Dear SPM list,
I'd be very grateful for any help on the following question:
I'm looking for activation that parametrically increases
with four versions of a stimulus, i.e. a perfectly
matching voxel would respond with strength 1 to stim1,
strength 2 to stim2 etc.
One way to look for this would be to have four conditions,
[ stim1 ... stim4 ] and run a contrast [ 1 2 3 4 ].
Another would be to make a parametrically modulated regressor
with a 1 when stim1 onsets happen, 2 when stim2 onsets happen etc.,
and then convolve that with an HRF before entering it into the
model as a column in the design matrix.
e.g. conv([0 0 0 1 0 0 ... 0 3 0 ... 2 0 ... 0 4 0 ...], hrf)
My question is: are these two methods mathematically equivalent?
If so, how can one show that? If not, which is preferable,
i.e. which method would do a better job of finding voxels
whose activity really does increase with increasing stim-type?
Any help greatly appreciated,
Raj
P.S. Another thing that's puzzling me is whether the [ 1 2 3 4 ] contrast
should be made to be zero-mean or not: [ -1.5 -0.5 0.5 1.5 ]
Am I right in thinking that if the null trials are left unmodeled
to form an implicit baseline, then it's better to use the
[ 1 2 3 4 ] version, because I'm looking for positively active
areas with respect to that baseline?
P.P.S. I'm not at all expert in stats or GLM stuff, so I'd
be especially grateful for any answers that spell out
enough steps in the argument to be understandable by a novice
like me! Thanks!
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