Hi Ajay-

As you've discovered, @SUMA_Make_Spec_FS does not convert the outputs of -qcache. There are two somewhat direct ways of doing cortical thickness analysis in AFNI/SUMA. The first is to use the -qcache outputs and convert using the mris_convert command you listed for each subject.

mris_convert -c ./Subject01/surf/lh.thickness.fwhm10.fsaverage.mgh \
$SUBJECTS_DIR/fsaverage/surf/lh.white \
Subject01.lh.thickness.fsaverage.gii

This will then give you a set of files that are both smoothed and transformed to the fsaverage brain. From here, you can then use 3dttest++ or other AFNI tools to perform your analysis of interest and view the results on the fsaverage brain (run @SUMA_Make_Spec_FS on the fsaverage). This will give you similar results to those analyses done using the Freesurfer tools (e.g. mri_glmfit). Since the results are already in "standard space" with the fsaverage, there is really no need to run MapIcosahedron on the results. Some details are talked about in the following two links:

[afni.nimh.nih.gov]
[blog.cogneurostats.com]

The second option that you have is to take the std.141.?h.thickness.niml.dset (or .gii) files that are output from @SUMA_Make_Spec_FS and smooth them with an AFNI tool (SurfSmooth) and then perform some group analysis on the results. You could still overlay those results on the fsaverage brain, assuming that you again ran @SUMA_Make_Spec_FS on that as well. I had setup a pipeline to do this on some of my own data, but was a bit nervous with the results differing from the Freesurfer tools (people smooth in different ways).

Regardless of approach, you will probably want to use SurfFWHM and slow_surf_clustsim.py for estimating significant clusters.

-Peter