Hi everyone,

I’m preprocessing three separate rs-fMRI datasets in CONN with one unified pipeline, but the native voxel sizes are quite different: [1×1×2 mm], [3.5×3.5×3.5 mm], and [3×3×3.75 mm]

I’m unsure what to do for spatial smoothing. If I choose a single FWHM for all three, what kernel size would you recommend that’s reasonable for all data, without excessively smoothing the high-resolution dataset?

My worry is that a standard kernel (7–8 mm) might oversmooth the 1×1×2 dataset,
but using different smoothing kernels per dataset could introduce artificial between-dataset differences, since these will be analyzed together in the same project.

In your experience, which one is better ?

use one fixed kernel for all (and if so, what range)?
resample/normalize everything to a common voxel size first and then smooth?
or use different kernels but account for it somehow at group level? (if so, how?)

Thanks a lot!
Shiva