Active conflict and national identity shape intergroup bias among Arabic and Hebrew speakers in Israel.

Congenital heart disease (CHD) is the most common congenital malformation, with a global prevalence of approximately 8 per 1,000 births. Advances in surgical management and perioperative care have markedly improved survival, with approximately 90% of children now surviving into adulthood. However, long-term neurodevelopmental sequelae have become increasingly apparent. Children with CHD frequently exhibit impairments across multiple domains from early childhood, including cognition, motor performance and language, alongside difficulties with executive functioning, social interaction, inattention and emotional problems that may persist into school age and beyond. These deficits can range from mild to severe, and have a prolonged impact on quality of life and educational attainment. In the United Kingdom, 50% of primary school children with CHD receive special educational needs provision. Magnetic resonance imaging (MRI) studies in CHD have demonstrated brain injury and altered early brain development, including smaller cortical grey matter, deep grey matter, white matter and cerebellar volumes, reduced cortical folding and altered hippocampal subfield morphometry in the neonatal period. Such alterations in neonatal brain development have been linked to neurodevelopmental outcomes in both toddlerhood and in middle childhood, including in cognitive and behavioural domains. However, relatively few studies have characterised behavioural outcomes in children with CHD during the preschool years, a period of socio-emotional and behavioural development that underpins later learning, school readiness and academic success. To our knowledge, the relationship between neonatal structural brain development and behavioural outcomes in preschool children with CHD remains largely unexplored. One approach used to investigate neonatal structural brain development involves assessing the anatomical covariance of regional brain morphometry across individuals by delineating structural covariance networks (SCNs). This whole-brain, data-driven approach uses Jacobian determinants derived from T2-weighted MRI to reflect the relative expansion or contraction of brain regions, which can be decomposed using independent component analysis (ICA) into spatially distinct SCNs without the need for predefined regions of interest. This method has been applied to characterise structural brain maturation in typical development, as well as alterations in clinical populations. We have previously reported altered structural covariance compared to controls in fetuses and neonates with CHD before cardiac surgery, involving deep grey matter structures, cingulate gyrus, corpus callosum, cerebellum and CSF spaces.