Our group has been using @auto_tlrc for aligning anatomical human T1 data to the MNI152 template. I recently attended an AFNI workshop and worked through warping/skull-stripping using @SSwarper and it sounds like this is the preferred option if you have the computing power to do it.
I’ve been running @SSwarper on some of our subjects to compare warping between our old @auto_tlrc method and this new nonlinear warping and wanted to run a side-by-side comparison by others (attached image).
On the left is the non-linear warping and skull-stripping done via @SSwarper (anatQQ dataset) and on the right is skull-stripped data warped via @auto_tlrc. The two don’t seem dramatically different but in the @SSwarper (left) image I feel as though there is some blurring between the sulci and gyri - the contrast just doesn’t seem as clear as it is in the linearly warped data on the right. Wouldn’t this negatively impact alignment and registration of functional data, by making it more difficult to find edges and by reducing contrast?
I want to be sold on @SSwarper but am not sure what I’m missing here.