Can cavum septum pellucidum width and length measurements also be reliable indicators of corpus callosum measurements in normal fetuses?

OBJECTIVE: To examine the association between cavum septum pellucidum (CSP) and corpus callosum (CC) length and width measurements in mid-trimester sonographic screening in normal fetuses. METHODS: This prospective cohort study examined 152 pregnant women who underwent mid-trimester sonographic fetal anomaly screening. CSP and CC lengths and their anterior, middle, and posterior width measurements were examined sonographically. The association between length and width measurements of both structures, gestational week and CSP ratio (length/width) were evaluated. RESULTS: The mean CSP length was 7.96 ± 1.09 mm, and the mean middle width was 3.43 ± 0.82 mm. The mean CC length was 20 ± 3.76 mm, and the mean middle width was 3.43 ± 0.82 mm. There was a positive correlation between CSP and CC lengths (r = 0.691, p < 0.001). There was also a significant correlation between CSP and CC anterior, middle and posterior widths (anterior (r = 0.366, p < 0.001), middle (r = 0.305, p < 0.001), and posterior (r = 0.233, p = 0.004)). All CSP and CC measurements were correlated with gestational age, biparietal diameter (BPD), and head circumference (HC) (p < 0.001, for all). The CSP ratio was not related to CC dimensions (p > 0.05, for all) and also decreased with the increase in BPD and HC dimensions (r = -0.186, p = 0.022, and r = -0.174, p = 0.032; respectively). CONCLUSION: In normal fetuses, the length and width of the CC and CSP structures developed in relation to each other, as well as to the gestational week, BPD, and HC dimensions. In addition, while the CSP ratio was not found to be associated with CC dimensions, it decreased due to the increase in BPD and HC sizes.

Prediction of fetal brain gestational age using multihead attention with Xception.

Accurate gestational age (GA) prediction is crucial for monitoring fetal development and ensuring optimal prenatal care. Traditional methods often face challenges in terms of precision and prediction efficiency. In this context, leveraging modern deep learning (DL) techniques is a promising solution. This paper introduces a novel DL approach for GA prediction using fetal brain images obtained via magnetic resonance imaging (MRI), which combines the strength of the Xception pretrained model with a multihead attention (MHA) mechanism. The proposed model was trained on a diverse dataset comprising 52,900 fetal brain images from 741 patients. The images encompass a GA ranging from 19 to 39 weeks. These pretrained models served as feature extraction components during the training process. The extracted features were subsequently used as the inputs of different configurable MHAs, which produced GA predictions in days. The proposed model achieved promising results with 8 attention heads, 32 dimensionality of the key space and 32 dimensionality of the value space, with an R-squared (R2) value of 96.5 %, a mean absolute error (MAE) of 3.80 days, and a Pearson correlation coefficient (PCC) of 98.50 % for the test set. Additionally, the 5-fold cross-validation results reinforce the model's reliability, with an average R2 of 95.94 %, an MAE of 3.61 days, and a PCC of 98.02 %. The proposed model excels in different anatomical views, notably the axial and sagittal views. A comparative analysis of multiple planes and a single plane highlights the effectiveness of the proposed model against other state-of-the-art (SOTA) models reported in the literature. The proposed model could help clinicians accurately predict GA.

Cellularity Defects Are Not Ubiquitous in the Brains of Fetuses With Down Syndrome.

Down syndrome (DS) is a genetic pathology characterized by various developmental defects. Unlike other clinical problems, intellectual disability is an invariant clinical trait of DS. Impairment of neurogenesis accompanied by brain hypotrophy is a typical neurodevelopmental phenotype of DS, suggesting that a reduction in the number of cells forming the brain may be a key determinant of intellectual disability. Previous evidence showed that fetuses with DS exhibit widespread hypocellularity in brain regions belonging to the temporal lobe memory systems, which may account for the typical explicit memory impairment that characterizes DS. In the current study, we have examined the basal ganglia, the insular cortex (INS), and the cingulate cortex (CCX) of fetuses with DS and age-matched controls (18-22 weeks of gestation), to establish whether cellularity defects involve regions that are not primarily involved in explicit memory. We found that fetuses with DS exhibit a notable hypocellularity in the putamen (-30%) and globus pallidus (-35%). In contrast, no cellularity differences were found in the INS and CCX, indicating that hypocellularity is not ubiquitous in the DS brain. The hypocellularity found in the basal ganglia, which are critically implicated in the control of movement, suggests that such alterations may contribute to the motor abnormalities of DS. The normal cytoarchitecture of the INS and CCX suggests that the alterations exhibited by people with DS in functions in which these regions are involved are not attributable to neuron paucity.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

A motion assessment method for reference stack selection in fetal brain MRI reconstruction based on tensor rank approximation.

