Good morning,
I apologize for misunderstanding, I probably should have better explained my situation:
I am analyzing fMRI data with GLM and I'm trying to model neural activity related to CO2 levels during voluntary breath hold (repetitions of 30s free breathing and 30 s breath hold). The problem is that I have to test a huge number of models and decide which one is the best. Model selection is made at subject level, voxel-by-voxel, and by considering whether R2adjusted or BIC.
This model selection approach resulted to be very consistent across subjects, and selected models are always grouped into clusters of neighbor voxels. Then, at group level, I've chosen for each voxel the inter-subject mode, i.e., the model being selected at that voxel for the greater number of subjects.
Now, being my final result a collection of results from per-voxel selected models, I need a per-voxel correction technique for multiple comparison, and this is why I can't use 3dClustSim. There is no "base condition", I only have a design matrix composed of some time series of interest that describes PETCO2 fluctuations, and some nuisance regressors. This is why I can't use randpermute.py. Is then any way in AFNI to perform group analysis by permuting many times somehow the regressors of interest?