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.
The 4D file contains values for each subject at the voxels on the boundary of the mean surface (defined by the mask file) as distance from the mean, in voxel units. (Note that the FSL person posted in 2013 that he may change the units to mm at some point.) Here is an example from the hippocampus, where the distance is positive (values can be negative as well).
Analysis options (from UserGuide)
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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 (threshold output/multiple comparison correction options)more options:
Output Multiple comparison options: (see table in randomise User Guide):
Possible options include -x, --T2, -F <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
To run a t test).
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):
