Study of the Term Neonatal Brain Injury with combined Diffuse Optical Tomography and Electroencephalography

This thesis describes the application of combined diffuse optical tomography (DOT) and electroencephalography (EEG) in the investigation of neonatal term brain injury. With hypoxic ischaemic encephalopathy (HIE) and perinatal stroke being the most frequent contributors to brain injury in the term neonatal population, the first part of the thesis focuses on the description and ongoing requirement for their further investigation. In continuation to that, the characteristics and unique properties of both DOT and EEG are described and explored. The combination of these two modalities was utilised in elucidating the relationship between neuronal activity and cerebral haemodynamics both in physiological processes as well as in disease, by the infant’s cot side. This work differs to previous studies using near-infrared technologies and EEG, as a denser whole head array was used, offering the potential of 3-dimensional image reconstruction of the cortical haemodynamic events in relation to electro-cortical activity. These methods were applied in the study of critically ill infants presenting with seizures in the first few days of life. The relevant results are presented in three separate chapters of the thesis. Distinct neurophysiological phenomena such as seizures and burst suppression were detected and studied in association to underlying HIE. On the grounds of a pre-existing pilot study of our research group, distinct prolonged de-oxygenated cortical areas were identified following electrical seizure activity. Further exploration of infants with seizures provided limited supporting evidence. The investigation of burst suppression in HIE led to the first ever identification of repeated, waveform, cortical haemodynamic events in response to bursts of electrical activity with some spatial correlation to regions of brain injury. Further analysis of the low frequencies within the diffuse optical signal in cases of perinatal stroke, showed a consistent interhemispheric difference between the healthy and stroke-affected brain regions. The limitations, prospects and conclusions are presented in the final chapter. The use of simultaneous DOT and EEG offers a unique neuro-monitoring and neuro-investigating tool in the neonatal intensive care environment, which is safe, portable, and cost-effective, Ongoing research is required for the exploration and development of the methodology and its potential diagnostic properties

Long-term outcome after hypothermia-treated hypoxic-ischaemic encephalopathy

Hypoxic-ischaemic encephalopathy (HIE) is a major cause of acquired brain injury in newborn infants. It is a potentially life-threatening condition that leaves survivors at substantial risk of life-long debilitating sequelae including cerebral palsy, epilepsy, intellectual disability, sensory disruption, behavioural issues, executive difficulties and autism spectrum disorder. More subtle cognitive impairments are common among survivors free of major neuromotor disability. Therapeutic hypothermia (TH) reduces the risk of death and disability in nearterm/term new-born infants with moderate and severe HIE. Outcomes in adolescence and adulthood following HIE treated with TH are not yet known. The majority of infants with HIE also suffer multi-organ dysfunction resulting from the hypoxic-ischaemic insult. The kidneys are particularly sensitive to hypoxia-ischaemia, with up to 72% of asphyxiated infants suffering acute kidney injury (AKI) prior to the advent of TH. Evidence point to AKI being independently associated with increased neonatal morbidity and mortality. To date, very little is known about long-term renal consequences following neonatal AKI in asphyxiated infants treated with TH. The overall aim of this thesis was to contribute to the improved treatment and care of infants with HIE by means of increased knowledge about the predictive value of early aEEG, neonatal AKI, and long-term outcomes in the era of TH. In a small population-based cohort, the predictive value of early amplitude-integrated EEG (aEEG) was demonstrated to be altered in infants treated with TH due to HIE. Poor outcome at the age of 1 year was only seen among infants with a persisting abnormal aEEG background pattern at and beyond 24 hours of age. In a population-based, prospective, longitudinal study including all children treated with TH between 2007 and 2009 in Stockholm, Sweden, we assessed neuromotor, neurologic, cognitive and behavioural outcomes at 6-8 and 10-12 years of age. Seventeen per cent of survivors developed CP. Survivors free of major neuromotor impairment had cognitive abilities within normal range. Repeated assessment in early adolescence revealed new deficits in 26% of children with previously favourable outcome. The proportion of children with executive difficulties in this patient population appears to be higher than in the general population. Outcomes in children with a history of moderate HIE remain heterogenous also in the era of TH. In a population-based cohort of all children treated with TH between 2007 and 2009 in Stockholm, Sweden, 45% suffered neonatal AKI. Severe AKI necessitating kidney support therapy was rare. Among infants with AKI, 20% fulfilled only the urinary output criterion of the neonatal modified KDIGO (Kidney Disease Improving Global Outcomes) definition. Mortality was higher among infants with AKI. At 10-12 years of age, 21% of children had decreased glomerular filtration rate (GFR) estimated from creatinine with the Schwartz-Lyon equation. A more in-depth assessment of renal functions in the above-mentioned population-based cohort demonstrated that renal sequelae (defined as decreased GFR, albuminuria, hypertension or normal high blood pressure, reduced renal volume on magnetic resonance imaging, or elevated Fibroblast Growth Factor 23) were rare at 10-12 years of age following perinatal asphyxia and TH. The Schwarz-Lyon equation appears to underestimate GFR in this patient population

