Neonatal Brain Research Group - Journal Articles

Permanent URI for this collection

https://hdl.handle.net/10468/628

Browse

Now showing 1 - 13 of 13

Altered inflammasome activation in neonatal encephalopathy persists in childhood
(John Wiley and Sons Ltd, 2021-07) Kelly, Lynne A.; O'Dea, Mary I.; Zareen, Zunera; Melo, A. M.; McKenna, Ellen; Strickland, Tammy; McEneaney, V.; Donoghue, Veronica; Boylan, Geraldine B.; Sweetman, Deirdre; Butler, J.; Vavasseur, Claudine; Miletin, Jan; El-Khuffash, Afif; O'Neill, Luke; O'Leary, John; Molloy, Eleanor J.; National Children's Research Centre; Health Research Board
Neonatal encephalopathy (NE) is characterized by altered neurological function in term infants and inflammation plays an important pathophysiological role. Inflammatory cytokines interleukin (IL)-1 beta, IL-1ra and IL-18 are activated by the nucleotide-binding and oligomerization domain (NOD)-, leucine-rich repeat domain (LRR)- and NOD-like receptor protein 3 (NLRP3) inflammasome; furthermore, we aimed to examine the role of the inflammasome multiprotein complex involved in proinflammatory responses from the newborn period to childhood in NE. Cytokine concentrations were measured by multiplex enzyme-linked immunosorbent assay (ELISA) in neonates and children with NE in the absence or presence of lipopolysaccharide (LPS) endotoxin. We then investigated expression of the NLRP3 inflammasome genes, NLRP3, IL-1 beta and ASC by polymerase chain reaction (PCR). Serum samples from 40 NE patients at days 1 and 3 of the first week of life and in 37 patients at age 4-7 years were analysed. An increase in serum IL-1ra and IL-18 in neonates with NE on days 1 and 3 was observed compared to neonatal controls. IL-1ra in NE was decreased to normal levels at school age, whereas serum IL-18 in NE was even higher at school age compared to school age controls and NE in the first week of life. Percentage of LPS response was higher in newborns compared to school-age NE. NLRP3 and IL-1 beta gene expression were up-regulated in the presence of LPS in NE neonates and NLRP3 gene expression remained up-regulated at school age in NE patients compared to controls. Increased inflammasome activation in the first day of life in NE persists in childhood, and may increase the window for therapeutic intervention.
Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy
(Elsevier, 2017-01-11) Murphy, Keelin; van der Aa, Niek E.; Negro, Simona; Groenendaal, Floris; de Vries, Linda S.; Viergever, Max A.; Boylan, Geraldine B.; Benders, Manon; Išgum, Ivana; Science Foundation Ireland; Irish Research Council
A fully automatic method for detection and quantification of ischemic lesions in diffusion-weighted MR images of neonatal hypoxic ischemic encephalopathy (HIE) is presented. Ischemic lesions are manually segmented by two independent observers in 1.5 T data from 20 subjects and an automatic algorithm using a random forest classifier is developed and trained on the annotations of observer 1. The algorithm obtains a median sensitivity and specificity of 0.72 and 0.99 respectively. F1-scores are calculated per subject for algorithm performance (median = 0.52) and observer 2 performance (median = 0.56). A paired t-test on the F1-scores shows no statistical difference between the algorithm and observer 2 performances. The method is applied to a larger dataset including 54 additional subjects scanned at both 1.5 T and 3.0 T. The algorithm findings are shown to correspond well with the injury pattern noted by clinicians in both 1.5 T and 3.0 T data and to have a strong relationship with outcome. The results of the automatic method are condensed to a single score for each subject which has significant correlation with an MR score assigned by experienced clinicians (p < 0.0001). This work represents a quantitative method of evaluating diffusion-weighted MR images in neonatal HIE and a first step in the development of an automatic system for more in-depth analysis and prognostication.
Can EEG accurately predict 2-year neurodevelopmental outcome for preterm infants?
(BMJ Publishing Group, 2021-09) Lloyd, Rhodri O.; O'Toole, John M.; Livingstone, Vicki; Filan, Peter; Boylan, Geraldine B.; Science Foundation Ireland
