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.

Permutation Tests in the Educational and Behavioral Sciences

Over the past two decades, permutation tests (PTs) have received much attention in the educational and behavioral sciences. The aim of this article is to review the theoretical developments of PTs, the active areas in the educational and behavioral research using PTs, and the types of analysis under which PTs have been applied. We obtained 224 published articles, which included 141 theoretical articles and 83 application articles. After scrutinizing each article, we are happy to see that (1) some researchers began to advocate introducing PTs into basic statistics training; (2) computing load for PTs may be reduced dramatically by some intelligent algorithms; (3) PTs began to be applied in new areas such as studies on the relationship between brain and behavior and the relationship between gene and behavior; (4) besides simple types of analysis such as independent two-group comparison, PTs can also be carried out under more complex situations such as multivariate analysis. However, we should also notice that PTs are still mostly used for simple analyses (e.g., randomness analysis).