I have a question regarding the functional activation in gray matter. Any options are very welcome.
I am planning to count the numbers of activated voxel in the left inferior parietal region. There are two approaches I used to generate the ROI mask. The first one (method A) is to draw the ROI in AFNI and then I will use this mask to extract the number of activated voxels within that region for each subject, which will be done in a tlrc space (see Figure ROI_overall_LIP2). The second one (method B) is to use the freesurfer & suma to generate a ROI mask in gray matter only and then I will use this mask to extract the number of activated voxels with the gray matter for each subject, which will be done in a native (orig) space (see ROI_GrayMatter_LIP1). The followings are my concerns, I think method 1 may be better than method 2 is because that method 2 have relatively much smaller number of voxels than the method 1, thus the cluster size will be relatively smaller, which may increase the false positive rate. However, I am also puzzling about that it seems also reasonable for me that activation we want to count is the ones in the gray matter. On the other hand, I think method 2 may be better than method 1 is because I could generate the ROI mask of gray matter with freesurfer and suma which is automated generated by system. However, if I am going to use the ROI generate by the method 1, I will need to manual draw the ROI, which may increase the bias.
I will be so appreciated for any suggestions or options.