Misconceptions in the use of the General Linear Model applied to functional MRI: a tutorial for junior neuro-imagers

This tutorial presents several misconceptions related to the use the General Linear Model (GLM) in functional Magnetic Resonance Imaging (fMRI). The goal is not to present mathematical proofs but to educate using examples and computer code (in Matlab). In particular, I address issues related to (i) model parameterization (modelling baseline or null events) and scaling of the design matrix; (ii) hemodynamic modelling using basis functions, and (iii) computing percentage signal change. Using a simple controlled block design and an alternating block design, I first show why 'baseline' should not be modelled (model over-parameterization), and how this affects effect sizes. I also show that, depending on what is tested; over-parameterization does not necessarily impact upon statistical results. Next, using a simple periodic vs. random event related design, I show how the haemodynamic model (haemodynamic function only or using derivatives) can affects parameter estimates, as well as detail the role of orthogonalization. I then relate the above results to the computation of percentage signal change. Finally, I discuss how these issues affect group analysis and give some recommendations.