Quantitative Folding Pattern Analysis of Early Primary Sulci in Human Fetuses with Brain Abnormalities

Automatic cortical surface parcellation in the fetal brain using attention-gated spherical U-net

Cortical surface parcellation for fetal brains is essential for the understanding of neurodevelopmental trajectories during gestations with regional analyses of brain structures and functions. This study proposes the attention-gated spherical U-net, a novel deep-learning model designed for automatic cortical surface parcellation of the fetal brain. We trained and validated the model using MRIs from 55 typically developing fetuses [gestational weeks: 32.9 ± 3.3 (mean ± SD), 27.4-38.7]. The proposed model was compared with the surface registration-based method, SPHARM-net, and the original spherical U-net. Our model demonstrated significantly higher accuracy in parcellation performance compared to previous methods, achieving an overall Dice coefficient of 0.899 ± 0.020. It also showed the lowest error in terms of the median boundary distance, 2.47 ± 1.322 (mm), and mean absolute percent error in surface area measurement, 10.40 ± 2.64 (%). In this study, we showed the efficacy of the attention gates in capturing the subtle but important information in fetal cortical surface parcellation. Our precise automatic parcellation model could increase sensitivity in detecting regional cortical anomalies and lead to the potential for early detection of neurodevelopmental disorders in fetuses.

The role of cortical structural variance in deep learning-based prediction of fetal brain age

Background

Deep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age.

Methods

We examined the association between the predicted brain age difference (PAD: predicted brain age-chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis.

Results

Our results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age.

Conclusion

These results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model.

A Prospective Multi-Institutional Study Comparing the Brain Development in the Third Trimester between Opioid-Exposed and Nonexposed Fetuses Using Advanced Fetal MR Imaging Techniques

BACKGROUND AND PURPOSE

While the adverse neurodevelopmental effects of prenatal opioid exposure on infants and children in the United States are well described, the underlying causative mechanisms have yet to be fully understood. This study aims to compare quantitative volumetric and surface-based features of the fetal brain between opioid-exposed fetuses and unexposed controls by using advanced MR imaging processing techniques.

MATERIALS AND METHODS

This is a multi-institutional IRB-approved study in which pregnant women with and without opioid use during the current pregnancy were prospectively recruited to undergo fetal MR imaging. A total of 14 opioid-exposed (31.4 ± 2.3 weeks of gestation) and 15 unexposed (31.4 ± 2.4 weeks) fetuses were included. Whole brain volume, cortical plate volume, surface area, sulcal depth, mean curvature, and gyrification index were computed as quantitative features by using our fetal brain MR imaging processing pipeline.

RESULTS

After correcting for gestational age, fetal sex, maternal education, polysubstance use, high blood pressure, and MR imaging acquisition site, all of the global morphologic features were significantly lower in the opioid-exposed fetuses compared with the unexposed fetuses, including brain volume, cortical volume, cortical surface area, sulcal depth, cortical mean curvature, and gyrification index. In regional analysis, the opioid-exposed fetuses showed significantly decreased surface area and sulcal depth in the bilateral Sylvian fissures, central sulci, parieto-occipital fissures, temporal cortices, and frontal cortices.

CONCLUSIONS

In this small cohort, prenatal opioid exposure was associated with altered fetal brain development in the third trimester. This adds to the growing body of literature demonstrating that prenatal opioid exposure affects the developing brain.

Association between placental oxygen transport and fetal brain cortical development: a study in monochorionic diamniotic twins

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.

Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI

BACKGROUND AND PURPOSE

The current imaging assessment of fetal brain gyrification is performed qualitatively and subjectively using sonography and MR imaging. A few previous studies have suggested methods for quantification of fetal gyrification based on 3D reconstructed MR imaging, which requires unique data and is time-consuming. In this study, we aimed to develop an automatic pipeline for gyrification assessment based on routinely acquired fetal 2D MR imaging data, to quantify normal changes with gestation, and to measure differences in fetuses with lissencephaly and polymicrogyria compared with controls.

