fMRI signal + - Neurometrika

Lessons from the GLM Example• To obtain optimal GLM fits, all known effects should be modelled inthe design matrix.• Non-modelled effects are moved to the residuals, substantiallyincreasing the error variance. This leads to poor fits and reducedstatistical power (the error variance is one component of the standarderror of contrasts).• Inspection of the residuals is a good diagnostic to assess thegoodness-of-fit of a GLM: If one observes prominent, low-frequencyfluctuations, it is likely that not all effects have been modelled or thatthe modelled time courses do not fit to the data. The latter might befor example, due to delayed fMRI responses with respect to predictors.• Confound predictors may be added to improve the fit, for example,predictors capturing low-frequency drifts (Fourier basis functions orDiscrete Cosine Transform, DCT, basis functions), if drifts are notremoved already during data preprocessing.