Early changes in brain structure correlate with language outcomes in children with neonatal encephalopathy.

Global patterns of brain injury correlate with motor, cognitive, and language outcomes in survivors of neonatal encephalopathy (NE). However, it is still unclear whether local changes in brain structure predict specific deficits. We therefore examined whether differences in brain structure at 6 months of age are associated with neurodevelopmental outcomes in this population. We enrolled 32 children with NE, performed structural brain MR imaging at 6 months, and assessed neurodevelopmental outcomes at 30 months. All subjects underwent T1-weighted imaging at 3 T using a 3D IR-SPGR sequence. Images were normalized in intensity and nonlinearly registered to a template constructed specifically for this population, creating a deformation field map. We then used deformation based morphometry (DBM) to correlate variation in the local volume of gray and white matter with composite scores on the Bayley Scales of Infant and Toddler Development (Bayley-III) at 30 months. Our general linear model included gestational age, sex, birth weight, and treatment with hypothermia as covariates. Regional brain volume was significantly associated with language scores, particularly in perisylvian cortical regions including the left supramarginal gyrus, posterior superior and middle temporal gyri, and right insula, as well as inferior frontoparietal subcortical white matter. We did not find significant correlations between regional brain volume and motor or cognitive scale scores. We conclude that, in children with a history of NE, local changes in the volume of perisylvian gray and white matter at 6 months are correlated with language outcome at 30 months. Quantitative measures of brain volume on early MRI may help identify infants at risk for poor language outcomes

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates

Neonatal seizures and therapeutic hypothermia for hypoxic-ischemic encephalopathy.

Neonatal seizures are associated with morbidity and mortality. Hypoxic-ischemic encephalopathy (HIE) is the most common cause of seizures in newborns. Neonatal animal models suggest that therapeutic hypothermia can reduce seizures and epileptiform activity in the setting of hypoxia-ischemia, however data from human studies have conflicting results. In this research highlight, we will discuss the findings of our recent study that demonstrated a decreased seizure burden in term newborns with moderate HIE treated with hypothermia

Perinatal neuroprotection update

Antepartum, intrapartum, and neonatal events can result in a spectrum of long-term neurological sequelae, including cerebral palsy, cognitive delay, schizophrenia, and autism spectrum disorders [1]. Advances in obstetrical and neonatal care have led to survival at earlier gestational ages and consequently increasing numbers of periviable infants who are at significant risk for long-term neurological deficits. Therefore, efforts to decrease and prevent cerebral insults attempt not only to decrease preterm delivery but also to improve neurological outcomes in infants delivered preterm. We recently published a comprehensive review addressing the impacts of magnesium sulfate, therapeutic hypothermia, delayed cord clamping, infections, and prevention of preterm delivery on the modification of neurological risk [2]. In this review, we will briefly provide updates to the aforementioned topics as well as an expansion on avoidance of toxin and infections, specifically the Zika virus

Critical congenital heart disease beyond HLHS and TGA: neonatal brain injury and early neurodevelopment.

BACKGROUND: Characterization of brain injury and neurodevelopmental (ND) outcomes in critical congenital heart disease (cCHD) has primarily focused on hypoplastic left heart syndrome (HLHS) and transposition of the great arteries (TGA). This study reports brain injury and ND outcomes among patients with heterogeneous cCHD diagnoses beyond HLHS and TGA. METHODS: This prospective cohort study included infants with HLHS, TGA, or heterogenous Other cCHD including left- or right-sided obstructive lesions, anomalous pulmonary venous return, and truncus arteriosus. Brain injury on perioperative brain MRI and ND outcomes on the Bayley-II at 30 months were compared. RESULTS: A total of 218 participants were included (HLHS = 60; TGA = 118; Other cCHD = 40, including 8 with genetic syndromes). Pre-operative (n = 209) and post-operative (n = 189) MRI showed similarly high brain injury rates across groups, regardless of cardiopulmonary bypass exposure. At 30 months, participants with Other cCHD had lower cognitive scores (p = 0.035) compared to those with HLHS and TGA, though worse ND outcome in this group was driven by those with genetic disorders. CONCLUSIONS: Frequency of brain injury and neurodevelopmental delay among patients with Other cCHD is similar to those with HLHS or TGA. Patients with all cCHD lesions are at risk for impaired outcomes; developmental and genetic screening is indicated. IMPACT: This study adds to literature on risk of brain injury in patients with critical congenital heart disease (cCHD) diagnoses other than hypoplastic left heart syndrome (HLHS) and transposition of the great arteries (TGA), a heterogenous cohort of patients that has often been excluded from imaging studies. Children with cCHD beyond HLHS and TGA have similarly high rates of acquired brain injury. The high rate of neurodevelopmental impairment in this heterogenous group of cCHD diagnoses beyond HLHS and TGA is primarily driven by patients with comorbid genetic syndromes such as 22q11.2 deletion syndrome