Slice-to-volume registration and super-resolution reconstruction are commonly used to generate 3D volumes of the fetal brain from 2D stacks of slices acquired in multiple orientations. A critical initial step in this pipeline is to select one stack with the minimum motion among all input stacks as a reference for registration. An accurate and unbiased motion assessment (MA) is thus crucial for successful selection. Here, we presented an MA method that determines the minimum motion stack based on 3D low-rank approximation using CANDECOMP/PARAFAC (CP) decomposition. Compared to the current 2D singular value decomposition (SVD) based method that requires flattening stacks into matrices to obtain ranks, in which the spatial information is lost, the CP-based method can factorize 3D stack into low-rank and sparse components in a computationally efficient manner. The difference between the original stack and its low-rank approximation was proposed as the motion indicator. Experiments on linearly and randomly simulated motion illustrated that CP demonstrated higher sensitivity in detecting small motion with a lower baseline bias, and achieved a higher assessment accuracy of 95.45% in identifying the minimum motion stack, compared to the SVD-based method with 58.18%. CP also showed superior motion assessment capabilities in real-data evaluations. Additionally, combining CP with the existing SRR-SVR pipeline significantly improved 3D volume reconstruction. The results indicated that our proposed CP showed superior performance compared to SVD-based methods with higher sensitivity to motion, assessment accuracy, and lower baseline bias, and can be used as a prior step to improve fetal brain reconstruction.

A bimodal taxonomy of adult human brain sulcal morphology related to timing of fetal sulcation and trans-sulcal gene expression gradients.

We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (n = 34,725; 45-82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bimodal distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex, and complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (n = 228; 21-36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.

Dynamic stress- and inflammatory-based regulation of psychiatric risk loci in human neurons.

The prenatal environment can alter neurodevelopmental and clinical trajectories, markedly increasing risk for psychiatric disorders in childhood and adolescence. To understand if and how fetal exposures to stress and inflammation exacerbate manifestation of genetic risk for complex brain disorders, we report a large-scale context-dependent massively parallel reporter assay (MPRA) in human neurons designed to catalogue genotype x environment (GxE) interactions. Across 240 genome-wide association study (GWAS) loci linked to ten brain traits/disorders, the impact of hydrocortisone, interleukin 6, and interferon alpha on transcriptional activity is empirically evaluated in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons. Of ~3,500 candidate regulatory risk elements (CREs), 11% of variants are active at baseline, whereas cue-specific CRE regulatory activity range from a high of 23% (hydrocortisone) to a low of 6% (IL-6). Cue-specific regulatory activity is driven, at least in part, by differences in transcription factor binding activity, the gene targets of which show unique enrichments for brain disorders as well as co-morbid metabolic and immune syndromes. The dynamic nature of genetic regulation informs the influence of environmental factors, reveals a mechanism underlying pleiotropy and variable penetrance, and identifies specific risk variants that confer greater disorder susceptibility after exposure to stress or inflammation. Understanding neurodevelopmental GxE interactions will inform mental health trajectories and uncover novel targets for therapeutic intervention.

Correlating maternal and cord-blood inflammatory markers and BDNF with human fetal brain activity recorded by magnetoencephalography: An exploratory study.

Background: During gestation, the brain development of the fetus is affected by many biological markers, where inflammatory processes and neurotrophic factors have been of particular interest in the past decade. Aim: This exploratory study is the first attempt to explore the relationships between biomarker levels in maternal and cord-blood samples and human fetal brain activity measured with non-invasive fetal magnetoencephalography (fMEG). Method: Twenty-three women were enrolled in this study for collection of maternal serum and fMEG tracings immediately prior to their scheduled cesarean delivery. Twelve of these women had a preexisting diabetic condition. At the time of delivery, umbilical cord blood was also collected. Biomarker levels from both maternal and cord blood were measured and subsequently analyzed for correlations with fetal brain activity in four frequency bands extracted from fMEG power spectral densities. Results: Relative power in the delta, alpha, and beta frequency bands exhibited moderate-sized correlations with maternal BDNF and cord-blood CRP levels before and after adjusting for confounding diabetic status. These correlations were negative for the delta band, and positive for the alpha and beta bands. Maternal CRP and cord-blood BDNF and IL-6 exhibited negligible correlations with relative power in all four bands. Diabetes did not appear to be a strong confounding factor affecting the studied biomarkers. Conclusions: Maternal BDNF levels and cord-blood CRP levels appear to have a direct correlation to fetal brain activity. Our findings indicate the potential use of these biomarkers in conjunction with fetal brain electrophysiology to track fetal neurodevelopment.

