Dear Dr. Nieto-Castanon and CONN experts,

I previously asked how I can find denoised ROI timeseries. After a more cautious look in the forum, I found that they are stored results/preprocessing. However, I have another related question. 

I am interested in task-based functional connectivity. My task has three explicit conditions. Since I am using whole-brain gPPI (doi: 10.1002/hbm.22532), I will conduct my first level analysis outside the CONN. As a second-level outcome, I have nxn matrix for each condition. That is, this is not a classic gPPI analysis (seed-to-voxel) but directed functional connectivity.

I have realized that effect of task is automatically included during denisoing step. I have seen a couple of posts that you are explaining the reasing behind this. It makes sense but I was thinking how this is compatible with SPM ROI extraction pipeline (https://en.wikibooks.org/wiki/SPM/Timese...). There, one need to adjust data for effect of task (using a F contrast), this tells SPM what is interesting in the GLM design (e.g., conditions) and regress out the other confounding variables (e.g., reliagnment parameters etc.). 

Actually, the method I use sounds similar to weighted ROI-to-ROI connectivity. If I am not wrong, you suggest regressing out the effect of task for this method too. If this is the case, I would also prefer regressing out the task effect. Nevertheless, I wonder whether if there is subtle difference between CONN and SPM in terms of modelling task effect for ROI extraction or if you tested this effect explicity.

Many thanks in advance!

Best,

Seda