Dear all,

I am currently analyzing a dataset (task-based) for a gPPI analysis based on the Brainnetome atlas.

In general, I have 6 conditions (3 - control conditions, and 3-treatment conditions), and for each condition pair, I have selected seeds based on my contrasts of the same condition pair (e.g. wtp_control_green vs wtp_treatment_green).

One region that shows significant activation difference is dlPFC, which has three different subregions based on the brainnetome. So, I wanted to combine all of the regions into one, so I created a mask and uploaded it with SETUP -> ROI -> (atlas, which is how andy's brain book recommends to do it). I did the denoising again, looked at my first level analysis, and saw that my mask was seperated into three different clusters (Can you explain why it did that?).

And also, if I go to the second level analysis, is it correct to do a F-Test with that?

Also, I have noticed the literature is not consistent about how to report results (with FWE cluster correction and the p-threshhold). I know that the standard is cluster correction 0.05, and a p-threshold of 0.0001, however, i also find p>0.005 for example. Are you aware of any literature, that is discussing this topic?

Thank you for your help in advance.

Best,

Negin