You can subdivide your output for particular regions of interest. The most obvious way to create tags is with the Tagset plugin. You can mark the tags there or read the tags from a file with this format below. The attribute in the header gets updated from that.

# Label_   _____x_____ _____y_____ _____z_____ ____val____ _t_
'AC'             0.125      -30.25       22.75           1   0
'PC'             0.125        -6.5        13.5           2   0
'CP'             0.125        -0.5          10           3   0

Multiple alignments would introduce multiple interpolations that may be more important to you. You can combine affine and nonlinear warps with 3dNwarpCat or with 3dNwarpApply.

Did you use the Slicer nrrd format here for the Allen mouse brain?
[download.alleninstitute.org]

The units are in um there, and the coordinate system is funky on the latest version. You can try the versions we have here (2012, 2017):
[afni.nimh.nih.gov]

In past work with similar microscopy data and this template, you will need to take into account the large amount of data and memory limits. Downsampling the data can help, depending on the needed final resolution. Also depending on the type of histology you are doing, you may want different kinds of alignment cost functions or blurring/feature sizes in the alignment process. I've used these commands for alignment with good results.

# align data to Allen mouse template with affine transformation
align_epi_anat.py -cost lpa+ZZ -dset1 channel0_0.025_center2017.nii.gz -dset2 allen_50u_dgtest_to3d+orig. \
-overwrite -suffix _al2allen2017_c -dset1_strip None -dset2_strip None \
-Allineate_opts '-source_automask+4 -twoblur 0.500000 -blok "RHDD(0.200000)" \
-twobest 11 -twopass -VERB -maxrot 10 -maxshf 5 -maxscl 1.5 -fineblur 0.2 -source_automask+2'

# nonlinear alignment
3dQwarp -base allen_50u_dgtest_to3d+orig. -source channel0_0.025_center2017_al2allen2017_c+orig. \
-prefix channel0_0.025_qw3 -workhard "0:2" -saveall -resample -lpa -maxlev 11

If you want, PM or email me for details.