Learning, Arts, and the Brain: The Dana Consortium Report on Arts and Cognition

Reports findings from multiple neuroscientific studies on the impact of arts training on the enhancement of other cognitive capacities, such as reading acquisition, sequence learning, geometrical reasoning, and memory

Numerical Activities and Information Learned at Home Link to the Exact Numeracy Skills in 5-6 Years-Old Children

It is currently accepted that certain activities within the family environment contribute to develop early numerical skills before schooling. However, it is unknown whether this early experience influences both the exact and the approximate representation of numbers, and if so, which is more important for numerical tasks. In the present study the mathematical performance of 110 children (mean age 5 years 11 months) was evaluated using a battery that included tests of approximate and exact numerical abilities, as well as everyday numerical problems. Moreover, children were assessed on their knowledge of number information learned at home. The parents of the participants provided information regarding daily activities of the children and socio-demographic characteristics of the family. The results showed that the amount of numerical information learned at home was a significant predictor of participants' performance on everyday numerical problems and exact number representations, even after taking account of age, memory span and socio-economic and educational status of the family. We also found that particular activities, such as board games, correlate with the children's counting skills, which are foundational for arithmetic. Crucially, tests relying on approximate representations were not predicted by the numerical knowledge acquired at home. The present research supports claims about the importance and nature of home experiences in the child's acquisition of mathematics

Non-invasive brain stimulation techniques for chronic pain

Copyright © 2014 The Cochrane Collaboration.Various devices are available that can electrically stimulate the brain without the need for surgery or any invasive treatment in order to manage chronic pain. There are four main treatment types: repetitive transcranial magnetic stimulation (rTMS) in which the brain is stimulated by a coil applied to the scalp, cranial electrotherapy stimulation (CES) in which electrodes are clipped to the ears or applied to the scalp, transcranial direct current stimulation (tDCS) and reduced impedance non-invasive cortical electrostimulation (RINCE) in which electrodes are applied to the scalp. These have been used to try to reduce pain by aiming to alter the activity of the brain, but the efficacy of these treatments is uncertain. This review update included 56 studies: 30 of rTMS, 11 of CES, 14 of tDCS and one of RINCE. We judged only three studies as having a low risk of bias. Low or very low-quality evidence suggests that low-frequency rTMS and rTMS applied to pre-frontal areas of the brain are not effective but that a single dose of high-frequency stimulation of the motor cortex area of the brain provides short-term pain relief. This effect appears to be small and may be exaggerated by a number of sources of bias. Studies that gave a course of multiple treatments of rTMS produced conflicting results with no overall effect seen when we pooled the results of these studies. Most studies of rTMS are small and there is substantial variation between studies in terms of the treatment methods used. Low-quality evidence does not suggest that CES or tDCS are effective treatments for chronic pain. A single small study of RINCE provided very low-quality evidence of a short-term effect on pain. For all forms of stimulation the evidence is not conclusive and uncertainty remains. The reporting of side effects varied across the studies. Of the studies that clearly reported side effects, short-lived and minor side effects such as headache, nausea and skin irritation were usually reported both after real and sham stimulation. There were two reports of seizure following real rTMS. While the broad conclusions for rTMS and CES have not changed substantially, the addition of this new evidence and the application of the GRADE system has modified some of our interpretation. Previous readers should re-read this update. More studies of rigorous design and adequate size are required to evaluate accurately all forms of non-invasive brain stimulation for the treatment of chronic pain

Bayesian belief network model for the safety assessment of nuclear computer-based systems

The formalism of Bayesian Belief Networks (BBNs) is being increasingly applied to probabilistic modelling and decision problems in a widening variety of fields. This method provides the advantages of a formal probabilistic model, presented in an easily assimilated visual form, together with the ready availability of efficient computational methods and tools for exploring model consequences. Here we formulate one BBN model of a part of the safety assessment task for computer and software based nuclear systems important to safety. Our model is developed from the perspective of an independent safety assessor who is presented with the task of evaluating evidence from disparate sources: the requirement specification and verification documentation of the system licensee and of the system manufacturer; the previous reputation of the various participants in the design process; knowledge of commercial pressures;information about tools and resources used; and many other sources. Based on these multiple sources of evidence, the independent assessor is ultimately obliged to make a decision as to whether or not the system should be licensed for operation within a particular nuclear plant environment. Our BBN model is a contribution towards a formal model of this decision problem. We restrict attention to a part of this problem: the safety analysis of the Computer System Specification documentation. As with other BBN applications we see this modelling activity as having several potential benefits. It employs a rigorous formalism as a focus for examination, discussion, and criticism of arguments about safety. It obliges the modeller to be very explicit about assumptions concerning probabilistic dependencies, correlations, and causal relationships. It allows sensitivity analyses to be carried out. Ultimately we envisage this BBN, or some later development of it, forming part of a larger model, which might well take the form of a larger BBN model, covering all sources of evidence about pre-operational life-cycle stages. This could provide an integrated model of all aspects of the task of the independent assessor, leading up to the final judgement about system safety in a particular context. We expect to offer some results of this further work later in the DeVa project

Building Data-Driven Pathways From Routinely Collected Hospital Data:A Case Study on Prostate Cancer

Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals