Dear Torben,
Thank you very much for your reply (two days ago)
I created a matrix with 4 sessions and 9 regressors by sessions (3
conditions convolved by the hrf + 6 motion parameters) and associated
the data. Then, I ran the instructions you kindly send to me
(SPM.xX.iG=4:9,13:18,22:27,31:36 and SPM.xX.iC(SPM.xX.iG)=[] ) and
estimated the matrix.
I tested different contrasts but I observed very few differences in
comparison with the classical way: the increase of p values at the
voxel-level was around .001 or .002. The only major difference is at the
set-level. The number of expected clusters was respectively 11 and 10
for motion parameters as regressors of interest vs of no interest (it is
similar) but the probability to find this number or cluster or more
increased from .222 to .331
Does this result correspond to what you usually have? or maybe I've done
something wrong!
Thank you
Cyril
PS: I also have some difficulties to understand the subtle difference
between 'of interest' and 'of no interest' when thinking at the
estimation of the model. I'm not an expert in mathematics, can someone
explain me the difference in simple words? What I suppose is that in the
case of regressors of interest, for Y=f(Xi + ...Xk) + Ei, the motion
parameters are included as observations (Xi) whereas in the case of
regressors of no interest, motion parameters are included in the E?
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