Hi,

Can you please let me know whether when defining your first-level gPPI analysis you are selecting the option that reads "gPPI model output: interaction coefficients (task-dependent connectivity-change)" or the one that reads "gPPI model output: main+interaction coefficients (task-dependent total-connectivity)" (the exact text may be slightly different in prevoius versions of CONN)? and whether your design was an event-related or a block- design?

My guess would be that you may have the second option selected (main+interaction) which, combined with your "any effects" (F-test, 1,0;0,1) between-tasks contrast, is making your second-level analysis evaluate in what areas there is significant connectivity with the seed in either of your two tasks (or both). If instead you would like to evaluate in what areas there is a significantly different connectivity with the seed between the two tasks you can do that using the "difference" ( [-1 1] ) between-tasks contrast.  The specific options and interpretation would change a bit depending on whether you have an event-related design where you often want to explore difference between each task and the design implicit baseline (when no stimuli/events were presented) vs. when you have a block design where any experimental baseline is often defined explicitly as a separate condition. 

Hope this helps

Alfonso

Originally posted by sfeuer45:

Hello all! 

I am doing some research related to task-based connectivity. We are using CONN's implementation of gPPI, and then performing GLM analysis to compare connectivity across participants between tasks. I've noticed that no matter what region I use as a seed and search for any effects (F-test, contrast 1,0;0,1;) between tasks, there is a huge cluster that is called significant by the GLM analysis that includes that region. It also often includes the other regions in the network containing the selected seed. 

For instance, in the attached file, we use the ACC within the salience network as a seed, and the results include a large cluster that combines the ACC with the rest of the salience network. 

I have a few questions about this that partially relate to my general unfamiliarity with the mathematics behind GLMs:

• Should we expect this to happen every time? (If not, am I using the model incorrectly?)


• Why does this happen / what does it mean for the connectivity of a seed region to vary with itself between tasks?

Please let me know if you need any more information to help—any information would be appreciated, as I've been thinking about this issue for multiple weeks.