Cortical folding alterations in fetuses with isolated non-severe ventriculomegaly

Volume development changes in the occipital lobe gyrus assessed by MRI in fetuses with isolated ventriculomegaly correlate with neurological development in infancy and early childhood

OBJECTIVE

This study was to systematically assess the occipital lobe gray and white matter volume of isolated ventriculomegaly (IVM) fetuses with MRI and to follow up the neurodevelopment of participants.

METHOD

MRI was used to evaluate 37 IVM fetuses and 37 control fetuses. The volume of gray and white matter in each fetal occipital gyrus was manually segmented and compared, and neurodevelopment was followed up and assessed in infancy and early childhood.

RESULT

Compared with the control group, the volume of gray matter in occipital lobe increased in the IVM group, and the incidence of neurodevelopmental delay increased.

CONCLUSION

We tested the hypothesis that prenatal diagnosis IVM represents a biological marker for development in fetal occipital lobe. Compared with the control group, the IVM group showed differences in occipital gray matter development and had a higher risk of neurodevelopmental delay.

Prenatal diagnosis and pregnancy outcomes in fetuses with ventriculomegaly

Objective

Genetic etiology plays a critical role in fetal ventriculomegaly (VM). However, the studies on chromosomal copy number variants (CNVs) in fetal VM are limited. This study aimed to investigate the chromosomal CNVs in fetuses with mild to moderate VM, and explore its genotype-phenotype correlation.

Methods

A total of 242 fetuses with mild to moderate VM detected by prenatal ultrasound were enrolled in our study from October 2018 to October 2022. All cases underwent chromosomal microarray analysis (CMA) and G-banding simultaneously. All VM cases were classified different subgroups according to the maternal age, severity, VM distribution and presence/absence of other ultrasound abnormalities. The pregnancy outcomes and health conditions after birth were followed up. We also performed a pooled analysis regarding likely pathogenic and pathogenic CNVs (LP/P CNVs) for VM.

Results

The detection rate of chromosomal abnormalities by karyotyping was 9.1% (22/242), whereas it was 16.5% (40/242) when CMA was conducted (P < 0.05). The total detection rate of chromosomal abnormalities by karyotyping and CMA was 21.1% (51/242). A 12.0% incremental yield of CMA over karyotyping was observed. The detection rate of total genetic variants in fetuses with bilateral VM was significantly higher than in fetuses with unilateral VM (30.0% vs. 16.7%, P = 0.017). No significant differences were discovered between isolated VM and non-isolated VM, or between mild and moderate VM, or between advanced maternal age (AMA) and non-AMA (all P > 0.05). 28 fetuses with VM were terminated and 214 fetuses were delivered: one presented developmental delay and one presented congenital heart disease. The VM cases with both positive CMA and karyotypic results had a higher rate of termination of pregnancy than those with either a positive CMA or karyotypic result, or both negative testing results (P < 0.001).

Conclusion

The combination of CMA and karyotyping should be adopted to improve the positive detection rate of chromosomal abnormalities for VM. The total genetic abnormalities detected using both techniques would affect the final pregnancy outcomes. LP/P CNVs at 16p11.2, 17p13, and 22q11.21 were identified as the top three chromosomal hotspots associated with VM, which would enable genetic counselors to provide more precise genetic counseling for VM pregnancies.

Fetal neuroimaging applications for diagnosis and counseling of brain anomalies: Current practice and future diagnostic strategies

Advances in fetal brain neuroimaging, especially fetal neurosonography and brain magnetic resonance imaging (MRI), allow safe and accurate anatomical assessments of fetal brain structures that serve as a foundation for prenatal diagnosis and counseling regarding fetal brain anomalies. Fetal neurosonography strategically assesses fetal brain anomalies suspected by screening ultrasound. Fetal brain MRI has unique technological features that overcome the anatomical limits of smaller fetal brain size and the unpredictable variable of intrauterine motion artifact. Recent studies of fetal brain MRI provide evidence of improved diagnostic and prognostic accuracy, beginning with prenatal diagnosis. Despite technological advances over the last several decades, the combined use of different qualitative structural biomarkers has limitations in providing an accurate prognosis. Quantitative analyses of fetal brain MRIs offer measurable imaging biomarkers that will more accurately associate with clinical outcomes. First-trimester ultrasound opens new opportunities for risk assessment and fetal brain anomaly diagnosis at the earliest time in pregnancy. This review includes a case vignette to illustrate how fetal brain MRI results interpreted by the fetal neurologist can improve diagnostic perspectives. The strength and limitations of conventional ultrasound and fetal brain MRI will be compared with recent research advances in quantitative methods to better correlate fetal neuroimaging biomarkers of neuropathology to predict functional childhood deficits. Discussion of these fetal sonogram and brain MRI advances will highlight the need for further interdisciplinary collaboration using complementary skills to continue improving clinical decision-making following precision medicine principles.

