I have been working with FSL software (melodic & dual_regression) to parcellate intrinsic connectivity networks both at a group and subject level. I successfully ran dual_regression and have the outputted z-maps for each subject and component. I want to create a summary statistic for each outputted z-map so that I can compare differences across participants using a dimensional framework. I am not interested in analyzing subgroups, even though all the FSL documentation is all about group differences.
I am wondering how I could calculate a summary statistic for each z-map that represents the network strengthen/integrity of a given subject. I basically want to know how over-expressed or under-expressed is a given network is for each subject. I want to use this approach to test a hypothesis regarding individual differences in symptoms of depression. I read this in a few books and papers, but no one has explained how to quantify these subject-level networks from nifti files into numeric summary scores that could be inputted into a multiple regression.
Attached here is my group ICA output that was manually capped at 30 components (melodic_IC.nii.gz) and a couple dual regression output files (original and z-maps) from two subjects to give you an idea about what I am working with. The 3rd component is of most interest as it most resembles the default mode network. Please help someone!
Upon more thinking and reading, I think the easiest solution is to use fslmeants to simply extract the mean beta weight across all voxels from the Stage-2 Output Maps. I did multiple sanity checks and find that the mean values and individual beta maps are fairly comparable (see bottom row of attached screenshot).
It also helps to apply smoothing prior to group ica (kernel = 3mm) to get cleaner components that more resemble functional networks in case anyone runs into this problem in the future.
- Badges
- Science topic
- Similar topics
- Books