Motor and cortico-striatal-thalamic connectivity alterations in intrauterine growth restriction

BACKGROUND: Intrauterine growth restriction is associated with short-and long-term neurodevelopmental problems. Structural brain changes underlying these alterations have been described with the use of different magnetic resonance-based methods that include changes in whole structural brain networks. However, evaluation of specific brain circuits and its correlation with related functions has not been investigated in intrauterine growth restriction.OBJECTIVES: In this study, we aimed to investigate differences in tractography-related metrics in cortico-striatal-thalamic and motor networks in intrauterine growth restricted children and whether these parameters were related with their specific function in order to explore its potential use as an imaging biomarker of altered neurodevelopment.METHODS: We included a group of 24 intrauterine growth restriction subjects and 27 control subjects that were scanned at 1 year old; we acquired T1-weighted and 30 directions diffusion magnetic resonance images. Each subject brain was segmented in 93 regions with the use of anatomical automatic labeling atlas, and deterministic tractography was performed. Brain regions included in motor and cortico-striatal-thalamic networks were defined based in functional and anatomic criteria. Within the streamlines that resulted from the whole brain tractography, those belonging to each specific circuit were selected and tractography-related metrics that included number of streamlines, fractional anisotropy, and integrity were calculated for each network. We evaluated differences between both groups and further explored the correlation of these parameters with the results of socioemotional, cognitive, and motor scales from Bayley Scale at 2 years of age.RESULTS: Reduced fractional anisotropy (cortico-striatal-thalamic, 0.319 +/- 0.018 vs 0.315 +/- 0.015; P =.010; motor, 0.322 +/- 0.019 vs 0.319 +/- 0.020; P =.019) and integrity cortico-striatal-thalamic (0.407 +/- 0.040 vs 0.399 +/- 0.034; P =.018; motor, 0.417 +/- 0.044 vs 0.409 +/- 0.046; P =.016) in both networks were observed in the intrauterine growth restriction group, with no differences in number of streamlines. More importantly, strong specific correlation was found between tractography-related metrics and its relative function in both networks in intrauterine growth restricted children. Motor network metrics were correlated specifically with motor scale results (fractional anisotropy: rho = 0.857; integrity: rho = 0.740); cortico-striatal-thalamic network metrics were correlated with cognitive (fractional anisotropy: rho = 0.793; integrity, rho = 0.762) and socioemotional scale (fractional anisotropy: rho = 0.850; integrity: rho = 0.877).CONCLUSIONS: These results support the existence of altered brain connectivity in intrauterine growth restriction demonstrated by altered connectivity in motor and cortico-striatal-thalamic networks, with reduced fractional anisotropy and integrity. The specific correlation between tractography-related metrics and neurodevelopmental outcomes in intrauterine growth restriction shows the potential to use this approach to develop imaging biomarkers to predict specific neurodevelopmental outcome in infants who are at risk because of intrauterine growth restriction and other prenatal diseases

Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother's cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep Convolutional Neural Networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization

Automatic deep learning-based pipeline for automatic delineation and measurement of fetal brain structures in routine mid-trimester ultrasound images

Introduction: The aim of this study was to develop a pipeline using state-of-the-art deep learning methods to automatically delineate and measure several of the most important brain structures in fetal brain ultrasound (US) images. Methods: The dataset was composed of 5,331 images of the fetal brain acquired during the routine mid-trimester US scan. Our proposed pipeline automatically performs the following three steps: brain plane classification (transventricular, transthalamic, or transcerebellar plane); brain structures delineation (9 different structures); and automatic measurement (from the structure delineations). The methods were trained on a subset of 4,331 images and each step was evaluated on the remaining 1,000 images. Results: Plane classification reached 98.6% average class accuracy. Brain structure delineation obtained an average pixel accuracy higher than 96% and a Jaccard index higher than 70%. Automatic measurements get an absolute error below 3.5% for the four standard head biometries (head circumference, biparietal diameter, occipitofrontal diameter, and cephalic index), 9% for transcerebellar diameter, 12% for cavum septi pellucidi ratio, and 26% for Sylvian fissure operculization degree. Conclusions: The proposed pipeline shows the potential of deep learning methods to delineate fetal head and brain structures and obtain automatic measures of each anatomical standard plane acquired during routine fetal US examination.The research leading to these results has received funding from the Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales,UK) and ASISA foundation.Peer ReviewedPostprint (published version

