Autism spectrum disorder (ASD) is increasingly conceptualized as a condition rooted in altered prenatal neurodevelopment, yet in vivo evidence from fetal brain imaging remains limited. Using retrospective fetal MRI and surface-based morphometry, we investigated cortical development in 15 fetuses later diagnosed with ASD (77%; mean gestational age [GA] = 26.7 weeks) without major structural brain abnormalities and compared them with 60 typically developing controls (57% male; mean GA = 28.4 weeks). Fetuses later diagnosed with ASD showed significantly reduced whole-brain inner cortical plate surface area compared with controls (β = -0.08 ± 0.02 SE, p = 0.002, partial η² = 0.13), corresponding to an estimated 7.7% reduction (predicted at GA = 28.1 weeks). Lobar mixed-effects analyses demonstrated broadly distributed reductions across all cortical lobes (FDR-corrected p = <0.001-0.024; Cohen's d = -0.06 to -0.10), with modest regional heterogeneity indicating relatively greater frontal and insular involvement (group×lobe: F = 19.31, p = 0.002, η² = 0.08). Surface area findings remained directionally stable across sensitivity analyses, including restriction to neurodevelopmentally confirmed controls and models accounting for image quality variability, although effect sizes were attenuated after quality adjustment. Normative modeling further demonstrated subtle negative deviations from typical prenatal cortical surface area trajectories in ASD (mean Z = -0.27, p = 0.018). These findings suggest that aspects of cortical morphogenesis may diverge prenatally in individuals later diagnosed with ASD and suggest the feasibility of fetal MRI-based surface morphometry for studying early neurodevelopmental variation associated with ASD risk.

Objective To explore the relationship between placental oxygen transport on MRI and fetal birth weight, and to identify associated transcriptional pathways.Study Design We conducted a prospective cohort study of six monochorionic twin pairs. For each pair, the twin with the higher placental oxygen time-to-plateau (TTP) was designated as Group A and the co-twin with the lower TTP as Group B. RNA sequencing of cord blood was performed to assess differential gene expression. Analyses were performed using paired methods to account for within-pair comparisons.Results On descriptive analysis, higher TTP twins (Group A) had lower mean birth weight than their lower TTP co-twins (Group B) (2121 ± 256 g vs 2397 ± 283 g), but this difference was not statistically significant on paired analysis (p = 0.51). TTP values were also not significantly different within pairs (p = 0.17). Across all twins, higher TTP was associated with lower birth weight (p = 0.02). No differentially expressed genes or pathways were identified.Conclusion In this cohort of monochorionic twin pairs, the twin with slower placental oxygen transport tended to have lower birth compared with its co-twin, although this difference was not statistically significant on paired analysis. Higher TTP was correlated with lower birth weight across twins, but no transcriptomic differences were identified. Larger paired datasets are needed to further explore these associations.

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 to 32 wk gestational age, GA), we developed a semiautomated 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 the late 2nd and early 3rd trimesters, 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 programs. SP development also showed region-specific hemispheric asymmetries, including leftward thickness and volume asymmetry in the 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.

Accurate fetal brain tissue segmentation and biometric measurement are essential for monitoring neurodevelopment and detecting abnormalities in utero. The Fetal Tissue Annotation (FeTA) Challenges have established robust multi-center benchmarks for evaluating state-of-the-art segmentation methods. This paper presents the results of the 2024 challenge edition, which introduced three key innovations. First, we introduced a topology-aware metric based on the Euler characteristic difference (ED) to overcome the performance plateau observed with traditional metrics like Dice or Hausdorff distance (HD), as the performance of the best models in segmentation surpassed the inter-rater variability. While the best teams reached similar scores in Dice (0.81-0.82) and HD95 (2.1–2.3 mm), ED provided greater discriminative power: the winning method achieved an ED of 20.9, representing roughly a 50% improvement over the second- and third-ranked teams despite comparable Dice scores. Second, we introduced a new 0.55T low-field MRI test set, which, when paired with high-quality super-resolution reconstruction, achieved the highest segmentation performance across all test cohorts (Dice=0.86, HD95=1.69, ED=6.26). This provides the first quantitative evidence that low-cost, low-field MRI can match or surpass high-field systems in automated fetal brain segmentation. Third, the new biometry estimation task exposed a clear performance gap: although the best model reached a mean average percentage error (MAPE) of 7.72%, most submissions failed to outperform a simple gestational-age-based linear regression model (MAPE=9.56%), and all remained above inter-rater variability with a MAPE of 5.38%. Finally, by analyzing the top-performing models from FeTA 2024 alongside those from previous challenge editions, we identify ensembles of 3D nnU-Net trained on both real and synthetic data with both image- and anatomy-level augmentations as the most effective approaches for fetal brain segmentation. Our quantitative analysis reveals that acquisition site, super-resolution strategy, and image quality are the primary sources of domain shift, informing recommendations to enhance the robustness and generalizability of automated fetal brain analysis methods.

Primary sulcal patterns, the spatial arrangement of the earliest cortical folds, emerge prenatally and remain essentially stable after birth. Alterations are associated with cognitive outcomes and hold promise as clinical biomarkers. However, detecting abnormalities is challenging due to the high topological variability and complexity of normal individual sulcal patterns. Here, we introduce an unsupervised generative model that quantifies individual sulcal pattern deviations against a normative baseline learned from 10,349 typically developing subjects spanning early childhood through adulthood. We combine a generative reconstruction error with a discriminator trained on diffusion-generated pseudo-atypical graphs to leverage their complementary strengths in sensitivity and robustness. The discriminator captures subtle anomalies and is robust to demographic and data quality variations, whereas the generative model enables severity stratification and demonstrates cross-site generalizability. Across two clinical cohorts spanning subtle to overt sulcal abnormalities (congenital heart disease and polymicrogyria), our model captured global and localized condition-related deviations and linked them to neurodevelopmental outcomes. By enabling population-scale, individualized quantification of sulcal deviations, this framework provides a foundation for future clinical risk stratification.Competing Interest StatementThe authors have declared no competing interest.Thrasher Research Fund, https://ror.org/03hnrwx04