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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).

Function reference ('help ztrans')

  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

Usage examples

  • 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);
ztrans.txt · Last modified: 2010/06/11 02:04 by jochen