Childhood Outcomes of Preterm Brain Abnormalities

Premature Birth Clinical Trial

Official title:

Predicting the Early Childhood Outcomes of Preterm Brain Shape Abnormalities

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03410810
• Other study ID #: CHLA-17-00323
• Status: Recruiting
• Start date: March 1, 2018
• Est. completion date: August 1, 2025

Study information

• Verified date: April 2024
• Source: Children's Hospital Los Angeles
• Contact: Natasha Lepore, Phd
• Phone: (323) 361-5088
• Email: nlepore[@]chla.usc.edu
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Description:

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. Investigators 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 9 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, 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. The expected findings could improve the ability to predict these outcomes and enable the design of early treatments - before years of pathological brain development and symptoms occur.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 80
• Est. completion date: August 1, 2025
• Est. primary completion date: August 1, 2025
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A to 8 Years

Eligibility

Inclusion Criteria:

• Preterm birth (Gestational Age 21-36 weeks)

• English or Spanish speaking families

• PVL and Grade I and II IVH will be considered

Exclusion Criteria:

• Shunt

• Intubation, Cpap, Nasal Ventilation

• Chromosomal/Genetic abnormalities

• Mitochondrial/Metabolic Diseases

• Treatment for extracorporeal membrane oxygenation (ECMO)

• Grade III and IV IVH (optional)

Related Conditions & MeSH terms

• Brain Development Abnormality
• Brain Lesion
• Congenital Abnormalities
• Neurodevelopmental Disorders
• Premature Birth

Intervention

Diagnostic Test:
• MRI
MRI analysis
• Neurodevelopmental/Neuropsychological Assessment
Standardized Cognitive and Developmental Tests

Locations

Country	Name	City	State
United States	Children's Hospital Los Angeles	Los Angeles	California

Sponsors (3)

Lead Sponsor	Collaborator
Children's Hospital Los Angeles	Arizona State University, University of Pittsburgh

Country where clinical trial is conducted

United States, 

References & Publications (2)

Lao Y, Wang Y, Shi J, Ceschin R, Nelson MD, Panigrahy A, Lepore N. Thalamic alterations in preterm neonates and their relation to ventral striatum disturbances revealed by a combined shape and pose analysis. Brain Struct Funct. 2016 Jan;221(1):487-506. doi: 10.1007/s00429-014-0921-7. Epub 2014 Nov 1. — View Citation

Paquette N, Shi J, Wang Y, Lao Y, Ceschin R, Nelson MD, Panigrahy A, Lepore N. Ventricular shape and relative position abnormalities in preterm neonates. Neuroimage Clin. 2017 May 28;15:483-493. doi: 10.1016/j.nicl.2017.05.025. eCollection 2017. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Changes in Surface Area and Thickness of Subcortical Structures	Measured in Voxel Size (mm)	2018 - 2022
Primary	Changes in Diffusion values of white matter Tracts	Measured in mm squared per second	2018 - 2022
Primary	Differences in developmental Quotient / Neuropsychological scores	Measured using standardized tests (Bayley-III and NIH toolbox)	2018 - 2022

External Links

•   Computational Imaging of Brain Organization Research Group (CIBORG) laboratory specializes in mathematical and numerical methods to study brain anatomy and function though magnetic resonance imaging.