A few recommendations:

1. Use @SSwarper instead of @auto_tlrc. It will compute a transformation to an updated version, MNI152_2009_template_SSW.nii.gz (2009c asymmetric) of the MNI template using both an affine and a nonlinear alignment, and it will skullstrip the data too. The output can be used easily with afni_proc.py for an FMRI pipeline.
2. Use the MNI152_2009c template (MNI152_2009_template.nii.gz) with @auto_tlrc. The original MNI-152 template is very blurry, and that seems to cause some problems getting a good alignment.
3. auto_warp.py - includes the nonlinear alignment, and it calls @auto_tlrc. Again with the MNI 2009c asymmetric in our distribution.

I would give priority to the first choice which is pretty robust to lots of different kinds of data. If you really want to make this work with @auto_tlrc and the original MNI152 with your data, I would need a copy of some example data, and I can look into it.