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

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

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

Partnering With Stakeholders to Inform the Co-Design of a Psychosocial Intervention for Prenatally Diagnosed Congenital Heart Disease

Input from diverse stakeholders is critical to the process of designing healthcare interventions. This study applied a novel mixed-methods, stakeholder-engaged approach to co-design a psychosocial intervention for mothers expecting a baby with congenital heart disease (CHD) and their partners to promote family wellbeing. The research team included parents and clinicians from 8 health systems. Participants were 41 diverse parents of children with prenatally diagnosed CHD across the 8 health systems. Qualitative data were collected through online crowdsourcing and quantitative data were collected through electronic surveys to inform intervention co-design. Phases of intervention co-design were: (I) Engage stakeholders in selection of intervention goals/outcomes; (II) Engage stakeholders in selection of intervention elements; (III) Obtain stakeholder input to increase intervention uptake/utility; (IV) Obtain stakeholder input on aspects of intervention design; and (V) Obtain stakeholder input on selection of outcome measures. Parent participants anticipated the resulting intervention, HEARTPrep, would be acceptable, useful, and feasible for parents expecting a baby with CHD. This model of intervention co-design could be used for the development of healthcare interventions across chronic diseases

Factors Associated With Attendance for Cardiac Neurodevelopmental Evaluation

BACKGROUND AND OBJECTIVES: Neurodevelopmental evaluation of toddlers with complex congenital heart disease is recommended but reported frequency is low. Data on barriers to attending neurodevelopmental follow-up are limited. This study aims to estimate the attendance rate for a toddler neurodevelopmental evaluation in a contemporary multicenter cohort and to assess patient and center level factors associated with attending this evaluation. METHODS: This is a retrospective cohort study of children born between September 2017 and September 2018 who underwent cardiopulmonary bypass in their first year of life at a center contributing data to the Cardiac Neurodevelopmental Outcome Collaborative and Pediatric Cardiac Critical Care Consortium clinical registries. The primary outcome was attendance for a neurodevelopmental evaluation between 11 and 30 months of age. Sociodemographic and medical characteristics and center factors specific to neurodevelopmental program design were considered as predictors for attendance. RESULTS: Among 2385 patients eligible from 16 cardiac centers, the attendance rate was 29.0% (692 of 2385), with a range of 7.8% to 54.3% across individual centers. In multivariable logistic regression models, hospital-initiated (versus family-initiated) scheduling for neurodevelopmental evaluation had the largest odds ratio in predicting attendance (odds ratio = 4.24, 95% confidence interval, 2.74-6.55). Other predictors of attendance included antenatal diagnosis, absence of Trisomy 21, higher Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery mortality category, longer postoperative length of stay, private insurance, and residing a shorter distance from the hospital. CONCLUSIONS: Attendance rates reflect some improvement but remain low. Changes to program infrastructure and design and minimizing barriers affecting access to care are essential components for improving neurodevelopmental care and outcomes for children with congenital heart disease

Dynamic patterns of cortical expansion during folding of the preterm human brain

During the third trimester of human brain development, the cerebral cortex undergoes dramatic surface expansion and folding. Physical models suggest that relatively rapid growth of the cortical gray matter helps drive this folding, and structural data suggest that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a unique method to estimate local growth from sequential cortical reconstructions. Using anatomically constrained multimodal surface matching (aMSM), we obtain accurate, physically guided point correspondence between younger and older cortical reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned two to four times during the period of rapid cortical expansion (28-38 wk postmenstrual age), we observe significant regional differences in growth across the cortical surface that are consistent with the emergence of new folds. Furthermore, these growth patterns shift over the course of development, with noninjured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully automated surface analysis, based on tools freely available to the brain-mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables

Prenatal to postnatal trajectory of brain growth in complex congenital heart disease

Altered brain development is a common feature of the neurological sequelae of complex congenital heart disease (CHD). These alterations include abnormalities in brain size and growth that begin prenatally and persist postnatally. However, the longitudinal trajectory of changes in brain volume from the prenatal to postnatal environment have not been investigated. We aimed to evaluate the trajectory of brain growth in a cohort of patients with complex CHD (n = 16) and healthy controls (n = 15) to test the hypothesis that patients with complex CHD would have smaller total brain volume (TBV) prenatally, which would become increasingly prominent by three months of age. Participants underwent fetal magnetic resonance imaging (MRI) at a mean of 32 weeks gestation, a preoperative/neonatal MRI shortly after birth, a postoperative MRI (CHD only), and a 3-month MRI to evaluate the trajectory of brain growth. Three-dimensional volumetric analysis was applied to the MRI data to measure TBV, as well as tissue-specific volumes of the cortical gray matter (CGM), white matter (WM), subcortical (deep nuclear) gray matter (SCGM), cerebellum, and cerebrospinal fluid (CSF). A random coefficients model was used to investigate longitudinal changes in TBV and demonstrated an altered trajectory of brain growth in the CHD population. The estimated slope for TBV from fetal to 3-month MRI was 11.5 cm3 per week for CHD infants compared to 16.7 cm3 per week for controls (p = 0.0002). Brain growth followed a similar trajectory for the CGM (p < 0.0001), SCGM (p = 0.002), and cerebellum (p = 0.005). There was no difference in growth of the WM (p = 0.30) or CSF (p = 0.085). Brain injury was associated with reduced TBV at 3-month MRI (p = 0.02). After removing infants with brain injury from the model, an altered trajectory of brain growth persisted in CHD infants (p = 0.006). These findings extend the existing literature by demonstrating longitudinal impairments in brain development in the CHD population and emphasize the global nature of disrupted brain growth from the prenatal environment through early infancy. Keywords: Brain volume, Magnetic resonance imaging, Congenital heart disease, Feta

Development of the data registry for the Cardiac Neurodevelopmental Outcome Collaborative

Children with congenital heart disease (CHD) can face neurodevelopmental, psychological, and behavioural difficulties beginning in infancy and continuing through adulthood. Despite overall improvements in medical care and a growing focus on neurodevelopmental screening and evaluation in recent years, neurodevelopmental disabilities, delays, and deficits remain a concern. The Cardiac Neurodevelopmental Outcome Collaborative was founded in 2016 with the goal of improving neurodevelopmental outcomes for individuals with CHD and pediatric heart disease. This paper describes the establishment of a centralised clinical data registry to standardize data collection across member institutions of the Cardiac Neurodevelopmental Outcome Collaborative. The goal of this registry is to foster collaboration for large, multi-centre research and quality improvement initiatives that will benefit individuals and families with CHD and improve their quality of life. We describe the components of the registry, initial research projects proposed using data from the registry, and lessons learned in the development of the registry