Objective: Establish if serial multichannel video electroencephalography (EEG) in preterm infants can accurately predict 2-year neurodevelopmental outcome. Design and patients: EEGs were recorded at three time points over the neonatal course for infants <32 weeks’ gestational age (GA). Monitoring commenced soon after birth and continued over the first 3 days. EEGs were repeated at approximately 32 and 35 weeks’ postmenstrual age (PMA). EEG scores were based on an age- specific grading scheme. Clinical score of neonatal morbidity risk and cranial ultrasound imaging were completed. Setting: Neonatal intensive care unit at Cork University Maternity Hospital, Ireland. Main outcome measures: Bayley Scales of Infant Development III at 2 years’ corrected age. Results: Sixty- seven infants were prospectively enrolled in the study and 57 had follow- up available (median GA 28.9 weeks (IQR 26.5–30.4)). Forty had normal outcome, 17 had abnormal outcome/died. All EEG time points were individually predictive of abnormal outcome; however, the 35- week EEG performed best. The area under the receiver operating characteristic curve (AUC) for this time point was 0.91 (95% CI 0.83 to 1), p<0.001. Comparatively, the clinical course AUC was 0.68 (95% CI 0.54 to 0.80, p=0.015), while abnormal cranial ultrasound was 0.58 (95% CI 0.41 to 0.75, p=0.342). Conclusion: Multichannel EEG is a strong predictor of 2- year outcome in preterm infants particularly when recorded around 35 weeks’ PMA. Infants at high risk of brain injury may benefit from early postnatal EEG recording which, if normal, is reassuring. Postnatal clinical complications can contribute to poor outcome; therefore, we state that a later EEG around 35 weeks has a role to play in prognostication.
Coagulation profiles are associated with early clinical outcomes in neonatal encephalopathy
(Frontiers Media S.A., 2019-10-01) Sweetman, Deirdre; Kelly, Lynne A.; Zareen, Zunera; Nolan, Beatrice; Murphy, John; Boylan, Geraldine B.; Donoghue, Veronica; Molloy, Eleanor J.; National Children's Research Centre; Royal College of Surgeons in Ireland
Introduction: Neonatal encephalopathy (NE) is associated with coagulation abnormalities. We aimed to investigate the serial alterations in coagulation profiles in term infants with NE and correlate with their clinical outcomes. This was a prospective cohort study in a tertiary referral, university-affiliated maternity hospital. Neonates exposed to perinatal asphyxia were recruited (n = 82) and 39 received therapeutic hypothermia. Infants had serial coagulation tests including platelets, prothrombin time (PT), activated partial thromboplastin time (aPTT) and fibrinogen in the first week of life. The main outcome measures included MRI brain and EEG seizures. Our results show that mortality was predicted on day 1 by decreased Fibrinogen (AUC = 0.95, p = 0.009) and by PT on day 2 with a cutoff of 22 s. An abnormal MRI was predicted by Fibrinogen on day 3 with a cut-off value of 2 g/L. For prediction of grade II/III NE, PT on day 2 of life was strongest with a cut-off value of 14 s. Only elevated APTT levels on day 1 of life were predictive of seizures (AUC = 0.65, p = 0.04). Conclusion: Coagulation parameters are strong predictors of outcomes such as abnormal NE grade, seizures, and mortality.
Early postnatal EEG features of perinatal arterial ischaemic stroke with seizures
(Public Library of Science, 2014) Low, Evonne; Mathieson, Sean R.; Stevenson, Nathan J.; Livingstone, Vicki; Ryan, C. Anthony; Bogue, Conor O.; Rennie, Janet M.; Boylan, Geraldine B.; Wellcome Trust, United Kingdom; Science Foundation Ireland; National Institute for Health Research, United Kingdom; Biomedical Research Centre, Oxford
Background: Stroke is the second most common cause of seizures in term neonates and is associated with abnormal long-term neurodevelopmental outcome in some cases. Objective: To aid diagnosis earlier in the postnatal period, our aim was to describe the characteristic EEG patterns in term neonates with perinatal arterial ischaemic stroke (PAIS) seizures. Design: Retrospective observational study. Patients: Neonates >37 weeks born between 2003 and 2011 in two hospitals. Method: Continuous multichannel video-EEG was used to analyze the background patterns and characteristics of seizures. Each EEG was assessed for continuity, symmetry, characteristic features and sleep cycling; morphology of electrographic seizures was also examined. Each seizure was categorized as electrographic-only or electroclinical; the percentage of seizure events for each seizure type was also summarized. Results: Nine neonates with PAIS seizures and EEG monitoring were identified. While EEG continuity was present in all cases, the background pattern showed suppression over the infarcted side; this was quite marked (>50% amplitude reduction) when the lesion was large. Characteristic unilateral bursts of theta activity with sharp or spike waves intermixed were seen in all cases. Sleep cycling was generally present but was more