Im having trouble interpreting my color map and understanding certain parts of the tutorial.
I did dSPM unconstrained, then flattened, permutation test on sources for two conditions (neutral vs valid trial for MEG auditory experiment).
I skipped the tutorial "Difference" because we simply want to compare significant activation and not the significant difference.
After doing the stats for Neutral vs Valid, the color bar for the t stat is -2 to 2 (blue to red respectively). From my understanding, red source activation means Neutral is more active than Valid condition, and when blue it means valid is stronger/more active. Am I correct or wrong? I did read through the difference tutorial and the workflows but I'm still not fully grasping if my interpretation is correct, nor its limitations about the "sign". I also don't want to pick a scout because it's an exploratory study.
Note, I did try to follow one part of the difference tut: I computed the difference of means (absolute value of average), put that one file in process2, and also put the one stat source map, then test>apply stats, but then I get an error "output argument "outputfiles" (and maybe others) not assigned during call to "process_extract_pthrest2>Run".
Sorry for the long question, would really appreciate some advice!
Yes, this is correct if when comparing magnitudes with non-parametric tests. Unconstrained+flatten, this produces strictly positive values, there is no problem of flow direction. However, the tutorial about differences of source maps explains why this is not going to give correctly you all the differences between your two conditions.
I computed the difference of means (absolute value of average), put that one file in process2, and also put the one stat source map, then test>apply stats, but then I get an error "output argument "outputfiles" (and maybe others) not assigned during call to "process_extract_pthrest2>Run".
This process "difference of means" does not compute any significance level associated with your data. It is just a difference of the average, there is no statistical threshold that you can apply to this data. Use a non-parametric test instead.
As a general recommendation, it is better to start following the tutorials using the example dataset instead of your own data. This way you can focus on understanding the tools, and make you understand all the manipulations before reproducing it on your own data.