MATERIALS AND METHODS

We included coronal T2-weighted MR imaging data of 162 fetuses retrospectively collected from 2 clinical sites: 134 controls, 12 with lissencephaly, 13 with polymicrogyria, and 3 with suspected lissencephaly based on sonography, yet with normal MR imaging diagnoses. Following brain segmentation, 5 gyrification parameters were calculated separately for each hemisphere on the basis of the area and ratio between the contours of the cerebrum and its convex hull. Seven machine learning classifiers were evaluated to differentiate control fetuses and fetuses with lissencephaly or polymicrogyria.

RESULTS

In control fetuses, all parameters changed significantly with gestational age (P < .05). Compared with controls, fetuses with lissencephaly showed significant reductions in all gyrification parameters (P ≤ .02). Similarly, significant reductions were detected for fetuses with polymicrogyria in several parameters (P ≤ .001). The 3 suspected fetuses showed normal gyrification values, supporting the MR imaging diagnosis. An XGBoost-linear algorithm achieved the best results for classification between fetuses with lissencephaly and control fetuses (n = 32), with an area under the curve of 0.90 and a recall of 0.83. Similarly, a random forest classifier showed the best performance for classification of fetuses with polymicrogyria and control fetuses (n = 33), with an area under the curve of 0.84 and a recall of 0.62.

CONCLUSIONS

This study presents a pipeline for automatic quantification of fetal brain gyrification and provides normal developmental curves from a large cohort. Our method significantly differentiated fetuses with lissencephaly and polymicrogyria, demonstrating lower gyrification values. The method can aid radiologic assessment, highlight fetuses at risk, and may improve early identification of fetuses with cortical malformations.

Population-wise labeling of sulcal graphs using multi-graph matching

Population-wise matching of the cortical folds is necessary to compute statistics, a required step for e.g. identifying biomarkers of neurological or psychiatric disorders. The difficulty arises from the massive inter-individual variations in the morphology and spatial organization of the folds. The task is challenging both methodologically and conceptually. In the widely used registration-based techniques, these variations are considered as noise and the matching of folds is only implicit. Alternative approaches are based on the extraction and explicit identification of the cortical folds. In particular, representing cortical folding patterns as graphs of sulcal basins-termed sulcal graphs-enables to formalize the task as a graph-matching problem. In this paper, we propose to address the problem of sulcal graph matching directly at the population level using multi-graph matching techniques. First, we motivate the relevance of the multi-graph matching framework in this context. We then present a procedure for generating populations of artificial sulcal graphs, which allows us to benchmark several state-of-the-art multi-graph matching methods. Our results on both artificial and real data demonstrate the effectiveness of multi-graph matching techniques in obtaining a population-wise consistent labeling of cortical folds at the sulcal basin level.

Mechanical hierarchy in the formation and modulation of cortical folding patterns

The important mechanical parameters and their hierarchy in the growth and folding of the human brain have not been thoroughly understood. In this study, we developed a multiscale mechanical model to investigate how the interplay between initial geometrical undulations, differential tangential growth in the cortical plate, and axonal connectivity form and regulate the folding patterns of the human brain in a hierarchical order. To do so, different growth scenarios with bilayer spherical models that features initial undulations on the cortex and uniform or heterogeneous distribution of axonal fibers in the white matter were developed, statistically analyzed, and validated by the imaging observations. The results showed that the differential tangential growth is the inducer of cortical folding, and in a hierarchal order, high-amplitude initial undulations on the surface and axonal fibers in the substrate regulate the folding patterns and determine the location of gyri and sulci. The locations with dense axonal fibers after folding settle in gyri rather than sulci. The statistical results also indicated that there is a strong correlation between the location of positive (outward) and negative (inward) initial undulations and the locations of gyri and sulci after folding, respectively. In addition, the locations of 3-hinge gyral folds are strongly correlated with the initial positive undulations and locations of dense axonal fibers. As another finding, it was revealed that there is a correlation between the density of axonal fibers and local gyrification index, which has been observed in imaging studies but not yet fundamentally explained. This study is the first step in understanding the linkage between abnormal gyrification (surface morphology) and disruption in connectivity that has been observed in some brain disorders such as Autism Spectrum Disorder. Moreover, the findings of the study directly contribute to the concept of the regularity and variability of folding patterns in individual human brains.

