Eye movement sequences during simple versus complex information processing of scenes in autism spectrum disorder

Minshew and Goldstein (1998) postulated that autism spectrum disorder (ASD) is a disorder of complex information processing. The current study was designed to investigate this hypothesis. Participants with and without ASD completed two scene perception tasks: a simple “spot the difference” task, where they had to say which one of a pair of pictures had a detail missing, and a complex “which one's weird” task, where they had to decide which one of a pair of pictures looks “weird”. Participants with ASD did not differ from TD participants in their ability to accurately identify the target picture in both tasks. However, analysis of the eye movement sequences showed that participants with ASD viewed scenes differently from normal controls exclusively for the complex task. This difference in eye movement patterns, and the method used to examine different patterns, adds to the knowledge base regarding eye movements and ASD. Our results are in accordance with Minshew and Goldstein's theory that complex, but not simple, information processing is impaired in ASD.<br/

Pipeline comparisons of convolutional neural networks for structural connectomes: predicting sex across 3,152 participants

With several initiatives well underway towards amassing large and high-quality population-based neuroimaging datasets, deep learning is set to push the boundaries of what is possible in classification and prediction in neuroimaging studies. This includes those that derive increasingly popular structural connectomes, which map out the connections (and their relative strengths) between brain regions. Here, we test different Convolutional Neural Network (CNN) models in a benchmark sex prediction task in a large sample of N=3,152 structural connectomes acquired from the UK Biobank, and compare results across different connectome processing choices. The best results (76.5% test accuracy) were achieved using Fractional Anisotropy (FA) weighted connectomes, without sparsification, and with a simple weight normalisation through division by the maximum FA value. We also confirm that for structural connectomes, a Graph CNN approach, the recently proposed BrainNetCNN, outperforms an image-based CNN

Exploring the efficacy of a graph classification GNN in learning non-linear graph metrics

Graph-structured data is common in many felds, including social networks, biological networks, and recommendation systems. The complexity of relationships in such data frequently necessitates the use of advanced modeling approaches to derive relevant insights. With the increasingly large network datasets being made available, deep learning is becoming a more relevant methodology for their exploration. Deep learning architectures which have graph inputs are called Graph Neural Networks (GNNs). One area in particular where great efforts have been made to gather population-wide data is in brain connectomics. The UK BioBank, for example has plans for up to 100,000 MRI scans which can be used for processing into brain connectomes. An important example of a graph classifcation GNN model for use on such data is the Brain Network Convolutional Neural Network (BrainNetCNN) model [1]. The BrainNetCNN is a CNN with special “cross-shaped” kernels for dealing with graph adjacency matrices. However, recent studies have repeatedly shown that the BrainNetCNN (among other GNNs) fails to outperform simpler, linear predictive models such as linear ridge regression in predicting population characteristics and clinical variables [2] [3] [4] [5]. This could be because most of the important characteristic/diagnostic information retrievable from brain networks is linear in nature, or there is still not enough data available to train GNNs on brain networks. But it could also be that developing more powerful models which can better identify more interesting relationships in the data with greater effciency will signifcantly improve predictive power. In order to begin analysing this, here we study how well the BrainNetCNN can learn non-linear patterns and structural characteristics– clustering coeffcient, routing effciency, degree variance, diffusion effciency, and assortativity– in three different types of synthetic graph datasets: Erdos-Renyi graphs, Barabasi-Albert graphs, and random geometric graphs. We use linear ridge regression as a baseline for comparison against linear modelling. We provide this baseline frstly to verify that BrainNetCNN can actually outperform linear models on non-linear metric learning, and secondly to enhance insights into model performance across the different graph metrics and graph datasets studied

The implementation of cardiac arrest treatment recommendations in English acute NHS trusts : a national survey

Purpose of the study: There are approximately 35 000 in-hospital cardiac arrests in the UK each year. Successful resuscitation requires integration of the medical science, training and education of clinicians and implementation of best practice in the clinical setting. In 2015, the International Liaison Committee on Resuscitation (ILCOR) published its latest resuscitation treatment recommendations. It is currently unknown the extent to which these treatment recommendations have been successfully implemented in practice in English NHS acute hospital trusts. Methods: We conducted an electronic survey of English acute NHS trusts to assess the implementation of key ILCOR resuscitation treatment recommendations in relation to in-hospital cardiac arrest practice at English NHS acute hospital trusts. Results: Of 137 eligible trusts, 73 responded to the survey (response rate 53.3%). The survey identified significant variation in the implementation of ILCOR recommendations. In particular, the use of waveform capnography (n=33, 45.2%) and ultrasound (n=29, 39.7%) was often reported to be available only in specialist areas. Post-resuscitation debriefing occurs following every in-hospital cardiac arrest in few trusts (5.5%, n=4), despite a strong ILCOR recommendation. In contrast, participation in a range of quality improvement strategies such as the National Cardiac Arrest Audit (90.4%, n=66) and resuscitation equipment provision/audit (91.8%, n=67) were high. Financial restrictions were identified by 65.8% (n=48) as the main barrier to guideline implementation. Conclusion: Our survey found that ILCOR treatment recommendations had not been fully implemented in most English NHS acute hospital trusts. Further work is required to better understand barriers to implementation

