My response was based on the assumption that you have EPI data for each session and a single anatomical dataset and that you would reanalyze the data with similar pipelines. Alignment of statistical results is almost impossible and not worthwhile if you have the original data. For the second session, you could align the EPI data to the anatomical data of the first run with "align_epi_anat.py -giant_move" or "-ginormous_move".
In most FMRI processing pipelines, however, you would probably use afni_proc.py to guide you instead. There are numerous examples in the program help that are probably useful. The alignment to a standard space template is now often done with our nonlinear warping tools. The typical processing pipelines will handle the obliquity of the datasets internally, so you won't have that as a separate step that introduces addition blurring from interpolation. A single anatomical dataset can be transformed to a standard template space for each subject and then the affine and nonlinear transformations can be applied to the motion-corrected and aligned EPI datasets. The @SSwarper script takes care of the alignment to the template and skullstripping. See these presentations for descriptions and examples:
See around slide 18 here:
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afni.nimh.nih.gov]
See last few slides here:
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afni.nimh.nih.gov]
