Comparing groups of subjects in fMRI studies: a review of the GLM approach.
FMRIB Technical Report TR00DL1
Didier G. Leibovici and Stephen Smith
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB),
Department of Clinical Neurology, University of Oxford, John Radcliffe Hospital,
Headley Way, Headington, Oxford, UK
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,