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size

iCartiGD: the Integrated Cartilage Gene Database

BACKGROUND: Diseases of cartilage, such as arthritis and degenerative disc disease, affect the majority of the general population, particularly with ageing. Discovery and understanding of the genes and pathways involved in cartilage biology will greatly assist research on the development, degeneration and disorders of cartilage. DESCRIPTION: We have established the Integrated Cartilage Gene Database (iCartiGD) of genes that are known, based on results from high throughput experiments, to be expressed in cartilage. Information about these genes is extracted automatically from public databases and presented as a single page report via a web-browser. A variety of flexible search options are provided and the chromosomal distribution of cartilage associated genes can be presented. CONCLUSION: iCartiGD provides a comprehensive source of information on genes known to be expressed in cartilage. It will remain current due to its automatic update capability and provide researchers with an easily accessible resource for studies involving cartilage. Genetic studies of the development and disorders of cartilage will benefit from this database

Presence of tumour capsule on contrast-enhanced CT is associated with improved outcomes of stereotactic body radiation therapy in hepatocellular carcinoma patients

Purpose Stereotactic body radiation therapy (SBRT) is a novel local therapy for the treatment of hepatocellular carcinoma (HCC). While effective, there is currently noreliable radiological marker to guide patient selection. In this study, we investigated the prognostic value of capsule appearanceon contrast-enhanced computed tomography (CT) for patients undergoing SBRT. Materials and Methods Between 2006 and 2017, 156 consecutive patients with Child-Pugh score class A/B and HCC ≥5cm that underwent SBRT were retrospectively analysed. Baseline triple-phase CTs of the abdomen were reviewed for the presence of capsule appearances and correlated with objective response rate (ORR), overall survival (OS), and pattern of treatment failure. Results Capsule appearance on CT was present in 83 (53.2%) patients.It was associated with improved ORR by Response Evaluation Criteria in Solid Tumours (RECIST) (60.2% vs 24.7%; p<0.001) andModified Response Evaluation Criteria in Solid Tumours(mRECIST) (ORR 78.3% vs 34.2%; p<0.001). The presence of a capsule was also associated with superior 2-year local control (89.1% vs. 51.4%; p<0.001) and 2-year OS (34.1% vs. 14.8%, p<0.01). Hepatic out-field failure was the dominant mode of progression, which was less common in patients with intact capsule (54.2% vs. 60.3%, p=0.01). Conclusion Capsule appearance on CT could potentially be a non-invasive prognostic marker for selecting HCC patients undergoing SBRT. Larger cohort is warranted to validate our findings

Mechanical chest compression devices at in-hospital cardiac arrest: A systematic review and meta-analysis

AIM: To summarise the evidence in relation to the routine use of mechanical chest compression devices during resuscitation from in-hospital cardiac arrest. METHODS: We conducted a systematic review of studies which compared the effect of the use of a mechanical chest compression device with manual chest compressions in adults that sustained an in-hospital cardiac arrest. Critical outcomes were survival with good neurological outcome, survival at hospital discharge or 30-days, and short-term survival (ROSC/1-h survival). Important outcomes included physiological outcomes. We synthesised results in a random-effects meta-analysis or narrative synthesis, as appropriate. Evidence quality in relation to each outcome was assessed using the GRADE system. DATA SOURCES: Studies were identified using electronic databases searches (Cochrane Central, MEDLINE, EMBASE, CINAHL), forward and backward citation searching, and review of reference lists of manufacturer documentation. RESULTS: Eight papers, containing nine studies [689 participants], were included. Three studies were randomised controlled trials. Meta-analyses showed an association between use of mechanical chest compression device and improved hospital or 30-day survival (odds ratio 2.34, 95% CI 1.42-3.85) and short-term survival (odds ratio 2.14, 95% CI 1.11-4.13). There was also evidence of improvements in physiological outcomes. Overall evidence quality in relation to all outcomes was very low. CONCLUSIONS: Mechanical chest compression devices may improve patient outcome, when used at in-hospital cardiac arrest. However, the quality of current evidence is very low. There is a need for randomised trials to evaluate the effect of mechanical chest compression devices on survival for in-hospital cardiac arrest