Fetal brain tissue annotation and segmentation challenge results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

Development of cortical folds in the human brain: An attempt to review biological hypotheses, early neuroimaging investigations and functional correlates

The folding of the human brain mostly takes place in utero, making it challenging to study. After a few pioneer studies looking into it in post-mortem foetal specimen, modern approaches based on neuroimaging have allowed the community to investigate the folding process in vivo, its normal progression, its early disturbances, and its relationship to later functional outcomes. In this review article, we aimed to first give an overview of the current hypotheses on the mechanisms governing cortical folding. After describing the methodological difficulties raised by its study in fetuses, neonates and infants with magnetic resonance imaging (MRI), we reported our current understanding of sulcal pattern emergence in the developing brain. We then highlighted the functional relevance of early sulcal development, through recent insights about hemispheric asymmetries and early factors influencing this dynamic such as prematurity. Finally, we outlined how longitudinal studies have started to relate early folding markers and the child's sensorimotor and cognitive outcome. Through this review, we hope to raise awareness on the potential of studying early sulcal patterns both from a fundamental and clinical perspective, as a window into early neurodevelopment and plasticity in relation to growth in utero and postnatal environment of the child.

Comprehensive quantitative analyses of fetal magnetic resonance imaging in isolated cerebral ventriculomegaly

Isolated cerebral ventriculomegaly (IVM) is the most common prenatally diagnosed brain anomaly occurs in 0.2-1 % of pregnancies. However, knowledge of fetal brain development in IVM is limited. There is no prenatal predictor for IVM to estimate individual risk of neurodevelopmental disability occurs in 10 % of children. To characterize brain development in fetuses with IVM and delineate their individual neuroanatomical variances, we performed comprehensive post-acquisition quantitative analysis of fetal magnetic resonance imaging (MRI). In volumetric analysis, brain MRI of fetuses with IVM (n = 20, 27.0 ± 4.6 weeks of gestation, mean ± SD) had revealed significantly increased volume in the whole brain, cortical plate, subcortical parenchyma, and cerebrum compared to the typically developing fetuses (controls, n = 28, 26.3 ± 5.0). In the cerebral sulcal developmental pattern analysis, fetuses with IVM had altered sulcal positional (both hemispheres) development and combined features of sulcal positional, depth, basin area, in both hemispheres compared to the controls. When comparing distribution of similarity index of individual fetuses, IVM group had shifted toward to lower values compared to the control. About 30 % of fetuses with IVM had no overlap with the distribution of control fetuses. This proof-of-concept study shows that quantitative analysis of fetal MRI can detect emerging subtle neuroanatomical abnormalities in fetuses with IVM and their individual variations.

Fetal Neurology: From Prenatal Counseling to Postnatal Follow-Up

Brain abnormalities detected in fetal life are being increasingly recognized. Child neurologists are often involved in fetal consultations, and specific fetal neurology training has been implemented in many countries. Pediatric neurologists are asked to examine the data available and to contribute to the definition of the long-term outcomes. Ventriculomegaly, posterior fossa malformations, and agenesis/dysgenesis of corpus callosum are among the most common reasons for antenatal neurological consultations. Fetuses with central nervous system and extra-CNS anomalies should ideally be managed in secondary/tertiary hospitals where obstetricians who are experts in fetal medicine and pediatric specialists are available. Obstetricians play a critical role in screening, performing detailed neurosonography, and referring to other specialists for additional investigations. Clinical geneticists are frequently asked to propose diagnostic tests and counsel complex fetal malformations whose phenotypes may differ from those during postnatal life. Advances in fetal MRI and genetic investigations can support the specialists involved in counseling. Nevertheless, data interpretation can be challenging, and it requires a high level of expertise in a multidisciplinary setting. Postnatally, child neurologists should be part of an integrated multidisciplinary follow-up, together with neonatologists and pediatricians. The neurodevelopmental outcomes should be assessed at least up to school age. Children should be evaluated with formal tests of their gross motor, cognitive, language, fine motor/visuo-perceptual skills, and their behavior. In this perspective, fetal neurology can be regarded as the beginning of a long journey which continues with a prolonged, structured follow-up, support to the families, and transition to adult life. A review of the most common conditions is presented, along with the long-term outcomes and a proposal of the neurodevelopmental follow-up of children with CNS malformation which are diagnosed in uterus.