T2*-Relaxometry MRI to Assess Third Trimester Placental and Fetal Brain Oxygenation and Placental Characteristics in Healthy Fetuses and Fetuses With Congenital Heart Disease.

BACKGROUND: Congenital heart disease (CHD) has been linked to impaired placental and fetal brain development. Assessing the placenta and fetal brain in parallel may help further our understanding of the relationship between development of these organs. HYPOTHESIS: 1) Placental and fetal brain oxygenation are correlated, 2) oxygenation in these organs is reduced in CHD compared to healthy controls, and 3) placental structure is altered in CHD. STUDY TYPE: Retrospective case-control. POPULATION: Fifty-one human fetuses with CHD (32 male; median [IQR] gestational age [GA] = 32.0 [30.9-32.9] weeks) and 30 from uncomplicated pregnancies with normal birth outcomes (18 male; median [IQR] GA = 34.5 [31.9-36.7] weeks). FIELD STRENGTH/SEQUENCE: 1.5 T single-shot multi-echo-gradient-echo echo-planar imaging. ASSESSMENT: Masking was performed using an automated nnUnet model. Mean brain and placental T2* and quantitative measures of placental texture, volume, and morphology were calculated. STATISTICAL TESTS: Spearman's correlation coefficient for determining the association between brain and placental T2*, and between brain and placental characteristics with GA. P-values for comparing brain T2*, placenta T2*, and placental characteristics between groups derived from ANOVA. Significance level P < 0.05. RESULTS: There was a significant positive association between placental and fetal brain T2* (⍴ = 0.46). Placental and fetal brain T2* showed a significant negative correlation with GA (placental T2* ⍴ = -0.65; fetal brain T2* ⍴ = -0.32). Both placental and fetal brain T2* values were significantly reduced in CHD, after adjusting for GA (placental T2*: control = 97 [±24] msec, CHD = 83 [±23] msec; brain T2*: control = 218 [±26] msec, CHD = 202 [±25] msec). Placental texture and morphology were also significantly altered in CHD (Texture: control = 0.84 [0.83-0.87], CHD = 0.80 [0.78-0.84]; Morphology: control = 9.9 [±2.2], CHD = 10.8 [±2.0]). For all fetuses, there was a significant positive association between placental T2* and placental texture (⍴ = 0.46). CONCLUSION: Placental and fetal brain T2* values are associated in healthy fetuses and those with CHD. Placental and fetal brain oxygenation are reduced in CHD. Placental appearance is significantly altered in CHD and shows associations with placental oxygenation, suggesting altered placental development and function may be related. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Efficient segmentation of fetal brain MRI based on the physical resolution.

BACKGROUND: The image resolution of fetal brain magnetic resonance imaging (MRI) is a critical factor in brain development measures, which is mainly determined by the physical resolution configured in the MRI sequence. However, fetal brain MRI are commonly reconstructed to 3D images with a higher apparent resolution, compared to the original physical resolution. PURPOSE: This work is to demonstrate that accurate segmentation can be achieved based on the MRI physical resolution, and the high apparent resolution segmentation can be achieved by a simple deep learning module. METHODS: This retrospective study included 150 adult and 80 fetal brain MRIs. The adult brain MRIs were acquired at a high physical resolution, which were downsampled to visualize and quantify its impacts on the segmentation accuracy. The physical resolution of fetal images was estimated based on MRI acquisition settings and the images were downsampled accordingly before segmentation and restored using multiple upsampling strategies. Segmentation accuracy of ConvNet models were evaluated on the original and downsampled images. Dice coefficients were calculated, and compared to the original data. RESULTS: When the apparent resolution was higher than the physical resolution, the accuracy of fetal brain segmentation had negligible degradation (accuracy reduced by 0.26%, 1.1%, and 1.8% with downsampling factors of 4/3, 2, and 4 in each dimension, without significant differences from the original data). Using a downsampling factor of 4 in each dimension, the proposed method provided 7× smaller and 10× faster models. CONCLUSION: Efficient and accurate fetal brain segmentation models can be developed based on the physical resolution of MRI acquisitions.

