Vanessa Kyriakopoulou

Tbx1 controls cardiac neural crest cell migration during arch artery development by regulating Gbx2 expression in the pharyngeal ectoderm

Elucidating the gene regulatory networks that govern pharyngeal arch artery (PAA) development is an important goal, as such knowledge can help to identify new genes involved in cardiovascular disease. The transcription factor Tbx1 plays a vital role in PAA development and is a major contributor to cardiovascular disease associated with DiGeorge syndrome. In this report, we used various genetic approaches to reveal part of a signalling network by which Tbx1 controls PAA development in mice. We investigated the crucial role played by the homeobox-containing transcription factor Gbx2 downstream of Tbx1. We found that PAA formation requires the pharyngeal surface ectoderm as a key signalling centre from which Gbx2, in response to Tbx1, triggers essential directional cues to the adjacent cardiac neural crest cells (cNCCs) en route to the caudal PAAs. Abrogation of this signal generates cNCC patterning defects leading to PAA abnormalities. Finally, we showed that the Slit/Robo signalling pathway is activated during cNCC migration and that components of this pathway are affected in Gbx2 and Tbx1 mutant embryos at the time of PAA development. We propose that the spatiotemporal control of this tightly orchestrated network of genes participates in crucial aspects of PAA development.

Great vessel development requires biallelic expression of Chd7 and Tbx1 in pharyngeal ectoderm in mice

Aortic arch artery patterning defects account for approximately 20% of congenital cardiovascular malformations and are observed frequently in velocardiofacial syndrome (VCFS). In the current study, we screened for chromosome rearrangements in patients suspected of VCFS, but who lacked a 22q11 deletion or TBX1 mutation. One individual displayed hemizygous CHD7, which encodes a chromodomain protein. CHD7 haploinsufficiency is the major cause of coloboma, heart defect, atresia choanae, retarded growth and development, genital hypoplasia, and ear anomalies/deafness (CHARGE) syndrome, but this patient lacked the major diagnostic features of coloboma and choanal atresia. Because a subset of CHARGE cases also display 22q11 deletions, we explored the embryological relationship between CHARGE and VCSF using mouse models. The hallmark of Tbx1 haploinsufficiency is hypo/aplasia of the fourth pharyngeal arch artery (PAA) at E10.5. Identical malformations were observed in Chd7 heterozygotes, with resulting aortic arch interruption at later stages. Other than Tbx1, Chd7 is the only gene reported to affect fourth PAA development by haploinsufficiency. Moreover, Tbx1+/-;Chd7+/- double heterozygotes demonstrated a synergistic interaction during fourth PAA, thymus, and ear morphogenesis. We could not rescue PAA morphogenesis by restoring neural crest Chd7 expression. Rather, biallelic expression of Chd7 and Tbx1 in the pharyngeal ectoderm was required for normal PAA development.

Automatic quantification of normal cortical folding patterns from fetal brain MRI

We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation-maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R(2)=0.99) confirming the close link between neurological development and cortical convolution. This allowed an age-dependent non-linear model to be accurately fitted to the folding measures. The model supports visual observations that, after a slow initial start, cortical folding increases rapidly between 25 and 30weeks and subsequently slows near birth. The model allows the accurate prediction of fetal age from an observed folding measure with a smaller error where growth is fastest. We also analysed regional patterns in folding by parcellating each fetal cortex using a nine-region anatomical atlas and found that Gompertz models fitted the change in lobar regions. Regional differences in growth rate were detected, with the parietal and posterior temporal lobes exhibiting the fastest growth, while the cingulate, frontal and medial temporal lobes developed more slowly.

Automated fetal brain segmentation from 2D MRI slices for motion correction

Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-to-volume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39 weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing.

Cortical Overgrowth in Fetuses With Isolated Ventriculomegaly

Mild cerebral ventricular enlargement is associated with schizophrenia, autism, epilepsy, and attention-deficit/hyperactivity disorder. Fetal ventriculomegaly is the most common central nervous system (CNS) abnormality affecting 1% of fetuses and is associated with cognitive, language, and behavioral impairments in childhood. Neurodevelopmental outcome is partially predictable by the 2-dimensional size of the ventricles in the absence of other abnormalities. We hypothesized that isolated fetal ventriculomegaly is a marker of altered brain development characterized by relative overgrowth and aimed to quantify brain growth using volumetric magnetic resonance imaging (MRI) in fetuses with isolated ventriculomegaly. Fetal brain MRI (1.5 T) was performed in 60 normal fetuses and 65 with isolated ventriculomegaly, across a gestational age range of 22-38 weeks. Volumetric analysis of the ventricles and supratentorial brain structures was performed on 3-dimensional reconstructed datasets. Fetuses with isolated ventriculomegaly had increased brain parenchyma volumes when compared with the control cohort (9.6%, P < 0.0001) with enlargement restricted to the cortical gray matter (17.2%, P = 0.002). The extracerebral cerebrospinal fluid and third and fourth ventricles were also enlarged. White matter, basal ganglia, and thalamic volumes were not significantly different between cohorts. The presence of relative cortical overgrowth in fetuses with ventriculomegaly may represent the neurobiological substrate for cognitive, language, and behavioral deficits in these children.

Localisation of the brain in fetal MRI using bundled SIFT features.

