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Fetal ventriculomegaly is the most common antenatally-diagnosed brain abnormality. Imaging studies in antenatal isolated ventriculomegaly demonstrate enlarged ventricles and cortical overgrowth which are also present in children with autism-spectrum disorder/condition (ASD). We investigate the presence of ASD traits in a cohort of children ( n  = 24 [20 males/4 females]) with isolated fetal ventriculomegaly, compared with 10 controls ( n  = 10 [6 males/4 females]). Neurodevelopmental outcome at school age included IQ, ASD traits (ADOS-2), sustained attention, neurological functioning, behaviour, executive function, sensory processing, co-ordination, and adaptive behaviours. Pre-school language development was assessed at 2 years. 37.5% of children, all male, in the ventriculomegaly cohort scored above threshold for autism/ASD classification. Pre-school language delay predicted an ADOS-2 autism/ASD classification with 73.3% specificity/66.7% sensitivity. Greater pre-school language delay was associated with more ASD symptoms. In this study, the neurodevelopment of children with isolated fetal ventriculomegaly, associated with altered cortical development, includes ASD traits, difficulties in sustained attention, working memory and sensation-seeking behaviours. Isolated fetal ventriculomegaly is the most common antenatally-diagnosed brain abnormality. Here, the authors show that isolated fetal ventriculomegaly is associated with autism spectrum disorder traits.

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22–38 weeks gestational age range.

The development of connectivity between the thalamus and maturing cortex is a fundamental process in the second half of human gestation, establishing the neural circuits that are the basis for several important brain functions. In this study, we acquired high-resolution in utero diffusion magnetic resonance imaging (MRI) from 140 fetuses as part of the Developing Human Connectome Project, to examine the emergence of thalamocortical white matter over the second to third trimester. We delineate developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. We then quantify microstructural tissue components along the tracts in fetal compartments that are critical substrates for white matter maturation, such as the subplate and intermediate zone. We identify patterns of change in the diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester, such as the disassembly of radial glial scaffolding and the lamination of the cortical plate. These maturational trajectories of MR signal in transient fetal compartments provide a normative reference to complement histological knowledge, facilitating future studies to establish how developmental disruptions in these regions contribute to pathophysiology.

A key feature of the fetal period is the rapid emergence of organised patterns of spontaneous brain activity. However, characterising this process in utero using functional MRI is inherently challenging and requires analytical methods which can capture the constituent developmental transformations. Here, we introduce a novel analytical framework, termed “maturational networks” (matnets), that achieves this by modelling functional networks as an emerging property of the developing brain. Compared to standard network analysis methods that assume consistent patterns of connectivity across development, our method incorporates age-related changes in connectivity directly into network estimation. We test its performance in a large neonatal sample, finding that the matnets approach characterises adult-like features of functional network architecture with a greater specificity than a standard group-ICA approach; for example, our approach is able to identify a nearly complete default mode network. In the in-utero brain, matnets enables us to reveal the richness of emerging functional connections and the hierarchy of their maturational relationships with remarkable anatomical specificity. We show that the associative areas play a central role within prenatal functional architecture, therefore indicating that functional connections of high-level associative areas start emerging prior to exposure to the extra-utero environment. An analytical framework termed “maturational networks” (matnets) is applied to MRI data from 144 fetuses in utero, characterising emerging functional connectivity in the fetal brain and revealing that human functional connections of high-level associative areas emerge prior to extra-utero environment exposure

Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements. A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29-36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers. Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25-35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19-39 weeks), based on 280 control fetuses, were produced for future research. This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.

