LISA generic framework (vlisa_generic)

The program vlisa_generic implements a generic framework for statistical inference of fMRI data [2018_Lohmann]. This program allows the user to supply his/her own permutation images. This can be used to do statistical inference in arbitrary scenarios for which no dedicated Lisa-Program exists.

The user must supply two files as input. The first file is an uncorrected map in which each voxel contains some test statistic uncorrected for multiple comparisons. The second file is a 4D file containing permuted maps supplied by the user. Each “time point” corresponds to one permutation.

These permuted maps are subsequently used by Lisa to derive statistical significance including multiple comparison correction. The output is a map thresholded such that FDR < alpha for every voxel. The default is alpha=0.05. The resulting image shows (1-FDR) so that larger values indicate higher significance.

vlisa_generic -in zmap.v -permutations permfile.v -out result.v

Note that this program also accepts input images in Nifti format (”.nii” or “.nii.gz”). In this case, the permutations file is a 4D image in which each volume represents a permutation. The output is in vista format. To convert the output to the Nifti format, use the following command:

vnifti -in result.v -out result.nii

Parameters of ‘vlisa_generic’:

-help

Prints usage information.

-in

Input map.

-permutations

Input file containing permutations.

-out

Output file.

-alpha

FDR significance level. Default: 0.05

-seed

Seed for random number generation. Default: 99402622

-radius

Bilateral parameter (radius in voxels). Default: 2

-rvar

Bilateral parameter (radiometric). Default: 2.0

-svar

Bilateral parameter (spatial). Default: 2

-filteriterations

Bilateral parameter (number of iterations). Default: 2

-cleanup

Whether to remove isolated voxels. Default: true

-j

Number of processors to use, ‘0’ to use all. Default: 0

References

[2018_Lohmann]

Lohmann G., Stelzer J., Lacosse E., Kumar V.J., Mueller K., Kuehn E., Grodd W., Scheffler K. (2018). LISA improves statistical analysis for fMRI. Nature Communications 9:4014 `(link) <https://www.nature.com/articles/s41467-018-06304-z> `_