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.

Gyral peaks and patterns in human brains

Cortical folding patterns are related to brain function, cognition, and behavior. Since the relationship has not been fully explained on a coarse scale, many efforts have been devoted to the identification of finer grained cortical landmarks, such as sulcal pits and gyral peaks, which were found to remain invariant across subjects and ages and the invariance may be related to gene mediated proto-map. However, gyral peaks were only investigated on macaque monkey brains, but not on human brains where the investigation is challenged due to high inter-individual variabilities. To this end, in this work, we successfully identified 96 gyral peaks both on the left and right hemispheres of human brains, respectively. These peaks are spatially consistent across individuals. Higher or sharper peaks are more consistent across subjects. Both structural and functional graph metrics of peaks are significantly different from other cortical regions, and more importantly, these nodal graph metrics are anti-correlated with the spatial consistency metrics within peaks. In addition, the distribution of peaks and various cortical anatomical, structural/functional connective features show hemispheric symmetry. These findings provide new clues to understanding the cortical landmarks, as well as their relationship with brain functions, cognition, behavior in both healthy and aberrant brains.

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.

Gyral peaks: Novel gyral landmarks in developing macaque brains

Cerebral cortex development undergoes a variety of processes, which provide valuable information for the study of the developmental mechanism of cortical folding as well as its relationship to brain structural architectures and brain functions. Despite the variability in the anatomy-function relationship on the higher-order cortex, recent studies have succeeded in identifying typical cortical landmarks, such as sulcal pits, that bestow specific functional and cognitive patterns and remain invariant across subjects and ages with their invariance being related to a gene-mediated proto-map. Inspired by the success of these studies, we aim in this study at defining and identifying novel cortical landmarks, termed gyral peaks, which are the local highest foci on gyri. By analyzing data from 156 MRI scans of 32 macaque monkeys with the age spanned from 0 to 36 months, we identified 39 and 37 gyral peaks on the left and right hemispheres, respectively. Our investigation suggests that these gyral peaks are spatially consistent across individuals and relatively stable within the age range of this dataset. Moreover, compared with other gyri, gyral peaks have a thicker cortex, higher mean curvature, more pronounced hub-like features in structural connective networks, and are closer to the borders of structural connectivity-based cortical parcellations. The spatial distribution of gyral peaks was shown to correlate with that of other cortical landmarks, including sulcal pits. These results provide insights into the spatial arrangement and temporal development of gyral peaks as well as their relation to brain structure and function.

Quantification of sulcal emergence timing and its variability in early fetal life: Hemispheric asymmetry and sex difference

Human fetal brains show regionally different temporal patterns of sulcal emergence following a regular timeline, which may be associated with spatiotemporal patterns of gene expression among cortical regions. This study aims to quantify the timing of sulcal emergence and its temporal variability across typically developing fetuses by fitting a logistic curve to presence or absence of sulcus. We found that the sulcal emergence started from the central to the temporo-parieto-occipital lobes and frontal lobe, and the temporal variability of emergence in most of the sulci was similar between 1 and 2 weeks. Small variability (< 1 week) was found in the left central and postcentral sulci and larger variability (>2 weeks) was shown in the bilateral occipitotemporal and left superior temporal sulci. The temporal variability showed a positive correlation with the emergence timing that may be associated with differential contributions between genetic and environmental factors. Our statistical analysis revealed that the right superior temporal sulcus emerged earlier than the left. Female fetuses showed a trend of earlier sulcal emergence in the right superior temporal sulcus, lower temporal variability in the right intraparietal sulcus, and higher variability in the right precentral sulcus compared to male fetuses. Our quantitative and statistical approach quantified the temporal patterns of sulcal emergence in detail that can be a reference for assessing the normality of developing fetal gyrification.

Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation

Accurate measurement of brain structures is essential for the evaluation of neonatal brain growth and development. The conventional methods use manual segmentation to measure brain tissues, which is very time-consuming and inefficient. Recent deep learning achieves excellent performance in computer vision, but it is still unsatisfactory for segmenting magnetic resonance images of neonatal brains because they are immature with unique attributes. In this paper, we propose a novel attention-modulated multi-branch convolutional neural network for neonatal brain tissue segmentation. The proposed network is built on the encoder-decoder framework by introducing both multi-scale convolutions in the encoding path and multi-branch attention modules in the decoding path. Multi-scale convolutions with different kernels are used to extract rich semantic features across large receptive fields in the encoding path. Multi-branch attention modules are used to capture abundant contextual information in the decoding path for segmenting brain tissues by fusing both local features and their corresponding global dependencies. Spatial attention connections between the encoding and decoding paths are designed to increase feature propagation for both avoiding information loss during downsampling and accelerating model training convergence. The proposed network was implemented in comparison with baseline methods on three neonatal brain datasets. Our network achieves the average Dice similarity coefficients/the average Hausdorff distances of 0.9116/8.1289, 0.9367/9.8212 and 0.8931/8.1612 on the customized dCBP2021 dataset, 0.8786/11.7863, 0.8965/13.4296 and 0.8539/10.462 on the public NBAtlas dataset, as well as 0.9253/7.7968, 0.9448/9.5472 and 0.9132/7.5877 on the public dHCP2017 dataset in partitioning the brain into gray matter, white matter and cerebrospinal fluid, respectively. The experimental results show that the proposed method achieves competitive state-of-the-art performance in neonatal brain tissue segmentation. The code and pre-trained models are available at https://github.com/zhangyongqin/AMCNN.

Gyral Hinges Account for the Highest Cost and the Highest Communication Capacity in a Corticocortical Network

Prior studies reported the global structure of brain networks exhibits the "small-world" and "rich-world" attributes. However, the underlying structural and functional architecture highlighted by these graph theory findings hasn't been explicitly related to the morphology of the cortex. This could be attributed to the lower resolution of used folding patterns, such as gyro-sulcal patterns. By defining a novel gyral folding pattern, termed gyral hinge (GH), which is the conjunction of ordinary gyri from multiple directions, we found GHs possess the highest length and cost in the white matter fiber connective network, and the shortest paths in the network tend to travel through GHs in their middle part. Based on these findings, we would hypothesize GHs could reside in the centers of a network core, thereby accounting for the highest cost and the highest communication capacity in a corticocortical network. The following results further support our hypothesis: 1) GHs possess stronger functional network integration capacity. 2) Higher cost is found on the connection with GHs to hinges and GHs to GHs. 3) Moving GHs introduces higher extra network cost. Our findings and hypotheses could reveal a profound relationship among the cortical folding patterns, axonal wiring architectures, and brain functions.

The Prenatal Morphomechanic Impact of Agenesis of the Corpus Callosum on Human Brain Structure and Asymmetry

Genetic, molecular, and physical forces together impact brain morphogenesis. The early impact of deficient midline crossing in agenesis of the Corpus Callosum (ACC) on prenatal human brain development and architecture is widely unknown. Here we analyze the changes of brain structure in 46 fetuses with ACC in vivo to identify their deviations from normal development. Cases of complete ACC show an increase in the thickness of the cerebral wall in the frontomedial regions and a reduction in the temporal, insular, medial occipital and lateral parietal regions, already present at midgestation. ACC is associated with a more symmetric configuration of the temporal lobes and increased frequency of atypical asymmetry patterns, indicating an early morphomechanic effect of callosal growth on human brain development affecting the thickness of the pallium along a ventro-dorsal gradient. Altered prenatal brain architecture in ACC emphasizes the importance of conformational forces introduced by emerging interhemispheric connectivity on the establishment of polygenically determined brain asymmetries.

