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Hi Martin,
Thanks for your reply, it's good to know that the mass-univariate approach is still appropriate here.
Since the aseg segmentations are volumetric, can I still use the *mris_preproc* and *mri_surf2surf* functions? If so, would the mri_preproc function look something like *mri_preproc --qdec-long /path/to/asegstats2table.long.table --target fsaverage --hemi rh --meas volume --out output_dir/rh.volume.mgh*
If not, and I need separate functions, would *mri_surf2surf* be replaced by *mri_vol2surf*?
On Thu, Jun 27, 2024 at 4:51 AM Reuter, Martin,Ph.D. < MREUTER@mgh.harvard.edu> wrote:
Hi Dan,
You could do either one. I would recommend the mass univariate for multiple independent test with the same design.
Best, Martin
On 26. Jun 2024, at 17:20, Dan Levitas djlevitas208@gmail.com wrote:
I recently performed an LME longitudinal mass-univariate (surface) analysis and am now trying to do something similar, but with subcortical (aseg) ROIs instead.
I first created an aseg table with the following command:
- asegstats2table \ --sd $subjects_dir \ --qdec-long
/path/to/long.qdec.table.dat \ --segno 4 10 11 12 17 18 26 43 49 50 51 53 54 58 251 253 255 \ --stats aseg.stats \ --tablefile $output_file*
The LME tutorial ( https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels) provides information and examples of both a univariate and mass-univariate approach. While univariate seems most appropriate in this case, the example details design matrix setup and testing of a single ROI (hippocampus), whereas I would like to assess several subcortical ROIs.
Would I simply create a loop structure to set up design matrices for each ROI (both hemispheres) and test them individually, or can I perform a mass-univariate approach to assess all ROIs at once?
Thanks,
Dan
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