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ft_connectivityanalysis.m
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function [stat] = ft_connectivityanalysis(cfg, data)
% FT_CONNECTIVITYANALYSIS computes various measures of connectivity between
% MEG/EEG channels or between source-level signals.
%
% Use as
% stat = ft_connectivityanalysis(cfg, data)
% stat = ft_connectivityanalysis(cfg, timelock)
% stat = ft_connectivityanalysis(cfg, freq)
% stat = ft_connectivityanalysis(cfg, source)
% where the first input argument is a configuration structure (see below)
% and the second argument is the output of FT_PREPROCESSING,
% FT_TIMELOCKANLAYSIS, FT_FREQANALYSIS, FT_MVARANALYSIS or FT_SOURCEANALYSIS.
%
% The different connectivity metrics are supported only for specific
% datatypes (see below).
%
% The configuration structure has to contain
% cfg.method = string, can be
% 'amplcorr', amplitude correlation, support for freq and source data
% 'coh', coherence, support for freq, freqmvar and source data.
% For partial coherence also specify cfg.partchannel, see below.
% For imaginary part of coherency or coherency also specify
% cfg.complex, see below.
% 'csd', cross-spectral density matrix, can also calculate partial
% csds - if cfg.partchannel is specified, support for freq
% and freqmvar data
% 'dtf', directed transfer function, support for freq and
% freqmvar data
% 'granger', granger causality, support for freq and freqmvar data
% 'pdc', partial directed coherence, support for freq and
% freqmvar data
% 'plv', phase-locking value, support for freq and freqmvar data
% 'powcorr', power correlation, support for freq and source data
% 'powcorr_ortho', power correlation with single trial
% orthogonalisation, support for source data
% 'ppc' pairwise phase consistency
% 'psi', phaseslope index, support for freq and freqmvar data
% 'wpli', weighted phase lag index (signed one,
% still have to take absolute value to get indication of
% strength of interaction. Note: measure has positive
% bias. Use wpli_debiased to avoid this.
% 'wpli_debiased' debiased weighted phase lag index
% (estimates squared wpli)
% 'wppc' weighted pairwise phase consistency
% 'corr' Pearson correlation, support for timelock or raw data
%
% Additional configuration options are
% cfg.channel = Nx1 cell-array containing a list of channels which are
% used for the subsequent computations. This only has an effect when
% the input data is univariate. See FT_CHANNELSELECTION
% cfg.channelcmb = Nx2 cell-array containing the channel combinations on
% which to compute the connectivity. This only has an effect when the
% input data is univariate. See FT_CHANNELCOMBINATION
% cfg.trials = Nx1 vector specifying which trials to include for the
% computation. This only has an effect when the input data contains
% repetitions.
% cfg.feedback = string, specifying the feedback presented to the user.
% Default is 'none'. See FT_PROGRESS
%
% For specific methods the cfg can also contain
% cfg.partchannel = cell-array containing a list of channels that need to
% be partialized out, support for method 'coh', 'csd', 'plv'
% cfg.complex = 'abs' (default), 'angle', 'complex', 'imag', 'real',
% '-logabs', support for method 'coh', 'csd', 'plv'
% cfg.removemean = 'yes' (default), or 'no', support for method
% 'powcorr' and 'amplcorr'.
% cfg.bandwidth = scalar, (default = Rayleigh frequency), needed for
% 'psi', half-bandwidth of the integration across frequencies (in Hz)
%
% To facilitate data-handling and distributed computing you can use
% cfg.inputfile = ...
% cfg.outputfile = ...
% If you specify one of these (or both) the input data will be read from a *.mat
% file on disk and/or the output data will be written to a *.mat file. These mat
% files should contain only a single variable, corresponding with the
% input/output structure.
%
% See also FT_PREPROCESSING, FT_TIMELOCKANALYSIS, FT_FREQANALYSIS,
% FT_MVARANALYSIS, FT_SOURCEANALYSIS, FT_NETWORKANALYSIS.
%
% For the implemented methods, see also FT_CONNECTIVITY_CORR,
% FT_CONNECTIVITY_GRANGER, FT_CONNECTIVITY_PPC, FT_CONNECTIVITY_WPLI,
% FT_CONNECTIVITY_PDC, FT_CONNECTIVITY_DTF, FT_CONNECTIVITY_PSI
% Undocumented options:
% cfg.refindx =
% cfg.jackknife =
% cfg.method = 'mi';
% cfg.granger.block =
% cfg.granger.conditional =
%
% Methods to be implemented
% 'xcorr', cross correlation function
% 'di', directionality index
% 'spearman' spearman's rank correlation
% Copyright (C) 2009, Jan-Mathijs Schoffelen, Andre Bastos, Martin Vinck, Robert Oostenveld
% Copyright (C) 2010-2011, Jan-Mathijs Schoffelen, Martin Vinck
% Copyright (C) 2012-2013, Jan-Mathijs Schoffelen
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the documentation and details.
