Learning to segment fetal brain tissue from noisy annotations

Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19-39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI

Overestimates of Survival after HAART: Implications for Global Scale-Up Efforts

Background: Monitoring the effectiveness of global antiretroviral therapy scale-up efforts in resource-limited settings is a global health priority, but is complicated by high rates of losses to follow-up after treatment initiation. Determining definitive outcomes of these lost patients, and the effects of losses to follow-up on estimates of survival and risk factors for death after HAART, are key to monitoring the effectiveness of global HAART scale-up efforts. Methodology/Principal Findings: A cohort study comparing clinical outcomes and risk factors for death after HAART initiation as reported before and after tracing of patients lost to follow-up was conducted in Botswana's National Antiretroviral Therapy Program. 410 HIV-infected adults consecutively presenting for HAART were evaluated. The main outcome measures were death or loss to follow-up within the first year after HAART initiation. Of 68 patients initially categorized as lost, over half (58.8%) were confirmed dead after tracing. Patient tracing resulted in reporting of significantly lower survival rates when death was used as the outcome and losses to follow-up were censored [1-year Kaplan Meier survival estimate 0.92 (95% confidence interval, 0.88–0.94 before tracing and 0.83 (95% confidence interval, 0.79–0.86) after tracing, log rank P<0.001]. In addition, a significantly increased risk of death after HAART among men [adjusted hazard ratio 1.74 (95% confidence interval, 1.05–2.87)] would have been missed had patients not been traced [adjusted hazard ratio 1.41 (95% confidence interval, 0.65–3.05)]. Conclusions/Significance: Due to high rates of death among patients lost to follow-up after HAART, survival rates may be inaccurate and important risk factors for death may be missed if patients are not actively traced. Patient tracing and uniform reporting of outcomes after HAART are needed to enable accurate monitoring of global HAART scale-up efforts

Quantification of sulcal emergence timing and its variability in early fetal life: Hemispheric asymmetry and sex difference

Human fetal brains show regionally different temporal patterns of sulcal emergence following a regular timeline, which may be associated with spatiotemporal patterns of gene expression among cortical regions. This study aims to quantify the timing of sulcal emergence and its temporal variability across typically developing fetuses by fitting a logistic curve to presence or absence of sulcus. We found that the sulcal emergence started from the central to the temporo-parieto-occipital lobes and frontal lobe, and the temporal variability of emergence in most of the sulci was similar between 1 and 2 weeks. Small variability (\u3c 1 week) was found in the left central and postcentral sulci and larger variability (\u3e2 weeks) was shown in the bilateral occipitotemporal and left superior temporal sulci. The temporal variability showed a positive correlation with the emergence timing that may be associated with differential contributions between genetic and environmental factors. Our statistical analysis revealed that the right superior temporal sulcus emerged earlier than the left. Female fetuses showed a trend of earlier sulcal emergence in the right superior temporal sulcus, lower temporal variability in the right intraparietal sulcus, and higher variability in the right precentral sulcus compared to male fetuses. Our quantitative and statistical approach quantified the temporal patterns of sulcal emergence in detail that can be a reference for assessing the normality of developing fetal gyrification

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth

Fetal cortical plate segmentation using fully convolutional networks with multiple plane aggregation

Fetal magnetic resonance imaging (MRI) has the potential to advance our understanding of human brain development by providing quantitative information of cortical plate (CP) developmen

Maternal social risk, gestational age at delivery, and cognitive outcomes among adolescents born extremely preterm

Background: Children born extremely preterm (EP) are at increased risk of cognitive deficits that persist into adulthood. Few large cohort studies have examined differential impairment of cognitive function in EP-born adolescents in relation to early life risk factors, including maternal social disadvantage, gestational age at delivery, and neonatal morbidities prevalent among EP neonates. Objectives: To assess cognitive abilities in relation to early life risk factors in an EP-born cohort at 15 years of age. Methods: 681 of 1198 surviving participants (57%) enrolled from 2002 to 2004 in the Extremely Low Gestational Age Newborn Study returned at age 15 years for an assessment of cognitive abilities with the Wechsler Abbreviated Scale of Intelligence-II and the NIH Toolbox Cognition Battery (NTCB) verbal cognition and fluid processing composites, the latter of which measured executive functions and processing speed. Three cognitive outcomes, WASI-II IQ, NTCB verbal cognition, and NTCB fluid processing, were analyzed for associations with maternal social disadvantage and gestational age. Mediation of maternal social disadvantage by gestational age and mediation of gestational age by neonatal morbidities were also examined. Results: Test scores were lower for NTCB fluid processing relative to IQ and NTCB verbal abilities. Social disadvantage and gestational age were associated with all three cognitive outcomes. Mediation analyses indicated partial mediation of gestational age associations with all three outcomes by neonatal morbidities but did not support mediation by gestational age of social risk associations with cognitive outcomes. Conclusions: Greater maternal social disadvantage and lower gestational age are associated with less favorable cognitive outcomes among EP-born adolescents at 15 years of age. Neonatal morbidities partially mediate associations between lower gestational age and cognitive outcomes. These findings highlight the need for improved medical and remedial interventions to mitigate risk of poor cognitive outcomes among EP-born adolescents

