Fetal brain development in small-for-gestational age (SGA) fetuses and normal controls

Prevalence of autism spectrum disorder diagnosis by birth weight, gestational age, and size for gestational age: a systematic review, meta-analysis, and meta-regression

We aimed to comprehensively pool the prevalence of autism spectrum disorder (ASD) diagnosis by birth weight, gestational age, and size for gestational age. PubMed, EMBASE, Web of Science, Ovid PsycINFO, and Cochrane Library were searched up to December 22, 2021. We pooled data using the random-effects model and quantified heterogeneity using the I2 statistic. Of 66 643 records initially identified, 75 studies were included in the meta-analysis. The pooled prevalence estimates of ASD diagnosis are as follows: very-low-birth weight, 3.1% (912 ASD/66,445 individuals); low-birth weight, 2.3% (5672 ASD/593,927 individuals); normal-birth weight, 0.5% (17,361 ASD/2,378,933 individuals); high-birth weight, 0.6% (4505 ASD/430,699 individuals); very preterm, 2.8% (2113 ASD/128,513 individuals); preterm, 2.1% (19 672 ASD/1 725 244 individuals); term, 0.6% (113,261 ASD/15,297,259 individuals); postterm, 0.6% (9419 ASD/1,138,215 individuals); small-for-gestational-age, 1.9% (6314 ASD/796,550 individuals); appropriate-for-gestational-age, 0.7% (21,026 ASD/5,936,704 individuals); and large-for-gestational-age, 0.6% (2607 ASD/635,666 individuals). Compared with the reference prevalence (those in normal-birth weight, term, and appropriate-for-gestational-age individuals), the prevalence estimates of ASD diagnosis in very-low-birth weight, low-birth weight, very preterm, preterm, and small-for-gestational-age individuals increased significantly, while those in high-birth weight, postterm, and large-for-gestational-age individuals did not change significantly. There were geographical differences in the prevalence estimates. This meta-analysis provided reliable estimates of the prevalence of ASD diagnosis by birth weight, gestational age, and size for gestational age, and suggested that low-birth weight (especially very-low-birth weight), preterm (especially very preterm), and small-for-gestational-age births, rather than high-birth weight, postterm, and large-for-gestational-age births, were associated with increased risk of ASD diagnosis. However, in view of marked between-study heterogeneity in most conditions, unknown effects of certain important confounders associated with ASD due to limited information in original articles, and included studies from a relatively small number of countries, the findings of this study should be interpreted with caution.

Subcortical Segmentation of the Fetal Brain in 3D Ultrasound using Deep Learning

The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and challenging due to low soft tissue contrast, speckle and shadowing artifacts. For this reason, we developed a convolutional neural network (CNN) for the automated segmentation of the choroid plexus (CP), lateral posterior ventricle horns (LPVH), cavum septum pellucidum et vergae (CSPV), and cerebellum (CB) from 3D ultrasound. As ground-truth labels are scarce and expensive to obtain, we applied few-shot learning, in which only a small number of manual annotations (n = 9) are used to train a CNN. We compared training a CNN with only a few individually annotated volumes versus many weakly labelled volumes obtained from atlas-based segmentations. This showed that segmentation performance close to intra-observer variability can be obtained with only a handful of manual annotations. Finally, the trained models were applied to a large number (n = 278) of ultrasound image volumes of a diverse, healthy population, obtaining novel US-specific growth curves of the respective structures during the second trimester of gestation.

Prenatal Ultrasound Diagnosis of Biometric changes in the Brain of Growth Restricted Fetuses. A Systematic Review of Literature

Fetal growth restriction (FGR) occurs when the fetus does not reach its intrauterine potential for growth and development as a result of compromise in placental function. It is a condition that affects 5 to 10% of pregnancies and is the second most common cause of perinatal morbidity and mortality. Children born with FGR are at risk of impaired neurological and cognitive development and cardiovascular or endocrine diseases in adulthood. The purpose of the present revision is to perform a literature search for evidence on the detection and assessment by ultrasound of brain injury linked to FGR during fetal life. Using a systematic approach and quantitative evaluation as study methodology, we reviewed ultrasound studies of the fetal brain structure of growth-restricted fetuses with objective quality measures. A total of eight studies were identified. High quality studies were identified for measurement of brain volumes; corpus callosum; brain fissure depth measurements, and cavum septi pellucidi width measurement. A low-quality study was available for transverse cerebellar diameter measurement in FGR. Further prospective randomized studies are needed to understand the changes that occur in the brain of fetuses with restricted growth, as well as their correlation with the changes in cognitive development observed.

Diagnostic Value of Diffusion Tensor Imaging for Infants' Brain Development Retardation Caused by Pre-Eclampsia

Objective

Pre-eclampsia (PE) can cause brain development delay in infants. This work aims to characterize the pattern differences of brain white matter development in premature infants under PE conditions and those without.

Methods

Eighty preterm infants delivered by women with PE were selected as the PE group, and ninety-six preterm infants of the same period born to women without high-risk perinatal factors were used as control. All infants underwent diffusion tensor imaging (DTI) examination. The fractional anisotropy (FA) was measured in five regions of interests (ROIs), including posterior limbs of internal capsule (PLIC), splenium of the corpus callosum (SCC), superior frontal gyrus (SFG), superior parietal lobule (SPL), and superior occipital gyrus (SOG). The relationship between the FA values and postmenstrual age (PMA) was analyzed.

Results

After adjusting for the birth weight and gestational ages, in the SCC and PLIC, the PMA and FA values showed a low-to-medium intensity positive correlation in the control group (r = 0.30, p=0.003; r = 0.53, p < 0.0001), while no positive relevance was detected in the PE group (r = 0.08, p=0.47; r = 0.19, p < 0.08). In the PE and control groups, in the SPL and SOG, the PMA and FA values showed a near-consistent positive correlation (r = 0.57, r = 0.55 vs. r = 0.31, r = 0.55; all p < 0.05). In the control group, in SFG, the PMA and FA values had a medium intensity positive correlation (r = 0.47, p < 0.0001), but there was no statistical difference in correlation in PE (r = 0.10, p=0.39).

Conclusion

PE may cause lagging brain development in the SCC, PLIC, and SFG during infancy. DTI may be an effective and sensitive detection tool.