Congenital heart disease (CHD) affects approximately 1% of live births in the United States and is the most prevalent congenital disorder. Despite advances in neonatal cardiovascular surgery improving survival, neurodevelopmental impairments remain prevalent, impacting motor skills, social behavior, and executive function. Motor deficits and long-term challenges in emotional regulation and memory are particularly common. Recent research using resting-state functional MRI (rs-fMRI) has revealed disorganized brain networks in newborns with CHD. However, those few prior rs-fMRI studies examining the impact of CHD have relied on predefined brain parcellations to compare group-level connectivity, limiting the ability to capture spatial alterations in neonatal brain networks in CHD. Understanding these network-level changes is critical for elucidating mechanisms of neurodevelopmental impairment and identifying early biomarkers of risk. To address these gaps, our study introduces two conceptual advances: 1) a data-driven approach to investigate atypical brain network development in high-risk CHD and 2) the use of a population-sized, independent dataset of healthy newborns to derive a normative set of neonatal brain networks. By analyzing a large rs-fMRI of human newborns (N=448; 219 females and 229 males), we identify atypical brain activity in the sensorimotor and limbic networks of newborns with complex CHD. Notably, before cardiovascular surgery, these networks are split into left and right hemispheric subnetworks. Postoperatively, these components coalesce into a singular, symmetric pattern resembling networks observed in healthy neonates. Our study highlights the potential of rs-fMRI to detect subtle, early functional disruptions in CHD and may inform future biomarkers of neurodevelopmental risk.Significant Statement Congenital heart disease, the most common congenital disorder, affects 1% of live births and is associated with persistent neurodevelopmental impairments despite improved surgical survival. These deficits, including motor, socio-emotional, and cognitive challenges, may stem from early brain network disruptions. Prior resting-state fMRI studies in CHD relied on predefined parcellations, limiting detection of subtle spatial alterations. In this study, we used a data-driven approach and leveraged an independent normative dataset to define resting-state networks. Comparing CHD patients and healthy controls against these independently derived networks, we reveal atypical sensorimotor and limbic network organization preoperatively, which normalizes post-surgery. These findings highlight the potential of rs-fMRI to identify early biomarkers of neurodevelopmental risk and guide targeted interventions in this high-risk population.
Kim et al. (Mon,) studied this question.