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- "_vertexNative" ==>> "--useReconNative --useRigidAlign" (volume differences)
- "_vertexNativeScale" ==>> "--useReconNative --useRigidAlign –useScale" --useScale" (shape differences)
- "_vertexMNI" ==>> "--useReconMNI --useRigidAlign" (volume differences accounting for head size)
- "_vertexMNIScale" ==>> "--useReconMNI --useRigidAlign –useScale"--useScale" (differences after accounting for head size/shape and structure size - hard to interpret but should have the lowest variability)
The output of this step is a 4D nifti file (*.nii.gz) with the name as indicated in the -o option, and a _mask file with a similar name. Additional files with the same name as the design matrix but with different extensions are also created: *.con for t-contrast, *.fts for f-contrast.
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[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -t des_2sample.con -f des_2sample.fts --fonly -D (output/multiple comparison correction options)
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Possible options include -x, --T2, -F <optional threshold><threshold*>, -S <threshold>
* At some point, this could also run without a threshold; however, it usually gives an error ("F missing non-optional argument" or similar).
Just as in SPM "Results" where we put in statistical tests and thresholds, we do the same with randomise (see here for initial details). A starting point might be
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[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -t des_2sample.con -f des_2sample.fts --fonly -D -F 3
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -t des_2sample.con -f des_2sample.fts --fonly -D -x
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -f t des_2sample.fts --fonly -D -x
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -con -f des_2sample.
ftsfts --fonly -D --T2
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -f t des_2sample.fts --fonly -D -S 3
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -con -f des_2sample.fts --fonly -D -
FS 3
Each analysis will generate a number of files, one for each contrast. There are maps of f statistics, p values, and corrected p values, regions that are significantly different based on the threshold if one was included (F statistic > 3 in the last example):
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