LISA onesample test (vlisa_onesample)

The program vlisa_onesample performs a one-sample t-test on a group of images including a correction for multiple comparisons using the LISA algorithm [2018_Lohmann]. The input is a list of 3D images resulting from a 1st level computation. For ease of use, the input images may be specified using wildcards as shown below. 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.

Note that a region-of-interest mask is required. The mask should exclude non-brain voxels, and it may cover the entire brain. In the example below, the mask is in the file “braimmask.nii”.

vlisa_onesample -in images_*.v -mask brainmask.nii -out result.v

Note that this program also accepts input images in Nifti format (“images_*.nii” or “images_*.nii.gz”), but the output is always 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_onesample’:

-help

Prints usage information.

-in

Input files.

-out

Output file.

-alpha

FDR significance level. Default: 0.05

-perm

Number of permutations. Default: 5000

-mask

Region of interest mask.

-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)