Deep robust residual network for super-resolution of 2D fetal brain MRI

Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR) ones to meet clinical and scientific requirements. To increase the quality of brain MRI, we study a robust residual-learning SR network (RRLSRN) to generate a sharp HR brain image from an LR input. Due to the Charbonnier loss can handle outliers well, and Gradient Difference Loss (GDL) can sharpen an image, we combined the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results. Two MRI datasets of adult brain, Kirby 21 and NAMIC, were used to train and verify the effectiveness of our model. To further verify the generalizability and robustness of the proposed model, we collected eight clinical fetal brain MRI 2D data for evaluation. The experimental results have shown that the proposed deep residual-learning network achieved superior performance and high efficiency over other compared methods.

Severe fetal symptomatic infection from human cytomegalovirus following non-primary maternal infection: report of two cases

Introduction Human cytomegalovirus (HCMV) is the most common congenital infection, expecially severe after a maternal primary infection; sequelae in neonates born to mothers experiencing a non-primary infection have been already reported. Hereby, two cases of severe fetal HCMV disease in seroimmune gravidas referred to our Unit are described. Cases presentation Case 1 A fetus at 21 weeks’ gestation with signs of anemia and brain abnormalities at ultrasound (US), described at magnetic resonance (MR) imaging as ependymal irregularity and bilateral asymmetric parenchimal thinning; amniotic fluid sample was positive for HCMV although the woman had a previous immunity; after termination of pregnancy, autopsy demonstrated a thicken layer of disorganized neurons on the right cortical plate, while on the left there was a morphological pattern coherent with polymicrogyria. Case 2 A fetus at 20 weeks’ gestation with anemia, moderate atrio-ventricular insufficiency, hepatosplenomegaly but no major cerebral lesions. Fetal blood was positive for HCMV, although unexpected for pre-pregnancy maternal immunity, and intrauterine transfusion was needed. A cesarean section at 34 weeks gestation was performed due to worsening condition of the fetus, who had a birthweight of 2210 grams, needed platelet transfusions but MR examination and clinical evaluation were normal. Conclusion The impact of non-primary maternal infection on pregnancy outcome is unknown and fetal brain damage in HCMV seroimmune transmitter-mothers can occur as a consequence of maternal re-infection or reactivation for a hypotetic different role of HCMV-primed CD4+ or CD8+ T-cells in fetal brain, with progressive brain lesions coexistent in the first case and with severe unexpected anemia in the second case. A previous maternal HCMV immunity should not exempt to test anemic fetuses for such infection, nor to consider a potential transplacental transmission.

Constructing local cell-specific networks from single-cell data

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

Through-plane super-resolution with autoencoders in diffusion magnetic resonance imaging of the developing human brain

ABSTRACTFetal brain diffusion magnetic resonance images are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network to enhance the through-plane resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and on the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize to fetal data with different levels of motion and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.

Single cell profiling of Hofbauer cells and fetal brain microglia reveals shared programs and functions

SummaryMaternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many of which are mediated by in utero microglial programming. Microglia remain inaccessible at birth and throughout development, thus identification of noninvasive biomarkers that can reflect fetal brain microglial programming may permit screening and intervention during critical developmental windows. Here we used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells). Single-cell RNA sequencing of murine fetal brain and placental macrophages demonstrated shared transcriptional programs. Comparison with human datasets demonstrated that placental resident macrophage signatures are highly conserved between mice and humans. Single-cell RNA-seq identified sex differences in fetal microglial and Hofbauer cell programs, and robust differences between placenta-associated maternal macrophage/monocyte (PAMM) populations in the context of a male versus a female fetus. We propose that Hofbauer cells, which are easily accessible at birth, provide novel insights into fetal brain microglial programs, potentially facilitating the early identification of offspring most vulnerable to neurodevelopmental disorders.