disturbed over the infarcted side. Seizures demonstrated a characteristic pattern; focal sharp waves/spike-polyspikes were seen at frequency of 1-2 Hz and phase reversal over the central region was common. Electrographic-only seizure events were more frequent compared to electroclinical seizure events (78 vs 22%). Conclusions: Focal electrographic and electroclinical seizures with ipsilateral suppression of the background activity and focal sharp waves are strong indicators of PAIS. Approximately 80% of seizure events were the result of clinically unsuspected seizures in neonates with PAIS. Prolonged and continuous multichannel video-EEG monitoring is advocated for adequate seizure surveillance.
A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial
(Elsevier, 2020-10) Pavel, Andreea; Rennie, Janet M.; de Vries, Linda S.; Blennow, Mats; Foran, Adrienne; Shah, Divyen K.; Pressler, Ronit; Kapellou, Olga; Dempsey, Eugene M.; Mathieson, Sean R.; Pavlidis, Elena; van Huffelen, Alexander C.; Livingstone, Vicki; Toet, Mona C.; Weeke, Lauren C.; Finder, Mikael; Mitra, Subhabrata; Murray, Deirdre M.; Marnane, William P.; Boylan, Geraldine B.; Wellcome Trust; Science Foundation Ireland; Nihon Kohden America
Background: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR).Methods: This multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure probability trend in real time (algorithm group) or cEEG monitoring alone (non algorithm group). The primary outcome was diagnostic accuracy (sensitivity, specificity, and false detection rate) of health-care professionals to identify neonates with electrographic seizures and seizure hours with and without the support of the ANSeR algorithm. Neonates with data on the outcome of interest were included in the analysis. This study is registered with ClinicalTrials.gov, NCT02431780.Findings: Between Feb 13, 2015, and Feb 7, 2017, 132 neonates were randomly assigned to the algorithm group and 132 to the non-algorithm group. Six neonates were excluded (four from the algorithm group and two from the non-algorithm group). Electrographic seizures were present in 32 (25.0%) of 128 neonates in the algorithm group and 38 (29.2%) of 130 neonates in the non-algorithm group. For recognition of neonates with electrographic seizures, sensitivity was 81.3% (95% CI 66.7-93.3) in the algorithm group and 89.5% (78.4-97.5) in the non-algorithm group; specificity was 84.4% (95% CI 76.9-91.0) in the algorithm group and 89.1% (82.5-94.7) in the non-algorithm group; and the false detection rate was 36.6% (95% CI 22.7-52.1) in the algorithm group and 22.7% (11.6-35.9) in the non-algorithm group. We identified 659 h in which seizures occurred (seizure hours): 268 h in the algorithm versus 391 h in the non algorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66.0%; 95% CI 53.8-77.3] of 268 h vs 177 [45.3%; 34.5-58.3] of 391 h; difference 20.8% [3.6-37.1]). No significant differences were seen in the percentage of neonates with seizures given at least one inappropriate antiseizure medication (37.5% [95% CI 25.0 to 56.3] vs 31.6% [21.1 to 47.4]; difference 5.9% [-14.0 to 26.3]).Interpretation ANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required.
Multichannel EEG abnormalities during the first 6 hours in infants with mild hypoxic-ischaemic encephalopathy
(Springer Nature, 2021-07) Garvey, Aisling A.; Pavel, Andreea; O'Toole, John M.; Walsh, Brian; Korotchikova, Irina; Livingstone, Vicki; Dempsey, Eugene M.; Murray, Deirdre M.; Boylan, Geraldine B.; Science Foundation Ireland; Molecular Medicine Ireland; Health Research Board; National Children’s Research Centre; Wellcome Trust
Background: Infants with mild HIE are at risk of significant disability at follow-up. In the pre-therapeutic hypothermia (TH) era, electroencephalography (EEG) within 6 hours of birth was most predictive of outcome. This study aims to identify and describe features of early EEG and heart rate variability (HRV) (<6 hours of age) in infants with mild HIE compared to healthy term infants. Methods: Infants >36 weeks with mild HIE, not undergoing TH, with EEG before 6 hours of age were identified from 4 prospective cohort studies conducted in the Cork University Maternity Services, Ireland (2003-2019). Control infants were taken from a contemporaneous study examining brain activity in healthy term infants. EEGs were qualitatively analysed by two neonatal neurophysiologists and quantitatively assessed using multiple features of amplitude, spectral shape and inter-hemispheric connectivity. Quantitative features of HRV were assessed in both the groups. Results: Fifty-eight infants with