Dear listmembers,
I am currently analyzing an parametric activation experiment in PET with the
polynomial regression technique as described by Buchel et al. (Neuroimage
4:60-66). Using a first- and second-order expansion, the results seem to
show that almost every voxel that has a significant linear relationship to
the parameter also has a significant second-order contribution. That is
confirmed by plotting the activation.
(1) To get a clearer picture of this, I'd like to formally test for voxels
that show a significant first-order but no significant second-order effect
and I'd appreciate advice on how to implement this.
(2) Secondly, it seems that the nonlinearity seen in the experiment
represents a sort of ceiling effect. In the above-mentioned paper it is
suggested to try cosine basis functions which may be more sensitive to this
kind of phenomenon. How do I construct these regressors from my parameter
values?
As always, thanks a lot,
best regards,
Andreas
Andreas Meyer-Lindenberg, M.D., Ph.D. (Dr. med. Dr. med. habil.)
Unit on Integrative Neuroimaging, Clinical Brain Disorders Branch,
National Institute of Mental Health
Bldg. 10 Room 4C101, 9000 Rockville Pike, Bethesda, MD 20892-1365, USA
email: [log in to unmask], phone: 301-496-9672, fax: 301-496-7437
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