Search

Abstract Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.

Background: Maternal inflammation can result from immune dysregulation and metabolic perturbations during pregnancy. Whether conditions associated with inflammation during pregnancy increase the likelihood of autism spectrum disorder (ASD) or other neurodevelopmental disorders (DDs) is not well understood. Methods: We conducted a case-control study among children born in California from 2011 to 2016 to investigate maternal immune-mediated and cardiometabolic conditions during pregnancy and risk of ASD (n = 311) and DDs (n = 1291) compared with children from the general population (n = 967). Data on maternal conditions and covariates were retrieved from electronic health records. Maternal genetic data were used to assess a causal relationship. Results: Using multivariable logistic regression, we found that mothers with asthma were more likely to deliver infants later diagnosed with ASD (odds ratio [OR] = 1.62, 95% CI: 1.15–2.29) or DDs (OR = 1.30, 95% CI: 1.02–1.64). Maternal obesity was also associated with child ASD (OR = 1.51, 95% CI: 1.07–2.13). Mothers with both asthma and extreme obesity had the greatest odds of delivering an infant later diagnosed with ASD (OR = 16.9, 95% CI: 5.13–55.71). These increased ASD odds were observed among female children only. Polygenic risk scores for obesity, asthma, and their combination showed no association with ASD risk. Mendelian randomization did not support a causal relationship between maternal conditions and ASD. Conclusions: Inflammatory conditions during pregnancy are associated with risk for neurodevelopmental disorders in children. These risks do not seem to be due to shared genetic risk; rather, inflammatory conditions may share nongenetic risk factors with neurodevelopmental disorders. Children whose mothers have both asthma and obesity during pregnancy may benefit from earlier screening and intervention.

This article describes the methods used to build a large-scale database of more than 250,000 electronic fetal monitoring (EFM) records linked to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome. The database can be used to investigate how birth outcome is related to clinical and EFM features. The main steps involved in building the database were: (1) Acquiring the raw EFM recording and clinical records for each birth. (2) Assigning each birth to an objectively defined outcome class that included normal, acidosis, and hypoxic-ischemic encephalopathy. (3) Removing all personal health information from the EFM recordings and clinical records. (4) Preprocessing the deidentified EFM records to eliminate duplicates, reformat the signals, combine signals from different sensors, and bridge gaps to generate signals in a format that can be readily analyzed. (5) Post-processing the repaired EFM recordings to extract key features of the fetal heart rate, uterine activity, and their relations. (6) Populating a database that links the clinical information, EFM records, and EFM features to support easy querying and retrieval. • A multi-step process is required to build a comprehensive database linking electronic temporal fetal monitoring signals to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome. • The current database documents more than 250,000 births including almost 4,000 acidosis and 400 HIE cases. This represents more than 80% of the births that occurred in 15 Northern California Kaiser Permanente Hospitals between 2011–2019. This is a valuable resource for studying the factors predictive of outcome. • The signal processing code and schemas for the database are freely available. The database will not be permitted to leave Kaiser firewalls, but a process is in place to allow interested investigators to access it.

Introduction MRI and MR spectroscopy (MRS) provide early biomarkers of brain injury and treatment response in neonates with hypoxic-ischaemic encephalopathy). Still, there are challenges to incorporating neuroimaging biomarkers into multisite randomised controlled trials. In this paper, we provide the rationale for incorporating MRI and MRS biomarkers into the multisite, phase III high-dose erythropoietin for asphyxia and encephalopathy (HEAL) Trial, the MRI/S protocol and describe the strategies used for harmonisation across multiple MRI platforms.Methods and analysis Neonates with moderate or severe encephalopathy enrolled in the multisite HEAL trial undergo MRI and MRS between 96 and 144 hours of age using standardised neuroimaging protocols. MRI and MRS data are processed centrally and used to determine a brain injury score and quantitative measures of lactate and n-acetylaspartate. Harmonisation is achieved through standardisation—thereby reducing intrasite and intersite variance, real-time quality assurance monitoring and phantom scans.Ethics and dissemination IRB approval was obtained at each participating site and written consent obtained from parents prior to participation in HEAL. Additional oversight is provided by an National Institutes of Health-appointed data safety monitoring board and medical monitor.Trial registration number NCT02811263; Pre-result.

Neurodevelopmental outcomes among infants with total serum bilirubin levels of 25 mg per deciliter or more with those among randomly selected controls are compared. When treated with phototherapy or exchange transfusion, total serum bilirubin levels in the range included in the study were not associated with adverse neurodevelopmental outcomes in infants born at or near term.