Fetal MRI is a rapidly emerging diagnostic imaging tool. Its main focus is currently on brain imaging, but there is a huge potential for whole body studies. We propose a method for accurate and robust localisation of the fetal brain in MRI when the image data is acquired as a stack of 2D slices misaligned due to fetal motion. We first detect possible brain locations in 2D images with a Bag-of-Words model using SIFT features aggregated within Maximally Stable Extremal Regions (called bundled SIFT), followed by a robust fitting of an axis-aligned 3D box to the selected regions. We rely on prior knowledge of the fetal brain development to define size and shape constraints. In a cross-validation experiment, we obtained a median error distance of 5.7mm from the ground truth and no missed detection on a database of 59 fetuses. This 2D approach thus allows a robust detection even in the presence of substantial fetal motion.

Construction of a fetal spatio-temporal cortical surface atlas from in utero MRI: Application of spectral surface matching.

In this study, we construct a spatio-temporal surface atlas of the developing cerebral cortex, which is an important tool for analysing and understanding normal and abnormal cortical development. In utero Magnetic Resonance Imaging (MRI) of 80 healthy fetuses was performed, with a gestational age range of 21.7 to 38.9 weeks. Topologically correct cortical surface models were extracted from reconstructed 3D MRI volumes. Accurate correspondences were obtained by applying a joint spectral analysis to cortices for sets of subjects close to a specific age. Sulcal alignment was found to be accurate in comparison to spherical demons, a state of the art registration technique for aligning 2D cortical representations (average Fréchet distance≈0.4 mm at 30 weeks). We construct consistent, unbiased average cortical surface templates, for each week of gestation, from age-matched groups of surfaces by applying kernel regression in the spectral domain. These were found to accurately capture the average cortical shape of individuals within the cohort, suggesting a good alignment of cortical geometry. Each spectral embedding and its corresponding cortical surface template provide a dual reference space where cortical geometry is aligned and a vertex-wise morphometric analysis can be undertaken.

Multidimensional Analysis of Fetal Posterior Fossa in Health and Disease

Fetal magnetic resonance imaging (MRI) is now routinely used to further investigate cerebellar malformations detected with ultrasound. However, the lack of 2D and 3D biometrics in the current literature hinders the detailed characterisation and classification of cerebellar anomalies. The main objectives of this fetal neuroimaging study were to provide normal posterior fossa growth trajectories during the second and third trimesters of pregnancy via semi-automatic segmentation of reconstructed fetal brain MR images and to assess common cerebellar malformations in comparison with the reference data. Using a 1.5-T MRI scanner, 143 MR images were obtained from 79 normal control and 53 fetuses with posterior fossa abnormalities that were grouped according to the severity of diagnosis on visual MRI inspections. All quantifications were performed on volumetric datasets, and supplemental outcome information was collected from the surviving infants. Normal growth trajectories of total brain, cerebellar, vermis, pons and fourth ventricle volumes showed significant correlations with 2D measurements and increased in second-order polynomial trends across gestation (Pearson r, p < 0.05). Comparison of normal controls to five abnormal cerebellum subgroups depicted significant alterations in volumes that could not be detected exclusively with 2D analysis (MANCOVA, p < 0.05). There were 15 terminations of pregnancy, 8 neonatal deaths, and a spectrum of genetic and neurodevelopmental outcomes in the assessed 24 children with cerebellar abnormalities. The given posterior fossa biometrics enhance the delineation of normal and abnormal cerebellar phenotypes on fetal MRI and confirm the advantages of utilizing advanced neuroimaging tools in clinical fetal research.

Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors

Automatic detection of the fetal brain in Magnetic Resonance (MR) Images is especially difficult due to arbitrary orientation of the fetus and possible movements during the scan. In this paper, we propose a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors. We calculate features for a set of 50 prenatal fast spin echo T2 volumes of the uterus and learn the appearance of the fetal brain in the feature space. We evaluate our novel classification method and show that we can localize the fetal brain with an accuracy of 100% and classify fetal brain voxels with an accuracy above 97%. Furthermore, we show how the classification process can be used for a direct segmentation of the brain by simple refinement methods within the raw MR scan data leading to a final segmentation with a Dice score above 0.90.

Automated Localization of Fetal Organs in MRI Using Random Forests with Steerable Features

Fetal MRI is an invaluable diagnostic tool complementary to ultrasound thanks to its high contrast and resolution. Motion artifacts and the arbitrary orientation of the fetus are two main challenges of fetal MRI. In this paper, we propose a method based on Random Forests with steerable features to automatically localize the heart, lungs and liver in fetal MRI. During training, all MR images are mapped into a standard coordinate system that is defined by landmarks on the fetal anatomy and normalized for fetal age. Image features are then extracted in this coordinate system. During testing, features are computed for different orientations with a search space constrained by previously detected landmarks. The method was tested on healthy fetuses as well as fetuses with intrauterine growth restriction (IUGR) from 20 to 38 weeks of gestation. The detection rate was above 90% for all organs of healthy fetuses in the absence of motion artifacts. In the presence of motion, the detection rate was 83% for the heart, 78% for the lungs and 67% for the liver. Growth restriction did not decrease the performance of the heart detection but had an impact on the detection of the lungs and liver. The proposed method can be used to initialize subsequent processing steps such as segmentation or motion correction, as well as automatically orient the 3D volume based on the fetal anatomy to facilitate clinical examination.