Ex-vivo mechanical sealing properties and toxicity of a bioadhesive patch as sealing system for fetal membrane iatrogenic defects

Preterm prelabor rupture of membranes (PPROM) is the most frequent complication of fetal surgery. Strategies to seal the membrane defect created by fetoscopy aiming to reduce the occurrence of PPROM have been attempted with little success. The objective of this study was to evaluate the ex-vivo mechanical sealing properties and toxicity of four different bioadhesives integrated in semi-rigid patches for fetal membranes. We performed and ex-vivo study using term human fetal membranes to compare the four integrated patches composed of silicone or silicone-polyurethane combined with dopaminated-hyaluronic acid or hydroxypropyl methylcellulose (HPMC). For mechanical sealing properties, membranes were mounted in a multiaxial inflation device with saline, perforated and sealed with the 4 combinations. We measured bursting pressure and maximum pressure free of leakage (n = 8). For toxicity, an organ culture of membranes sealed with the patches was used to measure pyknotic index (PI) and lactate dehydrogenase (LDH) concentration (n = 5). All bioadhesives achieved appropriate bursting pressures, but only HPMC forms achieved high maximum pressures free of leakage. Concerning toxicity, bioadhesives showed low PI and LDH levels, suggesting no cell toxicity. We conclude that a semi-rigid patch coated with HPMC achieved ex-vivo sealing of iatrogenic defects in fetal membranes with no signs of cell toxicity. These results warrant further research addressing long-term adhesiveness and feasibility as a sealing system for fetoscopy

Prenatal adverse environment is associated with epigenetic age deceleration at birth and hypomethylation at the hypoxia-responsive EP300 gene

Abstract Background: Obstetric complications have long been retrospectively associated with a wide range of short- and long-term health consequences, including neurodevelopmental alterations such as those observed in schizophrenia and other psychiatric disorders. However, prospective studies assessing fetal well-being during pregnancy tend to focus on perinatal complications as the final outcome of interest, while there is a scarcity of postnatal follow-up studies. In this study, the cerebroplacental ratio (CPR), a hemodynamic parameter reflecting fetal adaptation to hypoxic conditions, was analyzed in a sample of monozygotic monochorionic twins (60 subjects), part of them with prenatal complications, with regard to (i) epigenetic age acceleration, and (ii) DNA methylation at genes included in the polygenic risk score (PRS) for schizophrenia, and highly expressed in placental tissue. Results: Decreased CPR measured during the third trimester was associated with epigenetic age deceleration (β = 0.21, t = 3.362, p = 0.002). Exploration of DNA methylation at placentally expressed genes of the PRS for schizophrenia revealed methylation at cg06793497 (EP300 gene) to be associated with CPR (β = 0.021, t = 4.385; p = 0.00008, FDR-adjusted p = 0.11). This association was reinforced by means of an intrapair analysis in monozygotic twins discordant for prenatal suffering (β = 0.027, t = 3.924, p = 0.001). Conclusions: Prenatal adverse environment during the third trimester of pregnancy is associated with both (i) developmental immaturity in terms of epigenetic age, and (ii) decreased CpG-specific methylation in a gene involved in hypoxia response and schizophrenia genetic liability. Keywords: DNA methylation, Obstetric complications, Prenatal stress, Hypoxia, EP300 gene, Epigenetic clock, Monozygotic twins, Schizophreni

Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age

Background: Optimal prenatal care relies on accurate gestational age dating. After the first trimester, the accuracy of current gestational age estimation methods diminishes with increasing gestational age. Considering that, in many countries, access to first trimester crown rump length is still difficult owing to late booking, infrequent access to prenatal care, and unavailability of early ultrasound examination, the development of accurate methods for gestational age estimation in the second and third trimester of pregnancy remains an unsolved challenge in fetal medicine. Objective. This study aimed to evaluate the performance of an artificial intelligence method based on automated analysis of fetal brain morphology on standard cranial ultrasound sections to estimate the gestational age in second and third trimester fetuses compared with the current formulas using standard fetal biometry. Study Design: Standard transthalamic axial plane images from a total of 1394 patients undergoing routine fetal ultrasound were used to develop an artificial intelligence method to automatically estimate gestational age from the analysis of fetal brain information. We compared its performance—as stand alone or in combination with fetal biometric parameters—against 4 currently used fetal biometry formulas on a series of 3065 scans from 1992 patients undergoing second (n=1761) or third trimester (n=1298) routine ultrasound, with known gestational age estimated from crown rump length in the first trimester. Results: Overall, 95% confidence interval of the error in gestational age estimation was 14.2 days for the artificial intelligence method alone and 11.0 when used in combination with fetal biometric parameters, compared with 12.9 days of the best method using standard biometrics alone. In the third trimester, the lower 95% confidence interval errors were 14.3 days for artificial intelligence in combination with biometric parameters and 17 days for fetal biometrics, whereas in the second trimester, the 95% confidence interval error was 6.7 and 7, respectively. The performance differences were even larger in the small-for-gestational-age fetuses group (14.8 and 18.5, respectively). Conclusion: An automated artificial intelligence method using standard sonographic fetal planes yielded similar or lower error in gestational age estimation compared with fetal biometric parameters, especially in the third trimester. These results support further research to improve the performance of these methods in larger studies.The research leading to these results was partially funded by Transmural Biotech S.L. In addition, the research has received funding from “la Caixa” Foundation under grant agreements LCF/PR/GN14/10270005 and LCF/PR/GN18/10310003, the Instituto de Salud Carlos III (PI16/00861, PI17/00675) within the Plan Nacional de I+D+I and cofinanced by Instituto de Salud Carlos III— Subdirección General de Evaluación together with the Fondo Europeo de Desarrollo Regional (FEDER) “Una manera de hacer Europa,” Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, United Kingdom), Cellex Foundation, ASISA Foundation, and Agency for Management of University and Research Grants under grant 2017 SGR number 1531. In addition, E.E. has received funding from the Departament de Salut under grant number SLT008/18/00156.Peer ReviewedPostprint (published version

Long-term functional outcomes and correlation with regional brain connectivity by MRI diffusion tractography metrics in a near-term rabbit model of intrauterine growth restriction

Background: Intrauterine growth restriction (IUGR) affects 5-10% of all newborns and is associated with increased risk of memory, attention and anxiety problems in late childhood and adolescence. The neurostructural correlates of long-term abnormal neurodevelopment associated with IUGR are unknown. Thus, the aim of this study was to provide a comprehensive description of the long-term functional and neurostructural correlates of abnormal neurodevelopment associated with IUGR in a near-term rabbit model (delivered at 30 days of gestation) and evaluate the development of quantitative imaging biomarkers of abnormal neurodevelopment based on diffusion magnetic resonance imaging (MRI) parameters and connectivity. Methodology: At +70 postnatal days, 10 cases and 11 controls were functionally evaluated with the Open Field Behavioral Test which evaluates anxiety and attention and the Object Recognition Task that evaluates short-term memory and attention. Subsequently, brains were collected, fixed and a high resolution MRI was performed. Differences in diffusion parameters were analyzed by means of voxel-based and connectivity analysis measuring the number of fibers reconstructed within anxiety, attention and short-term memory networks over the total fibers. Principal Findings: The results of the neurobehavioral and cognitive assessment showed a significant higher degree of anxiety, attention and memory problems in cases compared to controls in most of the variables explored. Voxel-based analysis (VBA) revealed significant differences between groups in multiple brain regions mainly in grey matter structures, whereas connectivity analysis demonstrated lower ratios of fibers within the networks in cases, reaching the statistical significance only in the left hemisphere for both networks. Finally, VBA and connectivity results were also correlated with functional outcome. Conclusions: The rabbit model used reproduced long-term functional impairments and their neurostructural correlates of abnormal neurodevelopment associated with IUGR. The description of the pattern of microstructural changes underlying functional defects may help to develop biomarkers based in diffusion MRI and connectivity analysis

Vision based robot assistance in TTTS fetal surgery

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents an accurate and robust tracking vision algorithm for Fetoscopic Laser Photo-coagulation (FLP) surgery for Twin-Twin Transfusion Syndrome (TTTS). The aim of the proposed method is to assist surgeons during anastomosis localization, coagulation and review using a tele-operated robotic system. The algorithm computes the relative position of the fetoscope tool tip with respect to the placenta, via local vascular structure registration.The algorithm uses image features (local superficial vascular structures of the placenta’s surface) to automatically match consecutive fetoscopic images. It is composed of three sequential steps: image processing (filtering, binarization and vascular structures segmentation); relevant Points Of Interest (POIs) seletion; and image registration between consecutive images.The algorithm has to deal with the low quality of fetoscopic images, the liquid and dirty environment inside the placenta jointly with the thin diameter of the fetoscope optics and low amount of environment light reduces the image quality. The obtained images are blurred, noisy and with very poor color components.The tracking system has been tested using real video sequences of FLP surgery for TTTS. The computational performance enables real time tracking, locally guiding the robot over the placenta’s surface with enough accuracy.Peer ReviewedPostprint (author's final draft

