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roi_connect_compare.m
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roi_connect_compare.m
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% roi_connect_compare - Compare ROI in collection of datasets.
%
% Usage:
% roi_connect_compare(ALLEEG, datCond, 'key', 'val', ...);
%
% Inputs:
% ALLEEG - EEGLAB datasets
% datCond - [cell] list of datasets to compare { [1:10] [11:20 } to
% compare datasets 1-10 with 11:20
%
% Optional inputs:
%
% Output:
%
% Author: Arnaud Delorme
% 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] = roi_connect_compare(EEG, datCond, varargin)
if nargin < 2
help roi_activity;
return
end
% decode input parameters
% -----------------------
[g,addopts] = finputcheck(varargin, { 'measure' 'string' {} 'MIM';
'freqrange' {'real' 'cell' } [] [];
'quantile' 'real' { } [];
'plotsubjmat' 'string' { 'on' 'off' } 'off'; ...
'threshold' 'real' { } 1.1;
'outputdir' 'string' { } '.';
'network' 'string' { 'on' 'off' } 'off'; ...
'addrois' 'string' { 'on' 'off' } 'off'; ...
'pmask' 'string' { 'on' 'off' } 'off' }, 'pop_roi_connectplot', 'ignore');
if ischar(g), error(g); end
if isempty(g.freqrange)
error('You must define freqrange');
end
if ~iscell(g.freqrange)
g.freqrange = { g.freqrange };
end
% select data
% -----------
dataCell = cell(1, length(datCond));
freqs = EEG(1).roi.freqs;
for iFreq = 1:length(g.freqrange)
for iCond = 1:length(datCond)
for iSet = 1:length(datCond{iCond}) % can only index datasets
setNum = datCond{iCond}(iSet);
dataCell{iFreq, iCond}(:,:,iSet) = selectconnect(EEG(setNum).roi.(g.measure), freqs, g.freqrange{iFreq}, g.quantile);
end
end
end
% compute stats
% -------------
freqTextSig = cell(1, length(g.freqrange));
freqTextSig(:) = { '' };
if strcmpi(g.pmask, 'on')
freqTextSig = {};
for iFreq = 1:length(g.freqrange)
pcond = std_stat(dataCell(iFreq,:)', 'condstats', 'on', 'method', 'permutation', 'naccu', 50, 'mcorrect', 'fdr');
%pcond = std_stat(dataCell', 'condstats', 'on', 'method', 'permutation', 'naccu', 200); %, 'mcorrect', 'fdr'); pcond{1} = pcond{1} < 0.5;
pcond = pcond{1};
for iDiag = 1:size(pcond,1), pcond(iDiag, iDiag) = 1; end
if all(pcond(:) == 1)
fprintf('Frequency %1.1f to %1.1f -> Nothing significant\n', g.freqrange{iFreq}(1), g.freqrange{iFreq}(2));
freqTextSig{iFreq} = 'ns';
else
fprintf(2, 'Frequency %1.1f to %1.1f -> Something significant\n', g.freqrange{iFreq}(1), g.freqrange{iFreq}(2));
freqTextSig{iFreq} = 'significant';
for iCond = 1:length(datCond(:))
dataCell{iFreq, iCond} = bsxfun(@times,dataCell{iFreq, iCond}, pcond<1 );
end
end
end
end
% plot matrices
% -------------
if strcmpi(g.plotsubjmat, 'on')
for iFreq = 1:length(g.freqrange)
figure('position', [275 1075 1725 262]);
numDat = size(dataCell{1},3);
numRows = length(dataCell(:));
cl = [0.03 0.1];
%cl = [0.0 1];
for iCond = 1:numRows
for iDat = 1:numDat
subplot(numRows, numDat, iDat+numDat*(iCond-1));