Background Congenital Heart Disease (CHD) is the most common congenital abnormality. Survival rates are over 90%, however infants with CHD remain at high risk of attention and executive function impairments. These abilities are difficult to assess in toddlers because clinical assessments rely on language abilities which are commonly delayed in CHD. Our aim was to characterise visual attention in toddlers with CHD compared to controls and identify associations with parent‐rated effortful control. Methods Thirty toddlers with CHD (19 male, median (IQR) age at assessment 22.2 (22–23.1) months) and 66 controls from the developing human connectome project (36 male, age at assessment 22 (21.5–23.8) months) using eye‐tracking tasks designed to assess multiple components of visual attention. Analyses of co‐variance and regressions were used to identify differences between groups and relationships between gaze behaviours and parent‐rated effortful control. Results Toddlers with CHD were less accurate when switching behaviours (set‐shifting) [median (IQR) 79%, (28–100)] compared to controls [100% (86–100), pFDR = 0.032], with worse accuracy associated with lower parent‐rated effortful control in CHD but not controls (interaction pFDR = 0.028). Reaction times were slower during selective [CHD 1243 ms (986–1786), controls 1065 ms (0851–1397), pFDR<0.001] and exogenous attention tasks [CHD 312 ms (279–358), control 289 (249–331), (pFDR = 0.032) and endogenous attention was less mature (prolonged looks at facial stimuli CHD 670 ms (518–885), control 500 ms (250–625), (pFDR = 0.006). These results were unrelated to differences in cognition or socioeconomic status. In contrast, the allocation of attentional resources was preserved in CHD. Conclusions We identified a profile of altered attention and early executive functioning development in CHD. Eye‐tracking may provide clinically feasible, early objective measures of attention and executive function development in CHD.

The fetal brain shows accelerated growth in the latter half of gestation, and these changes can be captured by 2D and 3D biometry measurements. The aim of this study was to quantify brain growth in normal fetuses using Magnetic Resonance Imaging (MRI) and to produce reference biometry data and a freely available centile calculator ( https://www.developingbrain.co.uk/fetalcentiles/ ). A total of 127 MRI examinations (1.5 T) of fetuses with a normal brain appearance (21–38 gestational weeks) were included in this study. 2D and 3D biometric parameters were measured from slice-to-volume reconstructed images, including 3D measurements of supratentorial brain tissue, lateral ventricles, cortex, cerebellum and extra-cerebral CSF and 2D measurements of brain biparietal diameter and fronto-occipital length, skull biparietal diameter and occipitofrontal diameter, head circumference, transverse cerebellar diameter, extra-cerebral CSF, ventricular atrial diameter, and vermis height, width, and area. Centiles were constructed for each measurement. All participants were invited for developmental follow-up. All 2D and 3D measurements, except for atrial diameter, showed a significant positive correlation with gestational age. There was a sex effect on left and total lateral ventricular volumes and the degree of ventricular asymmetry. The 5th, 50th, and 95th centiles and a centile calculator were produced. Developmental follow-up was available for 73.1% of cases [mean chronological age 27.4 (±10.2) months]. We present normative reference charts for fetal brain MRI biometry at 21–38 gestational weeks. Developing growth trajectories will aid in the better understanding of normal fetal brain growth and subsequently of deviations from typical development in high-risk pregnancies or following premature delivery.

The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance images of fetal and/or neonatal brain from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, clinical, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.

Advances in fetal three‐dimensional (3D) ultrasound (US) and magnetic resonance imaging (MRI) have revolutionized the study of fetal brain development, enabling detailed analysis of brain structures and growth. Despite their complementary capabilities, these modalities capture fundamentally different physical signals, potentially leading to systematic differences in image‐derived phenotypes (IDPs). Here, we evaluate the agreement of IDPs between US and MRI by comparing the volumes of eight brain structures from 90 subjects derived using deep‐learning algorithms from majority same‐day imaging (days between scans: mean = 1.2, mode = 0 and max = 4). Excellent agreement (intra‐class correlation coefficient, ICC>0.75 $$ ICC>0.75 $$ ) was observed for the cerebellum, cavum septum pellucidum, thalamus, white matter and deep grey matter volumes, with significant correlations p<0.001 $$ \\left(p<0.001\\right) $$for most structures, except the ventricular system. Bland–Altman analysis revealed some systematic biases: intracranial and cortical plate volumes were larger on US than MRI, by an average of 35cm3 $$ 35\\ {\\mathrm{cm}}^3 $$and 4.1cm3 $$ 4.1\\ {\\mathrm{cm}}^3 $$ , respectively. Finally, we found the labels of the brainstem and ventricular system were not comparable between the modalities. These findings highlight the necessity of structure‐specific adjustments when interpreting fetal brain IPDs across modalities and underscore the complementary roles of US and MRI in advancing fetal neuroimaging. This study investigates the agreement of image derived‐phenotypes (IDPs) from eight fetal brain structures derived from same‐day MRI and 3D US volumes. Strong agreement was observed for the CSP, Th, CB, WMDGM, whereas, systematic biases were revealed for ICV and CoP.