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.

Regional Alterations in Cortical Sulcal Depth in Living Fetuses with Down Syndrome

Down syndrome (DS) is the most common genetic cause of developmental disabilities. Advanced analysis of brain magnetic resonance imaging (MRI) has been used to find brain abnormalities and their relationship to neurocognitive impairments in children and adolescents with DS. Because genetic factors affect brain development in early fetal life, there is a growing interest in analyzing brains from living fetuses with DS. In this study, we investigated regional sulcal folding depth as well as global cortical gyrification from fetal brain MRIs. Nine fetuses with DS (29.1 ± 4.24 gestational weeks [mean ± standard deviation]) were compared with 17 typically developing [TD] fetuses (28.4 ± 3.44). Fetuses with DS showed lower whole-brain average sulcal depths and gyrification index than TD fetuses. Significant decreases in sulcal depth were found in bilateral Sylvian fissures and right central and parieto-occipital sulci. On the other hand, significantly increased sulcal depth was shown in the left superior temporal sulcus, which is related to atypical hemispheric asymmetry of cortical folding. Moreover, these group differences increased as gestation progressed. This study demonstrates that regional sulcal depth is a sensitive marker for detecting alterations of cortical development in DS during fetal life, which may be associated with later neurocognitive impairment.

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.

Fetal Cortical Plate Segmentation Using Fully Convolutional Networks With Multiple Plane Aggregation

Fetal magnetic resonance imaging (MRI) has the potential to advance our understanding of human brain development by providing quantitative information of cortical plate (CP) development in vivo. However, for a reliable quantitative analysis of cortical volume and sulcal folding, accurate and automated segmentation of the CP is crucial. In this study, we propose a fully convolutional neural network for the automatic segmentation of the CP. We developed a novel hybrid loss function to improve the segmentation accuracy and adopted multi-view (axial, coronal, and sagittal) aggregation with a test-time augmentation method to reduce errors using three-dimensional (3D) information and multiple predictions. We evaluated our proposed method using the ten-fold cross-validation of 52 fetal brain MR images (22.9-31.4 weeks of gestation). The proposed method obtained Dice coefficients of 0.907 ± 0.027 and 0.906 ± 0.031 as well as a mean surface distance error of 0.182 ± 0.058 mm and 0.185 ± 0.069 mm for the left and right, respectively. In addition, the left and right CP volumes, surface area, and global mean curvature generated by automatic segmentation showed a high correlation with the values generated by manual segmentation (R 2 > 0.941). We also demonstrated that the proposed hybrid loss function and the combination of multi-view aggregation and test-time augmentation significantly improved the CP segmentation accuracy. Our proposed segmentation method will be useful for the automatic and reliable quantification of the cortical structure in the fetal brain.

Early-Emerging Sulcal Patterns Are Atypical in Fetuses with Congenital Heart Disease

Fetuses with congenital heart disease (CHD) have third trimester alterations in cortical development on brain magnetic resonance imaging (MRI). However, the intersulcal relationships contributing to global sulcal pattern remain unknown. This study applied a novel method for examining the geometric and topological relationships between sulci to fetal brain MRIs from 21-30 gestational weeks in CHD fetuses (n = 19) and typically developing (TD) fetuses (n = 17). Sulcal pattern similarity index (SI) to template fetal brain MRIs was determined for the position, area, and depth for corresponding sulcal basins and intersulcal relationships for each subject. CHD fetuses demonstrated altered global sulcal patterns in the left hemisphere compared with TD fetuses (TD [SI, mean ± SD]: 0.822 ± 0.023, CHD: 0.795 ± 0.030, P = 0.002). These differences were present in the earliest emerging sulci and were driven by differences in the position of corresponding sulcal basins (TD: 0.897 ± 0.024, CHD: 0.878 ± 0.019, P = 0.006) and intersulcal relationships (TD: 0.876 ± 0.031, CHD: 0.857 ± 0.018, P = 0.033). No differences in cortical gyrification index, mean curvature, or surface area were present. These data suggest our methods may be more sensitive than traditional measures for evaluating cortical developmental alterations early in gestation.

