examples:
basic: give 3 required inputs, all else is default
While a blur of 4.0 is the default, it is inculded for clarity.
slow_surf_clustsim.py -save_script surf.clustsim
-uvar spec_file sb23_lh_141_std.spec -uvar surf_vol sb23_SurfVol_aligned+orig -uvar blur 4.0 -uvar vol_mask mask_3mm+orig
more advanced, but still based on EPI analysis
Specify p-values, blur size and number of iterations, along with the script name and results directory, use 10000 iterations, instead of the default 1000.
- slow_surf_clustsim.py -save_script surf.clustsim
-uvar spec_file sb23_lh_141_std.spec -uvar surf_vol sb23_SurfVol_aligned+orig -uvar vol_mask mask_3mm+orig -uvar pthr_list 0.05 0.01 0.002 0.001 0.0002 0.0001 -uvar blur 8.0 -uvar niter 10000 -save_script csim.10000 -uvar results_dir clust.results.10000
basic, but on the surface (so no vol_mask is provided)
slow_surf_clustsim.py -save_script surf.sim.3
-on_surface yes -uvar blur 3.0 -uvar spec_file sb23_lh_141_std.spec -uvar surf_vol sb23_SurfVol_aligned+orig
- Note: it is appropriate to use a volume mask on the same grid as the data to
- be analyzed, which is to say either the EPI grid (for functional analysis) or perhaps the anatomical grid (for anatomical analysis, such as of thickness measures).
- Note: the niter values should match between this program and
- quick.alpha.vals.py.
applying the results:
The result of processing should be one z.max.* file for each uncorrected p-value input to the program (or each default). These files contain the maximum cluster sizes (in mm^2), per z-score/p-value, and are named using the corresponding p-value, e.g. z.max.area.0.001 corresponds to p=0.001.
To get the cluster size required for some uncorrected p-value, run quick.alpha.vals.py on the z.max.area file corresponding to the desired p-value, and note the cluster area required for the chosen corrected p.
For example, running this:
quick.alpha.vals.py -niter 1000 z.max.area.0.001might show that a minimum cluster size of 113 mm^2 would correspond to a corrected p=0.05.
Use of -niter should match that from slow_surf_clustsim.py.
script outline:
set control variables create and enter results directory convert p-value list (pthr_list) to z-scores (zthr_list) create dummy time series of length itersize for each iter ( iteration list )
3dcalc: generate noise volume 3dVol2Surf: map noise to surface SurfSmooth: blur to FWHM for each index ( itersize list )
- for each zthr ( zthr_list )
- SurfClust: make clust file clust.out.$iter.$index.$zthr
extract lists of maximum areas
terminal options:
-help : show this help
-hist : show module history
-show_default_cvars : list default control variables
-show_default_uvars : list default user variables
-show_valid_opts : list valid options
-ver : show current version
- other options
- -on_surface yes/no : if yes, start from noise on the surface
- (so no volume data is involved)
-print_script : print script to terminal -save_script FILE : save script to given file -uvar value ... : set the user variable
(use -show_default_uvars to see user vars)
-verb LEVEL : set the verbosity level