Hello Hossein,
This is great, thanks!
Just to push forward the convo meanwhile the Tutorials are out, let me try to ask a couple more Qs that might be helpful to all of us:
1. Granger causality is a good approach to differentiate between the source and the sink of information flow. The performance of GC depends on how accurate is the underlying model and assumptions. Also, the interpretation is not very easy when we have more than two signals in our model. That is why we only have bi-variate functions. However, If you have few sources it should have satisfactory results.
If I understand this correctly, then GC is better suited for testing the influence of one region to another (2 signals) or of a few ROIs on each other. Am I correct?
3. There are a couple of approaches for that. Sometimes you can find features from connectivity matrices (like network measures) and then compare those features between two groups. Another approach is to subtract one matrix from another one (if there are dependent groups like two conditions of a cognitive task) and compare the difference with a null hypothesis distribution to find out the connections with significant differences between groups.
Cool! My design is a within-subject, therefore I believe that this second scenario could fit my needs. Trying to implement it into BST, would you run a Process 2 A(Condition 1) - B (Condition 2) difference and then test the result on Process 1 --> test --> Parametric Test Against zero?
Cheers;