imagesc(dataCell{iCond}(:,:,iDat));
clim(cl);
axis off;
end
end
textsc('title', sprintf('Connectivity %g-%g Hz %s', g.freqrange{iFreq}(1), g.freqrange{iFreq}(2)), freqTextSig{iFreq});
end
end
% plot surfaces
% -------------
if isfield(EEG(1).roi.cortex, 'Faces')
for iFreq = 1:length(g.freqrange)
mn = inf;
mx = -inf;
for iCond = 1:length(datCond)
tmpData = mean(dataCell{iFreq, iCond},3);
mn = min(mn, min(tmpData(:)));
mx = max(mx, max(tmpData(:)));
end
load -mat cm17;
for iCond = 1:length(datCond)
tmpData = mean(dataCell{iFreq, iCond},3);
figure; allplots_cortex_BS(EEG(1).roi.cortex, tmpData, [mn mx*0.5], cm17a, '', 0.35);
textsc('title', sprintf('Connectivity %g-%g Hz condition %d', freqrange{iFreq}(1), freqrange{iFreq}(2)), iCond);
end
end
end
% define connectivity regions
% ---------------------------
if strcmpi(g.network, 'on')
tab1 = readtable('NGNetworkROIs_v4.txt','delimiter', char(9));
for iCond = 1:length(dataCell(:))
if strcmpi(g.addrois, 'on')
tab2 = readtable('NGNetworkROIs_area_definition_v2.txt','delimiter', char(9));
[EEG(1),net,dataCell{iCond}] = roi_definenetwork(EEG(1), tab1, 'addrois', tab2, 'connectmat', dataCell{iCond}, 'ignoremissing', 'on'); % adding missing ROIs
else
[EEG(1),net,dataCell{iCond}] = roi_definenetwork(EEG(1), tab1, 'connectmat', dataCell{iCond}, 'ignoremissing', 'on'); % adding missing ROIs
end
end
if size(dataCell,2) > 1
for iNetwork = 1:length(net)
netTmp = net([iNetwork iNetwork iNetwork]);
netTmp(1).name = [ netTmp(1).name 'Condition 1' ];
netTmp(2).name = [ netTmp(2).name 'Condition 2' ];
netTmp(2).name = [ netTmp(2).name 'Difference' ];
for iFreq = 1:length(g.freqrange)
tit = sprintf('MIM %g_%g Hz', g.freqrange{iFreq}(1), g.freqrange{iFreq}(2));
medthresh = nanmedian(dataCell{iFreq,1}(:))*g.threshold;
roi_networkplot(EEG(1), netTmp, [ dataCell(iFreq,:) {dataCell{iFreq,2}-dataCell{iFreq,1}}], 'threshold', medthresh, 'subplots', 'on', 'title', tit, 'columns', 3, addopts{:}); %, 'limits'); %, [0.05 0.08]);
tit(tit == ' ') = '_';
print('-djpeg', [g.outputdir filesep 'Connectivity_maps_MIM_' net(iNetwork).name '_' tit '.jpg']);
close;
end
end
else
for iFreq = 1:length(g.freqrange)
tit = sprintf('MIM %g_%g Hz', g.freqrange{iFreq}(1), g.freqrange{iFreq}(2));
roi_networkplot(EEG(1), net, dataCell{iFreq}, 'threshold', 0, 'subplots', 'on', 'title', tit, 'limits', [0.1 0.2]);
tit(tit == ' ') = '_';
print('-djpeg', [g.outputdir filesep 'Connectivity_maps_' tit '_fixed.jpg']);
close;
end
end
end
% select connection based on frequencies and freq range
% -----------------------------------------------------
function MIMtmp = selectconnect(CON, freqs, range, quant)
[~,fBeg] = min(abs(freqs-range(1)));
[~,fEnd] = min(abs(freqs-range(2)));
MIMtmp = mean(CON(fBeg:fEnd,:,:),1);
if ~isempty(quant)
TMPCON = MIMtmp(:);
TMPCON(TMPCON == 0) = [];
if quant < 0.5
quant = 1-quant;
end
mn = quantile(TMPCON, quant);
mx = quantile(TMPCON, 1-quant);
MIMtmp = (MIMtmp-mn)/(mx-mn);
end