Hi, Jim-

Well, for most data, I wouldn't even try manual/landmarking alignment steps. I would let the cost function do your work. There are a lot of features about alignment to note, and many of these are covered in this playlist:
[www.youtube.com]
... with the general AFNI Academy lectures homepage here:
https://www.youtube.com/c/afnibootcamp

I will send you a link to upload an example EPI-anat pair and I can take a look. Typically, we want to estimate the alignment as part of the FMRI processing pipeline, and then have this alignment be concatenated with any other alignment steps (going to standard space, motion correction, phase distortion correction, etc.)---afni_proc.py is *very* good and *very* convenient for this.

Picking the cost function appropriate for EPI-anat alignment is also key--typically lpc+ZZ is what we start with for standard EPI and T1w anats.

All alignment is helped by starting "close to" a good solution. If EPI and anatomical dsets are connected in the same session, then their coordinates should be similar and overlap should be good. Having good/reasonable coordinates is always a plus for many steps in alignment. If there is obliquity in either acquisition, they might *appear* further away from each on in the GUI than their header information actually knows where they should be.

Also, we typically skullstrip the anatomical before alignment---note here how the EPI and anat have very different features, such as the latter having a bright skull, a face, a neck, etc. We don't want those extraneous bits of non-brain to affect things. We typically include an @SSwarper command prior to running afni_proc.py, and hand over those results to be incorporated in the processing.

--pt