I've just read the Buchel et al. (1998) paper on zero, first and second
order terms in analyses of parametric fMRI studies. I'd like to conduct a
similar analysis/series of analyses on some data that I have that I
mentioned in a previous post regarding a parametric study design. To recap,
conditions run in the order 1412131 where conditions 2, 3 and 4 represent
increases in level of difficulty and condition 1 a control. If I understand
the Buchel et al. (1998) paper correctly, the approach I adopt should
proceed something like this:
Analysis (1): model the simple box-car between task and control
irrespective of the manipulation of difficulty (conditions 1212121, boxcar
-1 1) [zero-order]
Analysis (2): linear term as single covariate of interest (e.g., -3 -1 1 3
for conditions now entered as 1412131) with confound/covariate of no
interest being the simple boxcar function from (1) above [first-order]
Analysis (3): quadratic term as single covariate of interest (1 -3 -3 1)
now with 2 confounds/covariates of no interest entered, namely the boxcar
and linear terms from (1) and (2) above [second-order expansion]
With covariates (inc. confounds) convolved with a hrf.
Is this correct? And returning to the issue of interpretation raised
earlier - compare the SPM{F} maps from (1) to (3) above?
Sound reasonable?
regards,
Greig
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