Aberrant brain functional connectivity in newborns with congenital heart disease before cardiac surgery

Newborns with congenital heart disease (CHD) requiring open heart surgery are at increased risk for neurodevelopmental disabilities. Recent quantitative MRI studies have reported disrupted growth, microstructure, and metabolism in fetuses and newborns with complex CHD. To date, no study has examined whether functional brain connectivity is altered in this high-risk population after birth, before surgery. Our objective was to compare whole-brain functional connectivity of resting state networks in healthy, term newborns (n = 82) and in term neonates with CHD before surgery (n = 30) using graph theory and network-based statistics. We report for the first time intact global network topology – efficient and economic small world networks – but reduced regional functional connectivity involving critical brain regions (i.e. network hubs and/or rich club nodes) in newborns with CHD before surgery. These findings suggest the presence of early-life brain dysfunction in CHD which may be associated with neurodevelopmental impairments in the years following cardiac surgery. Additional studies are needed to evaluate the prognostic, diagnostic and surveillance potential of these findings

Functional properties of resting state networks in healthy full-term newborns.

Objective, early, and non-invasive assessment of brain function in high-risk newborns is critical to initiate timely interventions and to minimize long-term neurodevelopmental disabilities. A prerequisite to identifying deviations from normal, however, is the availability of baseline measures of brain function derived from healthy, full-term newborns. Recent advances in functional MRI combined with graph theoretic techniques may provide important, currently unavailable, quantitative markers of normal neurodevelopment. In the current study, we describe important properties of resting state networks in 60 healthy, full-term, unsedated newborns. The neonate brain exhibited an efficient and economical small world topology: densely connected nearby regions, sparse, but well integrated, distant connections, a small world index greater than 1, and global/local efficiency greater than network cost. These networks showed a heavy-tailed degree distribution, suggesting the presence of regions that are more richly connected to others (\u27hubs\u27). These hubs, identified using degree and betweenness centrality measures, show a more mature hub organization than previously reported. Targeted attacks on hubs show that neonate networks are more resilient than simulated scale-free networks. Networks fragmented faster and global efficiency decreased faster when betweenness, as opposed to degree, hubs were attacked suggesting a more influential role of betweenness hub in the neonate network

Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity

The architecture of the human connectome changes with brain maturation. Pivotal to understanding these changes is defining developmental periods when transitions in network topology occur. Here, using 110 resting-state functional connectivity data sets from healthy fetuses between 19 and 40 gestational weeks, we estimated optimal gestational-age (GA) cut points for dichotomizing fetuses into ‘young’ and ‘old’ groups based on global network features. We computed the small-world index, normalized clustering and path length, global and local efficiency, and modularity from connectivity matrices comprised 200 regions and their corresponding pairwise connectivity. We modeled the effect of GA at scan on each metric using separate repeated-measures generalized estimating equations. Our modeling strategy involved stratifying fetuses into ‘young’ and ‘old’ based on the scan occurring before or after a selected GA (i.e., 28 to 33). We then used the quasi-likelihood independence criterion statistic to compare model fit between ‘old’ and ‘young’ cohorts and determine optimal cut points for each graph metric. Trends for all metrics, except for global efficiency, decreased with increasing gestational age. Optimal cut points fell within 30–31 weeks for all metrics coinciding with developmental events that include a shift from endogenous neuronal activity to sensory-driven cortical patterns

Aberrant brain functional connectivity in newborns with congenital heart disease before cardiac surgery

Newborns with congenital heart disease (CHD) requiring open heart surgery are at increased risk for neurodevelopmental disabilities. Recent quantitative MRI studies have reported disrupted growth, microstructure, and metabolism in fetuses and newborns with complex CHD. To date, no study has examined whether functional brain connectivity is altered in this high-risk population after birth, before surgery. Our objective was to compare whole-brain functional connectivity of resting state networks in healthy, term newborns (n=82) and in term neonates with CHD before surgery (n=30) using graph theory and network-based statistics. We report for the first time intact global network topology – efficient and economic small world networks – but reduced regional functional connectivity involving critical brain regions (i.e. network hubs and/or rich club nodes) in newborns with CHD before surgery. These findings suggest the presence of early-life brain dysfunction in CHD which may be associated with neurodevelopmental impairments in the years following cardiac surgery. Additional studies are needed to evaluate the prognostic, diagnostic and surveillance potential of these findings. Keywords: Congenital heart disease, Neurodevelopment, Neonatal resting state networks, Graph theor

Examining the relationship between fetal cortical thickness, gestational age, and maternal psychological distress

In utero exposure to maternal stress, anxiety, and depression has been associated with reduced cortical thickness (CT), and CT changes, in turn, to adverse neuropsychiatric outcomes. Here, we investigated global and regional (G/RCT) changes associated with fetal exposure to maternal psychological distress in 265 brain MRI studies from 177 healthy fetuses of low-risk pregnant women. GCT was measured from cortical gray matter (CGM) voxels; RCT was estimated from 82 cortical regions. GCT and RCT in 87% of regions strongly correlated with GA. Fetal exposure was most strongly associated with RCT in the parahippocampal region, ventromedial prefrontal cortex, and supramarginal gyrus suggesting that cortical alterations commonly associated with prenatal exposure could emerge in-utero. However, we note that while regional fetal brain involvement conformed to patterns observed in newborns and children exposed to prenatal maternal psychological distress, the reported associations did not survive multiple comparisons correction. This could be because the effects are more subtle in this early developmental window or because majority of the pregnant women in our study did not experience high levels of maternal distress. It is our hope that the current findings will spur future hypothesis-driven studies that include a full spectrum of maternal mental health scores