%
% FieldTrip is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% FieldTrip is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id$
% these are used by the ft_preamble/ft_postamble function and scripts
ft_revision = '$Id$';
ft_nargin = nargin;
ft_nargout = nargout;
% do the general setup of the function
ft_defaults
ft_preamble init
ft_preamble debug
ft_preamble loadvar data
ft_preamble provenance data
ft_preamble trackconfig
% the ft_abort variable is set to true or false in ft_preamble_init
if ft_abort
return
end
% FIXME it should be checked carefully whether the following works
% check if the input data is valid for this function
% data = ft_checkdata(data, 'datatype', {'raw', 'timelock', 'freq', 'source'});
% set the defaults
cfg.feedback = ft_getopt(cfg, 'feedback', 'none');
cfg.channel = ft_getopt(cfg, 'channel', 'all');
cfg.channelcmb = ft_getopt(cfg, 'channelcmb', {'all' 'all'});
cfg.refindx = ft_getopt(cfg, 'refindx', 'all');
cfg.trials = ft_getopt(cfg, 'trials', 'all', 1);
cfg.complex = ft_getopt(cfg, 'complex', 'abs');
cfg.jackknife = ft_getopt(cfg, 'jackknife', 'no');
cfg.removemean = ft_getopt(cfg, 'removemean', 'yes');
cfg.partchannel = ft_getopt(cfg, 'partchannel', '');
cfg.parameter = ft_getopt(cfg, 'parameter', []);
hasjack = (isfield(data, 'method') && strcmp(data.method, 'jackknife')) || (isfield(data, 'dimord') && strcmp(data.dimord(1:6), 'rptjck'));
hasrpt = (isfield(data, 'dimord') && ~isempty(strfind(data.dimord, 'rpt'))) || (isfield(data, 'avg') && isfield(data.avg, 'mom')) || (isfield(data, 'trial') && isfield(data.trial, 'mom')); % FIXME old-fashioned pcc data
dojack = strcmp(cfg.jackknife, 'yes');
normrpt = 0; % default, has to be overruled e.g. in plv, because of single replicate normalisation
normpow = 1; % default, has to be overruled e.g. in csd
% select trials of interest
if ~strcmp(cfg.trials, 'all')
tmpcfg = [];
tmpcfg.trials = cfg.trials;
data = ft_selectdata(tmpcfg, data);
[cfg, data] = rollback_provenance(cfg, data);
end
% select channels/channelcombination of interest and set the cfg-options accordingly
if isfield(data, 'label')
selchan = cell(0, 1);
if ~isempty(cfg.channelcmb) && ~isequal(cfg.channelcmb, {'all' 'all'})
tmpcmb = ft_channelcombination(cfg.channelcmb, data.label);
tmpchan = unique(tmpcmb(:));
cfg.channelcmb = ft_channelcombination(cfg.channelcmb(:, 1:2), tmpchan, 1);
selchan = [selchan;unique(cfg.channelcmb(:))];
end
cfg.channel = ft_channelselection(cfg.channel, data.label);
selchan = [selchan;cfg.channel];
if ~isempty(cfg.partchannel)
cfg.partchannel = ft_channelselection(cfg.partchannel, data.label);
selchan = [selchan; cfg.partchannel];
end
tmpcfg = [];
tmpcfg.channel = unique(selchan);
data = ft_selectdata(tmpcfg, data);
% restore the provenance information
[cfg, data] = rollback_provenance(cfg, data);
elseif isfield(data, 'labelcmb')
cfg.channel = ft_channelselection(cfg.channel, unique(data.labelcmb(:)));
if ~isempty(cfg.partchannel)
ft_error('partialisation is only possible without linearly indexed bivariate data');
end
if ~isempty(cfg.channelcmb)
% FIXME do something extra here
end
% FIXME call selectdata
end
% FIXME check which methods require hasrpt
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% data bookkeeping - ensure that the input data is appropriate for the method
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
needrpt = 1; % logical flag to specify whether (pseudo)-repetitions are required in the lower level connectivity function (can be singleton)
switch cfg.method
case {'coh' 'csd'}
if ~isempty(cfg.partchannel)
if hasrpt && ~hasjack
ft_warning('partialisation on single trial observations is not supported, removing trial dimension');
try
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq'}, 'cmbrepresentation', 'fullfast');
inparam = 'crsspctrm';
hasrpt = 0;
catch
ft_error('partial coherence/csd is only supported for input allowing for a all-to-all csd representation');
end
else
try
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq'}, 'cmbrepresentation', 'full');
inparam = 'crsspctrm';
catch
ft_error('partial coherence/csd is only supported for input allowing for a all-to-all csd representation');
end
end
else
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq' 'source' 'source+mesh'});
inparam = 'crsspctrm';
end
if strcmp(cfg.method, 'csd')
normpow = 0;
outparam = 'crsspctrm';
elseif strcmp(cfg.method, 'coh')
outparam = 'cohspctrm';
end
dtype = ft_datatype(data);
switch dtype
case 'source'
if isempty(cfg.refindx), ft_error('indices of reference voxels need to be specified'); end
% if numel(cfg.refindx)>1, ft_error('more than one reference voxel is not yet supported'); end
otherwise
end
% FIXME think of accommodating partial coherence for source data with only a few references
