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.

Examining the altered arrangement and patterning of sulcal folds offers insights into the mechanisms of neurodevelopmental differences in psychiatric and neurological disorders. Previous sulcal pattern analysis used spectral graph matching of sulcal pit-based graph structures to assess deviations from normative sulcal patterns. However, challenges exist, including the absence of a standard criterion for defining a typical reference set, time-consuming cost of graph matching, user-defined feature weight sets, and assumptions about uniform node distribution. We developed a deep learning-based sulcal pattern analysis to address these challenges by adapting prototype-based graph neural networks to sulcal pattern graphs. Additionally, we proposed a prototype inverse-projection for better interpretability. Unlike other prototype-based models, our approach inversely projects prototypes onto individual node representations to calculate the inverse-projection weights, enabling efficient visualization of prototypes and focusing the model on selective regions. We evaluated our method through a classification task between healthy controls (n = 174, age = 15.4 ±1.9 [mean ± standard deviation, years]) and patients with congenital heart disease (n = 345, age = 15.8 ±4.7) from four cohort studies and a public dataset. Our approach demonstrated superior classification performance compared to other state-of-the-art models, supported by extensive ablative studies. Furthermore, we visualized and examined the learned prototypes to enhance understanding. We believe our method has the potential to be a sensitive and understandable tool for sulcal pattern analysis.

BACKGROUND: In adolescents and adults with complex congenital heart disease (CHD), abnormal cortical folding is a putative predictor of poor neurodevelopmental outcome. However, it is unknown when this relationship first emerges. We test the hypothesis that it begins in utero, when the brain starts to gyrify and folding patterns first become established.

METHODS: We carried out a prospective, longitudinal case-control study, acquiring foetal MRIs at two timepoints in utero, (Scan 1 = 20-30 Gestational Weeks (GW) and Scan 2 = 30-39 GW), then followed up participants at two years of age to assess neurodevelopmental outcomes. We used normative modelling to chart growth trajectories of surface features across 60 cortical regions in a control population (n = 157), then quantified the deviance of each foetus with CHD (n = 135) and explored the association with neurodevelopmental outcomes at two years of age.

FINDINGS: Differences in cortical development between CHD and Control foetuses only emerged after 30 GW, and lower regional cortical surface area growth was correlated with poorer neurodevelopmental outcomes at two years of age in the CHD group.

INTERPRETATION: This work highlights the third trimester specifically as a critical period in brain development for foetuses with CHD, where the reduced surface area expansion in specific cortical regions becomes consequential in later life, and predictive of neurodevelopmental outcome in toddlerhood.

FUNDING: This research was supported by the NINDS (R01NS114087, K23NS101120) and NIBIB (R01EB031170) of the NIH, PHN Scholar Award, AAN Clinical Research Training Fellowship, BBRF Young Investigator Awards, and the Farb Family Fund.

Normal cortical growth and the resulting folding patterns are crucial for normal brain function. Although cortical development is largely influenced by genetic factors, environmental factors in fetal life can modify the gene expression associated with brain development. As the placenta plays a vital role in shaping the fetal environment, affecting fetal growth through the exchange of oxygen and nutrients, placental oxygen transport might be one of the environmental factors that also affect early human cortical growth. In this study, we aimed to assess the placental oxygen transport during maternal hyperoxia and its impact on fetal brain development using MRI in identical twins to control for genetic and maternal factors. We enrolled 9 pregnant subjects with monochorionic diamniotic twins (30.03 ± 2.39 gestational weeks [mean ± SD]). We observed that the fetuses with slower placental oxygen delivery had reduced volumetric and surface growth of the cerebral cortex. Moreover, when the difference between placenta oxygen delivery increased between the twin pairs, sulcal folding patterns were more divergent. Thus, there is a significant relationship between placental oxygen transport and fetal brain cortical growth and folding in monochorionic twins.

Congenital heart disease affects 1% of infants and is associated with impaired neurodevelopment. Right- or left-sided sulcal features correlate with executive function among people with Tetralogy of Fallot or single ventricle congenital heart disease. Studies of multiple congenital heart disease types are needed to understand regional differences. Further, sulcal pattern has not been studied in people with d-transposition of the great arteries. Therefore, we assessed the relationship between sulcal pattern and executive function, general memory, and processing speed in a meta-regression of 247 participants with three congenital heart disease types (114 single ventricle, 92 d-transposition of the great arteries, and 41 Tetralogy of Fallot) and 94 participants without congenital heart disease. Higher right hemisphere sulcal pattern similarity was associated with improved executive function (Pearson r = 0.19, false discovery rate-adjusted P = 0.005), general memory (r = 0.15, false discovery rate P = 0.02), and processing speed (r = 0.17, false discovery rate P = 0.01) scores. These positive associations remained significant in for the d-transposition of the great arteries and Tetralogy of Fallot cohorts only in multivariable linear regression (estimated change β = 0.7, false discovery rate P = 0.004; β = 4.1, false discovery rate P = 0.03; and β = 5.4, false discovery rate P = 0.003, respectively). Duration of deep hypothermic circulatory arrest was also associated with outcomes in the multivariate model and regression tree analysis. This suggests that sulcal pattern may provide an early biomarker for prediction of later neurocognitive challenges among people with congenital heart disease.