ObjectivesPrenatal characterisation of craniofacial development remains a challenge for ultrasound.¹ We sought to develop an MRI protocol for the automated extraction of craniofacial measurements using 3D motion-corrected, slice-to-volume reconstructed (SVR) fetal MRI² and atlas-based label propagation of anatomical landmarks.Methods24 fetuses with genetically confirmed Down syndrome (DS) and 85 control fetuses were retrospectively selected if: scanned between 29–37 weeks GA; had maternal written informed consent via; fetal MRI [REC:07/H0707/105], dHCP [REC:14/LO/1169], PiP [REC:16/LO/1573], eBIDS [REC:19/LO/0667], or iFIND [REC:14/LO/1806]; had a 1.5T or 3T MRI protocol amenable to SVR from 2D acquisitions; and, if the reconstruction quality score was ‘good/excellent’.Using a control dataset, 4D spatiotemporal atlases were developed for 16 discrete time-points from 21–36 weeks GA range.³ A clinician reviewed the literature and 46 fetal MRI-reliable craniofacial landmarks for biometry were labelled in three atlases using research software (ITK-SNAP).4 The label propagation pipeline was followed by the calculation of the distances between selected landmark centre-points. The performance was tested on five datasets with DS, by comparing manual measurements to the automated distances. Lastly, we investigated the feasibility of this approach by comparing the automated DS biometry to the control groups with different acquisition protocols (fig 1).ResultsThe automated craniofacial anatomical landmarks were visually assessed for accuracy. No landmarks in the control group required modification. However, in the DS group 4 out of 120 automated landmarks required minor manual adjustment.Automated biometry, compared to manual measurements, showed small mean paired relative errors of <10%, except for the foramen magnum measurements (figure 2). The differences were primarily caused by variability in multiplanar manual adjustment of images and suboptimal regional visibility of finer features. The process of verifying correct positioning of landmarks was significantly faster than extracting manual biometry (5 vs 25 minutes/case).There were no significant differences in measurements within the control cohorts and between different acquisition parameters (1.5T, 3T; TE=80ms, TE=180ms, TE=250ms – see figure 2). However, there were significant differences between DS and control cohorts in the OFD, ASBL and HPL distances (ANOVA, p<0.001). These differences are likely associated with shorter/wider skulls (brachycephaly) and smaller mid-facies (midface hypoplasia) in DS, which is consistent with ultrasound and neonatal findings.5ConclusionWe present the first automated atlas-based label propagation protocol using 3D motion-corrected MRI for 12 fetal craniofacial measurements across varied echo times and field strengths. The method shows differences in craniofacial growth between fetuses with Down syndrome and control subjects.ReferencesMak ASL, Leung KY. Prenatal ultrasonography of craniofacial abnormalities. Ultrasonography 2019. doi:10.14366/usg.18031Kuklisova-Murgasova M, Quaghebeur G, Rutherford MA, Hajnal J V., Schnabel JA. Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med Image Anal 2012; 16: 1550.Uus A, Matthew J, Grigorescu I, Jupp S, Grande LC, Price A, et al. Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2021; 12959 LNCS: 168–178.ITK-SNAP tool. http://www.itksnap.org/pmwiki/pmwiki.phpVicente A, Bravo-González LA, López-Romero A, Muñoz CS, Sánchez-Meca J. Craniofacial morphology in down syndrome: a systematic review and meta-analysis. Sci Reports 2020 101 2020;10:1–14.Abstract 560 Figure 1