case {'wpli'}
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq'});
inparam = 'crsspctrm';
outparam = 'wplispctrm';
debiaswpli = 0;
if hasjack, ft_error('to compute wpli, data should be in rpt format'); end
case {'wpli_debiased'}
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq'});
inparam = 'crsspctrm';
outparam = 'wpli_debiasedspctrm';
debiaswpli = 1;
if hasjack, ft_error('to compute wpli, data should be in rpt format'); end
case {'ppc'}
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq'});
inparam = 'crsspctrm';
outparam = 'ppcspctrm';
weightppc = 0;
if hasjack, ft_error('to compute ppc, data should be in rpt format'); end
case {'wppc'}
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq'});
inparam = 'crsspctrm';
outparam = 'wppcspctrm';
weightppc = 1;
if hasjack, ft_error('to compute wppc, data should be in rpt format'); end
case {'plv'}
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq' 'source'});
inparam = 'crsspctrm';
outparam = 'plvspctrm';
normrpt = 1;
case {'corr'}
data = ft_checkdata(data, 'datatype', {'raw' 'timelock'});
if isfield(data, 'cov')
% it looks like a timelock with a cov, which is perfectly valid as input
data = ft_checkdata(data, 'datatype', 'timelock');
else
% it does not have a cov, the covariance will be computed on the fly further down
data = ft_checkdata(data, 'datatype', 'raw');
end
inparam = 'cov';
outparam = cfg.method;
case {'amplcorr' 'powcorr'}
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq' 'source' 'source+mesh'});
dtype = ft_datatype(data);
switch dtype
case {'freq' 'freqmvar'}
inparam = 'powcovspctrm';
case {'source' 'source+mesh'}
inparam = 'powcov';
if isempty(cfg.refindx), ft_error('indices of reference voxels need to be specified'); end
% if numel(cfg.refindx)>1, ft_error('more than one reference voxel is not yet supported'); end
otherwise
end
outparam = [cfg.method, 'spctrm'];
case {'granger' 'instantaneous_causality' 'total_interdependence' 'transfer' 'iis'}
% create subcfg for the spectral factorization
if ~isfield(cfg, 'granger')
cfg.granger = [];
end
cfg.granger.conditional = ft_getopt(cfg.granger, 'conditional', 'no');
cfg.granger.block = ft_getopt(cfg.granger, 'block', []);
cfg.granger.channelcmb = ft_getopt(cfg.granger, 'channelcmb', cfg.channelcmb);
cfg = removefields(cfg, 'channelcmb');
data = ft_checkdata(data, 'datatype', {'mvar' 'freqmvar' 'freq'});
inparam = {'transfer', 'noisecov', 'crsspctrm'};
if strcmp(cfg.method, 'granger'), outparam = 'grangerspctrm'; end
if strcmp(cfg.method, 'instantaneous_causality'), outparam = 'instantspctrm'; end
if strcmp(cfg.method, 'total_interdependence'), outparam = 'totispctrm'; end
if strcmp(cfg.method, 'transfer'), outparam = {'transfer' 'noisecov' 'crsspctrm'}; end
if strcmp(cfg.method, 'iis'), outparam = 'iis'; end
% check whether the frequency bins are more or less equidistant
dfreq = diff(data.freq)./mean(diff(data.freq));
assert(all(dfreq>0.999) && all(dfreq<1.001), ['non equidistant frequency bins are not supported for method ',cfg.method]);
case {'ddtf'}
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq'});
inparam = {'transfer' 'crsspctrm'};
outparam = [cfg.method, 'spctrm'];
case {'dtf' 'pdc' 'gpdc'}
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq'});
inparam = 'transfer';
outparam = [cfg.method, 'spctrm'];
case {'psi'}
cfg.bandwidth = ft_getopt(cfg, 'bandwidth', []);
cfg.normalize = ft_getopt(cfg, 'normalize', 'no');
assert(~isempty(cfg.bandwidth), 'you need to supply cfg.bandwidth with ''psi'' as method');
data = ft_checkdata(data, 'datatype', {'freqmvar' 'freq'});
inparam = 'crsspctrm';
outparam = 'psispctrm';
% check whether the frequency bins are more or less equidistant
dfreq = diff(data.freq)./mean(diff(data.freq));
assert(all(dfreq>0.999) && all(dfreq<1.001), 'non equidistant frequency bins are not supported for method ''psi''');
case {'powcorr_ortho'}
data = ft_checkdata(data, 'datatype', {'source', 'freq'});
% inparam = 'avg.mom';
inparam = 'mom';
outparam = 'powcorrspctrm';
case {'mi'}
% create the subcfg for the mutual information
if ~isfield(cfg, 'mi'), cfg.mi = []; end
cfg.mi.numbin = ft_getopt(cfg.mi, 'numbin', 10);
cfg.mi.lags = ft_getopt(cfg.mi, 'lags', 0);
% what are the input requirements?
data = ft_checkdata(data, 'datatype', {'raw' 'timelock' 'freq' 'source'});
dtype = ft_datatype(data);
if strcmp(dtype, 'timelock')
if ~isfield(data, 'trial')
inparam = 'avg';
else
inparam = 'trial';
end
hasrpt = (isfield(data, 'dimord') && ~isempty(strfind(data.dimord, 'rpt')));
elseif strcmp(dtype, 'raw')
inparam = 'trial';
hasrpt = 1;
elseif strcmp(dtype, 'freq')
inparam = 'something';
else
inparam = 'something else';
end
outparam = 'mi';
needrpt = 1;
case {'di'}
% wat eigenlijk?
