The subplate (SP) is a transient fetal brain compartment supporting neuronal migration, axonal ingrowth, and early cortical activity, yet the dynamics of its regional development remain poorly understood in vivo. Using T2-weighted fetal MRI of 68 typically developing fetuses (22-32 weeks gestational age, GA), we developed a semi-automated pipeline to quantify regional SP morphology (thickness, surface area, and volume). SP characteristics scaled strongly with GA and residual brain volume and showed marked regional differences. After correcting for geometric confounds, regional variation of SP thickness persisted, with highest values in parietal and perisylvian regions, suggesting that SP thickness may serve as a sensitive marker of intrinsic developmental differences. Between late 2nd and early 3rd trimester, mean SP thickness increased by 39.2% with large variation across regions (±11.0 SD), whereas surface area growth was more uniform (64.3% ±0.7 SD). Continuous growth trajectories clustered into distinct spatiotemporal profiles: early-developing regions (e.g., pericentral and medial occipital cortices) contrasted with later-developing regions (prefrontal, temporal, and parietal cortices). These patterns partially recapitulate primary-to-association, medial-to-lateral, and posterior-to-anterior maturational hierarchies, pointing to organized developmental program. SP development also showed region-specific hemispheric asymmetries, including leftward thickness and volume asymmetry in superior temporal and precentral gyri. Some asymmetries amplified, others attenuated or reversed with age, suggesting both transient states and potential precursors of postnatal lateralization. Together, these findings provide a framework for regional SP quantification and position SP morphology, particularly thickness, as a promising early biomarker that might link fetal SP changes to subsequent cortical development and neurodevelopmental outcomes.

Fetal brain development is a complex and dynamic process, and its disruption can lead to significant neurological disorders. Early detection of brain aberrations during pregnancy is critical for optimizing postnatal medical intervention. We propose a deep generative anomaly detection framework, conditional cyclic variational autoencoding generative adversarial network (CCVAEGAN), that can identify structural brain anomalies using fetal brain magnetic resonance imaging. CCVAEGAN leverages covariate conditioning on gestational age and cyclic consistency training to generate high-fidelity normative fetal brain images to enhance anomaly detection across various neurodevelopmental stages and diagnoses. Using MRI data from typically developing and clinically abnormal fetuses across multiple sites, CCVAEGAN achieves superior image generation quality and anomaly detection accuracy than other comparable models, consistently producing anatomically precise images with lower reconstruction errors and higher structural similarities. Anomaly detection experiments yielded near-perfect AUROC values (>0.99) across various anomaly score metrics, and visual assessments confirmed the model's ability to localize and characterize structural abnormalities. Additionally, external validation on separated-site cohorts demonstrated the generalizability of the CCVAEGAN framework, showing robust detection performance despite data variations. These findings demonstrate CCVAEGAN's potential as a powerful tool for automated, objective anomaly screening, that could significantly enhance the efficiency of clinical workflows for early diagnosis of fetal brain anomalies. Furthermore, this approach has the potential universality to apply to other medical imaging not limited to specific organs or imaging modalities in the future.

OBJECTIVES: Maternal obesity increases a child's risk of neurodevelopmental impairment. However, little is known about the impact of maternal obesity on fetal brain development.

METHODS: We prospectively recruited 20 healthy pregnant women across the range of pre-pregnancy or first-trimester body mass index (BMI) and performed fetal brain magnetic resonance imaging (MRI) of their healthy singleton fetuses. We examined correlations between early pregnancy maternal BMI and regional brain volume of living fetuses using volumetric MRI analysis.

RESULTS: Of 20 fetuses, there were 8 males and 12 females (median gestational age at MRI acquisition was 24.3 weeks, range: 19.7-33.3 weeks, median maternal age was 33.3 years, range: 22.0-37.4 years). There were no significant differences in clinical demographics between overweight (OW, 25≤BMI<30)/obese (OB, BMI≥30 kg/m2) (n=12) and normal BMI (18.5≤BMI<25) (n=8) groups. Fetuses in the OW/OB group had significantly larger left cortical plate (p=0.0003), right cortical plate (p=0.0002), and whole cerebellum (p=0.049) compared to the normal BMI group. In the OW/OB BMI group, cortical plate volume was larger relative to other brain regions after 28 weeks.

CONCLUSIONS: This pilot study supports the concept that maternal obesity impacts fetal brain volume, detectable via MRI in living fetuses using quantitative analysis.

Neurodevelopmental impairments associated with congenital heart disease (CHD) may arise from perturbations in brain developmental pathways, including the formation of sulcal patterns. While genetic factors contribute to sulcal features, the association of noncoding de novo variants (ncDNVs) with sulcal patterns in people with CHD remains poorly understood. Leveraging deep learning models, we examined the predicted impact of ncDNVs on gene regulatory signals. Predicted impact was compared between participants with CHD and a jointly called cohort without CHD. We then assessed the relationship of the predicted impact of ncDNVs with their sulcal folding patterns. ncDNVs predicted to increase H3K9me2 modification were associated with larger disruptions in right parietal sulcal patterns in the CHD cohort. Genes predicted to be regulated by these ncDNVs were enriched for functions related to neuronal development. This highlights the potential of deep learning models to generate hypotheses about the role of noncoding variants in brain development.

Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a two-dimensional (2D) single-channel convolutional neural network (CNN) with multiplanar MRI sections showed the potential to detect fetuses with VM. This study examines the diagnostic performance of a deep learning-based fetal brain age prediction model to distinguish fetuses with VM (n = 317) from typically developing fetuses (n = 183), the severity of VM, and the presence of associated CNS abnormalities. The predicted age difference (PAD) was measured by subtracting the predicted brain age from the gestational age in fetuses with VM and typical development. PAD and absolute value of PAD (AAD) were compared between VM and typically developing fetuses. In addition, PAD and AAD were compared between subgroups by VM severity and the presence of associated CNS abnormalities in VM. Fetuses with VM showed significantly larger AAD than typically developing fetuses (P < .001), and fetuses with severe VM showed larger AAD than those with moderate VM (P = .004). Fetuses with VM and associated CNS abnormalities had significantly lower PAD than fetuses with isolated VM (P = .005). These findings suggest that fetal brain age prediction using the 2D single-channel CNN method has the clinical ability to assist in identifying not only the enlargement of the ventricles but also the presence of associated CNS abnormalities. Keywords: MR-Fetal (Fetal MRI), Brain/Brain Stem, Fetus, Supervised Learning, Machine Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms Supplemental material is available for this article. ©RSNA, 2025.