FIRST uses the standard FSL "randomise" non parametric statistical engine to calculate statistical values and perform correction for multiple comparisons. Since there are different types of corrections for multiple comparisons, it is recommended to create method-specific subfolders of the design subfolders within which to run randomise and save resutls.
The shape analysis subfodlers contain a text file with example scripts.
Running Analysis
Randomize is like "Results" in SPM in that it does a statistical test on a contrast. The inputs (analysis and mask) are the outputs from the previous step.
[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)
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, we do the same with randomise (see here for initial details). A starting point might be
-F 3
to look at bi-directional effects (--fonly), after de-meaning the data (-D), with an F threshold of 3, with cluster-based correction for multiple comparisons. The guide (see Running vertex analysis) suggests always using demean and F-test only).
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).
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 des_2sample.mat -t 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. 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):
