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 .. index:: lisa_generic 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) `_