Greater Neighborhood Disadvantage Is Associated with Alterations in Fetal Functional Brain Network Structure.

OBJECTIVE: To determine the association between neighborhood disadvantage (ND) and functional brain development of in utero fetuses. STUDY DESIGN: We conducted an observational study using Social Vulnerability Index (SVI) scores to assess the impact of ND on a prospectively recruited sample of healthy pregnant women from Washington, DC. Using 79 functional magnetic resonance imaging scans from 68 healthy pregnancies at a mean gestational age of 33.12 weeks, we characterized the overall functional brain network structure using a graph metric approach. We used linear mixed effects models to assess the relationship between SVI and gestational age on 5 graph metrics, adjusting for multiple scans. RESULTS: Exposure to greater ND was associated with less well integrated functional brain networks, as observed by longer characteristic path lengths and diminished global efficiency (GE), as well as diminished small world propensity (SWP). Across gestational ages, however, the association between SVI and network integration diminished to a negligible relationship in the third trimester. Conversely, SWP was significant across pregnancy, but the relationship changed such that there was a negative association with SWP earlier in the second trimester that inverted around the transition to the third trimester to a positive association. CONCLUSIONS: These data directly connect ND and altered functional brain maturation in fetuses. Our results suggest that, even before birth, proximity to environmental stressors in the wider neighborhood environment are associated with altered brain development.

Toward a more informative representation of the fetal-neonatal brain connectome using variational autoencoder.

Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal-neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the life span. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal-neonatal brain development, including nonlinear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called variational autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting-state data in healthy adults. Here, we demonstrated that nonlinear brain features, that is, latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, nonlinear fetal-neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.

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.

FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net.

An important step in the preprocessing of resting state functional magnetic resonance images (rs-fMRI) is the separation of brain from non-brain voxels. Widely used imaging tools such as FSL's BET2 and AFNI's 3dSkullStrip accomplish this task effectively in children and adults. In fetal functional brain imaging, however, the presence of maternal tissue around the brain coupled with the non-standard position of the fetal head limit the usefulness of these tools. Accurate brain masks are thus generated manually, a time-consuming and tedious process that slows down preprocessing of fetal rs-fMRI. Recently, deep learning-based segmentation models such as convolutional neural networks (CNNs) have been increasingly used for automated segmentation of medical images, including the fetal brain. Here, we propose a computationally efficient end-to-end generative adversarial neural network (GAN) for segmenting the fetal brain. This method, which we call FetalGAN, yielded whole brain masks that closely approximated the manually labeled ground truth. FetalGAN performed better than 3D U-Net model and BET2: FetalGAN, Dice score = 0.973 ± 0.013, precision = 0.977 ± 0.015; 3D U-Net, Dice score = 0.954 ± 0.054, precision = 0.967 ± 0.037; BET2, Dice score = 0.856 ± 0.084, precision = 0.758 ± 0.113. FetalGAN was also faster than 3D U-Net and the manual method (7.35 s vs. 10.25 s vs. ∼5 min/volume). To the best of our knowledge, this is the first successful implementation of 3D CNN with GAN on fetal fMRI brain images and represents a significant advance in fully automating processing of rs-MRI images.

Measuring intrauterine growth in healthy pregnancies using quantitative magnetic resonance imaging.

OBJECTIVE: The aim of this study was to determine in utero fetal-placental growth patterns using in vivo three-dimensional (3D) quantitative magnetic resonance imaging (qMRI). STUDY DESIGN: Healthy women with singleton pregnancies underwent fetal MRI to measure fetal body, placenta, and amniotic space volumes. The fetal-placental ratio (FPR) was derived using 3D fetal body and placental volumes (PV). Descriptive statistics were used to describe the association of each measurement with increasing gestational age (GA) at MRI. RESULTS: Fifty-eight (58) women underwent fetal MRI between 16 and 38 completed weeks gestation (mean = 28.12 ± 6.33). PV and FPR varied linearly with GA at MRI (rPV,GA = 0.83, rFPR,GA = 0.89, p value < 0.001). Fetal volume varied non-linearly with GA (p value < 0.01). CONCLUSIONS: We describe in-utero growth trajectories of fetal-placental volumes in healthy pregnancies using qMRI. Understanding healthy in utero development can establish normative benchmarks where departures from normal may identify early in utero placental failure prior to the onset of fetal harm.

Association of Prenatal Maternal Anxiety With Fetal Regional Brain Connectivity.

Importance: Maternal psychological distress during pregnancy is associated with adverse obstetric outcomes and neuropsychiatric deficits in children. Currently unavailable in vivo interrogation of fetal brain function could provide critical insights into the onset and timing of altered neurodevelopmental trajectories. Objective: To investigate the association between prenatal maternal stress, anxiety, and depression and in vivo fetal brain resting state functional connectivity. Design, Setting, and Participants: This cohort study included pregnant women scanned between January 2016 and April 2019. A total of 50 pregnant women with healthy pregnancies were prospectively recruited from low-risk obstetric clinics in the Washington DC area and were scanned at Children's National in Washington DC. Exposures: Maternal stress, anxiety, and depression. Main Outcomes and Measures: The association of prenatal maternal stress, anxiety, and depression with whole-brain connectivity was analyzed using multivariate distance matrix regression. Prenatal maternal stress, anxiety, and depression were assessed using the Perceived Stress Scale, Spielberger State Anxiety Inventory and Spielberger Trait Anxiety Inventory, and the Edinburgh Postnatal Depression Scale, respectively. Whole-brain connectivity was measured from 100 functionally defined regions of interest. Results: This study analyzed 59 resting-state functional connectivity magnetic resonance image data sets from the fetuses (mean [SD] gestational age, 33.52 [4 weeks]) of 50 healthy pregnant women (mean [SD] age, 33.77 [5.51]). Mean (SD) scores for the questionnaires were as follows: Spielberger State Anxiety Inventory, 26.66 (6.72) (range, 20-48); Spielberger Trait Anxiety Inventory, 28.09 (6.62) (range, 20-50); Perceived Stress Scale, 9.27 (5.13) (range, 1-25); and Edinburgh Postnatal Depression Scale 3.24 (2.84) (range, 0-14). Prenatal maternal anxiety scores measured using the Spielberger Trait and State Anxiety Inventories were associated with differences in fetal connectivity (Spielberger State Anxiety Inventory: pseudo-R2 = 0.019, P = .04; Spielberger Trait Anxiety Inventory: pseudo-R2 = 0.021, P = .007). Interhemispheric connections, such as those involving the parietofrontal and occipital association cortices, were associated with reduced maternal prenatal anxiety, and those between the brainstem and sensorimotor areas were associated with higher anxiety scores. Conclusions and Relevance: In this cohort study, an association was found between prenatal maternal anxiety and disturbances in fetal brain functional connectivity, suggesting altered fetal programming. Early onset of functional deviations suggests the need for more widespread screening of pregnant women for symptoms of anxiety.