Electroencephalogram studies of hypoxic ischemia in fetal and neonatal animal models

Atlas-Free Automatic Segmentation of Sheep Brain MRI

Automated 3D brain segmentation methods have been shown to produce fast, reliable, and reproducible segmentations from magnetic resonance imaging (MRI) sequences for the anatomical structures of the human brain. Despite the extensive experimental research utility of large animal species such as the sheep, there is limited literature on the segmentation of their brains relative to that of humans. The availability of automatic segmentation algorithms for animal brain models can have significant impact for experimental explorations, such as treatment planning and studying brain injuries. The neuroanatomical similarities in size and structure between sheep and humans, plus their long lifespan and docility, make them an ideal animal model for investigating automatic segmentation methods.This work, for the first time, proposes an atlas-free fully automatic sheep brain segmentation tool that only requires structural MR images (T1-MPRAGE images) to segment the entire sheep brain in less than one minute. We trained a convolutional neural network (CNN) model - namely a four-layer U-Net - on data from eleven adult sheep brains (training and validation: 8 sheep, testing: 3 sheep), with a high overall Dice overlap score of 93.7%.Clinical relevance- Upon future validation on larger datasets, our atlas-free automatic segmentation tool can have clinical utility and contribute towards developing robust and fully automatic segmentation tools which could compete with atlas-based tools currently available.

Brief hypoxia in late gestation sheep causes prolonged disruption of fetal electrographic, breathing behaviours and can result in early labour

KEY POINTS

Brief episodes of severe fetal hypoxia can arise in late gestation as a result of interruption of normal umbilical blood flow Systemic parameters and blood chemistry indicate complete recovery within 1-2 hours, although the long-term effects on fetal brain functions are unknown Fetal sheep were subjected to umbilical cord occlusion (UCO) for 10 min at 131 days of gestation, and then monitored intensively until onset of labour or delivery (<145 days of gestation) Normal patterns of fetal behaviour, including breathing movements, episodes of high and low voltage electorcortical activity, eye movements and postural (neck) muscle activity, were disrupted for 3-10 days after the UCO Preterm labour and delivery occurred in a significant number of the pregnancies after UCO compared to the control (sham-UCO) cohort.

ABSTRACT

Complications arising from antepartum events such as impaired umbilical blood flow can cause significant fetal hypoxia. These complications can be unpredictable, as well as difficult to detect, and thus we lack a detailed understanding of the (patho)physiological changes that occur between the antenatal in utero event and birth. In the present study, we assessed the consequences of brief (∼10 min) umbilical cord occlusion (UCO) in fetal sheep at ∼0.88 gestation on fetal plasma cortisol concentrations and fetal behaviour [electrocortical (EcoG), electo-oculargram (EOG), nuchal muscle electromyography (EMG) and breathing activities] in the days following UCO. UCO caused a rapid onset of fetal hypoxaemia, hypercapnia, and acidosis; however, by 6 h, all blood parameters and cardiovascular status were normalized and not different from the control (Sham-UCO) cohort. Subsequently, the incidence of fetal breathing movements decreased compared to the control group, and abnormal behavioural patterns developed over the days following UCO and leading up to the onset of labour, which included increased high voltage and sub-low voltage ECoG and EOG activities, as well as decreased nuchal EMG activity. Fetuses subjected to UCO went into labour 7.9 ± 3.6 days post-UCO (139.5 ± 3.2 days of gestation) compared to the control group fetuses at 13.6 ± 3.3 days post-sham UCO (144 ± 2.2 days of gestation; P < 0.05), despite comparable increases in fetal plasma cortisol and a similar body weight at birth. Thus, a single transient episode of complete UCO late in gestation in fetal sheep can result in prolonged effects on fetal brain activity and premature labour, suggesting persisting effects on fetal cerebral metabolism.

