Howdy-
From an AFNI perspective, the main workhorse tool for nonlinear (NL) alignment is 3dQwarp:
Cox RW, Glen DR (2013). Nonlinear warping in AFNI.
OHBM: https://afni.nimh.nih.gov/pub/dist/HBM2013/Cox_Poster_HBM2013.pdf
In modern times, we recommend using a particular wrapper for this program, depending on whether you are doing human or animal studies; each includes both skullstripping (SS) and nonlinear warping, because these processes can help each other: for humans, @SSwarper, and for animals, @animal_warper. In each case, the outputs can be directly input into afni_proc.py for your full, FMRI single subject processing.
It is hard to imagine any case where you have an analysis that ends with having multiple subjects in a single space that you would not do nonlinear alignment. Having poor alignment basically means that parts of a particular gyrus whose results should be piled together across subjects are not really well compiled, and hence you don’t get a strong build up of the similarities-- it is kind of like having very blurry, noisy data. I can’t imagine a scenario where just affine alignment would be preferred-- even in cases of having non-standard structures in a brain (e.g., a tumor, or changes in shape due to surgery), one can use masks to help reduce the effects of those structures (at least with 3dQwarp et al).
You can see more thoughts on alignment in general (as well as in specific cases) here:
AFNI Academy alignment series
Note that NL alignment is not perfect. And the brain is a tricky subject to align nonlinearly: the variability of structure even within a healthy, homogeneous group is such that some people will have different numbers of sulci and gyri in a particular location-- how can a diffeomorphic, NL warp overcome that kind of difference?? So, there will still be imperfect alignment of structures across any group. And you still need to visually check all your results-- that’s why we try to include automatic images of alignment to help that important step of running a program. (And NL alignment does take longer-- that’s just life.)
Note that you also have to consider cost functions carefully with your alignment-- see here for more details:
Saad ZS, Glen DR, Chen G, Beauchamp MS, Desai R, Cox RW (2009). A new method for improving functional-to-structural MRI alignment using local Pearson correlation. Neuroimage 44 839–848. doi: 10.1016/j.neuroimage.2008.09.037.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649831/
Our general rule: when you are aligning brains of different subjects (e.g., sub-001 to template, or sub-001 to sub-002), nonlinear alignment is always recommended.
–pt