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Multimodal characterisation of the infant response to retinopathy of prematurity screening and treatment

Retinopathy of prematurity (ROP) is a condition which affects premature infants and is a cause of childhood blindness. Screening is performed repeatedly during the preterm period, using binocular indirect ophthalmoscopy (BIO), to identify disease at a treatable stage; unfortunately screening and treatment are considered to be painful and stressful for infants. Pain and stress during the preterm period can lead to negative consequences for infant development. Reduction of pain and stress during ROP procedures has therefore been attempted using pharmacological and non-pharmacological strategies. However, it is challenging to evaluate the effectiveness of such interventions due to limitations in the accurate measurement of infant pain and stress. In this thesis, novel approaches to quantifying infant pain and stress evoked by ROP procedures are presented. Infant brain activity was characterised using quantitative electroencephalography (EEG) analysis to test the hypothesis that ROP screening evokes noxious-related changes in infant brain activity. The results of this study suggest BIO ROP screening evokes a significant increase in higher frequency brain activity (12 - 30 Hz), and that increase in relative beta power may be a measure of nociception in preterm infants. Infant cardiac autonomic reactivity was characterised using heart rate variability (HRV) analysis to test the hypothesis that ROP screening evokes stress-related autonomic changes. The results of this study suggest BIO ROP screening evokes significant reduction in HRV measures of parasympathetic nervous system activity, indicating a physiological stress response in preterm infants. An approach to characterising the infant response to non-contact ultra-widefield photography (Optos screening) and ROP treatment was also demonstrated. Recruitment of subjects was curtailed by the outbreak of COVID-19, therefore the investigations are presented as an example of approaches which could be performed in a larger sample size. In summary, the research described in this thesis aims to contribute to understanding of the infant experience of ROP procedures; to characterise changes in noxious-related brain activity and stress-related cardiac reactivity evoked by BIO ROP screening, and to use these measures to investigate the infant response to an alternative screening method and to ROP treatments. Improved understanding of the infant experience of ROP screening and treatment may allow clinicians to better identify and treat infant pain and stress during essential clinical procedures

Individualised Clinical Neuroimaging in the Developing Brain: Abnormality Detection

