I have a question about where to apply GLM on the resting-stat fMRI data when I have a regressor.
Solution 1: Apply GLM after finishing all preprocessing steps of afni_proc.py.
GLM will be just 3ddeconvolve with only my regressor applied to the errts.subj.fanaticor+tlrc .
Solution 2: Modify the outcome of afni_proc.py (proc.subj) and add the regressor to the GLM step applied after volume registration. Then estimate the stat from the outcome of the GLM analysis.
What is the recommended approach? And what are other regressors should be included besides my regressor (motion, ventricle signal, …) in this case?
Really appreciate your help!
Are you running seed-based correlation analysis? Is the effect associated with the EEG regressor considered as a confounding effect or an effect of interest in this context?
No, it is not a seed-based analysis. It is a whole-brain analysis to find the fMRI correlated maps with the EEG regressor i.e. the brain regions that are associated with the regressor. I think it is more of effect of interest.
The situation would be similar to a task-based analysis: build one model for the whole analysis at the individual level. So just directly add the EEG regressor to the model with 3dDeconvolve (your solution #2).
You can pass that regressor to afni_proc.py via something like:
-regress_extra_stim_files subj_1234_EEG_reg.1D \
-regress_extra_stim_labels EEG_reg \
Thank you so much for the quick answer!
Thank you, Rick, for the explanation!