How do I make contrasts for a deconvolution analysis? What sort of contrasts should I report?
Generally, deconvolution analyses of the sort implemented by AFNI’s 3dDeconvolve work on a finite impulse response (FIR) model, in which each peristimulus timepoint for each condition out to a threshold timepoint is represented by a separate column in the design matrix. In this case, a given ‘condition’ (or trial type) is represented in the matrix not by one column but by several. The readout of the parameter values across those peristimulus timepoints then gives you a nice peristimulus timecourse, but how do you evaluate that timecourse within the GLM statistical framework? There are a couple of ways; in general, the Ward (ContrastsPapers) is the best reference to describe them. A couple obvious ones, though. First, an F-contrast containing a single constraint for each column of a given condition will test the ‘omnibus’ hypothesis for that condition – the hypothesis that there’s some parameter significantly different from zero somewhere in the peristimulus timecourse, or more, simply,
