Understanding what SPM does to establish significant 'activations'

I recently came across a web-page I should have committed to memory years ago, when I was first starting to get to grips with SPM analysis:

Matthew Brett's Introduction to SPM Statistics

It's a fantastically straightforward guide to how SPM analysis uses a regression model defined by your contrasts to establish the voxels in your scanner images that have significant activations.

It doesn't take too much understanding on top of what you get from this web-page to appreciate that when you're specifying that you want onsets modeled as a haemodynamic response function (hrf), the software is simply building a timecourse by stacking hrfs on top of one another according to your design-defined onsets.  It them fits the regression which is now defined by parameters resulting from the estimated hrf timecourse rather than task difficulty values from 1-5.

I'd say this should be required reading for all those getting to grips with SPM.