Note that this reference documentation is identical to the help that is displayed in Matlab when you type “help ft_volumenormalise”.

  FT_VOLUMENORMALISE normalises anatomical and functional volume data
  to a template anatomical MRI.
 
  Use as
    [volume] = ft_volumenormalise(cfg, volume)
 
  The input volume should be the result from FT_SOURCEINTERPOLATE.
  Alternatively, the input can contain a single anatomical MRI that
  was read with READ_FCDC_MRI, or you can specify a filename of an
  anatomical MRI.
 
  Configuration options are:
    cfg.spmversion  = 'spm8' (default) or 'spm2'
    cfg.template    = filename of the template anatomical MRI (default is the 'T1.mnc' (spm2) or 'T1.nii' (spm8)
                      in the (spm-directory)/templates/)
    cfg.parameter   = cell-array with the functional data which has to
                      be normalised, can be 'all'
    cfg.downsample  = integer number (default = 1, i.e. no downsampling)
    cfg.coordinates = 'spm, 'ctf' or empty for interactive (default = [])
    cfg.name        = string for output filename
    cfg.write       = 'no' (default) or 'yes', writes the segmented volumes to SPM2
                      compatible analyze-file, with the suffix
                      _anatomy for the anatomical MRI volumeFT_
                      _param   for each of the functional volumes
    cfg.nonlinear   = 'yes' (default) or 'no', estimates a nonlinear transformation
                      in addition to the linear affine registratFT_ion. If a reasonably
                      accurate normalisation is sufficient, a purely linearly transformed
                      image allows for 'reverse-normalisation', which might come in handy
                      when for example a region of interest is dFT_efined on the normalised
                      group-average.