Chromosomal microarray analysis vs. karyotyping for fetal ventriculomegaly: a meta-analysis

BACKGROUND

Chromosomal abnormalities are important causes of ventriculomegaly (VM). In mild and isolated cases of fetal VM, obstetricians rarely give clear indications for pregnancy termination. We aimed to calculate the incidence of chromosomal abnormalities and incremental yield of chromosomal microarray analysis (CMA) in VM, providing more information on genetic counseling and prognostic evaluation for fetuses with VM.

METHODS

The Chinese language databases Wanfang Data, China National Knowledge Infrastructure, and China Biomedical Literature Database (from January 1, 1991 to April 29, 2020) and English language databases PubMed, Embase, and Cochrane Library (from January 1, 1945 to April 29, 2020) were systematically searched for articles on fetal VM. Diagnostic criteria were based on ultrasonographic or magnetic resonance imaging (MRI) assessment of lateral ventricular atrium width: ≥10 to <15 mm for mild VM, and ≥15 mm for severe VM. Isolated VM was defined by the absence of structural abnormalities other than VM detected by ultrasonography or MRI. R software was used for the meta-analysis to determine the incidence of chromosomal abnormalities and incremental yield of CMA in VM, and the combined rate and 95% confidence interval (CI) were calculated.

RESULTS

Twenty-three articles involving 1635 patients were included. The incidence of chromosomal abnormalities in VM was 9% (95% CI: 5%-12%) and incremental yield of CMA in VM was 11% (95% CI: 7%-16%). The incidences of chromosomal abnormalities in mild, severe, isolated, and non-isolated VM were 9% (95% CI: 4%-16%), 5% (95% CI: 1%-11%), 3% (95% CI: 1%-6%), and 13% (95% CI: 4%-25%), respectively.

CONCLUSIONS

Applying CMA in VM improved the detection rate of abnormalities. When VM is confirmed by ultrasound or MRI, obstetricians should recommend fetal karyotype analysis to exclude chromosomal abnormalities. Moreover, CMA should be recommended preferentially in pregnant women with fetal VM who are undergoing invasive prenatal diagnosis. CMA cannot completely replace chromosome karyotype analysis.

Morphometric Analysis of Brain in Newborn with Congenital Diaphragmatic Hernia

Congenital diaphragmatic hernia (CDH) is a severe pediatric disorder with herniation of abdominal viscera into the thoracic cavity. Since neurodevelopmental impairment constitutes a common outcome, we performed morphometric magnetic resonance imaging (MRI) analysis on CDH infants to investigate cortical parameters such as cortical thickness (CT) and local gyrification index (LGI). By assessing CT and LGI distributions and their correlations with variables which might have an impact on oxygen delivery (total lung volume, TLV), we aimed to detect how altered perfusion affects cortical development in CDH. A group of CDH patients received both prenatal (i.e., fetal stage) and postnatal MRI. From postnatal high-resolution T2-weighted images, mean CT and LGI distributions of 16 CDH were computed and statistically compared to those of 13 controls. Moreover, TLV measures obtained from fetal MRI were further correlated to LGI. Compared to controls, CDH infants exhibited areas of hypogiria within bilateral fronto-temporo-parietal labels, while no differences were found for CT. LGI significantly correlated with TLV within bilateral temporal lobes and left frontal lobe, involving language- and auditory-related brain areas. Although the causes of neurodevelopmental impairment in CDH are still unclear, our results may suggest their link with altered cortical maturation and possible impaired oxygen perfusion.

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segmentations, we have developed a new and powerful deep learning segmentation method. Our method exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections. We evaluated our method quantitatively based on several performance measures and expert evaluations. Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique. We achieved average Dice similarity coefficient of 0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned in the gestational age range of 16 to 39 weeks (28.6± 5.3). With a computation time of less than 1 minute per fetal brain, our method can facilitate and accelerate large-scale studies on normal and altered fetal brain cortical maturation and folding.

Quantitative MRI Analyses of Regional Brain Growth in Living Fetuses with Down Syndrome

Down syndrome (DS) is the most common liveborn autosomal chromosomal anomaly and is a major cause of developmental disability. Atypical brain development and the resulting intellectual disability originate during the fetal period. Perinatal interventions to correct such aberrant development are on the horizon in preclinical studies. However, we lack tools to sensitively measure aberrant structural brain development in living human fetuses with DS. In this study, we aimed to develop safe and precise neuroimaging measures to monitor fetal brain development in DS. We measured growth patterns of regional brain structures in 10 fetal brains with DS (29.1 ± 4.2, weeks of gestation, mean ± SD, range 21.7~35.1) and 12 control fetuses (25.2 ± 5.0, range 18.6~33.3) using regional volumetric analysis of fetal brain MRI. All cases with DS had confirmed karyotypes. We performed non-linear regression models to compare fitted regional growth curves between DS and controls. We found decreased growth trajectories of the cortical plate (P = 0.033), the subcortical parenchyma (P = 0.010), and the cerebellar hemispheres (P < 0.0001) in DS compared to controls. This study provides proof of principle that regional volumetric analysis of fetal brain MRI facilitates successful evaluation of brain development in living fetuses with DS.