otherwise
ft_error('unknown method % s', cfg.method);
end
dtype = ft_datatype(data);
% FIXME throw an error if cfg.complex~='abs', and dojack==1
% FIXME throw an error if no replicates and cfg.method='plv'
% FIXME trial selection has to be implemented still
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% data bookkeeping - check whether the required inparam is present in the data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if any(~isfield(data, inparam)) || (isfield(data, 'crsspctrm') && (ischar(inparam) && strcmp(inparam, 'crsspctrm')))
if iscell(inparam)
% in the case of multiple inparams, use the first one to check the
% input data (e.g. checking for 'transfer' for requested granger)
inparam = inparam{1};
end
switch dtype
case {'freq' 'freqmvar'}
if strcmp(inparam, 'crsspctrm')
if isfield(data, 'fourierspctrm')
[data, powindx, hasrpt] = univariate2bivariate(data, 'fourierspctrm', 'crsspctrm', dtype, 'cmb', cfg.channelcmb, 'keeprpt', normrpt);
elseif strcmp(inparam, 'crsspctrm') && isfield(data, 'powspctrm')
% if input data is old-fashioned, i.e. contains powandcsd
[data, powindx, hasrpt] = univariate2bivariate(data, 'powandcsd', 'crsspctrm', dtype, 'cmb', cfg.channelcmb, 'keeprpt', normrpt);
elseif isfield(data, 'labelcmb')
powindx = labelcmb2indx(data.labelcmb);
else
powindx = [];
end
elseif strcmp(inparam, 'powcovspctrm')
if isfield(data, 'powspctrm')
[data, powindx] = univariate2bivariate(data, 'powspctrm', 'powcovspctrm', dtype, 'demeanflag', strcmp(cfg.removemean, 'yes'), 'cmb', cfg.channelcmb, 'sqrtflag', strcmp(cfg.method, 'amplcorr'));
elseif isfield(data, 'fourierspctrm')
[data, powindx] = univariate2bivariate(data, 'fourierspctrm', 'powcovspctrm', dtype, 'demeanflag', strcmp(cfg.removemean, 'yes'), 'cmb', cfg.channelcmb, 'sqrtflag', strcmp(cfg.method, 'amplcorr'));
end
elseif strcmp(inparam, 'transfer')
if isfield(data, 'fourierspctrm')
% FIXME this is fast but throws away the trial dimension, consider
% a way to keep trial information if needed, but use the fast way
% if possible
data = ft_checkdata(data, 'cmbrepresentation', 'fullfast');
hasrpt = 0;
elseif isfield(data, 'powspctrm')
data = ft_checkdata(data, 'cmbrepresentation', 'full');
end
% convert the inparam back to cell array in the case of granger
if any(strcmp(cfg.method, {'granger' 'instantaneous_causality' 'total_interdependence' 'transfer' 'iis'}))
inparam = {'transfer' 'noisecov' 'crsspctrm'};
tmpcfg = ft_checkconfig(cfg, 'createsubcfg', {'granger'});
optarg = ft_cfg2keyval(tmpcfg.granger);
elseif strcmp(cfg.method, 'ddtf')
inparam = {'transfer' 'crsspctrm'};
tmpcfg = ft_checkconfig(cfg, 'createsubcfg', {'ddtf'});
optarg = ft_cfg2keyval(tmpcfg.ddtf);
else
tmpcfg = ft_checkconfig(cfg, 'createsubcfg', {cfg.method});
optarg = ft_cfg2keyval(tmpcfg.(cfg.method));
end
% compute the transfer matrix
data = ft_connectivity_csd2transfer(data, optarg{:});
end
case {'source' 'source+mesh'}
if ischar(cfg.refindx) && strcmp(cfg.refindx, 'all')
cfg.refindx = 1:size(data.pos,1);
elseif ischar(cfg.refindx)
ft_error('cfg.refindx should be a 1xN vector, or ''all''');
end
if strcmp(inparam, 'crsspctrm')
[data, powindx, hasrpt] = univariate2bivariate(data, 'mom', 'crsspctrm', dtype, 'cmb', cfg.refindx, 'keeprpt', 0);
% [data, powindx, hasrpt] = univariate2bivariate(data, 'fourierspctrm', 'crsspctrm', dtype, 0, cfg.refindx, [], 1);
elseif strcmp(inparam, 'powcov')
if isfield(data, 'pow')
[data, powindx, hasrpt] = univariate2bivariate(data, 'pow', 'powcov', dtype, 'demeanflag', strcmp(cfg.removemean, 'yes'), 'cmb', cfg.refindx, 'sqrtflag', strcmp(cfg.method, 'amplcorr'), 'keeprpt', 0);
elseif isfield(data, 'mom')
[data, powindx, hasrpt] = univariate2bivariate(data, 'mom', 'powcov', dtype, 'demeanflag', strcmp(cfg.removemean, 'yes'), 'cmb', cfg.refindx, 'sqrtflag', strcmp(cfg.method, 'amplcorr'), 'keeprpt', 0);
end
end
case 'comp'
[data, powindx, hasrpt] = univariate2bivariate(data, 'trial', 'cov', dtype, 'demeanflag', strcmp(cfg.removemean, 'yes'), 'cmb', cfg.channelcmb, 'sqrtflag', false, 'keeprpt', 1);
end % switch dtype
elseif (isfield(data, 'crsspctrm') && (ischar(inparam) && strcmp(inparam, 'crsspctrm')))
% this means that there is a sparse crsspctrm in the data