Optimal method for fetal brain age prediction using multiplanar slices from structural magnetic resonance imaging

The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979

Abnormal prenatal brain development in Chiari II malformation

IntroductionThe Chiari II is a relatively common birth defect that is associated with open spinal abnormalities and is characterized by caudal migration of the posterior fossa contents through the foramen magnum. The pathophysiology of Chiari II is not entirely known, and the neurobiological substrate beyond posterior fossa findings remains unexplored. We aimed to identify brain regions altered in Chiari II fetuses between 17 and 26 GW.MethodsWe used in vivo structural T2-weighted MRIs of 31 fetuses (6 controls and 25 cases with Chiari II).ResultsThe results of our study indicated altered development of diencephalon and proliferative zones (ventricular and subventricular zones) in fetuses with a Chiari II malformation compared to controls. Specifically, fetuses with Chiari II showed significantly smaller volumes of the diencephalon and significantly larger volumes of lateral ventricles and proliferative zones.DiscussionWe conclude that regional brain development should be taken into consideration when evaluating prenatal brain development in fetuses with Chiari II

Risk factors for health impairments in children after hospitalization for acute COVID-19 or MIS-C

ObjectiveTo identify risk factors for persistent impairments after pediatric hospitalization for acute coronavirus disease 2019 (COVID-19) or multisystem inflammatory syndrome in children (MIS-C) during the SARS-CoV-2 pandemic.MethodsAcross 25 U.S. Overcoming COVID-19 Network hospitals, we conducted a prospective cohort study of patients <21-years-old hospitalized for acute COVID-19 or MIS-C (May 2020 to March 2022) surveyed 2- to 4-months post-admission. Multivariable regression was used to calculate adjusted risk ratios (aRR) and 95% confidence intervals (CI).ResultsOf 232 children with acute COVID-19, 71 (30.6%) had persistent symptoms and 50 (21.6%) had activity impairments at follow-up; for MIS-C (n = 241), 56 (23.2%) had persistent symptoms and 58 (24.1%) had activity impairments. In adjusted analyses of patients with acute COVID-19, receipt of mechanical ventilation was associated with persistent symptoms [aRR 1.83 (95% CI: 1.07, 3.13)] whereas obesity [aRR 2.18 (95% CI: 1.05, 4.51)] and greater organ system involvement [aRR 1.35 (95% CI: 1.13, 1.61)] were associated with activity impairment. For patients with MIS-C, having a pre-existing respiratory condition was associated with persistent symptoms [aRR 3.04 (95% CI: 1.70, 5.41)] whereas obesity [aRR 1.86 (95% CI: 1.09, 3.15)] and greater organ system involvement [aRR 1.26 (1.00, 1.58)] were associated with activity impairments.DiscussionAmong patients hospitalized, nearly one in three hospitalized with acute COVID-19 and one in four hospitalized with MIS-C had persistent impairments for ≥2 months post-hospitalization. Persistent impairments were associated with more severe illness and underlying health conditions, identifying populations to target for follow-up

Fetal Brain Development in Congenital Heart Disease

Neurodevelopmental impairments are the most common extracardiac morbidities among patients with complex congenital heart disease (CHD) across the lifespan. Robust clinical research in this area has revealed several cardiac, medical, and social factors that can contribute to neurodevelopmental outcome in the context of CHD. Studies using brain magnetic resonance imaging (MRI) have been instrumental in identifying quantitative and qualitative difference in brain structure and maturation in this patient population. Full-term newborns with complex CHD are known to have abnormal microstructural and metabolic brain development with patterns similar to those seen in premature infants at approximately 34 to 36 weeks' gestation. With the advent of fetal brain MRI, these brain abnormalities are now documented as they begin in utero, as early as the third trimester. Importantly, disturbed brain development in utero is now known to be independently associated with neurodevelopmental outcome in early childhood, making the prenatal period an important timeframe for potential interventions. Advances in fetal brain MRI provide a robust imaging tool to use in future neuroprotective clinical trials. The causes of abnormal fetal brain development are multifactorial and include cardiovascular physiology, genetic abnormalities, placental impairment, and other environmental and social factors. This review provides an overview of current knowledge of brain development in the context of CHD, common prenatal imaging tools to evaluate the developing fetal brain in CHD, and known risk factors contributing to brain immaturity