The role of G-CSF neuroprotective effects in neonatal hypoxic-ischemic encephalopathy (HIE): current status

Hypoxic-ischemic encephalopathy (HIE) is an important cause of permanent damage to central nervous system (CNS) that may result in neonatal death or manifest later as mental retardation, epilepsy, cerebral palsy, or developmental delay. The primary cause of this condition is systemic hypoxemia and/or reduced cerebral blood flow with long-lasting neurological disabilities and neurodevelopmental impairment in neonates. About 20 to 25% of infants with HIE die in the neonatal period, and 25-30% of survivors are left with permanent neurodevelopmental abnormalities. The mechanisms of hypoxia-ischemia (HI) include activation and/or stimulation of myriad of cascades such as increased excitotoxicity, oxidative stress, N-methyl-D-aspartic acid (NMDA) receptor hyperexcitability, mitochondrial collapse, inflammation, cell swelling, impaired maturation, and loss of trophic support. Different therapeutic modalities have been implicated in managing neonatal HIE, though translation of most of these regimens into clinical practices is still limited. Therapeutic hypothermia, for instance, is the most widely used standard treatment in neonates with HIE as studies have shown that it can inhibit many steps in the excito-oxidative cascade including secondary energy failure, increases in brain lactic acid, glutamate, and nitric oxide concentration. Granulocyte-colony stimulating factor (G-CSF) is a glycoprotein that has been implicated in stimulation of cell survival, proliferation, and function of neutrophil precursors and mature neutrophils. Extensive studies both in vivo and ex vivo have shown the neuroprotective effect of G-CSF in neurodegenerative diseases and neonatal brain damage via inhibition of apoptosis and inflammation. Yet, there are still few experimentation models of neonatal HIE and G-CSF's effectiveness, and extrapolation of adult stroke models is challenging because of the evolving brain. Here, we review current studies and/or researches of G-CSF's crucial role in regulating these cytokines and apoptotic mediators triggered following neonatal brain injury, as well as driving neurogenesis and angiogenesis post-HI insults.

The role of pineal microRNA-325 in regulating circadian rhythms after neonatal hypoxic-ischemic brain damage

Circadian rhythm disorder is a common, but often neglected, consequence of neonatal hypoxic-ischemic brain damage (HIBD). However, the underlying molecular mechanisms remain largely unknown. We previously showed that, in a rat model of HIBD, up-regulation of microRNA-325 (miR-325) in the pineal gland is responsible for the suppression of Aanat, a key enzyme involved in melatonin synthesis and circadian rhythm regulation. To better understand the mechanism by which miR-325 affects circadian rhythms in neonates with HIBD, we compared clinical samples from neonates with HIBD and samples from healthy neonates recruited from the First Affiliated Hospital of Soochow University (Dushuhu Branch) in 2019. We found that circulating miR-325 levels correlated positively with the severity of sleep and circadian rhythm disorders in neonates with HIBD. Furthermore, a luciferase reporter gene assay revealed that LIM homeobox 3 (LHX3) is a novel downstream target of miR-325. In addition, in miR-325 knock-down mice, the transcription factor LHX3 exhibited an miR-325-dependent circadian pattern of expression in the pineal gland. We established a neonatal mouse model of HIBD by performing double-layer ligation of the left common carotid artery and exposing the pups to a low-oxygen environment for 2 hours. Lhx3 mRNA expression was significantly down-regulated in these mice and partially rescued in miR-325 knockout mice subjected to the same conditions. Finally, we showed that improvement in circadian rhythm-related behaviors in animals with HIBD was dependent on both miR-325 and LHX3. Taken together, our findings suggest that the miR-325-LHX3 axis is responsible for regulating circadian rhythms and provide novel insights into the identification of potential therapeutic targets for circadian rhythm disorders in patients with neonatal HIBD. The clinical trial was approved by Institutional Review Board of Children's Hospital of Soochow University (approval No. 2015028) on July 20, 2015. Animal experiments were approved by Animal Care and Use Committee, School of Medicine, Soochow University, China (approval No. XD-2016-1) on January 15, 2016.

Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep

Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not know when an insult may have started, and thus which phase of injury the brain may be experiencing. To improve diagnosis, prognosis and treatment efficacy, we need to establish biomarkers which denote phases of injury. Our pre-clinical research, using preterm fetal sheep, show that micro-scale EEG patterns (e.g. spikes and sharp waves), superimposed on suppressed EEG background, primarily occur during the early recovery from an HI insult (0-6 h), and that numbers of events within the first 2 h are strongly predictive of neural survival. Thus, real-time automated algorithms that could reliably identify EEG patterns in this phase will help clinicians to determine the phases of injury, to help guide treatment options. We have previously developed successful automated machine learning approaches for accurate identification and quantification of HI micro-scale EEG patterns in preterm fetal sheep post-HI. This paper introduces, for the first time, a novel online fusion strategy that employs a high-level wavelet-Fourier (WF) spectral feature extraction method in conjunction with a deep convolutional neural network (CNN) classifier for accurate identification of micro-scale preterm fetal sheep post-HI sharp waves in 1024Hz EEG recordings, along with 256Hz down-sampled data. The classifier was trained and tested over 4120 EEG segments within the first 2 hours latent phase recordings. The WF-CNN classifier can robustly identify sharp waves with considerable high-performance of 99.86% in 1024Hz and 99.5% in 256Hz data. The method is an alternative deep-structure approach with competitive high-accuracy compared to our computationally-intensive WS-CNN sharp wave classifier.

Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep

Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biomarkers to identify at risk infants. Using our pre-clinical fetal sheep models, we have demonstrated that micro-scale patterns evolve over a profoundly suppressed EEG background within the first 6 hours of recovery, post HI insult. In particular, we have shown that high-frequency micro-scale spike transients (in the gamma frequency band, 80-120Hz) emerge immediately after an HI event, with much higher numbers around 2-2.5 h of the insult, with numbers gradually declining thereafter. We have also shown that the automatically quantified sharp waves in this phase are predictive of neural outcome. Initiation of some neuroprotective treatments within this limited window of opportunity, such as therapeutic hypothermia, optimally reduces neural injury. In clinical practice, it is hard to determine the exact timing of the injury, therefore, reliable automatic identification of EEG transients could be beneficial to help specify the phases of injury. Our team has previously developed successful machine- and deep-learning strategies for the identification of post-HI EEG patterns in an HI preterm fetal sheep model.This paper introduces, for the first time, a novel online fusion approach to train an 11-layers deep convolutional neural network (CNN) classifier using Wavelet-Fourier (WF) spectral features of EEG segments for accurate identification of high-frequency micro-scale spike transients in 1024Hz EEG recordings in our preterm fetal sheep. Sets of robust features were extracted using reverse biorthogonal wavelet (rbio2.8 at scale 7) and considering an 80-120Hz spectral frequency range. The WF-CNN classifier was able to accurately identify spike transients with a reliable high-performance of 99.03±0.86%.Clinical relevance-Results confirm the expertise of the method for the identification of similar patterns in the EEG of neonates in the early hours after birth.

Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG

Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at risk infants at birth. Using pre-clinical data from our fetal sheep models, we have shown that micro-scale EEG patterns, such as high-frequency spikes and sharp waves, evolve superimposed on a significantly suppressed background during the early hours of recovery (0-6 h), after an HI insult. In particular, we have demonstrated that the number of micro-scale gamma spike transients peaks within the first 2-2.5 hours of the insult and automatically quantified sharp waves in this period are predictive of neural outcome. This period of time is optimal for the initiation of neuroprotection treatments such as therapeutic hypothermia, which has a limited window of opportunity for implementation of 6 h or less after an HI insult. Clinically, it is hard to determine when an insult has started and thus the window of opportunity for treatment. Thus, reliable automatic algorithms that could accurately identify EEG patterns that denote the phase of injury is a valuable clinical tool. We have previously developed successful machine-learning strategies for the identification of HI micro-scale EEG patterns in a preterm fetal sheep model of HI. This paper employs, for the first time, reverse biorthogonal Wavelet-Scalograms (WS) as the inputs to a 17-layer deep-trained convolutional neural network (CNN) for the precise identification of high-frequency micro-scale spike transients that occur in the 80-120Hz gamma band during first 2 h period of an HI insult. The rbio-WS-CNN classifier robustly identified spike transients with an exceptionally high-performance of 99.82%.Clinical relevance-The suggested classifier would effectively identify and quantify EEG patterns of a similar morphology in preterm newborns during recovery from an HI-insult.

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic–ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia–ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80–120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms.