http://hdl.handle.net/10468/628 2017-09-01T09:50:01Z http://hdl.handle.net/10468/3550 Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy 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 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. 2017-01-11T00:00:00Z http://hdl.handle.net/10468/3623 Neurotrophic factors: from neurodevelopmental regulators to novel therapies for Parkinson's disease. Hegarty, Shane V.; O'Keefe, Gerard W.; Sullivan, Aideen M. 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. 2014-10-01T00:00:00Z http://hdl.handle.net/10468/2332 Early postnatal EEG features of perinatal arterial ischaemic stroke with seizures Low, Evonne; Mathieson, Sean R.; Stevenson, Nathan J.; Livingstone, Vicki; Ryan, C. Anthony; Bogue, Conor O.; Rennie, Janet M.; Boylan, Geraldine B. 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. 2014-01-01T00:00:00Z http://hdl.handle.net/10468/836 A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity Stevenson, Nathan J.; O'Toole, John M.; Rankine, Luke J.; Boylan, Geraldine B.; Boashash, Boualem 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). 2012-05-01T00:00:00Z