mild HIE and sixteen healthy term infants were included. Seventy-two percent of infants with mild HIE had at least one abnormal EEG feature on qualitative analysis and quantitative EEG analysis revealed significant differences in spectral features between the two groups. HRV analysis did not differentiate between the groups. Conclusions: Qualitative and quantitative analysis of the EEG before 6 hours of age identified abnormal EEG features in mild HIE, which could aid in the objective identification of cases for future TH trials in mild HIE. Impact: Infants with mild HIE currently do not meet selection criteria for TH yet may be at risk of significant disability at follow-up. In the pre-TH era, EEG within 6 hours of birth was most predictive of outcome; however, TH has delayed this predictive value. 72% of infants with mild HIE had at least one abnormal EEG feature in the first 6 hours on qualitative assessment. Quantitative EEG analysis revealed significant differences in spectral features between infants with mild HIE and healthy term infants. Quantitative EEG features may aid in the objective identification of cases for future TH trials in mild HIE.
Multiple-view time-frequency distribution based on the empirical mode decomposition
(Institution of Engineering and Technology (IET), 2010-08) Stevenson, Nathan J.; Mesbah, Mostefa; Boashash, Boualem
This study proposes a novel, composite time-frequency distribution (TFD) constructed using a multiple-view approach. This composite TFD utilises the intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) to generate each view that are then combined using the arithmetic mean. This process has the potential to eliminate the inter-component interference generated by a quadratic TFD (QTFD), as the IMFs of the EMD are, in general, monocomponent signals. The formulation of the multiple-view TFD in the ambiguity domain results in faster computation, compared to a convolutive implementation in the time-frequency domain, and a more robust TFD in the presence of noise. The composite TFD, referred to as the EMD-TFD, was shown to generate a heuristically more accurate representation of the distribution of time-frequency energy in a signal. It was also shown to have performance comparable to the Wigner-Ville distribution when estimating the instantaneous frequency of multiple signal components in the presence of noise.
Neurotrophic factors: from neurodevelopmental regulators to novel therapies for Parkinson's disease.
(Medknow Publications, 2014-10) Hegarty, Shane V.; O'Keefe, Gerard W.; Sullivan, Aideen M.; Irish Research Council; Health Research Board; Science Foundation Ireland
Neuroprotection and neuroregeneration are two of the most promising disease-modifying therapies for the incurable and widespread Parkinson’s disease. In Parkinson’s disease, progressive degeneration of nigrostriatal dopaminergic neurons causes debilitating motor symptoms. Neurotrophic factors play important regulatory roles in the development, survival and maintenance of specific neuronal populations. These factors have the potential to slow down, halt or reverse the loss of nigrostriatal dopaminergic neurons in Parkinson’s disease. Several neurotrophic factors have been investigated in this regard. This review article discusses the neurodevelopmental roles and therapeutic potential of three dopaminergic neurotrophic factors: glial cell line-derived neurotrophic factor, neurturin and growth/differentiation factor 5.
Non-invasive cardiac output monitoring in neonates
(Frontiers Media S.A., 2021-01) O'Neill, Roisin; Dempsey, Eugene M.; Garvey, Aisling A.; Schwarz, Christoph E.; Deutsche Forschungsgemeinschaft; National Children's Research Centre
Circulatory monitoring is currently limited to heart rate and blood pressure assessment in the majority of neonatal units globally. Non-invasive cardiac output monitoring (NiCO) in term and preterm neonates is increasing, where it has the potential to enhance our understanding and management of overall circulatory status. In this narrative review, we summarized 33 studies including almost 2,000 term and preterm neonates. The majority of studies evaluated interchangeability with echocardiography. Studies were performed in various clinical settings including the delivery room, patent ductus arteriosus assessment, patient positioning, red blood cell transfusion, and therapeutic hypothermia for hypoxic ischemic encephalopathy. This review presents an overview of NiCO in neonatal care, focusing on technical and practical aspects as well as current available evidence. We discuss potential goals for future research.
