I am trying to run tPAC analyses on brainstorm using MEG data and I am stuck at a few points, I hope you can help me with some steps as I am fairly new to this field.
In my analysis steps, I am splitting my data into 2 different times (same condition, different times). One epoch is 5s long, the other is 9 seconds long. My first question is concerning the baseline time for DC offset; in the tutorials, it was mentioned to use the first 100ms as a baseline. However, I don't have a clear baseline (no pre-stimulus), is it ok to use the whole epoch duration?
My second question is whether the data covariance matrix used for the beamformer needs to be different for each split? I also want to compare the 5s epoch between tasks (different conditions). I am also wondering whether I need to use different data covariance matrices for each condition, or is it valid to use a single data covariance matrix estimated from grouping the conditions together?
My next question concerns the constrained versus unconstrained methods when computing sources using beamformer. I'd like to know how important it is to use the unconstrained method (which was recommended in the tutorials) when I have individual anatomy files. For the purpose of my project, I don't think it is necessary but would like to hear about your opinions.
Once I finish computing the sources, I want to run tPAC in source space using scouts, and I have received counsel to use surrogate data values to z-score the output values. However, I think this option has been removed in the new updates (?) and I am unable to find how to generate surrogate values. If it works, then my next steps would be in this order:
- run tPAC analysis in source space
- generate surrogate data (from the data that I use for this analysis)
- z-score transform tPAC output values WRT surrogate data
- perform statistical tests
Lastly, I am wondering about the comodulogram generated from tPAC maps; what does the measure "level of coupling" represent, and how can I reach it in the output file?