Perinatal neuroanatomical structure is incredibly intricate and, at time of birth, is undergoing continuous change due to interweaving developmental processes (growth, myelination and gyrification). While there is some small variability in structure and rates of development, all follow proscribed pathways with well documented milestones. Brain injury or other disruption of these processes can result in poor neurodevelopmental outcomes or mortality, making their early identification critical to estimate, and potentially forestall, negative effects. MRI is an increasingly used method of investigating suspected neonatal encephalopathies and injuries.Identification of these injuries and malformations is more challenging in neonates compared to adults due to the brain’s continuously evolving appearance. This makes radiological review of neonatal MRI an intensive and time-consuming task which, in an ideal setting, requires a team of highly skilled clinicians and radiologists with complementary training and extensive experience. To assist this review process, some localisation method that highlights areas likely to contain tissue abnormalities would be highly desirable, as it could quickly draw attention to these locations. In addition, identifying neonates whose MRI is likely to contain some form of pathology could allow for review prioritisation.In this thesis, I first investigated using normative models of neonatal tissue intensity for brain tissue abnormality detection. I applied voxel-wise Gaussian process (GP) regression to a training cohort of neonates with no obvious lesions, all born preterm (<37 weeks) but imaged between 28-55 weeks. Gestational age at birth (GA), postmenstrual age at scan (PMA) and sex were used as input variables and voxel intensity as the output variable. GPs output a mean value and its variance inferred from neonates within the training cohort whose demographic information most closely matched those of the prediction target. The voxel specific models were put together to form a synthesised typical image and standard deviation image derived from the variance outputs. Z-score abnormality maps were constructed by taking the difference between neonates actual MRI and GP-calculated synthetic image and scaling by their standard deviation map. Higher Z-score map values indicate voxels more likely to contain abnormal tissue intensity. Using manually delineated masks of common brain injuries seen in a subset of neonates, these abnormality Z-score maps demonstrated good detection performance using area under the curve of receiver operating characteristic scores, with the exception of small punctate lesions.The initial voxel-wise models had substantial false positives around the edges of the brain where there is large typical heterogeneity. I next investigated if incorporating local structural information into predictive models could improve their ability to accommodate typical anatomical heterogeneity seen across individual brains and improve the accuracy of synthetic images and abnormality detection. To achieve this, voxel intensity values in a patch surrounding the prediction target were appended to the design matrix, alongside GMA, PMA and sex. The patch-based synthetic images were able to match an individual’s brain structure more closely and had lower false positives in normal appearing tissue. However, a weakness was that the centre of some larger lesions was included in the predictions (thereby classified as ‘healthy’ tissue), having a deleterious effect on their coverage, increasing false negatives. This was offset by much better coverage of smaller, more subtle lesions, to the extent that overall performance was higher compared to that seen in the earlier model.I also investigated if the Z-score abnormality maps could be used to classify neonates with MRI positive brain injury from those with normal appearing brains. While many machine learning algorism see frequent use in neuroimaging classification tasks, I opted for a logistic regression model due to its high levels of interpretability and simple implementation. Using the histograms of the Z-score abnormality maps as inputs, the model demonstrated good performance, being able to correctly identify neonates with injuries, but not those with subtle lesions like punctate lesions, whilst minimising false identification of neonates with normal appearing brains.To ascertain if performance could be improved, I explored multiple classification methods. Specifically, the use of other more complex classifiers (random forest, support vector machines, GP classification) and the use of a regional abnormal voxel count, that allowed localisation of lesioned tissue rather than the more global detection ability of the Z-score histograms. Using these innovations, I investigated their application towards a specific pathology; hypoxic ischemic encephalopathy (HIE). This is a good test for the system, as HIE has high incidence rates, multiple associated lesion types and a time dependant appearance. Further, I wanted to know if, given a positive HIE diagnosis, the Z-score abnormality maps could be used to predict long-term outcomes (normal vs poor). Several models demonstrated an excellent ability to separate HIE and healthy control neonates achieving >90% accuracy, a statistically significant result even after false discovery rate (FDR) correction (p-value < 0.05). While the outcome prediction models achieved reasonable accuracy, >70% in multiple models, none of these were statistically significant after FDR correction.Overall, this work demonstrates how normative modelling can be used to create individual voxel-wise / image-wise estimation of tissue abnormality for neonatal MRI across a range of gestational ages. It further demonstrates that these abnormality maps can be utilised for additional tasks, in this instance, three increasingly challenging neurological classification problems. These include the separation of neonates with and without MRI positive lesions, identification of neonates with a specific pathological condition (HIE) and prediction of long-term functional outcome (normal vs poor). Within a radiological setting, these classifications task can be considered analogous to three radiological challenges, image triage, diagnostic detection and estimation of developmental prognosis, important for the clinical team but also infants and their families

Prediction of short-term health outcomes in preterm neonates from heart-rate variability and blood pressure using boosted decision trees

