Comparing groups of subjects in fMRI studies: a review of the GLM approach

A review of the different approaches using the General Linear Model (GLM) to analyse multi-subject fMRI studies is presented. The first part of the paper attempts to expose the approaches with the least amount of statistic knowledge whilst the second part embeds those approaches in the GLM framework necessitating more statistical mathematical awareness, but enabling more advanced applications. Fixed and Random subject analysis are then re-expressed within the GLM, also taking into account autocorrelation of the measures. This occurs because of the time series aspect of the data, and also if the groups come from repeated sessions (e.g. different conditions) ; the spatial autocovariance is usually treated at the end, as the model is voxelwise. General hypothesis testing makes use of unidimensional contrasts, as well as multidimensional contrasts leading to different statistical maps (e.g. t-maps, F-maps).