Hi Dr. Greve,
This works for me if I define seeds into subject-wise anatomical space.
Now, location of seed regions across subjects might differ because these are defined in subject-wise anatomical space. Hence subject-wise connectivity maps are generated from subject-wise seeds but these can be displayed only on fsaverage space, not on subject-wise space. That means connectivity maps are normalized here and we can do 2nd level analysis (keeping subject-wise seeds as subject-wise (not normalized)).
(a). If above points are correct, then it's fine to calculate 2nd level maps but I am not sure how seeds can be overlaid on 2nd level maps or 1st level maps because seeds are not normalized but both 1st and 2nd level connectivity maps are. Is there any way I can make sure that self connectivity is giving correlation coefficient ~1.
(b). If above points are incorrect i.e. if subject-wise connectivity maps are not normalized and are in subject-wise space then (i) why can't we overlay those on subject-wise connectivity maps on subject-wise surfaces and (ii) how second level maps can be calculated from un-normalized 1st-level level maps?
For second level analysis, I am using isxconcat-sess and mri_glmfit.
Could you please clarify this for me?
Thanks,
Sahil