Temporal Patterns of Emergence and Spatial Distribution of Sulcal Pits During Fetal Life

Sulcal pits are thought to represent the first cortical folds of primary sulci during neurodevelopment. The uniform spatial distribution of sulcal pits across individuals is hypothesized to be predetermined by a human-specific protomap which is related to functional localization under genetic controls in early fetal life. Thus, it is important to characterize temporal and spatial patterns of sulcal pits in the fetal brain that would provide additional information of functional development of the human brain and crucial insights into abnormal cortical maturation. In this paper, we investigated temporal patterns of emergence and spatial distribution of sulcal pits using 48 typically developing fetal brains in the second half of gestation. We found that the position and spatial variance of sulcal pits in the fetal brain are similar to those in the adult brain, and they are also temporally uniform against dynamic brain growth during fetal life. Furthermore, timing of pit emergence shows a regionally diverse pattern that may be associated with the subdivisions of the protomap. Our findings suggest that sulcal pits in the fetal brain are useful anatomical landmarks containing detailed information of functional localization in early cortical development and maintaining their spatial distribution throughout the human lifetime.

Abnormal Left-Hemispheric Sulcal Patterns Correlate with Neurodevelopmental Outcomes in Subjects with Single Ventricular Congenital Heart Disease

Neurodevelopmental abnormalities are the most common noncardiac complications in patients with congenital heart disease (CHD). Prenatal brain abnormalities may be due to reduced oxygenation, genetic factors, or less commonly, teratogens. Understanding the contribution of these factors is essential to improve outcomes. Because primary sulcal patterns are prenatally determined and under strong genetic control, we hypothesized that they are influenced by genetic variants in CHD. In this study, we reveal significant alterations in sulcal patterns among subjects with single ventricle CHD (n = 115, 14.7 ± 2.9 years [mean ± standard deviation]) compared with controls (n = 45, 15.5 ± 2.4 years) using a graph-based pattern-analysis technique. Among patients with CHD, the left hemisphere demonstrated decreased sulcal pattern similarity to controls in the left temporal and parietal lobes, as well as the bilateral frontal lobes. Temporal and parietal lobes demonstrated an abnormally asymmetric left-right pattern of sulcal basin area in CHD subjects. Sulcal pattern similarity to control was positively correlated with working memory, processing speed, and executive function. Exome analysis identified damaging de novo variants only in CHD subjects with more atypical sulcal patterns. Together, these findings suggest that sulcal pattern analysis may be useful in characterizing genetically influenced, atypical early brain development and neurodevelopmental risk in subjects with CHD.

Disorganized Patterns of Sulcal Position in Fetal Brains with Agenesis of Corpus Callosum

Fetuses with isolated agenesis of the corpus callosum (ACC) are associated with a broad spectrum of neurodevelopmental disability that cannot be specifically predicted in prenatal neuroimaging. We hypothesized that ACC may be associated with aberrant cortical folding. In this study, we determined altered patterning of early primary sulci development in fetuses with isolated ACC using novel quantitative sulcal pattern analysis which measures deviations of regional sulcal features (position, depth, and area) and their intersulcal relationships in 7 fetuses with isolated ACC (27.1 ± 3.8 weeks of gestation, mean ± SD) and 17 typically developing (TD) fetuses (25.7 ± 2.0 weeks) from normal templates. Fetuses with ACC showed significant alterations in absolute sulcal positions and relative intersulcal positional relationship compared to TD fetuses, which were not detected by traditional gyrification index. Our results reveal altered sulcal positional development even in isolated ACC that is present as early as the second trimester and continues throughout the fetal period. It might originate from altered white matter connections and portend functional variances in later life.

