Negative long-term effects of excessive prenatal alcohol exposure (PAE) and prenatal tobacco exposure (PTE) on risk for multiple adverse outcomes have been well established. PAE is the leading cause of preventable intellectual disability, and smoking during pregnancy is one of the most modifiable causes of perinatal morbidity and mortality.^1,2 Understanding the associations of quantity, timing, and various combinations of in utero alcohol and smoking exposures with early brain function could help identify mechanisms that underlie adverse long-term neurobehavioral outcomes.

Late intrauterine growth restriction (IUGR) is a fetal pathological condition characterized by chronic hypoxia secondary to placental insufficiency, resulting in an abnormal rate of fetal growth. This pathology has been associated with increased fetal and neonatal morbidity and mortality. In standard clinical practice, late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This study presents a radial basis function support vector machine (RBF-SVM) classification based on quantitative features extracted from fetal heart rate (FHR) signals acquired using routine cardiotocography (CTG) in a population of 160 healthy and 102 late IUGR fetuses. First, the individual performance of each time, frequency, and nonlinear feature was tested. To improve the unsatisfactory results of univariate analysis we firstly adopted a Recursive Feature Elimination approach to select the best subset of FHR-based parameters contributing to the discrimination of healthy vs. late IUGR fetuses. A fine tuning of the RBF-SVM model parameters resulted in a satisfactory classification performance in the training set (accuracy 0.93, sensitivity 0.93, specificity 0.84). Comparable results were obtained when applying the model on a totally independent testing set. This investigation supports the use of a multivariate approach for the in utero identification of late IUGR condition based on quantitative FHR features encompassing different domains. The proposed model allows describing the relationships among features beyond the traditional linear approaches, thus improving the classification performance. This framework has the potential to be proposed as a screening tool for the identification of late IUGR fetuses.

Sleep is a central activity in human adults and characterizes most of the newborn infant life. During sleep, autonomic control acts to modulate heart rate variability (HRV) and respiration. Mechanisms underlying cardiorespiratory interactions in different sleep states have been studied but are not yet fully understood. Signal processing approaches have focused on cardiorespiratory analysis to elucidate this co-regulation. This manuscript proposes to analyze heart rate (HR), respiratory variability and their interrelationship in newborn infants to characterize cardiorespiratory interactions in different sleep states (active vs. quiet). We are searching for indices that could detect regulation alteration or malfunction, potentially leading to infant distress. We have analyzed inter-beat (RR) interval series and respiration in a population of 151 newborns, and followed up with 33 at 1 month of age. RR interval series were obtained by recognizing peaks of the QRS complex in the electrocardiogram (ECG), corresponding to the ventricles depolarization. Univariate time domain, frequency domain and entropy measures were applied. In addition, Transfer Entropy was considered as a bivariate approach able to quantify the bidirectional information flow from one signal (respiration) to another (RR series). Results confirm the validity of the proposed approach. Overall, HRV is higher in active sleep, while high frequency (HF) power characterizes more quiet sleep. Entropy analysis provides higher indices for SampEn and Quadratic Sample entropy (QSE) in quiet sleep. Transfer Entropy values were higher in quiet sleep and point to a major influence of respiration on the RR series. At 1 month of age, time domain parameters show an increase in HR and a decrease in variability. No entropy differences were found across ages. The parameters employed in this study help to quantify the potential for infants to adapt their cardiorespiratory responses as they mature. Thus, they could be useful as early markers of risk for infant cardiorespiratory vulnerabilities.

The term ‘executive functions’ (EFs) refers to a set of skills that support flexible control over thought and action. Classic EFs (working memory, inhibitory control, and cognitive flexibility) do not show measurable stable function until after the third year of life and continue to develop into early adulthood. However, even at the earliest ages, these EFs are shown to have value for predicting school readiness and academic achievement. They continue to have predictive value for success, mental health, and general well-being across the lifespan including in ageing populations. As such, understanding the developing brain and cognitive developmental dynamics that set the stage for the development of EFs, in the first three years of life, is crucial for developing programming that supports healthy EFs development. The goal of this manuscript is to describe the goals, hypotheses, participant populations, and methodology of the Khula Study. Khula is a multi-modal multi-site longitudinal birth cohort study designed to characterise emerging EFs in the first 1000 days of life in global majority settings. Most research to date has been conducted in highincome countries rather than low- and middle-income countries that comprise most of the world’s child population. We assert that understanding and supporting EF development has global importance, but this must be done with the understanding that EFs are skills that develop within the context of adaptation to one’s environment. As such, the Khula Study aims to understand which EF influences are common across cultures but also which are culture specific. We will address these questions by incorporating data from South Africa and Malawi to understand influences on EF development and outcomes for children living in these contexts. We enrolled 394 mothers (84% antenatally) from Gugulethu in Cape Town, South Africa and 507 mothers (42% antenatally) from Blantyre, Malawi.

Objective : Though the mutual influence of cardiovascular and respiratory rhythms in healthy newborns has been documented, its full characterization is still pending. In general, the activity of many physiological subsystems has a well-expressed rhythmic character, and often an interdependency between physiological rhythms emerges early in development. Traditional methods of data analysis only address the quantification of the strength of subsystem interactions. In this work, we will investigate system interrelationships in terms of the possible presence of causal or directional interplays. Approach : In this paper, we propose a methodological application that quantifies phase coupling and its directionality in a population of newborn infants born between 35 and 40 weeks of gestational age (GA). The aim is to assess whether GA at birth significantly influences the development of phase synchronization and the directionality of the coupling between the cardiovascular and respiratory system activity. Several studies indicating irregular cardiorespiratory coupling as a leading cause of several pathologies underscore the need to investigate this phenomenon in this at-risk population. Main results : Results from our investigation show a different directionality profile as a function of GA and sleep state. Significance : These findings are a contribution to the understanding of higher risk for the documented negative outcomes in the late preterm population. Moreover, these parameters could provide a tool for the development of early markers of cardiorespiratory dysregulation in infants.