Background and Objective: Efficient management of low blood pressure (BP) in preterm neonates remains challenging with considerable variability in clinical practice. There is currently no clear consensus on what constitutes a limit for low BP that is a risk to the preterm brain. It is argued that a personalised approach rather than a population based threshold is more appropriate. This work aims to assist healthcare professionals in assessing preterm wellbeing during episodes of low BP in order to decide when and whether hypotension treatment should be initiated. In particular, the study investigates the relationship between heart rate variability (HRV) and BP in preterm infants and its relevance to a short-term health outcome. Methods: The study is performed on a large clinically collected dataset of 831 h from 23 preterm infants of less than 32 weeks gestational age. The statistical predictive power of common HRV features is first assessed with respect to the outcome. A decision support system, based on boosted decision trees (XGboost), was developed to continuously estimate the probability of neonatal morbidity based on the feature vector of HRV characteristics and the mean arterial blood pressure. Results: It is shown that the predictive power of the extracted features improves when observed during episodes of hypotension. A single best HRV feature achieves an AUC of 0.87. Combining multiple HRV features extracted during hypotensive episodes with the classifier achieves an AUC of 0.97, using a leave-one-patient-out performance assessment. Finally it is shown that good performance can even be achieved using continuous HRV recordings, rather than only focusing on hypotensive events – this had the benefit of not requiring invasive BP monitoring. Conclusions: The work presents a promising step towards the use of multimodal data in providing objective decision support for the prediction of short-term outcome in preterm infants with hypotensive episodes

The role of infection/inflammation, the TNF family of cytokines and myeloid cells in perinatal hypoxia-ischaemia brain injury

Synergy between materno-foetal infection and hypoxic-ischaemic insult around the time of birth is a known contributing factor to perinatal brain damage. This is a common precursor to cerebral palsy and other neurological deficits, affecting 2 to 5 per 1000 live births. Endotoxin up-regulates several molecules, including the TNF cluster of pro-inflammatory cytokines. Our group has explored the role of this cluster and shown that its deletion abolishes LPS sensitization to neonatal hypoxic-ischaemic insult. In this study we wanted to first investigate the effects of LPS-mediated sensitization across multiple wild type strains (C57BL/5, 129SVJ, BALB/c, CD1 and FVB) in order to then further characterize the TNF cluster, by studying the individual effects of TNFα, LTα and LTβ members of this cluster, using either global gene deletion, or peripheral myeloid/macrophage-specific deletion of the floxed TNFα allele with MLys::Cre (MLys+). Additionally, we decided to also look at the acquired cellular immune system, using the athymic nude mouse model of T cell deficiency (nu). At P7, littermates for each of the wild type strains, wild-type and homozygous knock-out offspring of heterozygous animals listed above underwent hypoxic-ischaemic insult, consisting of unilateral carotid occlusion followed 2 hours recovery before being placed in a hypoxic chamber for 30min with continuous 8% oxygen exposure. 12 hours prior, animals received a single intraperitoneal injection of 0.6µg/g LPS or saline as a control. 1/3 of animals in the wild type strains group underwent hypoxia-ischaemia alone as a control for saline treatment. LPS pre-treatment resulted in substantial increase inflammation, neuronal injury and infarct in all wild type strains, as well as in the phenotypically wild type littermates of the homozygous mutant animals. Mice lacking both copies of the LTα gene revealed a clear reduction in LPS-mediated sensitization. In reverse, global deletion of LTβ had a detrimental effect, with significant increase in brain damage. Global deletion of TNFα showed a trend towards greater damage, but deletion just in MLys+ macrophages was strongly protective, pointing to a dual role for the TNFα gene depending on in which cell-type it is expressed. Finally, nude animals (nu/nu) demonstrated a complete lack of LPS-mediated sensitization to subsequent hypoxic-ischaemic insult, suggesting that LPS sensitization may require T cell function

