Nisanova

Nisanova performs a voxelwise multi-factor analysis of variance of neuroimaging data.

Invocation

java  Nisanova 

Nisanova window

Information to be entered

• Design file
  Text file with design levels and corresponding image files.
• Output directory
  Where the output images will be placed.
• Output stem
  String to use as the base for output image names.
• Factor names
  Names of all the factors as listed in the design file. These names should be short, since they will become part of the filenames for the output images (except for the first factor, which is the random factor). See the man page for nisanova for more details.
• Threshold
  All voxel timecourses whose mean is below this threshold do not have an anova computed. The output map value for those voxesl will default to zero. Choose this value such that most brain matter is above the threshold, and most non-brain matter is below it.
• Use mask file
  Enter an analyze file, same dimensions as the functional images going into the anova. All voxel timecourses whose corresponding voxel in the mask file is zero will not have an anova computed. The output map value for those voxels will default to zero.
• Verbose mode
  Checking this will print out each slice number as it is processed as well as how many pixels are completed. At the end, it will print out the names of the images written out.
• Check cell counts
  Checking this box will produce a table of counts for each combination of factor levels in the design file so as to verify a balanced design or to diagnose an unbalanced one.
• Save afni maps
  Checking this box will produce afni format (fift) F map datasets in addition to the analyze format F maps. Afni F maps will have the same name as the analyze maps, but will have the afni .HEAD .BRIK extension. fift format has 2 images per map: the statistical map and an intensity image which is a copy of the first image put in the anova.

• Menubar
• Toolbar
• Output window