Fetal Brain Biometric Measurements on 3D Super-Resolution Reconstructed T2-Weighted MRI: An Intra- and Inter-observer Agreement Study

We present the comparison of two-dimensional (2D) fetal brain biometry on magnetic resonance (MR) images using orthogonal 2D T2-weighted sequences (T2WSs) vs. one 3D super-resolution (SR) reconstructed volume and evaluation of the level of confidence and concordance between an experienced pediatric radiologist (obs1) and a junior radiologist (obs2). Twenty-five normal fetal brain MRI scans (18-34 weeks of gestation) including orthogonal 3-mm-thick T2WSs were analyzed retrospectively. One 3D SR volume was reconstructed per subject based on multiple series of T2WSs. The two observers performed 11 2D biometric measurements (specifying their level of confidence) on T2WS and SR volumes. Measurements were compared using the paired Wilcoxon rank sum test between observers for each dataset (T2WS and SR) and between T2WS and SR for each observer. Bland-Altman plots were used to assess the agreement between each pair of measurements. Measurements were made with low confidence in three subjects by obs1 and in 11 subjects by obs2 (mostly concerning the length of the corpus callosum on T2WS). Inter-rater intra-dataset comparisons showed no significant difference (p > 0.05), except for brain axial biparietal diameter (BIP) on T2WS and for brain and skull coronal BIP and coronal transverse cerebellar diameter (DTC) on SR. None of them remained significant after correction for multiple comparisons. Inter-dataset intra-rater comparisons showed statistical differences in brain axial and coronal BIP for both observers, skull coronal BIP for obs1, and axial and coronal DTC for obs2. After correction for multiple comparisons, only axial brain BIP remained significantly different, but differences were small (2.95 ± 1.73 mm). SR allows similar fetal brain biometry as compared to using the conventional T2WS while improving the level of confidence in the measurements and using a single reconstructed volume.

Global and Regional Changes in Cortical Development Assessed by MRI in Fetuses with Isolated Nonsevere Ventriculomegaly Correlate with Neonatal Neurobehavior

BACKGROUND AND PURPOSE

Fetuses with isolated nonsevere ventriculomegaly (INSVM) are at risk of presenting neurodevelopmental delay. However, the currently used clinical parameters are insufficient to select cases with high risk and determine whether subtle changes in brain development are present and might be a risk factor. The aim of this study was to perform a comprehensive evaluation of cortical development in INSVM by magnetic resonance (MR) imaging and assess its association with neonatal neurobehavior.

MATERIALS AND METHODS

Thirty-two INSVM fetuses and 29 healthy controls between 26-28 weeks of gestation were evaluated using MR imaging. We compared sulci and fissure depth, cortical maturation grading of specific areas and sulci and volumes of different brain regions obtained from 3D brain reconstruction of cases and controls. Neonatal outcome was assessed by using the Neonatal Behavioral Assessment Scale at a mean of 4 ± 2 weeks after birth.

RESULTS

Fetuses with INSVM showed less profound and underdeveloped sulcation, including the Sylvian fissure (mean depth: controls 16.8 ± 1.9 mm, versus INSVM 16.0 ± 1.6 mm; P = .01), and reduced global cortical grading (mean score: controls 42.9 ± 10.2 mm, versus INSVM: 37.8 ± 9.9 mm; P = .01). Fetuses with isolated nonsevere ventriculomegaly showed a mean global increase of gray matter volume (controls, 276.8 ± 46.0 ×10 mm³, versus INSVM 277.5 ± 49.3 ×10 mm³, P = .01), but decreased mean cortical volume in the frontal lobe (left: controls, 53.2 ± 8.8 ×10 mm³, versus INSVM 52.4 ± 5.4 ×10 mm³; P = < .01). Sulcal depth and brain volumes were significantly associated with the Neonatal Behavioral Assessment Scale severity (P = .005, Nagelkerke R² = 0.732).

