Predictive Role of F2-Isoprostanes as Biomarkers for Brain Damage after Neonatal Surgery.

OBJECTIVE: Neonates have a high risk of oxidative stress during anesthetic procedures. The predictive role of oxidative stress biomarkers on the occurrence of brain injury in the perioperative period has not been reported before. METHODS: A prospective cohort study of patients requiring major surgery in the neonatal period was conducted. Biomarker levels of nonprotein-bound iron (NPBI) in plasma and F2-isoprostane in plasma and urine before and after surgical intervention were determined. Brain injury was assessed using postoperative MRI. RESULTS: In total, 61 neonates were included, median gestational age at 39 weeks (range 31-42) and weight at 3000 grams (1400-4400). Mild to moderate brain lesions were found in 66%. Logistic regression analysis showed a significant difference between plasma NPBI in patients with nonparenchymal injury versus no brain injury: 1.34 umol/L was identified as correlation threshold for nonparenchymal injury (sensitivity 67%, specificity 91%). In the multivariable analysis, correcting for GA, no other significant relation was found with the oxidative stress biomarkers and risk factors. CONCLUSION: Oxidative stress seems to occur during anaesthesia in this cohort of neonates. Plasma nonprotein-bound iron showed to be associated with nonparenchymal injury after surgery, with values of 1.34 umol/L or higher. Risk factors should be elucidated in a more homogeneous patient group

Effect of allopurinol in addition to hypothermia treatment in neonates for hypoxic-ischemic brain injury on neurocognitive outcome (ALBINO) : Study protocol of a blinded randomized placebo-controlled parallel group multicenter trial for superiority (phase III)

Background: Perinatal asphyxia and resulting hypoxic-ischemic encephalopathy is a major cause of death and long-term disability in term born neonates. Up to 20,000 infants each year are affected by HIE in Europe and even more in regions with lower level of perinatal care. The only established therapy to improve outcome in these infants is therapeutic hypothermia. Allopurinol is a xanthine oxidase inhibitor that reduces the production of oxygen radicals as superoxide, which contributes to secondary energy failure and apoptosis in neurons and glial cells after reperfusion of hypoxic brain tissue and may further improve outcome if administered in addition to therapeutic hypothermia. Methods: This study on the effects of ALlopurinol in addition to hypothermia treatment for hypoxic-ischemic Brain Injury on Neurocognitive Outcome (ALBINO), is a European double-blinded randomized placebo-controlled parallel group multicenter trial (Phase III) to evaluate the effect of postnatal allopurinol administered in addition to standard of care (including therapeutic hypothermia if indicated) on the incidence of death and severe neurodevelopmental impairment at 24 months of age in newborns with perinatal hypoxic-ischemic insult and signs of potentially evolving encephalopathy. Allopurinol or placebo will be given in addition to therapeutic hypothermia (where indicated) to infants with a gestational age ≥ 36 weeks and a birth weight ≥ 2500 g, with severe perinatal asphyxia and potentially evolving encephalopathy. The primary endpoint of this study will be death or severe neurodevelopmental impairment versus survival without severe neurodevelopmental impairment at the age of two years. Effects on brain injury by magnetic resonance imaging and cerebral ultrasound, electric brain activity, concentrations of peroxidation products and S100B, will also be studied along with effects on heart function and pharmacokinetics of allopurinol after iv-infusion. Discussion: This trial will provide data to assess the efficacy and safety of early postnatal allopurinol in term infants with evolving hypoxic-ischemic encephalopathy. If proven efficacious and safe, allopurinol could become part of a neuroprotective pharmacological treatment strategy in addition to therapeutic hypothermia in children with perinatal asphyxia. Trial registration: NCT03162653, www.ClinicalTrials.gov, May 22, 2017. © 2019 The Author(s).Peer reviewe

Shape variability of the central sulcus in the developing brain: a longitudinal descriptive and predictive study in preterm infants

