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $132,057
- **Award type:** 1
- **Project period:** 2022-08-10 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10507663

## Citation

> US National Institutes of Health, RePORTER application 10507663, Predicting long-term outcomes in preterm infants using multimodal neuroimaging techniques and environmental factors (1K99HD109507-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10507663. Licensed CC0.

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