else
powindx = [];
end % ensure that the bivariate measure exists
% do some additional work if single trial normalisation is required
% for example when plv needs to be computed
if normrpt && hasrpt
if strcmp(inparam, 'crsspctrm')
tmp = data.(inparam);
nrpt = size(tmp, 1);
ft_progress('init', cfg.feedback, 'normalising...');
for k = 1:nrpt
ft_progress(k/nrpt, 'normalising amplitude of replicate % d from % d to 1\n', k, nrpt);
tmp(k, :, :, :, :) = tmp(k, :, :, :, :)./abs(tmp(k, :, :, :, :));
end
ft_progress('close');
data.(inparam) = tmp;
end
end
% convert the labels for the partialisation channels into indices
% do the same for the labels of the channels that should be kept
% convert the labels in the output to reflect the partialisation
if ~isempty(cfg.partchannel)
allchannel = ft_channelselection(cfg.channel, data.label);
pchanindx = match_str(allchannel, cfg.partchannel);
kchanindx = setdiff(1:numel(allchannel), pchanindx);
keepchn = allchannel(kchanindx);
cfg.pchanindx = pchanindx;
cfg.allchanindx = kchanindx;
partstr = '';
for k = 1:numel(cfg.partchannel)
partstr = [partstr, '-', cfg.partchannel{k}];
end
for k = 1:numel(keepchn)
keepchn{k} = [keepchn{k}, '\', partstr(2:end)];
end
data.label = keepchn; % update labels to remove the partialed channels
% FIXME consider keeping track of which channels have been partialised
else
cfg.pchanindx = [];
cfg.allchanindx = [];
end
% check if jackknife is required
if hasrpt && dojack && hasjack
% do nothing
elseif hasrpt && dojack && ~(exist('debiaswpli', 'var') || exist('weightppc', 'var'))
% compute leave-one-outs
% assume the inparam(s) are well-behaved, i.e. they have the 'rpt'
% dimension as the first dimension
if iscell(inparam)
for k = 1:numel(inparam)
nrpt = size(data.(inparam{k}),1);
sumdat = sum(data.(inparam{k}),1);
data.(inparam{k}) = (sumdat(ones(nrpt,1),:,:,:,:,:) - data.(inparam{k}))./(nrpt-1);
clear sumdat;
end
else
nrpt = size(data.(inparam),1);
sumdat = sum(data.(inparam),1);
data.(inparam) = (sumdat(ones(nrpt,1),:,:,:,:,:) - data.(inparam))./(nrpt-1);
clear sumdat;
end
hasjack = 1;
elseif hasrpt && ~(exist('debiaswpli', 'var') || exist('weightppc', 'var') || any(strcmp({'powcorr_ortho';'mi'},cfg.method)))% || needrpt)
% create dof variable
if isfield(data, 'dof')
dof = data.dof;
elseif isfield(data, 'cumtapcnt')
dof = sum(data.cumtapcnt);
end
tmpcfg = [];
tmpcfg.avgoverrpt = 'yes';
data = ft_selectdata(tmpcfg, data);
hasrpt = 0;
else
% nothing required
end
% ensure that the first dimension is singleton if ~hasrpt
if ~hasrpt && needrpt
if ischar(inparam)
data.(inparam) = reshape(data.(inparam), [1 size(data.(inparam))]);
else
for k = 1:numel(inparam)
data.(inparam{k}) = reshape(data.(inparam{k}), [1 size(data.(inparam{k}))]);
end
end
if isfield(data, 'dimord')
data.dimord = ['rpt_', data.dimord];
elseif ~strcmp(dtype, 'raw')
data.([inparam, 'dimord']) = ['rpt_', data.([inparam, 'dimord'])];
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% compute the desired connectivity metric by calling the appropriate ft_connectivity_XXX function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
switch cfg.method
case 'coh'
% coherence (unsquared), if cfg.complex = 'imag' imaginary part of coherency
optarg = {'complex', cfg.complex, 'dimord', data.dimord, 'feedback', cfg.feedback, 'pownorm', normpow, 'hasjack', hasjack};
if ~isempty(cfg.pchanindx), optarg = cat(2, optarg, {'pchanindx', cfg.pchanindx, 'allchanindx', cfg.allchanindx}); end
if exist('powindx', 'var'), optarg = cat(2, optarg, {'powindx', powindx}); end
[datout, varout, nrpt] = ft_connectivity_corr(data.(inparam), optarg{:});
case 'csd'
% cross-spectral density (e.g. useful if partialisation is required)
optarg = {'complex', cfg.complex, 'dimord', data.dimord, 'feedback', cfg.feedback, 'pownorm', normpow, 'hasjack', hasjack};
if ~isempty(cfg.pchanindx), optarg = cat(2, optarg, {'pchanindx', cfg.pchanindx, 'allchanindx', cfg.allchanindx}); end
if exist('powindx', 'var'), optarg = cat(2, optarg, {'powindx', powindx}); end
[datout, varout, nrpt] = ft_connectivity_corr(data.(inparam), optarg{:});
case {'wpli' 'wpli_debiased'}
% weighted pli or debiased weighted phase lag index.