FetMRQC: A robust quality control system for multi-centric fetal brain MRI.

Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts' ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC's predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain.

First-trimester ultrasound of the cerebral lateral ventricles in fetuses with open spina bifida: a retrospective cohort study.

BACKGROUND: Beyond 18 weeks of gestation, an increased size of the fetal lateral ventricles is reported in most fetuses with open spina bifida. In the first trimester of pregnancy, the definition of ventriculomegaly is based on the ratio of the size of the choroid plexus to the size of the ventricular space or the entire fetal head. However, contrary to what is observed from the midtrimester of pregnancy, in most fetuses with open spina bifida at 11 to 13 weeks of gestation, the amount of fluid in the ventricular system seems to be reduced rather than increased. OBJECTIVE: This study aimed to compare the biometry of the lateral ventricles at 11 0/7 to 13 6/7 weeks of gestation between normal fetuses and those with confirmed open spina bifida. STUDY DESIGN: This was a retrospective cohort study that included all cases of isolated open spina bifida detected at 11 0/7 to 13 6/7 weeks of gestation over a period of 5 years and a group of structurally normal fetuses attending at our center over a period of 1 year for the aneuploidy screening as controls. Transventricular axial views of the fetal brain obtained from cases and controls were extracted from the archive for post hoc measurement of cerebral ventricles. The choroid plexus-to-lateral ventricle length ratio, sum of the choroid plexus-to-lateral ventricle area ratio, choroid plexus area-to-fetal head area ratio, and mean choroid plexus length-to-occipitofrontal diameter ratio were calculated for both groups. The measurements obtained from the 2 groups were compared, and the association between each parameter and open spina bifida was investigated. RESULTS: A total of 10 fetuses with open spina bifida were compared with 358 controls. Compared with controls, fetuses with open spina bifida showed a significantly smaller size of the cerebral ventricle measurements, as expressed by larger values of choroid plexus-to-lateral ventricle area ratio (0.49 vs 0.72, respectively; P<.001), choroid plexus-to-lateral ventricle length ratio (0.70 vs 0.79, respectively; P<.001), choroid plexus area-to-fetal head area ratio (0.28 vs 0.33, respectively; P=.006), and choroid plexus length-to-occipitofrontal diameter ratio (0.52 vs 0.60, respectively; P<.001). The choroid plexus-to-lateral ventricle area ratio was found to be the most accurate predictor of open spina bifida, with an area under the curve of 0.88, a sensitivity of 90%, and a specificity of 82%. CONCLUSION: At 11 0/7 to 13 6/7 weeks of gestation, open spina bifida is consistently associated with a reduced amount of fluid in the lateral cerebral ventricles of the fetus, as expressed by a significantly increased choroid plexus-to-lateral ventricle length ratio, choroid plexus-to-lateral ventricle area ratio, choroid plexus area-to-fetal head area ratio, and choroid plexus length-to-occipitofrontal diameter ratio.

Maternal obesity alters fetal neuroinflammation in a murine model of preterm birth.

BACKGROUND: Preterm birth from intrauterine infection is a leading cause of neonatal neurologic morbidity. Likewise, maternal obesity is associated with intra-amniotic infection and inflammation. Whether maternal obesity is a risk factor for fetal brain injury that occurs with premature birth remains unknown. This study hypothesized that maternal obesity intensifies fetal neuroinflammation in the setting of premature delivery. OBJECTIVE: This study aimed to examine the influence of maternal obesity on perinatal neuroinflammatory responses that arise with preterm birth using a murine model. STUDY DESIGN: Dams with obesity were generated via a high-fat diet that was maintained throughout pregnancy. In parallel, dams without obesity (normal) received a control diet. All dams were paired with males on normal diet. Pregnant dams were randomized to receive an intrauterine administration of bacterial endotoxin (lipopolysaccharide) or the vehicle (phosphate-buffered saline) on embryo day 15.5 of what is typically a 19- to 21-day gestation. Fetal brains were harvested 6 hours after intrauterine administrations, and the expressions of key inflammatory cytokines (Il1b, Il6, and Tnf) and panels of metabolic, immune, and inflammatory genes were analyzed. RESULTS: With the phosphate-buffered saline, there was no difference in gene expression related to maternal obesity. There were substantial differences in Il6 and immune/inflammatory expression profiles in fetal brains from dams with obesity vs normal dams that received lipopolysaccharide. Few differences were observed among the metabolic genes examined under these conditions. The gene expression pattern associated with maternal obesity correlated with pathways related to white matter injury. CONCLUSION: The expression of neuroinflammatory markers instigated by bacterial endotoxin via intrauterine lipopolysaccharide was greater in embryo brains obtained from dams with obesity. Expression profiles suggest that in combination with intrauterine inflammation, maternal obesity may increase the risk of fetal white matter injury. Further investigation is warranted to understand the relationship between maternal health and neurologic outcomes associated with prematurity.

