1,721,075 research outputs found
Susceptibility weighted image analysis methods for hypoxic-ischaemic encephalopathy prognosis
No full textNeonatal hypoxic-ischaemic encephalopathy (HIE) is a major cause of newborn deaths and neurodevelopmental abnormalities around the world. Susceptibility weighted imaging can provide assistance in the prognosis of neonatal HIE. The propose of this research is to develop a new automated system to assess neonatal brain injury and developmental outcome by detecting and analysing vessel features in SWI images. In this research, a dataset of SWI images acquired from 42 infants with neonatal HIE is used for feature extraction. Firstly, the ridges representing the veins in the SWI images are detecting to obtain features including the width, intensity value, length of veins, and Hessian eigenvalues for ridges. The normalized histograms of these features are used as feature vector for classification. Individual or concatenated feature vectors are fed into kNN and random forest classifiers to predict the neurological outcomes of infants with HIE at the age of 24 months. We select the balanced SWI dataset to avoid the bias. The feature vectors containing width, intensity, length and eigenvalue show a promising classification accuracy of 78.67% ± 2.58%. Then we use the feature vectors to train support vector regression and random forest regression models for predicting the motor score and cognitive score of infants with HIE assessed by Barley-III at the age of 24 months. Our mean relative errors for cognitive and motor outcome scores are 0.113±0.13 and 0.109±0.067 respectively. The features derived from the ridges of the veins are good predictors of neurological outcome in infants with neonatal HIE. Further, we design a supervised classifier for automatic prognosis of automated detection of SWI signs of HIE. This classifier also enables to determination of brain regions which have been affected by hypoxicischaemic by extracting appropriate features from SWI images. Our classifier can classify the veins in the SWI images into normal and abnormal group by clinical assessment outcomes. The number and location of abnormal veins in the brain of HIE neonates will predict the neurodevelopmental outcomes of infants with HIE at the age of 24 months. Our classifier proposed in this study demonstrates a superior performance in HIE prognosis for the dataset associated with cognitive and motor outcomes. The accuracy of early prediction of motor outcome at 2 years of age using SWI images in newborns by our classifier achieves 75% ± 13.9%. We also employed the linear regression, polynomial regression, and support vector regression model to predict outcomes and the lower mean relative errors for motor and cognitive outcomes are 0.088±0.073 and 0.101±0.11 respectively. Then we extract the feature vectors of global and local brain by the histogram of oriented gradient descriptor. We obtain the brain regions associated with motor and cognitive function by image registration. The histogram of oriented gradient feature vectors of these brain regions are fed into the kNN and random forest classifiers to predict the motor and cognitive outcome. The result shows an effective classification for cognitive outcome, which achieved an accuracy of 76.25±10.9. In addition, we propose a convolutional neural network model to classify the SWI images with HIE. Due to the lack of a large dataset, transfer learning method with fine-tuning a pre-trained ResNet 50 is introduced. we train a convolutional neural network model to classify the SWI images with HIE. Due to the lack of data, transfer learning method with fine-tuning a pre-trained ResNet 50 network is introduced. The balanced datasets are selected randomly to avoid bias in classification. Then we develop a rule-based system to improve the classification performance, with an accuracy of 0.933 ± 0.086. We also compute heatmaps produced by the Grad-CAM technique to analyze which areas of SWI images contributed more to the classification results. Our research demonstrates that the features derived from the vascular ridges improve the prognostic value of SWI images in HIE. Furthermore, our findings suggest that it is possible to predict neurological, motor, and cognitive outcomes by numerical analysis of their neonatal SWI images and to identify brain regions on SWI affected by HIE
Hypoxic-ischaemic encephalopathy prognosis using susceptibility weighted image analysis based on histogram orientation gradient
No full textThe aim of this study is to analyse the susceptibility-weighted magnetic resonance images (SWI) by using Histogram of Oriented Gradients (HOG) as a global feature to identify areas of the neonatal brain affected by Hypoxic-ischaemic encephalopathy (HIE). 42 infants with neonatal HIE have undergone under SW imaging in the neonatal period and have been investigated through neurodevelopmental assessment at 24 months of age. HOG features are used to represent the whole brain SW images and the region of interest separated from the brain image registration algorithm. We use k-nearest neighbours (kNN) and random forest to classify the SWI images into normal and abnormal groups, and then we compare our results to our previous work. The result shows an effective classification, which achieved an accuracy of 76.25±10.9. Our research suggests that automated analysis of neonatal SWI images can identify brain regions affected by HIE on SWI images and predict motor and cognitive outcomes
Ridge detection and analysis of susceptibility-weighted magnetic resonance imaging in neonatal hypoxic-ischaemic encephalopathy
No full textThe purpose of this study is to develop a new automated system to classify susceptibility weighted images (SWI) obtained to evaluate neonatal hypoxic-ischaemic injury, by detecting and analyzing ridges within these images. SW images can depict abnormal cerebral venous contrast as a consequence of abnormal blood flow, perfusion and thus oxygenation in babies with HIE. In this research, a dataset of SWI-MRI images, acquired from 42 infants with HIE during the neonatal period, features are obtained based on ridge analysis of SW images including the width of blood vessels, the change in intensity of the veins’ pixels in comparison with neighboring pixels, the length of blood vessels and Hessian eigenvalues for ridges are extracted. Normalized histogram parameters in the single or combined features are used to classify SWIs by kNN and random forest classifiers. The mean and standard deviation of the classification accuracies are derived by randomly selecting 11 datasets ten times from those with normal neurological outcome (n=31) at age 24 months and those with abnormal neurological outcome (n=11), to avoids classification biases due to any imbalanced data. The feature vectors containing width, intensity, length and eigenvalue show a promising classification accuracy of 78.67% ± 2.58%. The features derived from the ridges of the blood vessels have a good discriminative power for prediction of neurological outcome in infants with neonatal HIE. We also employ Support Vector Regression (SVR) to predict the scores of motor and cognitive outcomes assessed 24 months after the birth. Our mean relative errors for cognitive and motor outcome scores are 0.113±0.13 and 0.109±0.067 respectively
Caring for patients in mental health services during COVID-19 outbreak in China
No full textThis article reflects on some radical changes made in mental health services in China which include the implementation of the initial triage system and the special isolation ward, the early screening and testing for both patients and staff, the smaller teams working on rotating shifts on-site, and the adequate provision of PPE. These measures would be of great value as a reference to the effective delivery of mental health services in other countries through this pandemic
Rule-based deep learning method for prognosis of neonatal hypoxic-ischemic encephalopathy by using susceptibility weighted image analysis
No full textObjective: susceptibility weighted imaging (SWI) of neonatal hypoxic-ischemic brain injury can provide assistance in the prognosis of neonatal hypoxic-ischemic encephalopathy (HIE). We propose a convolutional neural network model to classify SWI images with HIE.Materials and methods: due to the lack of a large dataset, transfer learning method with fine-tuning a pre-trained ResNet 50 is introduced. We randomly select 11 datasets from patients with normal neurology outcomes (n = 31) and patients with abnormal neurology outcomes (n = 11) at 24 months of age to avoid bias in classification due to any imbalance in the data.Results: we develop a rule-based system to improve the classification performance, with an accuracy of 0.93 ± 0.09. We also compute heatmaps produced by the Grad-CAM technique to analyze which areas of SWI images contributed more to the classification patients with abnormal neurology outcome.Conclusion: such regions that are important in the classification accuracy can interpret the relationship between the brain regions affected by hypoxic-ischemic and neurodevelopmental outcomes of infants with HIE at the age of 2 years
Potential neuroimmunological targets in the treatment of anxiety disorders
Full text linkIn the translation of psychoneuroimmunology research into clinical practice, one critical step is to identify biomarkers for improved diagnosis and targeting of interventions. Inflammatory markers deserve special attention due to their crucial role linking various health conditions and disorders. In this chapter, we discuss the pivotal roles of cytokines in signalling to the brain and leading to behavioural changes. This is followed by a review of recent research findings into neuroimmunology of depression, and immunomodulating effects of antidepressants. The rest of the chapter focuses on neuroinflammatory hypothesis in anxiety disorders, and provides an overview of current research evidence on inflammatory responses in anxious state and anxiety disorders. Research suggestions are recommended, including study design, risk factors, medication effects, and measurement strategies. Clinical and pharmacotherapeutic implications and future research directions are also discussed in the final sectio
Automatic veins analysis of susceptibility weighted image in hypoxic-ischaemic encephalopathy
No full textBackground and objective: the purpose of this study is to evaluate venous vascular structure and distribution as prognostic indicators of developmental outcomes for infants with neonatal hypoxic-ischaemic encephalopathy (HIE) by detecting and analysing ridges representing vessels on susceptibility-weighted magnetic resonance images (SWIs). Methods: forty-two infants with neonatal HIE underwent SWI in the neonatal period and neurodevelopmental assessment at age 2 years. Normalised histograms of the width, intensity, length and Hessian eigenvalues extracted from the ridge analysis of each patient's SWI are applied as feature vectors to feed into supervised classifiers such as the kNN and random forest (RF) classifiers to predict their neurodevelopmental outcomes. Here we also propose a supervised classifier for automatic prognosis of automated detection of SWI signs of HIE. Our classifier proposed in this paper demonstrates a superior performance in HIE prognosis for the datasets associated with cognitive and motor outcomes and it also enables to determination of brain regions which have been affected by hypoxia-ischaemia by extracting appropriate features from SWI images. Results: the feature vectors containing width, intensity, length, and eigenvalue show a promising classification accuracy of 78.67% ± 2.58Linear regression, polynomial regression, and support vector regression (SVR) models predicted outcomes and the lower mean relative errors (MRE) for motor and cognitive outcomes are 0.088 ± 0.073 and 0.101 ± 0.11 respectively. Conclusion: the features derived from the vascular ridges improve the prognostic value of SWI in HIE. Our findings suggest that it is possible to predict neurological, motor, and cognitive outcomes by numerical analysis of neonatal SW images and to identify brain regions on SWI affected by hypoxia-ischaemia.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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