optarg = {'feedback', cfg.feedback, 'dojack', dojack, 'debias', debiaswpli};
[datout, varout, nrpt] = ft_connectivity_wpli(data.(inparam), optarg{:});
case {'wppc' 'ppc'}
% weighted pairwise phase consistency or pairwise phase consistency
optarg = {'feedback', cfg.feedback, 'dojack', dojack, 'weighted', weightppc};
[datout, varout, nrpt] = ft_connectivity_ppc(data.(inparam), optarg{:});
case 'plv'
% phase locking value
optarg = {'complex', cfg.complex, 'dimord', data.dimord, 'feedback', cfg.feedback, 'pownorm', normpow, 'hasjack', hasjack};
if ~isempty(cfg.pchanindx), optarg = cat(2, optarg, {'pchanindx', cfg.pchanindx, 'allchanindx', cfg.allchanindx}); end
if exist('powindx', 'var'), optarg = cat(2, optarg, {'powindx', powindx}); end
[datout, varout, nrpt] = ft_connectivity_corr(data.(inparam), optarg{:});
case 'amplcorr'
% amplitude correlation
if isfield(data, 'dimord')
dimord = data.dimord;
else
dimord = data.([inparam, 'dimord']);
end
optarg = {'feedback', cfg.feedback, 'dimord', dimord, 'complex', 'real', 'pownorm', 1, 'pchanindx', [], 'hasjack', hasjack};
if exist('powindx', 'var'), optarg = cat(2, optarg, {'powindx', powindx}); end
[datout, varout, nrpt] = ft_connectivity_corr(data.(inparam), optarg{:});
case 'powcorr'
% power correlation
if isfield(data, 'dimord')
dimord = data.dimord;
else
dimord = data.([inparam, 'dimord']);
end
optarg = {'feedback', cfg.feedback, 'dimord', dimord, 'complex', 'real', 'pownorm', 1, 'pchanindx', [], 'hasjack', hasjack};
if exist('powindx', 'var'), optarg = cat(2, optarg, {'powindx', powindx}); end
[datout, varout, nrpt] = ft_connectivity_corr(data.(inparam), optarg{:});
case 'transfer'
% the necessary stuff has already been computed
datout = data.transfer;
noisecov = data.noisecov;
crsspctrm = data.crsspctrm;
if ~hasrpt
datout = shiftdim(datout,1);
noisecov = shiftdim(noisecov,1);
crsspctrm = shiftdim(crsspctrm,1);
end
case {'granger' 'instantaneous_causality' 'total_interdependence' 'iis'}
% granger causality
if ft_datatype(data, 'freq') || ft_datatype(data, 'freqmvar')
if isfield(data, 'labelcmb') && isfield(cfg.granger, 'sfmethod') && strcmp(cfg.granger.sfmethod, 'bivariate_conditional')
% create a powindx variable that ft_connectivity_granger can use to
% do the conditioning
[indx, label, blockindx, blocklabel] = labelcmb2indx(data.labelcmb);
cmbindx12 = labelcmb2indx(cfg.granger.channelcmb(:,1:2), label);
cmbindx23 = labelcmb2indx(cfg.granger.channelcmb(:,2:3), label);
cmbindx = [cmbindx12 cmbindx23(:,2); cmbindx12(:,[2 1]) cmbindx23(:,2)];
powindx.cmbindx = indx;
powindx.blockindx = blockindx;
powindx.outindx = cmbindx;
newlabelcmb = cell(size(cmbindx,1),2);
for k = 1:size(newlabelcmb,1)
newlabelcmb{k,1} = sprintf('%s|%s',label{cmbindx(k,2)},label{cmbindx(k,3)}); % deliberate swap of 2/1 as per the conventional definition in conditional granger computation
newlabelcmb{k,2} = sprintf('%s|%s',label{cmbindx(k,1)},label{cmbindx(k,3)});
end
data.labelcmb = newlabelcmb;
elseif isfield(data, 'labelcmb') && ~istrue(cfg.granger.conditional)
% multiple pairwise non-parametric transfer functions
% linearly indexed
% The following is very slow, one may make assumptions regarding
% the order of the channels -> csd2transfer gives combinations in
% quadruplets, where the first and fourth are auto-combinations,
% and the second and third are cross-combinations
% powindx = labelcmb2indx(data.labelcmb);
%
% The following is not needed anymore, because ft_connectivity_granger
% relies on some hard-coded assumptions for the channel-pair ordering.
% Otherwise it becomes just too slow.
% powindx = zeros(size(data.labelcmb));
% for k = 1:size(powindx, 1)/4
% ix = ((k-1)*4+1):k*4;
% powindx(ix, :) = [1 1;4 1;1 4;4 4] + (k-1)*4;
% end
powindx = [];
if isfield(data, 'label')
% this field should be removed
data = rmfield(data, 'label');
end
elseif isfield(data, 'labelcmb') && istrue(cfg.granger.conditional)
% conditional (blockwise) needs linearly represented cross-spectra,
% that have been produced by ft_connectivity_csd2transfer
%
% each row in Nx2 cell-array tmp refers to a comparison
% tmp{k, 1} represents the ordered blocks
% for the full trivariate model: the second element drives the
% first element, while the rest is partialed out.
% tmp{k, 2} represents the ordered blocks where the driving block
% is left out
blocks = unique(data.blockindx);
nblocks = numel(blocks);
cnt = 0;
for k = 1:nblocks
for m = (k+1):nblocks
cnt = cnt+1;
rest = setdiff(reshape(blocks,[1 numel(blocks)]), [k m]); % make sure to reshape blocks into 1xn vector
tmp{cnt, 1} = [k m rest];
tmp{cnt, 2} = [k rest];
newlabelcmb{cnt, 1} = data.block(m).name; % note the index swap: convention is driver in left column
newlabelcmb{cnt, 2} = data.block(k).name;
cnt = cnt+1;
tmp{cnt, 1} = [m k rest];
tmp{cnt, 2} = [m rest];
newlabelcmb{cnt, 1} = data.block(k).name;
newlabelcmb{cnt, 2} = data.block(m).name;
end
end
% make a temporary label list
tmp2 = cell(numel(data.labelcmb),1);
for m = 1:numel(data.labelcmb)
tok = tokenize(data.labelcmb{m}, '[');
tmp2{m} = tok{1};
end
label = cat(1,data.block.label);%unique(tmp2);
[powindx.cmbindx, powindx.n] = blockindx2cmbindx(data.labelcmb, {label data.blockindx}, tmp);
data.labelcmb = newlabelcmb;
if isfield(data, 'label')
% this field should be removed
data = rmfield(data, 'label');
end
elseif isfield(cfg.granger, 'block') && ~isempty(cfg.granger.block)
% make a temporary label list
if isfield(data, 'label')
label = data.label;
else
tmp = cell(numel(data.labelcmb),1);
for m = 1:numel(data.labelcmb)
tok = tokenize(data.labelcmb{m}, '[');
tmp{m} = tok{1};
end
label = unique(tmp);
end
% blockwise granger
for k = 1:numel(cfg.granger.block)
%newlabel{k, 1} = cat(2, cfg.granger.block(k).label{:});
newlabel{k,1} = cfg.granger.block(k).name;
powindx{k,1} = match_str(label, cfg.granger.block(k).label);
end
data.label = newlabel;
else
powindx = [];
end
% fs = cfg.fsample; % FIXME do we really need this, or is this related to how noisecov is defined and normalised?