Automatic brain segmentation in preterm infants with post-hemorrhagic hydrocephalus using 3D Bayesian U-Net

Post-hemorrhagic hydrocephalus (PHH) is a severe complication of intraventricular hemorrhage (IVH) in very preterm infants. PHH monitoring and treatment decisions rely heavily on manual and subjective two-dimensional measurements of the ventricles. Automatic and reliable three-dimensional (3D) measurements of the ventricles may provide a more accurate assessment of PHH, and lead to improved monitoring and treatment decisions. To accurately and efficiently obtain these 3D measurements, automatic segmentation of the ventricles can be explored. However, this segmentation is challenging due to the large ventricular anatomical shape variability in preterm infants diagnosed with PHH. This study aims to (a) propose a Bayesian U-Net method using 3D spatial concrete dropout for automatic brain segmentation (with uncertainty assessment) of preterm infants with PHH; and (b) compare the Bayesian method to three reference methods: DenseNet, U-Net, and ensemble learning using DenseNets and U-Nets. A total of 41 T -weighted MRIs from 27 preterm infants were manually segmented into lateral ventricles, external CSF, white and cortical gray matter, brainstem, and cerebellum. These segmentations were used as ground truth for model evaluation. All methods were trained and evaluated using 4-fold cross-validation and segmentation endpoints, with additional uncertainty endpoints for the Bayesian method. In the lateral ventricles, segmentation endpoint values for the DenseNet, U-Net, ensemble learning, and Bayesian U-Net methods were mean Dice score = 0.814 ± 0.213, 0.944 ± 0.041, 0.942 ± 0.042, and 0.948 ± 0.034 respectively. Uncertainty endpoint values for the Bayesian U-Net were mean recall = 0.953 ± 0.037, mean negative predictive value = 0.998 ± 0.005, mean accuracy = 0.906 ± 0.032, and mean AUC = 0.949 ± 0.031. To conclude, the Bayesian U-Net showed the best segmentation results across all methods and provided accurate uncertainty maps. This method may be used in clinical practice for automatic brain segmentation of preterm infants with PHH, and lead to better PHH monitoring and more informed treatment decisions

Functional brain network properties of healthy full-term newborns quantified by scalp and source-reconstructed EEG

OBJECTIVE: Identifying the functional brain network properties of term low-risk newborns using high-density EEG (HD-EEG) and comparing these properties with those of established functional magnetic resonance image (fMRI) - based networks. METHODS: HD-EEG was collected from 113 low-risk term newborns before delivery hospital discharge and within 72 hours of birth. Functional brain networks were reconstructed using coherence at the scalp and source levels in delta, theta, alpha, beta, and gamma frequency bands. These networks were characterized for the global and local network architecture. RESULTS: Source-level networks in all the frequency bands identified the presence of the efficient small world (small-world propensity (SWP) \u3e 0.6) architecture with four distinct modules linked by hub regions and rich-club (coefficient \u3e 1) topology. The modular regions included primary, association, limbic, paralimbic, and subcortical regions, which have been demonstrated in fMRI studies. In contrast, scalp-level networks did not display consistent small world architecture (SWP \u3c 0.6), and also identified only 2-3 modules in each frequency band.The modular regions of the scalp-network primarily included frontal and occipital regions. CONCLUSIONS: Our findings show that EEG sources in low-risk newborns corroborate fMRI-based connectivity results. SIGNIFICANCE: EEG source analysis characterizes functional connectivity at the bedside of low-risk newborn infants soon after birth

Electroencephalogram in low-risk term newborns predicts neurodevelopmental metrics at age two years

OBJECTIVE: To determine whether neurodevelopmental biomarkers at 2 years of age are already present in the newborns\u27 EEG at birth. METHODS: Low-risk term newborns were enrolled and studied utilizing EEG prior to discharge from the birth hospital. A 14-channel EEG montage (scalp-level) and source signals were calculated using the EEG. Their spectral power was calculated for each of the five frequency bands. Cognitive, language and motor skills were assessed using the Bayley Scales of Infant Development-III at age 2 years. The relationship between the spectral power in each frequency band and neurodevelopmental scores were quantified using the Spearman\u27s r. The role of gender, gestational age (GA) and delivery mode, if found significant (P \u3c 0.05), were controlled by analyzing partial correlation. RESULTS: We studied 47 newborns and found a significant association between gender, and delivery mode with EEG power. Scalp- and source-level spectral powers were positively associated with cognitive and language scores. At the source level, significant associations were identified in the parietal and occipital regions. CONCLUSIONS: Electrophysiological biomarkers of neurodevelopment at age 2 years are already present at birth in low-risk term infants. SIGNIFICANCE: Low-risk newborns\u27 EEG utility as a screening tool to optimize neurodevelopmental outcome warrants further evaluation