Functional brain maturation and sleep organisation in neonates with congenital heart disease

The impact of sleep in high-risk infants

Most of an infant's day is devoted to sleep - and normal sleep is vital to normal brain development. Sleep disruptions may impair overall health, well-being, and neurodevelopment. Disruptors of sleep and circadian health, such as noise, light, respiratory support, and clinical interventions, are highly prevalent in hospital and nursing care facilities. These factors particularly affect infants who already have an increased risk of sleep disorders and their consequences due to an underlying disease. Preterm infants and infants with disorders such as neonatal abstinence syndrome, craniofacial malformations, congenital heart disease, hypoxic-ischemic encephalopathy, Chiari-malformation/myelomeningocele, congenital musculoskeletal disease, and Down syndrome are all at high risk for impaired development of sleep-wake cycling and for sleep-disordered breathing. Since abnormal sleep is a potentially treatable risk factor for impaired neurodevelopment, there is an urgent need for effective monitoring, timely interventions, and treatment strategies to improve sleep physiology and thereby optimize overall neurodevelopment in these high-risk populations. IMPACT: Healthy sleep plays a fundamental role in normal infant brain development. Many factors can disrupt sleep during a hospital stay. This is particularly important for infants who have an increased risk of sleep disorders due to neonatal disorders such as prematurity, congenital heart disease, or Chiari malformation. Sleep protective strategies are readily available and need to be systematically implemented into hospital care.

Neuromonitoring practices for neonates with congenital heart disease: a scoping review

Neonates with congenital heart disease (CHD) are at risk for adverse neurodevelopmental outcomes. This scoping review summarizes neuromonitoring methods in neonates with CHD. We identified 84 studies investigating the use of near-infrared spectroscopy (NIRS) (n = 37), electroencephalography (EEG) (n = 20), amplitude-integrated electroencephalography (aEEG) (n = 10), transcranial Doppler sonography (TCD) (n = 6), and multimodal monitoring (n = 11). NIRS was used to evaluate cerebral oxygenation, identify risk thresholds and adverse events in the intensive care unit (ICU), and outcomes. EEG was utilized to screen for seizures and to predict adverse outcomes. Studies of aEEG have focused on characterizing background patterns, detecting seizures, and outcomes. Studies of TCD have focused on correlation with short-term clinical outcomes. Multimodal monitoring studies characterized cerebral physiologic dynamics. Most of the studies were performed in single centers, had a limited number of neonates (range 3-183), demonstrated variability in neuromonitoring practices, and lacked standardized approaches to neurodevelopmental testing. We identified areas of improvement for future research: (1) large multicenter studies to evaluate developmental correlates of neuromonitoring practices; (2) guidelines to standardize neurodevelopmental testing methodologies; (3) research to address geographic variation in resource utilization; (4) integration and synchronization of multimodal monitoring; and (5) research to establish a standardized framework for neuromonitoring techniques across diverse settings. IMPACT: This scoping review summarizes the literature regarding neuromonitoring practices in neonates with congenital heart disease (CHD). The identification of low cerebral oxygenation thresholds with NIRS may be used to identify neonates at risk for adverse events in the ICU or adverse neurodevelopmental outcomes. Postoperative neuromonitoring with continuous EEG screening for subclinical seizures and status epilepticus, allow for early and appropriate therapy. Future studies should focus on enrolling larger multicenter cohorts of neonates with CHD with a standardized framework of neuromonitoring practices in this population. Postoperative neurodevelopmental testing should utilize standard assessments and testing intervals.

Near-Infrared Spectroscopy for Neonatal Sleep Classification

Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, particularly important in preterm infants who are at an increased risk of immature brain development. An accurate classification of neonatal sleep states can contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) and heart rate (HR) serving as key components in sleep assessment systems for neonates. Recent studies have demonstrated the feasibility of extracting both RR and HR using near-infrared spectroscopy (NIRS) in neonates. This study introduces a comprehensive sleep classification approach leveraging high-frequency NIRS signals recorded at a sampling rate of 100 Hz from a cohort of nine preterm infants admitted to a neonatal intensive care unit. Eight distinct features were extracted from the raw NIRS signals, including HR, RR, motion-related parameters, and proxies for neural activity. These features served as inputs for a deep convolutional neural network (CNN) model designed for the classification of AS and QS sleep states. The performance of the proposed CNN model was evaluated using two cross-validation approaches: ten-fold cross-validation of data pooling and five-fold cross-validation, where each fold contains two independently recorded NIRS data. The accuracy, balanced accuracy, F1-score, Kappa, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) were employed to assess the classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, and XGBoost (XGB), were conducted. Our results reveal the CNN model's superior performance, achieving an average accuracy of 88%, a balanced accuracy of 94%, an F1-score of 91%, Kappa of 95%, and an AUC-ROC of 96% in data pooling cross-validation. Furthermore, in both cross-validation methods, RF and XGB demonstrated accuracy levels closely comparable to the CNN classifier. These findings underscore the feasibility of leveraging high-frequency NIRS data, coupled with NIRS-based HR and RR extraction, for assessing sleep states in neonates, even in an intensive care setting. The user-friendliness, portability, and reduced sensor complexity of the approach suggest its potential applications in various less-demanding settings. This research thus presents a promising avenue for advancing neonatal sleep assessment and its implications for infant health and development.

Neonatal Seizures: New Evidence, Classification, and Guidelines

Neonates are susceptible to seizures due to their unique physiology and combination of risks associated with gestation, delivery, and the immediate postnatal period. Advances in neonatal care have improved outcomes for some of our most fragile patients, but there are persistent challenges for epileptologists in identifying neonatal seizures, diagnosing etiologies, and providing the most appropriate care, with an ultimate goal to maximize patient outcomes. In just the last few years, there have been critical advances in the state of the science, as well as new evidence-based guidelines for diagnosis, classification, and treatment of neonatal seizures. This review will provide updated knowledge about the pathophysiology of neonatal seizures, classification of the provoked seizures and neonatal epilepsies, state of the art guidance on EEG monitoring in the neonatal ICU, current treatment guidelines for neonatal seizures, and potential for future advancement in treatment.

Associations between Maternal Risk Factors and Intrinsic Placental and Fetal Brain Functional Properties in Congenital Heart Disease

The relationship between maternal risk factors (MRFs) (particularly pre-gravid obesity, diabetes, and hypertension) and congenital heart disease (CHD) to placental and fetal brain outcomes is poorly understood. Here, we tested the hypothesis that MRF and CHD would be associated with reduced intrinsic placental and fetal brain function using a novel non-invasive technique. Pregnant participants with and without MRF and fetal CHD were prospectively recruited and underwent feto-placental MRI. Using intrinsic properties of blood oxygen level dependent imaging (BOLD) we quantified spatiotemporal variance of placenta and fetal brain. MRFs and CHD were correlated with functional characteristics of the placenta and fetal brain. Co-morbid MRF (hypertension, diabetes, and obesity) reduced spatiotemporal functional variance of placenta and fetal brain (p < 0.05). CHD predicted reduced fetal brain temporal variance compared to non-CHD (p < 0.05). The presence of both MRF and CHD was associated with reduced intrinsic pBOLD temporal variance (p = 0.047). There were no significant interactions of MRFs and CHD status on either temporal or spatial variance of intrinsic brain BOLD. MRF and CHD reduced functional characteristic of placenta and brain in fetuses. MRF modification and management during pregnancy may have the potential to not only provide additional risk stratification but may also improve neurodevelopmental outcomes.