Predicting long-term outcomes in preterm infants using multimodal neuroimaging techniques and environmental factors
- Institution
- Stanford University, CA
- Principal investigator
- Elveda Gozdas
- Funding
- $249K
- Duration
- 2025–2028
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Still being scored
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.
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