reporting the results

Dear moderator,

I performed cortical thickness analysis as below

  1. cortical parcellation using freesurfer
  2. perform @SUMA_Make_Spec_FS
  3. peform mris_convert -c lh.thickness lh.white lh.thickness.asc
  4. perform 1dcat lh.thickness.asc’[4]’ > rh.thickness.1D.dset ← what’s mean the [4] ?
  5. perform MapIcosahedrone -overwrite -ld 90 -fix_cut_ssurfaces -dset_map lh.thickness.1D.dset -spec lh.spec -prefix std.90/
  6. perform SurfSmooth -met HEAT_07 -spec std.90.lh.spec -surf_A std.90.lh.smoothwm.asc -target_fwhm 30 -input std.90.lh.thickness.niml.dset -out lh.thickness.hk3.niml.dset
  7. 3dttest++ -prefix lh.thickness.niml.dset -set A ~~ -setB ~~~

after making the result file “lh.thickness.hk3.niml.dset”
how can I see the result image file using SUMA (could you tell me the exact command?)
then, how can I see the statistical result? how can I make the analysis result table (cluster size, F or T, p-value, peak coordinates, brain regions… etc)
Is there any documents for it?

I usually suggest that people use the Freesurfer pipeline for the group analyses[/url]. However, if you want to use AFNI/SUMA, I wrote a [url=]blog post a while ago on one pipeline that has worked well for me, and produces results nearly identical to Freesurfer.

The advantage of the pipeline in that post is that it uses the Freesurfer pipeline for smoothing and getting all subjects onto the fsaverage (group space). I found some differences between the AFNI’s SurfSmooth and Freesurfer’s tools that I haven’t fully flushed out.

It looks like your pipeline extracts the cortical thickness column from the asc file (that’s what the [4] does). I’m not sure that you necessarily need to deal either that step or with the MapIcosahedron step. You could run @SUMA_Make_Spec_FS with -ld 90 to automatically get the thickness file that you would otherwise take multiple steps to generate.

To view the results of your t-test in SUMA, you could do:

suma -spec Subject1/std.90.Subject1_lh.spec

And then use the surface controller to load the t-test results. Since your t-test results are on a standard mesh, you could use any brain as your underlay. However, you might consider using the fsaverage (or making your own version of it), as that is somewhat more expected when reporting results. You can then use SurfClust to get out a table of your clusters.

Again, I would recommend using the Freesurfer pipeline, and barring that, consider the impacts of smoothing and how much you feel like “being different” is worthwhile in terms of reviewer comments.

In suma, load the niml.dset files using the “Load Dset” button in the surface’s object controller. You can switch among various dsets with the “Switch Dset”. Show only 1 coloring dset with the ‘1’ button toggle or show multiple dsets at the same time.