A nonlinear model of newborn EEG with nonstationary inputs
(Springer Verlag, 2010-09) Stevenson, Nathan J.; Mesbah, Mostefa; Boylan, Geraldine B.; Colditz, Paul B.; Boashash, Boualem; McIntire, Larry V.
Newborn EEG is a complex multiple channel signal that displays nonstationary and nonlinear characteristics. Recent studies have focussed on characterizing the manifestation of seizure on the EEG for the purpose of automated seizure detection. This paper describes a novel model of newborn EEG that can be used to improve seizure detection algorithms. The new model is based on a nonlinear dynamic system; the Duffing oscillator. The Duffing oscillator is driven by a nonstationary impulse train to simulate newborn EEG seizure and white Gaussian noise to simulate newborn EEG background. The use of a nonlinear dynamic system reduces the number of parameters required in the model and produces more realistic, life-like EEG compared with existing models. This model was shown to account for 54% of the linear variation in the time domain, for seizure, and 85% of the linear variation in the frequency domain, for background. This constitutes an improvement in combined performance of 6%, with a reduction from 48 to 4 model parameters, compared to an optimized implementation of the best performing existing model.
A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity
(Elsevier, 2012-05) Stevenson, Nathan J.; O'Toole, John M.; Rankine, Luke J.; Boylan, Geraldine B.; Boashash, Boualem; Science Foundation Ireland
Automated methods of neonatal EEG seizure detection attempt to highlight the evolving, stereotypical, pseudo-periodic, nature of EEG seizure while rejecting the nonstationary, modulated, coloured stochastic background in the presence of various EEG artefacts. An important aspect of neonatal seizure detection is, therefore, the accurate representation and detection of pseudo-periodicity in the neonatal EEG. This paper describes a method of detecting pseudo-periodic components associated with neonatal EEG seizure based on a novel signal representation; the nonstationary frequency marginal (NFM). The NFM can be considered as an alternative time-frequency distribution (TFD) frequency marginal. This method integrates the TFD along data-dependent, time-frequency paths that are automatically extracted from the TFD using an edge linking procedure and has the advantage of reducing the dimension of a TFD. The reduction in dimension simplifies the process of estimating a decision statistic designed for the detection of the pseudo-periodicity associated with neonatal EEG seizure. The use of the NFM resulted in a significant detection improvement compared to existing stationary and nonstationary methods. The decision statistic estimated using the NFM was then combined with a measurement of EEG amplitude and nominal pre- and post-processing stages to form a seizure detection algorithm. This algorithm was tested on a neonatal EEG database of 18 neonates, 826 hrs in length with 1389 seizures, and achieved comparable performance to existing second generation algorithms (a median receiver operating characteristic area of 0.902; IQR 0.835-0.943 across 18 neonates).
Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants
(Springer Nature, 2019-03-19) O'Toole, John M.; Pavlidis, Elena; Korotchikova, Irina; Boylan, Geraldine B.; Stevenson, Nathan J.; Science Foundation Ireland; Horizon 2020
For the premature newborn, little is known about changes in brain activity during transition to extra-uterine life. We aim to quantify these changes in relation to the longer-term maturation of the developing brain. We analysed EEG for up to 72 hours after birth from 28 infants born <32 weeks of gestation. These infants had favourable neurodevelopment at 2 years of age and were without significant neurological compromise at time of EEG monitoring. Quantitative EEG was generated using features representing EEG power, discontinuity, spectral distribution, and inter-hemispheric connectivity. We found rapid changes in cortical activity over the 3 days distinct from slower changes associated with gestational age: for many features, evolution over 1 day after birth is equivalent to approximately 1 to 2.5 weeks of maturation. Considerable changes in the EEG immediately after birth implies that postnatal adaption significantly influences cerebral activity for early preterm infants. Postnatal age, in addition to gestational age, should be considered when analysing preterm EEG within the first few days after birth.

Browse

Browsing Neonatal Brain Research Group - Journal Articles by Title

Results Per Page

Sort Options