Long-term reorganization of structural brain networks in a rabbit model of intrauterine growth restriction

Characterization of brain changes produced by intrauterine growth restriction (IUGR) is among the main challenges of modern fetal medicine and pediatrics. This condition affects 5-10% of all pregnancies and is associated with a wide range of neurodevelopmental disorders. Better understanding of the brain reorganization produced by IUGR opens a window of opportunity to find potential imaging biomarkers in order to identify the infants with a high risk of having neurodevelopmental problems and apply therapies to improve their outcomes. Structural brain networks obtained from diffusion magnetic resonance imaging (MRI) is a promising tool to study brain reorganization and to be used as a biomarker of neurodevelopmental alterations. In the present study this technique is applied to a rabbit animal model of IUGR, which presents some advantages including a controlled environment and the possibility to obtain high quality MRI with long acquisition times. Using a Q-Ball diffusion model, and a previously published rabbit brain MRI atlas, structural brain networks of 15 IUGR and 14 control rabbits at 70 days of age (equivalent to pre-adolescence human age) were obtained. The analysis of graph theory features showed a decreased network infrastructure (degree and binary global efficiency) associated with IUGR condition and a set of generalized fractional anisotropy (GFA) weighted measures associated with abnormal neurobehavior. Interestingly, when assessing the brain network organization independently of network infrastructure by means of normalized networks, IUGR showed increased global and local efficiencies. We hypothesize that this effect could reflect a compensatory response to reduced infrastructure in IUGR. These results present new evidence on the long-term persistence of the brain reorganization produced by IUGR that could underlie behavioral and developmental alterations previously described. The described changes in network organization have the potential to be used as biomarkers to monitor brain changes produced by experimental therapies in IUGR animal model

Non-invasive monitoring of pH and oxygen using miniaturized electrochemical sensors in an animal model of acute hypoxia

Background: One of the most prevalent causes of fetal hypoxia leading to stillbirth is placental insufficiency. Hemodynamic changes evaluated with Doppler ultrasound have been used as a surrogate marker of fetal hypoxia. However, Doppler evaluation cannot be performed continuously. As a first step, the present work aimed to evaluate the performance of miniaturized electrochemical sensors in the continuous monitoring of oxygen and pH changes in a model of acute hypoxia-acidosis. Methods: pH and oxygen electrochemical sensors were evaluated in a ventilatory hypoxia rabbit model. The ventilator hypoxia protocol included 3 differential phases: basal (100% FiO2), the hypoxia-acidosis period (10% FiO2) and recovery (100% FiO2). Sensors were tested in blood tissue (ex vivo sensing) and in muscular tissue (in vivo sensing). pH electrochemical and oxygen sensors were evaluated on the day of insertion (short-term evaluation) and pH electrochemical sensors were also tested after 5 days of insertion (long-term evaluation). pH and oxygen sensing were registered throughout the ventilatory hypoxia protocol (basal, hypoxia-acidosis, and recovery) and were compared with blood gas metabolites results from carotid artery catheterization (obtained with the EPOC blood analyzer). Finally, histological assessment was performed on the sensor insertion site. One-way ANOVA was used for the analysis of the evolution of acid-based metabolites and electrochemical sensor signaling results; a t-test was used for pre- and post-calibration analyses; and chi-square analyses for categorical variables. Results: At the short-term evaluation, both the pH and oxygen electrochemical sensors distinguished the basal and hypoxia-acidosis periods in both the in vivo and ex vivo sensing. However, only the ex vivo sensing detected the recovery period. In the long-term evaluation, the pH electrochemical sensor signal seemed to lose sensibility. Finally, histological assessment revealed no signs of alteration on the day of evaluation (short-term), whereas in the long-term evaluation a sub-acute inflammatory reaction adjacent to the implantation site was detected. Conclusions: Miniaturized electrochemical sensors represent a new generation of tools for the continuous monitoring of hypoxia-acidosis, which is especially indicated in high-risk pregnancies. Further studies including more tissue-compatible material would be required in order to improve long-term electrochemical sensing