Despite growing evidence of links between sulcation and function in the adult brain, the folding dynamics, occurring mostly before normal-term-birth, is vastly unknown. Looking into the development of cortical sulci in infants can give us keys to address fundamental questions: what is the sulcal shape variability in the developing brain? When are the shape features encoded? How are these morphological parameters related to further functional development? In this study, we aimed to investigate the shape variability of the developing central sulcus, which is the frontier between the primary somatosensory and motor cortices. We studied a cohort of 71 extremely preterm infants scanned twice using MRI - once around 30 weeks post-menstrual age (w PMA) and once at term-equivalent age, around 40w PMA -, in order to quantify the sulcus's shape variability using manifold learning, regardless of age-group or hemisphere. We then used these shape descriptors to evaluate the sulcus's variability at both ages and to assess hemispheric and age-group specificities. This led us to propose a description of ten shape features capturing the variability in the central sulcus of preterm infants. Our results suggested that most of these features (8/10) are encoded as early as 30w PMA. We unprecedentedly observed hemispheric asymmetries at both ages, and the one captured at term-equivalent age seems to correspond with the asymmetry pattern previously reported in adults. We further trained classifiers in order to explore the predictive value of these shape features on manual performance at 5 years of age (handedness and fine motor outcome). The central sulcus's shape alone showed a limited but relevant predictive capacity in both cases. The study of sulcal shape features during early neurodevelopment may participate to a better comprehension of the complex links between morphological and functional organization of the developing brain

Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Convolutional neural network-based regression for quantification of brain characteristics using MRI

\u3cp\u3ePreterm birth is connected to impairments and altered brain growth. Compared to their term born peers, preterm infants have a higher risk of behavioral and cognitive problems since most part of their brain development is in extra-uterine conditions. This paper presents different deep learning approaches with the objective of quantifying the volumes of 8 brain tissues and 5 other image-based descriptors that quantify the state of brain development. Two datasets were used: one with 86 MR brain images of patients around 30 weeks PMA and the other with 153 patients around 40 weeks PMA. Two approaches were evaluated: (1) using the full image as 3D input and (2) using multiple image slices as 3D input, both achieving promising results. A second study, using a dataset of MR brain images of rats, was also performed to assess the performance of this method with other brains. A 2D approach was used to estimate the volumes of 3 rat brain tissues.\u3c/p\u3

Automatic segmentation of the preterm neonatal brain with MRI using supervised classification

Cortical folding ensues around 13-14 weeks gestational age and a qualitative analysis of the cortex around this period is required to observe and better understand the folds arousal. A quantitative assessment of cortical folding can be based on the cortical surface area, extracted from segmentations of unmyelinated white matter (UWM), cortical grey matter (CoGM) and cerebrospinal uid in the extracerebral space (CSF). This work presents a method for automatic segmentation of these tissue types in preterm infants. A set of T1- and T2-weighted images of ten infants scanned at 30 weeks postmenstrual age was used. The reference standard was obtained by manual expert segmentation. The method employs supervised pixel classification in three subsequent stages. The classification is performed based on the set of spatial and texture features. Segmentation results are evaluated in terms of Dice coefficient (DC), Hausdorff distance (HD), and modified Hausdorff distance (MHD) defined as 95th percentile of the HD. The method achieved average DC of 0.94 for UWM, 0.73 for CoGM and 0.86 for CSF. The average HD and MHD were 6.89 mm and 0.34 mm for UWM, 6.49 mm and 0.82 mm for CoGM, and 7.09 mm and 0.79 mm for CSF, respectively. The presented method can provide volumetric measurements of the segmented tissues, and it enables quantification of cortical characteristics. Therefore, the method provides a basis for evaluation of clinical relevance of these biomarkers in the given population. © 2013 SPIE

Automatic segmentation of MR brain images with a convolutional neural network

\u3cp\u3eAutomatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.\u3c/p\u3