Clinical Research Directory

Childhood Outcomes of Preterm Brain Abnormalities

Sponsor: Children's Hospital Los Angeles

Summary

Prematurely born children are at higher risk of cognitive impairments and behavioral disorders than full-term children. There is growing evidence of significant volumetric and shape abnormalities in subcortical structures of premature neonates, which may be associated to negative long-term neurodevelopmental outcomes. The general objective is to look directly at the long-term neurodevelopmental implications of these neonatal subcortical structures abnormalities. Investigators propose to develop biomarkers of prematurity by comparing the morphological and diffusion properties of subcortical structures between preterm, with and without associated brain injuries, and full-term neonates using brain MRI. By combining subcortical morphological and diffusion properties, investigators hypothesize to 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. The specific aims and general methodology are: 1) Build a new toolbox for neonatal subcortical structures analyses that combine a group lasso-based analysis of significant regions of shape changes, a structural correlation network analysis, a neonatal tractography, and tensor-based analysis on tracts; 2) Ascertain biomarkers of prematurity in neonates with different patterns of abnormalities using correlational and connectivity analysis within and between structures features; 3) Assess the predictive potential of subcortical imaging on neurodevelopmental outcomes by correlating neonatal imaging results with long-term neurodevelopmental scores at 9 and 18 months, and 6-8 years, follow-up. In each of these aims, investigators will use advanced neuroimaging analysis developed by their group and collaborator, including multivariate tensor-based morphometry and multivariate tract-based analysis. 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, investigators will use sparse and multi-task learning to determine which of the biomarkers of prematurity at birth are the best predictors of long-term outcome. Once implemented, these methods will be available to compare subcortical structures for other pathologies in newborns and children.

Official title: Predicting the Early Childhood Outcomes of Preterm Brain Shape Abnormalities

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