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roi_network.m
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roi_network.m
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% ****************************************************
% This function is mostly obsolete as it was replaced
% by roi_networkplot() which can plot any measure
% (instead of just coherence for this function)
% It might still be useful for real time calculation
% ****************************************************
%
% roi_network() - compute connectivity between ROIs
%
% Usage:
% EEG = roi_network(EEG, 'key', 'val', ...);
%
% Inputs:
% EEG - EEGLAB dataset with ROI activity computed
%
% Optional inputs (choose at least one):
% 'nfft' - [integer] FFT padding. Default is twice the sampling rate.
% 'freqdb' - ['on'|'off'] compute spctral activity in dB. Default is 'on'.
% 'freqrange' - [cell] frequency ranges. Default is { [4 6] [ 8 12] [18 22] }
% for theta (4 to 6 Hz), alpha and beta.
% 'processfreq' - [struct of func] how to process spectral data. Default is
% processfreq.theta = @(x)x(:,1);
% processfreq.alpha = @(x)x(:,2);
% processfreq.beta = @(x)x(:,3);
% 'processconnect' - [struct of func] how to process connectivity data.
% Default is (the diverder is the number of non zero values)
% processconnect.theta = @(x)sum(sum(x(:,:,1)))/((size(x,1).^2)-size(x,1));
% processconnect.alpha = @(x)sum(sum(x(:,:,2)))/((size(x,1).^2)-size(x,1));
% processconnect.beta = @(x)sum(sum(x(:,:,3)))/((size(x,1).^2)-size(x,1));
% 'plotmode' - ['2D'|'3D'|'both'] plot in 2-D, 3-D or both. Default
% is 2D.
%
% Output:
% EEG - EEG structure with EEG.roi field updated and now containing
% connectivity information.
% results - result with the fields defined as input.
% Copyright (C) Arnaud Delorme, [email protected]
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above copyright notice,
% this list of conditions and the following disclaimer.
%
% 2. Redistributions in binary form must reproduce the above copyright notice,
% this list of conditions and the following disclaimer in the documentation
% and/or other materials provided with the distribution.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
% THE POSSIBILITY OF SUCH DAMAGE.
function [EEG,results,loretaFile,imgFileName,txtFileName] = roi_network(EEG, varargin)
if EEG.trials == 1 % fast call
opt = struct(varargin{:});
if ~isfield(opt, 'networkfile')
error('No field networkfile given as input');
end
% other paratemers
if ~isfield(opt, 'roilist'), opt.roilist = []; end
if ~isfield(opt, 'nfft'), opt.nfft = EEG.srate*2; end
if ~isfield(opt, 'freqdb'), opt.freqdb = 1; end
if ~isfield(opt, 'freqrange') opt.freqrange = { [4 6] [ 8 12] [18 22] }; end
if ~isfield(opt, 'processfreq') opt.processfreq = []; end
if ~isfield(opt, 'processconnect') opt.processconnect = []; end
if ~isfield(opt, 'plotnetworkfile') opt.plotnetworkfile = ''; end
else
opt = finputcheck( varargin, { ...
'networkfile' '' {} '';
'nfft' 'integer' {} EEG.srate*2;
'freqdb' 'integer' {} 1;
'measureoutput' 'string' {'on' 'off'} 'off';
'roilist' 'integer' {} [];
'freqrange' 'cell' {} { [4 6] [ 8 12] [18 22] };
'freqname' 'cell' {} { 'theta' 'alpha' 'beta' };
'processfreq' '' {} [];
'precomputed' 'struct' {} struct([]);
'leadfield' 'string' {} '';
'sourcemodel' 'string' {} '';
'plotnetworkfile' '' {} '';
'plotmode' 'string' {'2D' '3D' 'both' 'off' } '2D';
'plotloretafile' '' {} '';
'loretalimits' '' {} [];
'processconnect' '' {} [] }, 'roi_network');
end
if ischar(opt), error(opt); end
if isempty(opt.processfreq)
for iFreq = 1:length(opt.freqrange)
opt.processfreq.(opt.freqname{iFreq}) = @(x)x(:,iFreq);
end
end
if isempty(opt.processconnect)
for iFreq = 1:length(opt.freqrange)
opt.processconnect.(opt.freqname{iFreq}) = @(x)sum(sum(x(:,:,iFreq)))/((size(x,1).^2)-size(x,1)); % the diverder is the number of non zero values
end
end
if isempty(opt.networkfile)
dipfitdefs;
EEG = pop_dipfit_settings( EEG, 'hdmfile', template_models(2).hdmfile,'coordformat','MNI', ...
'mrifile', template_models(2).mrifile,'chanfile',template_models(2).chanfile,...
