After running the individual segmentations, checking the output, and creating the files, we are ready to run the "Vertex Analysis." A model is created with a single command, but there are several options.
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Creating Statistical Model
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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 con -f des_2sample.fts --fonly -D (output/multiple comparison correction options)
Output Multiple comparison options: (see table in randomise User Guide):
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, so we do the same with randomise (see here for initial details). A starting point might be
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The "-o" indicates the output file name, so you probably want to label this with the test parameters (e.g., -o L_Hipp_vertexMNI_2sample_rand_F3 for the suggested options).
Probably Incorrect Example: t-tests
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Examples
Refer to table in randomise guide for files that are created (randomise User Guide).
[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
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des_2sample.mat -t
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Note: the T option can include a threshold (e.g., "-T 2" for t-statistic threshold of 2), but this is optional.
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des_2sample.con -f des_2sample.fts --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 -t des_2sample.con -f des_2sample.fts --fonly -D -S 3
Each analysis will generate a number of files, one for each contrast
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. There are maps of
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f statistics, p values, and corrected p values,
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regions that are significantly different based on the threshold if one was included (
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F statistic
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> 3 in the
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last example):
