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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.

Creating Statistical Model

This is the equivalent of "Estimating" the SPM.mat file in SPM.

first_utils --vertexAnalysis --usebvars -i concatenated_bvars -d design.mat -o output_basename [--useReconNative --useRigidAlign ] [--useReconMNI] [--usePCAfilter -n number_of_modes]

Based on earlier examples, this is one possible command line (assuming you are in the directory with the files):

[user@localhost temp]$ first_utils --vertexAnalysis --usebvars -i L_Hipp_all.bvars  -d design.mat -o L_Hipp_vertexMNI --useReconMNI  

The various options are specified in the User Guide under Vertex Analysis, Usage. It is important to come up with a naming convention for the output files. I suggest starting with the structure name, adding "_vertex" to indicate this file is the output of the vertex analysis, then add suffixes. Here is a convention:

  • "_vertexNative" ==>> "--useReconNative --useRigidAlign" 
  • "_vertexNativeScale" ==>> "--useReconNative --useRigidAlign –useScale
  • "_vertexMNI" ==>> "--useReconMNI --useRigidAlign" 
  • "_vertexMNIScale" ==>> "--useReconMNI --useRigidAlign –useScale"

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 extenstions are also created: *.con for t-contrast, *.fts for f-contrast, 

Running Analysis

Randomize is like "Results" in SPM in that it does a statistical test on a contrast. The inputs are the outputs from the previous step.

[user@localhost temp]$ randomise -i L_Hipp_vertexMNI.nii.gz -m L_Hipp_vertexMNI_mask.nii.gz -o L_Hipp_vertexMNI_rand -d design.mat -t design.con -f design.fts (more options required)

more options:
Just as in SPM Results we put in statistical tests (F or t) and thresholds, so we do the same with randomise (see here for initial details). A starting point might be 

--fonly -D -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. 

Note: the *.fts file is only needed for f tests; it can be left out for t tests.

The "-o" indicates the output file name, so you probably want to label this with the test parameters (e.g., -o L_Hipp_vertexMNI_rand_D_F3 for the suggested options).

Example 1: t-tests

To run a t test

[user@localhost temp]$ randomise -i L_Hipp_vertexMNI.nii.gz -m L_Hipp_vertexMNI_mask.nii.gz -o L_Hipp_vertexMNI_rand -d design.mat -t design.con -T 2

 

 






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