'coord_transform',[0 0 0 0 0 -1.5708 1 1 1] ,'chansel',[1:EEG.nbchan] );
chans = { 'FP1','FP2','F3','F4','C3','C4','P3','P4','O1','O2','F7','F8','T3','T4','T5','T6','FZ','CZ','PZ' };
if ischar(opt.leadfield)
tmpLeadfield = load('-mat', opt.leadfield);
if isfield(tmpLeadfield, 'label') && ~isequal(upper({EEG.chanlocs.labels}), upper(tmpLeadfield.label))
error('Electrode name inconsistency');
end
end
options = { ...
'headmodel' template_models(2).hdmfile ...
'leadfield' opt.leadfield ...
'sourcemodel' opt.sourcemodel ... % 'sourcemodel' '/data/matlab/eeglab/plugins/roiconnect/LORETA-Talairach-BAs.mat' ...
'sourcemodel2mni' [] ...
'sourcemodelatlas' 'BrainDx' ... % 'sourcemodelatlas' 'LORETA-Talairach-BAs' ...
'downsample' 1 ...
'nPCA' 1 ...
'model' 'eLoreta' ...
'roiactivity' 'on', ...
'exportvoxact' 'on'
};
% 'trgc' 'off' ...
% 'crossspec' 'off' ...
% 'morder' 20 ...
EEG = pop_roi_activity(EEG, options{:});
source_voxel_data = EEG.roi.source_voxel_data;
if isempty(opt.roilist)
opt.roilist = 1:EEG.roi.nROI; % list of ROI necessary to compute connectivity
end
tmp = load('-mat', 'supportfiles\BrainDx_sourcemodel.mat');
loreta_ROIS = tmp.Atlas.Scouts;
loreta_Networks = [];
else
if ischar(opt.networkfile)
opt.networkfile = load('-mat', opt.networkfile);
end
loreta_P = opt.networkfile.loreta_P;
loreta_Networks = opt.networkfile.loreta_Networks;
loreta_ROIS = opt.networkfile.loreta_ROIS;
if isempty(opt.roilist)
opt.roilist = unique([loreta_Networks.ROI_inds]); % list of ROI necessary to compute connectivity
end
% project to source space
source_voxel_data = reshape(EEG.data(:, :)'*loreta_P(:, :), size(EEG.data,2)*size(EEG.data,3), size(loreta_P,2), 3);
end
% Computing spectrum
% ALSO IMPLEMENT USING ROI_ACTIVITY
sz = size(source_voxel_data);
tmpdata = reshape(source_voxel_data, sz(1), sz(2)*sz(3)); % THIS IS MOSTLY WRONG HERE AS EPOCHS ARE CONCATENATED
source_voxel_spec = pwelch(tmpdata, EEG.srate, EEG.srate/2, opt.nfft, EEG.srate); % assuming 1 second of data
source_voxel_spec = reshape(source_voxel_spec, size(source_voxel_spec,1), sz(2), sz(3));
source_voxel_spec = mean(source_voxel_spec(2:size(source_voxel_spec,1),:,:,:),length(sz)); % frequency selection 2 to 31 (1Hz to 30Hz)
freqs = linspace(0, EEG.srate/2, floor(opt.nfft/2)+1);
freqs = freqs(2:end); % remove DC (match the output of PSD)
% Plot loreta file
if ~isempty(opt.plotloretafile)
loretaFile = opt.plotloretafile;
options = { 'freqrange', opt.freqrange, 'limits', opt.loretalimits, 'saveasfile', opt.plotloretafile, 'precomputed', opt.precomputed };
if strcmpi(opt.plotmode, 'off') options = [ options { 'noplot' 'on' } ]; end
loretaMeasures = roi_sourceplot(freqs, source_voxel_spec', opt.sourcemodel, options{:});
end
% Compute ROI activity
for ind_roi = opt.roilist
% data used for connectivity analysis
spatiallyFilteredDataTmp = roi_getact( source_voxel_data, loreta_ROIS(ind_roi).Vertices, 1, 0); % Warning no zscore here; also PCA=1 is too low
spatiallyFilteredSpecTmp = roi_getact( source_voxel_spec, loreta_ROIS(ind_roi).Vertices, 1, 0);
if ind_roi == 1
spatiallyFilteredData = zeros(max(opt.roilist), length(spatiallyFilteredDataTmp));
spatiallyFilteredSpec = zeros(max(opt.roilist), length(spatiallyFilteredSpecTmp));
end
spatiallyFilteredData(ind_roi,:) = spatiallyFilteredDataTmp;
spatiallyFilteredSpec(ind_roi,:) = spatiallyFilteredSpecTmp;
end
loretaSpec = spatiallyFilteredSpec';
% select frequency bands
for iSpec = 1:length(opt.freqrange)
