Review of Boulez on Music Today translated by Susan Bradshaw and Richard Rodney Bennett

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A systematic review with meta-analysis of comprehensive interventions for preschool children with autism spectrum disorder (ASD): study protocol

En: BMJ Open, No. 2, doi: 10.1136/bmjopen-2011-000679Introduction: The aims of this study are to (1) conduct a systematic review of the intervention literature in preschool children with autism spectrum disorder (ASD), including types of interventions that are tested and the classification of outcome measures used and (2) to undertake a meta-analysis of the studies, allowing for the first time the comparison of different approaches to intervention using comparative outcomes. There are a number of alternative modalities of intervention for preschool children with ASD in use with different theoretical background and orientation, each of which tend to use different trial designs and outcome measures. There is at this time an urgent need for comprehensive systematic review and meta-analyses of intervention studies for preschool children with ASD, covering studies of adequate quality across different intervention types and measurement methods, with a view to identifying the best current evidence for preschool interventions in the disorde . Methods and analysis: The authors will perform a systematic review of randomised controlled trials for preschool children with ASD aged 0e6 years, along with a meta-analysis of qualifying studies across intervention modality. The authors will classify the interventions for preschool children with ASD under three models: behaviour, multimodal developmental and communication focused. First, the authors will perform a systematic review. Then, the authors will conduct a meta-analysis by comparing the three models with various outcomes using an inverse variance method in a random effect model. The authors will synthesise each outcome of the studies for the three models using standardised mean differences

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data.

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest.This work was supported by a MRC Centenary Early Career Award (MR/J500410/1). The example datasets were collected using support from an MRC DTP studentship and an MRC grant (G0900593).This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.neuroimage.2016.02.05