CONCLUSIONS

INSVM fetuses showed differences in cortical development, including regions far from the lateral ventricles, that are associated with neonatal neurobehavior. These results suggest the possible use of these parameters to identify cases at higher risk of altered neurodevelopment.

A novel approach to multiple anatomical shape analysis: Application to fetal ventriculomegaly

Fetal ventriculomegaly (VM) is a condition in which one or both lateral ventricles are enlarged, and is diagnosed as an atrial diameter larger than 10 mm. Evidence of altered cortical folding associated with VM has been shown in the literature. However, existing works use a single scalar value such as diagnosis or lateral ventricular volume to characterize VM and study its relationship with alterations in cortical folding, thus failing to reveal the spatially-heterogeneous associations. In this work, we propose a novel approach to identify fine-grained associations between cortical folding and ventricular enlargement by leveraging the vertex-wise correlations between their growth patterns in terms of area expansion and curvature. Our approach comprises three steps. In the first step, we define a joint graph Laplacian matrix using cortex-to-ventricle correlations. The joint Laplacian is built based on multiple cortical features. Next, we propose a spectral embedding of the cortex-to-ventricle graph into a common underlying space where its nodes are projected according to the joint ventricle-cortex growth patterns. In this low-dimensional joint ventricle-cortex space, associated growth patterns lie close to each other. In the final step, we perform hierarchical clustering in the joint embedded space to identify associated sub-regions between cortex and ventricle. Using a dataset of 25 healthy fetuses and 23 fetuses with isolated non-severe VM within the age range of 26-29 gestational weeks, our approach reveals clinically relevant and heterogeneous regional associations. Cortical regions forming these associations are further validated using statistical analysis, revealing regions with altered folding that are significantly associated with ventricular dilation.

Revealing Regional Associations of Cortical Folding Alterations with In Utero Ventricular Dilation Using Joint Spectral Embedding

Fetal ventriculomegaly (VM) is a condition with dilation of one or both lateral ventricles, and is diagnosed as an atrial diameter larger than 10 mm. Evidence of altered cortical folding associated with VM has been shown in the literature. However, existing studies use a holistic approach (i.e., ventricle as a whole) based on diagnosis or ventricular volume, thus failing to reveal the spatially-heterogeneous association patterns between cortex and ventricle. To address this issue, we develop a novel method to identify spatially fine-scaled association maps between cortical development and VM by leveraging vertex-wise correlations between the growth patterns of both ventricular and cortical surfaces in terms of area expansion and curvature information. Our approach comprises multiple steps. In the first step, we define a joint graph Laplacian matrix using cortex-to-ventricle correlations. Next, we propose a spectral embedding of the cortex-to-ventricle graph into a common underlying space where their joint growth patterns are projected. More importantly, in the joint ventricle-cortex space, the vertices of associated regions from both cortical and ventricular surfaces would lie close to each other. In the final step, we perform clustering in the joint embedded space to identify associated sub-regions between cortex and ventricle. Using a dataset of 25 healthy fetuses and 23 fetuses with isolated non-severe VM within the age range of 26-29 gestational weeks, our results show that the proposed approach is able to reveal clinically relevant and meaningful regional associations.

Learning to combine complementary segmentation methods for fetal and 6-month infant brain MRI segmentation

Segmentation of brain structures during the pre-natal and early post-natal periods is the first step for subsequent analysis of brain development. Segmentation techniques can be roughly divided into two families. The first, which we denote as registration-based techniques, rely on initial estimates derived by registration to one (or several) templates. The second family, denoted as learning-based techniques, relate imaging (and spatial) features to their corresponding anatomical labels. Each approach has its own qualities and both are complementary to each other. In this paper, we explore two ensembling strategies, namely, stacking and cascading to combine the strengths of both families. We present experiments on segmentation of 6-month infant brains and a cohort of fetuses with isolated non-severe ventriculomegaly (INSVM). INSVM is diagnosed when ventricles are mildly enlarged and no other anomalies are apparent. Prognosis is difficult based solely on the degree of ventricular enlargement. In order to find markers for a more reliable prognosis, we use the resulting segmentations to find abnormalities in the cortical folding of INSVM fetuses. Segmentation results show that either combination strategy outperform all of the individual methods, thus demonstrating the capability of learning systematic combinations that lead to an overall improvement. In particular, the cascading strategy outperforms the ensembling one, the former one obtaining top 5, 7 and 13 results (out of 21 teams) in the segmentation of white matter, gray matter and cerebro-spinal fluid in the iSeg2017 MICCAI Segmentation Challenge. The resulting segmentations reveal that INSVM fetuses have a less convoluted cortex. This points to cortical folding abnormalities as potential markers of later neurodevelopmental outcomes.