Automatic labeling of cortical sulci for the human fetal brain based on spatio-temporal information of gyrification

Accurate parcellation and labeling of primary cortical sulci in the human fetal brain is useful for regional analysis of brain development. However, human fetal brains show large spatio-temporal changes in brain size, cortical folding patterns, and relative position/size of cortical regions, making accurate automatic sulcal labeling challenging. Here, we introduce a novel sulcal labeling method for the fetal brain using spatio-temporal gyrification information from multiple fetal templates. First, spatial probability maps of primary sulci are generated on the templates from 23 to 33 gestational weeks and registered to an individual brain. Second, temporal weights, which determine the level of contribution to the labeling for each template, are defined by similarity of gyrification between the individual and the template brains. We combine the weighted sulcal probability maps from the multiple templates and adopt sulcal basin-wise approach to assign sulcal labels to each basin. Our labeling method was applied to 25 fetuses (22.9-29.6 gestational weeks), and the labeling accuracy was compared to manually assigned sulcal labels using the Dice coefficient. Moreover, our multi-template basin-wise approach was compared to a single-template approach, which does not consider the temporal dynamics of gyrification, and a fully-vertex-wise approach. The mean accuracy of our approach was 0.958 across subjects, significantly higher than the accuracies of the other approaches. This novel approach shows highly accurate sulcal labeling and provides a reliable means to examine characteristics of cortical regions in the fetal brain.

Exploring early human brain development with structural and physiological neuroimaging

Early brain development, from the embryonic period to infancy, is characterized by rapid structural and functional changes. These changes can be studied using structural and physiological neuroimaging methods. In order to optimally acquire and accurately interpret this data, concepts from adult neuroimaging cannot be directly transferred. Instead, one must have a basic understanding of fetal and neonatal structural and physiological brain development, and the important modulators of this process. Here, we first review the major developmental milestones of transient cerebral structures and structural connectivity (axonal connectivity) followed by a summary of the contributions from ex vivo and in vivo MRI. Next, we discuss the basic biology of neuronal circuitry development (synaptic connectivity, i.e. ensemble of direct chemical and electrical connections between neurons), physiology of neurovascular coupling, baseline metabolic needs of the fetus and the infant, and functional connectivity (defined as statistical dependence of low-frequency spontaneous fluctuations seen with functional magnetic resonance imaging (fMRI)). The complementary roles of magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS) are discussed. We include a section on modulators of brain development where we focus on the placenta and emerging placental MRI approaches. In each section we discuss key technical limitations of the imaging modalities and some of the limitations arising due to the biology of the system. Although neuroimaging approaches have contributed significantly to our understanding of early brain development, there is much yet to be done and a dire need for technical innovations and scientific discoveries to realize the future potential of early fetal and infant interventions to avert long term disease.

Sulcal pits and patterns in developing human brains

Spatial distribution and specific geometric and topological patterning of early sulcal folds have been hypothesized to be under stronger genetic control and are more associated with optimal organization of cortical functional areas and their white matter connections, compared to later developing sulci. Several previous studies of sulcal pit (putative first sulcal fold) distribution and sulcal pattern analyses using graph structures have provided evidence of the importance of sulcal pits and patterns as remarkable anatomical features closely related to human brain function, suggesting additional insights concerning the anatomical and functional development of the human brain. Recently, early sulcal folding patterns have been observed in healthy fetuses and fetuses with brain abnormalities such as polymicrogyria and agenesis of corpus callosum. Graph-based quantitative sulcal pattern analysis has shown high sensitivity in detecting emerging subtle abnormalities in cerebral cortical growth in early fetal stages that are difficult to detect via qualitative visual assessment or using traditional cortical measures such as gyrification index and curvature. It has proven effective for characterizing genetically influenced early cortical folding development. Future studies will be aimed at better understanding a comprehensive map of spatio-temporal dynamics of fetal cortical folding in a large longitudinal cohort in order to examine individual clinical fetal MRIs and predict postnatal neurodevelopmental outcomes from early fetal life.