freqRangeTmp = intersect( find(freqs >= opt.freqrange{iSpec}(1)), find(freqs <= opt.freqrange{iSpec}(2)) );
loretaSpecSelect(:,iSpec) = mean(abs(loretaSpec(freqRangeTmp,:)).^2,1); % mean power in frequency range
if opt.freqdb
loretaSpecSelect(:,iSpec) = 10*log10(abs(loretaSpecSelect(:,iSpec)).^2);
end
end
% compute metric of interest
processfreqFields = fieldnames(opt.processfreq);
for iProcess = 1:length(processfreqFields)
results.(['loreta_regions_' processfreqFields{iProcess}]) = feval(opt.processfreq.(processfreqFields{iProcess}), loretaSpecSelect);
end
% compute cross-spectral density for each network
% -----------------------------------------------
if ~isempty(opt.processconnect)
for iNet = 1:length(loreta_Networks)
if 1
% ALSO IMPLEMENT USING ROI_CONNECT
[restmp,connectSpecSelect{iNet}] = roi_csnetworkact( spatiallyFilteredData, loreta_Networks(iNet).ROI_inds, 'nfft', opt.nfft, 'postprocess', opt.processconnect, 'freqranges', opt.freqrange);
% copy results
fields = fieldnames(restmp);
for iField = 1:length(fields)
meanField = [ loreta_Networks(iNet).name '_' fields{iField} ];
detailField = [ loreta_Networks(iNet).name '_' fields{iField} '_details' ];
results.(meanField) = restmp.(fields{iField});
results.(detailField) = connectSpecSelect{iNet}(:,:,iField);
% reuse data
if isfield(opt.precomputed, meanField)
restmp.(fields{iField}) = opt.precomputed.(meanField);
end
if isfield(opt.precomputed, detailField)
connectSpecSelect{iNet}(:,:,iField) = opt.precomputed.(detailField);
end
end
else
networkData = spatiallyFilteredData(loreta_Networks(iNet).ROI_inds,:);
S = cpsd_welch(networkData,size(networkData,2),0,g.measure.nfft);
[nchan, nchan, nfreq] = size(S);
% imaginary part of cross-spectral density
% ----------------------------------------
absiCOH = S;
for ifreq = 1:nfreq
absiCOH(:, :, ifreq) = squeeze(S(:, :, ifreq)) ./ sqrt(diag(squeeze(S(:, :, ifreq)))*diag(squeeze(S(:, :, ifreq)))');
end
absiCOH = abs(imag(absiCOH));
% frequency selection
% -------------------
connectSpecSelect = zeros(size(absiCOH,1), size(absiCOH,2), length(opt.freqrange));
for iSpec = 1:length(g.measure.freqrange)
freqRangeTmp = intersect( find(freqs >= opt.freqrange{iSpec}(1)), find(freqs <= opt.freqrange{iSpec}(2)) );
connectSpecSelect(:,:,iSpec) = mean(absiCOH(:,:,freqRangeTmp),3); % mean power in frequency range
end
connectprocessFields = fieldnames(opt.processconnect);
for iProcess = 1:length(connectprocessFields)
results.([ loreta_Networks(iNet).name '_' connectprocessFields{iProcess} ]) = feval(opt.processconnect.(connectprocessFields{iProcess}), connectSpecSelect);
end
end
end
if ~isempty(loreta_Networks) && ~isempty(opt.plotnetworkfile) && ~strcmpi(opt.plotmode, 'off')
imgFileName = {};
txtFileName = {};
for iField = 1:length(fields)
connectTmp = cellfun(@(x)x(:,:,iField), connectSpecSelect, 'uniformoutput', false);
[imgFileNameTmp,txtFileNameTmp] = roi_networkplot(opt.networkfile, connectTmp, 'title', fields{iField}, 'filename' ,[opt.plotnetworkfile '_' fields{iField} ], 'plotmode', opt.plotmode);
imgFileName = [ imgFileName imgFileNameTmp ];
txtFileName = [ txtFileName txtFileNameTmp ];
end
else
imgFileName = {};
txtFileName = {};
end
end
if strcmpi(opt.measureoutput, 'on')
out.measures = [];
fields = fieldnames(results);
for iField = 1:length(fields)
out.measures.(fields{iField}).mean = results.(fields{iField});
end
fields = fieldnames(loretaMeasures);
for iField = 1:length(fields)
out.measures.(fields{iField}).mean = loretaMeasures.(fields{iField});
end
results = out;
end