Under certain circumstances (e.g. data that is arbitrarily scaled and comes from several sources has to be compared), one way to normalize (re-scale) the data is to apply z-transformation (setting the mean of values in the desired dimension to 0 and the standard deviation to 1).

  ztrans  - perform z-transformation on time course
 
  FORMAT:       [ztc, zf, zsh] = ztrans(tc [, dim [, tp]])
 
  Input fields:
 
        tc          time course data
        dim         temporal dimension (default: first non-singleton)
        tp          time points (indices of dim to use for normalization)
 
  Output fields:
 
        ztc         z-transformed time course
        zf          z-transformation factor
        zsh         z-transformation shift
 
  See also psctrans

• z-transforming a single time-course (e.g. of a region or voxel):

  ztc = ztrans(tc);

• z-transformation of an entire VTC (ensuring that dimension is 1):

  vtc.VTCData = ztrans(vtc.VTCData, 1);

• z-transformation of 4D-NII VoxelData (ensuring that dimension is 4):

  nii.VoxelData = ztrans(nii.VoxelData, 4);

• also obtaining the information about the scaling factor and shift:

  [ztc, zfactor, zshift] = ztrans(tc);

• only using values where no experimental condition in an SDM is active:

  % find timepoints to use
  tp = all(abs(sdm.SDMMatrix(:, 1:sdm.FirstConfoundPredictor-1)) < 0.1, 2);
   
  % apply z-transformation with those as normalization basis
  vtc.VTCData = ztrans(vtc.VTCData, 1, tp);