Development and Validation of a Paralimbic Related Subcortical Brain Dysmaturation MRI Score in Infants with Congenital Heart Disease

Abstract

AbstractBackgroundBrain magnetic resonance imaging (MRI) of infants with congenital heart disease (CHD) shows brain immaturity assessed via a cortical-based semi-quantitative score. Our primary aim was to develop an infant paralimbic-related subcortical-based semi-quantitative dysmaturation score, a brain dysplasia score (BDS), to detect abnormalities in CHD infants and predict clinical outcomes. Our secondary aim was to validate our BDS in a preclinical mouse model of hypoplastic left heart syndrome.MethodsA paralimbic-related subcortical BDS, derived from structural MRIs of infants with CHD, was correlated with clinical risk factors, regional cerebral volumes, feeding and 18-month neurodevelopmental outcomes. The BDS was validated in a known CHD mouse model namedOhiawith two disease-causing genes,Sap130andPchda9. To relate clinical findings, RNA-Seq was completed onOhiaanimals.FindingsBDS showed high incidence of paralimbic-related subcortical abnormalities (including olfactory, cerebellar, and hippocampal abnormalities) in CHD infants (n=215) compared to healthy controls (n=92). BDS correlated with reduced cortical maturation, developmental delay, poor language and feeding outcomes, and increased length of stay.Ohiaanimals (n=63) showed similar BDS findings, and RNA-Seq analysis showed altered neurodevelopmental and feeding pathways.Sap130mutants correlated with a more severe BDS whereasPcdha9correlated with a milder phenotype.InterpretationOur BDS is sensitive to dysmaturational differences between CHD and healthy controls, and predictive of poor outcomes. A similar spectrum of paralimbic-related subcortical abnormalities exists between human andOhiamutants suggesting a common genetic mechanistic etiology.FundingNational Library of Medicine, Department of Defense, National Heart, Lung, and Blood Institute, National Institute on Aging, Southern California Clinical and Translational Sciences Institute, Additional Ventures Foundation, Saban Research Institute, Children’s Hospital Los Angeles Clinical Services Research Grant, and National Institute of Nursing Research. Funding award numbers can be found in the acknowledgment section.Research In ContextEvidence beforeThe number of clinical and research MRI studies in neonatal/infant CHD subjects has increased dramatically in the last two decades. Previous studies have developed brain MRI scores that have focused on cortical structural maturation and acquired brain injury. Paralimbic-related subcortical regions are important for the development of cognitive and visuomotor functions in early development. Levering a large infant brain MRI dataset and a large-scale genetic mouse screen, we theorized that a paralimbic-related subcortical brain MRI score could assist clinicians with outcome prediction in CHD infants.Added ValueThis work aims to develop a subcortical morphological scoring system that could be applied to either clinical or research MRI scans and could improve the ability of clinicians and neuroradiologists to predict not only those at risk for suboptimal neurodevelopmental outcomes but also associated co-morbidities. We discovered not only are there paralimbic-related subcortical structural abnormalities that a brain MRI score can detect but also that this score predicted poor language outcomes, poor feeding outcomes, and increased post-surgical length of stay. We also found that the genetic model of hypoplastic left heart syndrome, the most severe form of CHD, also demonstrated a similar pattern of paralimbic related subcortical brain abnormalities.ImplicationsThis novel scoring system developed by our group has implications for early detection of at-risk CHD individuals for poor outcomes, both neurodevelopmental and quality of life. This subcortical paralimbic brain dysplasia score is a simple tool that can be easily added to neuroradiological workflows that can lead to better outcome prediction for children with CHD. Our scoring system helps us to better serve our population, allowing clinicians and researchers to prognosticate highest risk individuals who will benefit from the earliest forms of intervention.