Script to produce a residual time series cleaned by ANATICOR model.

@ANATICOR    <-ts TimeSeriesVol>
             <-polort polort>
             <-motion motion.1D>
             <-aseg aseg.nii>
             <-prefix output>
             [<-radius r >]
             [<-view VIEW>]
             [<-nuisance nuisance.1D>]
             [-verb] [-dirty] [-echo]

   -ts TimeSeriesVol: Time series volume
      The time series should have had the following done already:
         Despiking (if necessary)
         RetroIcor, and RVT correction
         Time shifting, and volume registration
    We strongly recommend you do the preprocessing with afni_proc.py,
      for example:
           afni_proc.py  -subj_id ID  -dsets EPI+orig.HEAD \
               -blocks despike ricor tshift volreg regress \
               -tcat_remove_first_trs 4 \
               -ricor_regs_nfirst 0 \
               -ricor_regs oba.slibase.1D \
               -ricor_regress_method per-run \
    This is an example for preprocessing, and you should carefully
      look into your study design and the script made by afni_proc.py.
      See the RETROICOR examples in the help text of afni_proc.py.
   -polort polort: Polynomial for linear trend removal.
                   Use the same order as for afni_proc.py
   -motion motion.1D: head motion parameters from 3dvolreg
                      Also created by afni_proc.py
   -aseg aseg.nii: aseg file from FreeSurfer's segmentation.
                   This aseg volume must be in register with the EPI
                   time series. Otherwise you're wasting your time.
                   This script will automatically make tissue masks
                   from this file. Do not replace aseg with aparc
                   volumes for example. If you want other methods
                   for providing tissue masks, complain to HJJ,
                   Email address below.
   -prefix output: Use output (residual time series) for a prefix
   -radius r: The radius of a local sphere mask, r mm
              default = 15 mm, which what was used in HJJ et al. 2010
              for high resolution 1.7x1.7x3mm data. For typical, about
              3x3x5 resolution, a radius of 30 mm seems to do fine.
              You should check out the coverage of WMeLocal regressor
              using -coverage option.
   -view VIEW: Set the view of the output data. Default is +orig
               Choose from +orig, +acpc, or +tlrc.
   -nuisance nuisance.1D: Other nuisance regressors.
              Each regressor is a column in .1D file
   -no_ventricles: not include LVe regressor
   -Rsq_WMe: produce an explained variance map for WMeLOCAL regressor.
             This may help measuring the sptial pattern of local
             artifact like the paper of Jo et al. (2010, Neuroimage).
   -coverage: produce a spatial coverage map of WMeLOCAL regressor
   -dirty: Keep temporary files
   -verb: Verbose flag
   -echo: Echo each script command for debugging

Please reference the following paper if you publish results from
 this script.
'Mapping sources of correlation in resting state FMRI, with
 artifact detection and removal'
       Jo, et al., Neuroimage, Vol 52 (2), 571-582, 2010.

Written by Hang Joon Jo.
           hangjoon.jo@nih.gov (Last Update on 12/15/2010)