if ~exist('powindx', 'var'), powindx = []; end
if strcmp(cfg.method, 'granger'), methodstr = 'granger'; end
if strcmp(cfg.method, 'instantaneous_causality'), methodstr = 'instantaneous'; end
if strcmp(cfg.method, 'total_interdependence'), methodstr = 'total'; end
if strcmp(cfg.method, 'iis'), methodstr = 'iis'; end
optarg = {'hasjack', hasjack, 'method', methodstr, 'powindx', powindx, 'dimord', data.dimord};
[datout, varout, nrpt] = ft_connectivity_granger(data.transfer, data.noisecov, data.crsspctrm, optarg{:});
if strcmp(cfg.method, 'iis')
data.freq = nan;
end
else
ft_error('granger for time domain data is not yet implemented');
end
case {'dtf' 'ddtf'}
% directed transfer function
if isfield(data, 'labelcmb')
powindx = labelcmb2indx(data.labelcmb);
else
powindx = [];
end
optarg = {'feedback', cfg.feedback, 'powindx', powindx, 'hasjack', hasjack};
hasrpt = ~isempty(strfind(data.dimord, 'rpt'));
if hasrpt
nrpt = size(data.transfer, 1);
datin = data.transfer;
else
nrpt = 1;
datin = reshape(data.transfer, [1 size(data.transfer)]);
data.crsspctrm = reshape(data.crsspctrm, [1 size(data.crsspctrm)]);
end
if strcmp(cfg.method, 'ddtf'), optarg = cat(2, optarg, {'crsspctrm' data.crsspctrm}); end
[datout, varout, nrpt] = ft_connectivity_dtf(datin, optarg{:});
case {'pdc' 'gpdc'}
% partial directed coherence
if isfield(data, 'labelcmb')
powindx = labelcmb2indx(data.labelcmb);
else
powindx = [];
end
optarg = {'feedback', cfg.feedback, 'powindx', powindx, 'hasjack', hasjack};
if strcmp(cfg.method, 'gpdc'), optarg = cat(2, optarg, {'noisecov' data.noisecov}); end
hasrpt = ~isempty(strfind(data.dimord, 'rpt'));
if hasrpt
nrpt = size(data.(inparam), 1);
datin = data.(inparam);
else
nrpt = 1;
datin = reshape(data.(inparam), [1 size(data.(inparam))]);
end
[datout, varout, nrpt] = ft_connectivity_pdc(datin, optarg{:});
case 'psi'
% phase slope index
nbin = nearest(data.freq, data.freq(1)+cfg.bandwidth)-1;
optarg = {'feedback', cfg.feedback, 'dimord', data.dimord, 'nbin', nbin, 'normalize', cfg.normalize, 'hasrpt', hasrpt, 'hasjack', hasjack};
if exist('powindx', 'var'), optarg = cat(2, optarg, {'powindx', powindx}); end
[datout, varout, nrpt] = ft_connectivity_psi(data.(inparam), optarg{:});
case 'powcorr_ortho'
% Joerg Hipp's power correlation method
optarg = {'refindx', cfg.refindx, 'tapvec', data.cumtapcnt};
if isfield(data, 'mom')
% this is expected to be a single frequency
%dat = cat(2, data.mom{data.inside}).';
% HACK
dimord = getdimord(data, 'mom');
dimtok = tokenize(dimord, '_');
posdim = find(strcmp(dimtok, '{pos}'));
posdim = 4; % we concatenate across positions...
rptdim = find(~cellfun('isempty',strfind(dimtok, 'rpt')));
rptdim = rptdim-1; % the posdim has to be taken into account...
dat = cat(4, data.mom{data.inside});
dat = permute(dat,[posdim rptdim setdiff(1:ndims(dat),[posdim rptdim])]);
datout = ft_connectivity_powcorr_ortho(dat, optarg{:});
% HACK continued: format the output according to the inside and
% refindx specifications
if ischar(cfg.refindx) && strcmp(cfg.refindx, 'all')
% create all-to-all output
tmp = zeros(numel(data.inside));
tmp(data.inside,data.inside) = datout;
datout = tmp;
clear tmp;
outdimord = 'pos_pos_freq';
else
% create all-to-few output
tmp = zeros(numel(data.inside), numel(cfg.refindx));
tmp(data.inside, :) = datout;
datout = tmp;
clear tmp;
outdimord = 'pos_pos_freq';
end
elseif strcmp(data.dimord, 'rpttap_chan_freq')
% loop over all frequencies
[nrpttap, nchan, nfreq] = size(data.fourierspctrm);
datout = cell(1, nfreq);
for i=1:length(data.freq)
dat = reshape(data.fourierspctrm(:,:,i)', nrpttap, nchan).';
datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:});
end
datout = cat(3, datout{:});
% HACK otherwise I don't know how to inform the code further down about the dimord
data.dimord = 'rpttap_chan_chan_freq';
else
ft_error('unsupported data representation');
end
varout = [];
nrpt = numel(data.cumtapcnt);
case 'mi'
% mutual information using the information breakdown toolbox
% presence of the toolbox is checked in the low-level function
if ~strcmp(dtype, 'raw') && (numel(cfg.mi.lags)>1 || cfg.mi.lags~=0)
ft_error('computation of lagged mutual information is only possible with ''raw'' data in the input');
end
switch dtype
case 'raw'
% ensure the lags to be in samples, not in seconds.
