Hi Mahen,

> I won't be doing any group analysis, and so far TENT and GAM are
> giving me very different BOLD response shapes. In terms of
> 3dDeconvolve results, the ROIs are fairly similar, with the TENT
> results seeming more robust. Considering that I am just using these
> results to 1) create masks, and 2) "clean up" my signal, are there
> any theoretical concerns I should have using one or the other
> concering these different response shapes?

Since you've already tried both two approaches and their results are similar, I'd most likely go with TENT because it gives way much more flexibility in capturing the shape differences of BOLD response across brain regions and across tasks/conditions.

> I can't seem to use ExamineXmat to examine the matrices.

Alternatively you may try xmat_tool.py. See

xmat_tool.py -help | less

> I'm not quite sure about how some aspects of the regression modeling
> works. Considering that I want to utilise stimulus-driven signal change
> in my final ROC (and probably MVPA) analyses, I know I want to remove
> some sort of scanner-driven signal change. I'd assumed that this was
> just linear drift, but as you mentioned, there are higher orders of trend -
> how would you recommend me going about this? What other parts of
> the baseline should I remove?

You can start with a higher order of polynomial fit for the slow drift and add option -bout in 3Deconvolve. The you can check the significance of those polynomial coefficients, and decide whether you want to decrease the order or not.

Gang



Edited 1 time(s). Last edit at 10/26/2012 10:33AM by Gang.