Electrophysiological Analysis in an Animal Model of Dystonia

Dystonia is a movement disorder characterized by patterned, repetitive, and sustained muscle contractions that cause ineffective and often painful movements. The overall goal of this project was to understand the physiological mechanisms of dystonia in a rodent model as a basis for developing innovative treatments for secondary dystonias. The first half of the project was focused at developing essential techniques for systematically investigating the movement disorder in these animals. For achieving this, an innovative, multi-faceted approach was pursued starting with electromyographic (EMG) analysis for animal model validation, gait analysis for dystonia quantification, and development of a novel stereotaxic apparatus for recording brain activity during awake conditions. The later half of the project was focused on understanding how brain circuitry produces abnormal motor control in dystonia. Single and multi-unit neuronal activity was collected from individual basal ganglia nuclei along with EMG recordings to characterize the abnormal patterns of firing in dystonic animals and determine how neurons within individual nuclei communicate in dystonia, respectively. The findings of the current project have lead to new insights into the pathophysiology and treatment of secondary kernicteric dystonia and other secondary dystonia in humans

Intelligent monitoring and interpretation of preterm physiological signals using machine learning

Every year, more than one in ten babies are born prematurely. In Ireland of the 70000 babies delivered every year, 4500 are born too early. Premature babies are at a higher risk of complications, which may lead to both short-term and long-term adverse health outcomes. The neonatal population is especially vulnerable and a delay in the identification of medical conditions, as well as delays in the initiating the correct treatment, may be fatal. After birth, preterms are admitted to the neonatal intensive care unit (NICU), where a continuous flow of information in the form of physiological signals is available. Physiological signals can assist clinicians in decision making related to the diagnosis and treatment of various diseases. This information, however, can be highly complex, and usually requires expert analysis which may not be available at all times. The work conducted in this thesis develops a decision support systems for the intelligent monitoring of preterms in the NICU. This will allow for an accurate estimation of the current health status of the preterm neonate as well as the prediction of possible long-term complications. This thesis is comprised of three main work packages (WP), each addressing health complication of preterm on three different stages of life. At the first 12 hours of life the health status is quantified using the clinical risk index for babies (CRIB). This is followed by the assessment of the preterm’s well-being at discharge from the NICU using the clinical course score (CCS). Finally, the long-term neurodevelopmental follow-up is assessed using the Bayley III scales of development at two years. This is schematically represented in Figure 1 along with the main findings and contributions. Low blood pressure (BP) or hypotension is a recognised problem in preterm infants particularly during the first 72 hours of life. Hypotension may cause decreased cerebral perfusion, resulting in deprived oxygen delivery to the brain. Deciding when and whether to treat hypotension relies on our understanding of the relation between BP, oxygenation and brain activity. The electroencephalogram (EEG) is the most commonly used technology to assess the ‘brain health’ of a newborn. The first WP investigates the relationship between short-term dynamics in BP and EEG energy in the preterm on a large dataset of continuous multi-channel unedited EEG recordings in the context of the health status measured by the CRIB score. The obtained results indicate that a higher risk of mortality for the preterm is associated with a lower level of nonlinear interaction between EEG and BP. The level of coupling between these two systems can potentially serve as an additional source of information when deciding whether or not to intervene in the preterm. The electrocardiogram (ECG) is also routinely recorded in preterm infants. Analysis of heart rate variability (HRV) provides a non-invasive assessment of both the sympathetic and parasympathetic control of the heart rate. A novel automated objective decision support tool for the prediction of the short-term outcome (CCS) in preterm neonates who may have low BP is proposed in the second WP. Combining multiple HRV features extracted during hypotensive episodes, the classifier achieved an AUC of 0.97 for the task of short-term outcome prediction, using a leave-one-patient-out performance assessment. The developed system is based on the boosted decision tree classifier and allows for the continuous monitoring of the preterm. The proposed system is validated on a large clinically collected dataset of multimodal recordings from preterm neonates. If the correct treatment is initiated promptly after diagnosis, it can potentially improve the neurodevelopmental outcome of the preterm infant. The third WP presents a pilot study investigating the predictive capability of the early EEG recorded at discharge from the NICU with respect to the 2-year neurodevelopmental outcome using machine learning techniques. Two methods are used: 1) classical feature-based classifier, and 2) end-to-end deep learning. This is a fundamental study in this area, especially in the context of applying end-to-end learning to the preterm EEG for the problem of long-term outcome prediction. It is shown that for the available labelled dataset of 37 preterm neonates, the classical feature-based approach outperformed the end-to-end deep learning technique. A discussion of the obtained result as well as a section highlighting the possible limitations and areas that need to be investigated in the future are provided