cfg.mi.lags = round(cfg.mi.lags.*data.fsample);
dat = catnan(data.trial, max(abs(cfg.mi.lags)));
if ischar(cfg.refindx) && strcmp(cfg.refindx, 'all')
outdimord = 'chan_chan';
elseif numel(cfg.refindx)==1
outdimord = 'chan';
else
ft_error('at present cfg.refindx should be either ''all'', or scalar');
end
if numel(cfg.mi.lags)>1
data.time = cfg.mi.lags./data.fsample;
outdimord = [outdimord, '_time'];
else
data = rmfield(data, 'time');
end
case 'timelock'
dat = data.(inparam);
dat = reshape(permute(dat, [2 3 1]), [size(dat, 2) size(dat, 1)*size(dat, 3)]);
data = rmfield(data, 'time');
if ischar(cfg.refindx) && strcmp(cfg.refindx, 'all')
outdimord = 'chan_chan';
elseif numel(cfg.refindx)==1
outdimord = 'chan';
else
ft_error('at present cfg.refindx should be either ''all'', or scalar');
end
%data.dimord = 'chan_chan';
case 'freq'
ft_error('not yet implemented');
case 'source'
% for the time being work with mom
% dat = cat(2, data.mom{data.inside}).';
dat = cat(1, data.mom{data.inside});
% dat = abs(dat);
end
optarg = {'numbin', cfg.mi.numbin, 'lags', cfg.mi.lags, 'refindx', cfg.refindx};
[datout] = ft_connectivity_mutualinformation(dat, optarg{:});
varout = [];
nrpt = [];
case 'corr'
% pearson's correlation coefficient
optarg = {'dimord', getdimord(data, inparam), 'feedback', cfg.feedback, 'hasjack', hasjack};
if ~isempty(cfg.pchanindx), optarg = cat(2, optarg, {'pchanindx', cfg.pchanindx, 'allchanindx', cfg.allchanindx}); end
[datout, varout, nrpt] = ft_connectivity_corr(data.(inparam), optarg{:});
case 'xcorr'
% cross-correlation function
ft_error('method %s is not yet implemented', cfg.method);
case 'spearman'
% spearman's rank correlation
ft_error('method %s is not yet implemented', cfg.method);
case 'di'
% directionality index
ft_error('method %s is not yet implemented', cfg.method);
otherwise
ft_error('unknown method %s', cfg.method);
end % switch method
% remove the auto combinations if necessary -> FIXME this is granger specific and thus could move to ft_connectivity_granger
if (strcmp(cfg.method, 'granger') || strcmp(cfg.method, 'instantaneous_causality') || strcmp(cfg.method, 'total_interdependence')) && isfield(cfg, 'granger') && isfield(cfg.granger, 'sfmethod') && strcmp(cfg.granger.sfmethod, 'bivariate')
% remove the auto-combinations based on the order in the data
switch dtype
case {'freq' 'freqmvar'}
keepchn = 1:size(datout, 1);
keepchn = mod(keepchn, 4)==2 | mod(keepchn, 4)==3;
datout = datout(keepchn, :, :, :, :);
if ~isempty(varout)
varout = varout(keepchn, :, :, :, :);
end
data.labelcmb = data.labelcmb(keepchn, :);
case 'source'
% not yet implemented
end
end
if exist('powindx', 'var') && ~isempty(powindx)
% based on powindx
switch dtype
case {'freq' 'freqmvar'}
if isfield(data, 'labelcmb') && ~isstruct(powindx)
keepchn = powindx(:, 1) ~= powindx(:, 2);
datout = datout(keepchn, :, :, :, :);
if ~isempty(varout)
if all(size(varout)==size(nrpt))
nrpt = nrpt(keepchn, :, :, :, :);
end
varout = varout(keepchn, :, :, :, :);
end
data.labelcmb = data.labelcmb(keepchn, :);
end
case 'source'
nvox = size(unique(data.pos(:, 1:3), 'rows'), 1);
ncmb = size(data.pos, 1)/nvox-1;
remove = (powindx(:, 1) == powindx(:, 2)) & ((1:size(powindx, 1))' > nvox*ncmb);
keepchn = ~remove;
datout = datout(keepchn, :, :, :, :);
if ~isempty(varout)
varout = varout(keepchn, :, :, :, :);
end
inside = false(zeros(1, size(data.pos, 1)));
inside(data.inside) = true;
inside = inside(keepchn);
% data.inside = find(inside)';
% data.outside = find(inside==0)';
data.pos = data.pos(keepchn, :);
end % switch dtype
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% create the output structure
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
switch dtype
case {'freq' 'freqmvar'}
stat = keepfields(data, {'label', 'labelcmb', 'grad', 'elec'});