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

  FT_CONNECTIVITY_CORR computes correlation, coherence or a related quantity from a
  data-matrix containing a covariance or cross-spectral density. It implements the
  methods as described in the following papers:
  Coherence: Rosenberg et al, The Fourier approach to the identification of
  functional coupling between neuronal spike trains. Prog Biophys Molec
  Biol 1989; 53; 1-31
  Partial coherence: Rosenberg et al, Identification of patterns of
  neuronal connectivity - partial spectra, partial coherence, and neuronal
  interactions. J. Neurosci. Methods, 1998; 83; 57-72
  Phase locking value: Lachaux et al, Measuring phase sychrony in brain
  signals. Human Brain Mapping, 1999; 8; 194-208.
  Imaginary part of coherency: Nolte et al, Identifying true brain
  interaction from EEG data using the imaginary part of coherence. Clinical
  Neurophysiology, 2004; 115; 2292-2307
  Use as
    [c, v, n] = ft_connectivity_corr(input, ...)
  The input data should be an array organized as
    Repetitions x Channel x Channel (x Frequency) (x Time)
    Repetitions x Channelcombination (x Frequency) (x Time)
  If the input already contains an average, the first dimension should be singleton.
  Furthermore, the input data can be complex-valued cross spectral densities, or
  real-valued covariance estimates. If the former is the case, the output will be
  coherence (or a derived metric), if the latter is the case, the output will be the
  correlation coefficient.
  Additional optional input arguments come as key-value pairs:
    hasjack   = 0 or 1 specifying whether the Repetitions represent
                leave-one-out samples
    complex   = 'abs', 'angle', 'real', 'imag', 'complex', 'logabs' for
                post-processing of coherency
    feedback  = 'none', 'text', 'textbar' type of feedback showing progress of
    dimord    = specifying how the input matrix should be interpreted
    powindx   = required if the input data contain linearly indexed
                channel pairs. should be an Nx2 matrix indexing on each
                row for the respective channel pair the indices of the
                corresponding auto-spectra
    pownorm   = flag that specifies whether normalisation with the
                product of the power should be performed (thus should
                be true when correlation/coherence is requested, and
                false when covariance or cross-spectral density is
  Partialisation can be performed when the input data is (chan x chan). The
  following options need to be specified:
    pchanindx   = index-vector to the channels that need to be
    allchanindx = index-vector to all channels that are used
                  (including the "to-be-partialised" ones).
  The output c contains the correlation/coherence, v is a variance estimate
  which only can be computed if the data contains leave-one-out samples,
  and n is the number of repetitions in the input data.