Unraveling the Molecular Mechanisms of the Neurodevelopmental Consequences of Fetal Protein Deficiency: Insights From Rodent Models and Public Health Implications.

Fetal brain development requires increased maternal protein intake to ensure that offspring reach their optimal cognitive potential in infancy and adulthood. While protein deficiency remains a prevalent issue in developing countries, it is also reemerging in Western societies due to the growing adoption of plant-based diets, some of which are monotonous and may fail to provide sufficient amino acids crucial for the brain's critical developmental phase. Confounding variables in human nutritional research have impeded our understanding of the precise impact of protein deficiency on fetal neurodevelopment, as well as its implications for childhood neurocognitive performance. Moreover, it remains unclear whether such deficiency could predispose to mental health problems in adulthood, mirroring observations in individuals exposed to prenatal famine. In this review, we sought to evaluate mechanistic data derived from rodent models, placing special emphasis on the involvement of neuroendocrine axes, the influence of sex and timing, epigenetic modifications, and cellular metabolism. Despite notable progress, critical knowledge gaps remain, including understanding the long-term reversibility of effects due to fetal protein restriction and the interplay between genetic predisposition and environmental factors. Enhancing our understanding of the precise mechanisms that connect prenatal nutrition to brain development in future research endeavors can be significantly advanced by integrating multiomics approaches and utilizing additional alternative models such as nonhuman primates. Furthermore, it is crucial to investigate potential interventions aimed at alleviating adverse outcomes. Ultimately, this research has profound implications for guiding public health strategies aimed at raising awareness about the crucial role of optimal maternal nutrition in supporting fetal neurodevelopment.

The Developmental Origins of Health and Disease theory posits that suboptimal conditions during early life exert a profound influence on adult health, potentially predisposing individuals to conditions such as neuropsychiatric disorders. By reviewing studies in rodents, we identified common mechanisms of how inadequate fetal protein uptake alters brain development and may contribute to anxiety, impaired memory function, and altered metabolism in adulthood. Adequate protein consumption during pregnancy is therefore critical to support healthy brain development.

Anatomically constrained tractography of the fetal brain.

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and the challenging nature of tractography, existing methods tend to produce highly inaccurate results. They generate many false streamlines while failing to reconstruct the streamlines that constitute the major white matter tracts. In this paper, we advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. We develop a deep learning method to compute the segmentation automatically. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve the tractography results. It enables the reconstruction of highly curved tracts such as optic radiations. Importantly, our method infers the tissue segmentation and streamline propagation direction from a diffusion tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans. The proposed method can facilitate the study of fetal brain white matter tracts with dMRI.

Effects of Mediterranean Diet or Mindfulness-Based Stress Reduction during Pregnancy on Fetal Brain Development Detected by Neurosonography: A Secondary Analysis of a Randomized Clinical Trial (IMPACT BCN).

INTRODUCTION: We investigated whether structured maternal lifestyle interventions based on Mediterranean diet or stress reduction influence fetal-infant neurodevelopment detected by detailed fetal neurosonography and Ages and Stages Questionnaires 3rd edition (ASQ) at 12 months old. METHODS: This was a secondary analysis of a randomized clinical trial (2017-2020), including 1,221 singleton pregnancies at high risk for small-for-gestational age. Participants were randomized into three groups at 19-23 weeks' gestation: Mediterranean diet intervention, stress reduction program, or usual care. A detailed neurosonography was performed on 881 participants at mean (SD) 33.4 (1.1) weeks' gestation. Neurosonographic measurements were done offline. ASQ was performed on 276 infants at 1 year of corrected age. RESULTS: Biparietal diameter was similar among study groups. Mediterranean diet group fetuses had deeper insula (26.80 [1.68] versus 26.63 [1.75], mm, p = 0.02) and longer corpus callosum (42.98 [2.44] versus 42.62 [2.27], mm, p = 0.04), with a lower rate of suboptimal score infants in ASQ problem-solving domain (6.2 vs. 16.3%, p = 0.03). Stress reduction group fetuses had deeper insula (26.90 [1.75] versus 26.63 [1.75], mm, p = 0.04) and lower rates of suboptimal score infants in ASQ fine motor domain (4.3 vs. 12.8%, p = 0.04), compared to usual care group fetuses. CONCLUSION: Maternal structured intervention during pregnancy of the trial has the potential to modify offspring's neurodevelopment.

Effects of mid­gestational sevoflurane and magnesium sulfate on maternal oxidative stress, inflammation and fetal brain histopathology.

Models of inflammation, oxidative stress, hyperoxia and hypoxia have demonstrated that magnesium sulfate (MgSO4), a commonly used drug in obstetrics, has neuroprotective potential. In the present study, the effects of MgSO4 treatment on inflammation, oxidative stress and fetal brain histopathology were evaluated in an experimental rat model following sevoflurane (Sv) exposure during the mid-gestational period. Rats were randomly divided into groups: C (control; no injections or anesthesia), Sv (exposure to 2.5% Sv for 2 h), MgSO4 (administered 270 mg/kg MgSO4 intraperitoneally) and Sv + MgSO4 (Sv administered 30 min after MgSO4 injection). Inflammatory and oxidative stress markers were measured in the serum and neurotoxicity was investigated histopathologically in fetal brain tissue. Short-term mid-gestational exposure to a 1.1 minimum alveolar concentration of Sv did not significantly increase the levels of any of the measured biochemical markers, except for TNF-α. Histopathological evaluations demonstrated no findings suggestive of pathological apoptosis, neuroinflammation or oxidative stress-induced cell damage. MgSO4 injection prior to anesthesia caused no significant differences in biochemical or histopathological marker levels compared to the C and Sv groups. The present study indicated that short-term exposure to Sv could potentially be considered a harmless external stimulus to the fetal brain.

Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain.

In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a significant challenge in skill acquisition. We aim to build a US plane localization system for 3D visualization, training, and guidance without integrating additional sensors. This work builds on top of our previous work, which predicts the six-dimensional (6D) pose of arbitrarily oriented US planes slicing the fetal brain with respect to a normalized reference frame using a convolutional neural network (CNN) regression network. Here, we analyze in detail the assumptions of the normalized fetal brain reference frame and quantify its accuracy with respect to the acquisition of transventricular (TV) standard plane (SP) for fetal biometry. We investigate the impact of registration quality in the training and testing data and its subsequent effect on trained models. Finally, we introduce data augmentations and larger training sets that improve the results of our previous work, achieving median errors of 2.97 mm and 6.63° for translation and rotation, respectively.

Maternal SARS-CoV-2 impacts fetal placental macrophage programs and placenta-derived microglial models of neurodevelopment.

BACKGROUND: The SARS-CoV-2 virus activates maternal and placental immune responses. Such activation in the setting of other infections during pregnancy is known to impact fetal brain development. The effects of maternal immune activation on neurodevelopment are mediated at least in part by fetal brain microglia. However, microglia are inaccessible for direct analysis, and there are no validated non-invasive surrogate models to evaluate in utero microglial priming and function. We have previously demonstrated shared transcriptional programs between microglia and Hofbauer cells (HBCs, or fetal placental macrophages) in mouse models. METHODS AND RESULTS: We assessed the impact of maternal SARS-CoV-2 on HBCs isolated from 24 term placentas (N = 10 SARS-CoV-2 positive cases, 14 negative controls). Using single-cell RNA-sequencing, we demonstrated that HBC subpopulations exhibit distinct cellular programs, with specific subpopulations differentially impacted by SARS-CoV-2. Assessment of differentially expressed genes implied impaired phagocytosis, a key function of both HBCs and microglia, in some subclusters. Leveraging previously validated models of microglial synaptic pruning, we showed that HBCs isolated from placentas of SARS-CoV-2 positive pregnancies can be transdifferentiated into microglia-like cells (HBC-iMGs), with impaired synaptic pruning behavior compared to HBC models from negative controls. CONCLUSION: These findings suggest that HBCs isolated at birth can be used to create personalized cellular models of offspring microglial programming.