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roi_definenetwork.m
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roi_definenetwork.m
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% roi_definenetwork() - define network between brain areas. Requires to
% have an Atlas loaded in EEG.roi.atlas
% Usage:
% [EEG, networks] = roi_definenetwork(EEG, netTable, 'key', 'val');
% [roi, networks] = roi_definenetwork(roi, netTable, 'key', 'val');
%
% Inputs:
% EEG - EEGLAB dataset with ROI activity computed and Atlas loaded
% roi - EEGLAB EEG.roi substructure connectivity and Atlas
% netTable - [string|table] Define network based on existing
% ROIs. If a string is provided, the file is loaded as a
% table. First row contains the names of the new ROI.
% other rows contain the name of the areas to group (see
% example). Networks are defined as groups of ROIs.
%
% Optional input:
% 'addrois' - [string|table] Define additional ROIs based on existing
% ROIs (see example).
% 'ignoremissing' - ['on'|'off'] ignore missing names 'on' or issue
% an error 'off'. Default is 'off'.
% 'connectmat' - [array] connectivity matrix. When provided return
% the new connectivity with added ROI ('addrois' input)
%
% Output:
% EEG - EEG structure with EEG.roi.atlas.Scout and EEG.roi.atlas.networks
% field updated and now containing new ROI or network.
% networks - Same as EEG.roi.atlas.networks
% connectmat - Updated connectivity matrix (when provided as input)
%
% Example:
% DNM = [1 2 3 4 5]';
% EEG = roi_definenetwork(EEG, table(DNM)); % define network DNM comprising ROI 1, 2, 3, 4 and 5
%
% Example:
% DNM = { 'Brodmann area 10L' 'Brodmann area 10R' }';
% EEG = roi_definenetwork(EEG, table(DNM)); % define network DNM comprising ROI name 24L and 24R
%
% Example:
% A = { 'Brodmann area 10L' 'Brodmann area 10R' }';
% A = { 'Brodmann area 31L' 'Brodmann area 31R' }';
% DNM = { 'A' 'B' }';
% EEG = roi_definenetwork(EEG, table(DNM), 'addrois', table(A,B)); % define network DNM comprising ROI A and B
%
% Example:
% [EEG, net] = roi_definenetwork(EEG, 'NGNetworkROIs_v4.txt', 'addrois', 'NGNetworkROIs_area_definition_v2.txt', 'ignoremissing', 'on');
%
% 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,networks,connectmat] = roi_definenetwork(EEG, roiTable, varargin)
if nargin < 2
help roi_definenetwork;
return
end
if isfield(EEG, 'roi')
roi = EEG.roi;
flagEEG = true;
else
roi = EEG;
flagEEG = false;
end
g = finputcheck(varargin, { ...
'ignoremissing' 'string' {'on' 'off'} 'off';
'addrois' '' {} [];
'connectmat' '' {} [];
}, 'roi_definenetwork');
if isstr(g)
error(g);
end
if ischar(roiTable)
if ~exist(roiTable, 'file')
p = fileparts(which('roi_definenetwork'));
roiTable2 = fullfile(p, roiTable);
if ~exist(roiTable2, 'file')
error('File not found %s', roiTable);
end
roiTable = roiTable2;
end
roiTable = readtable(roiTable,'Delimiter', char(9));
end
if ischar(g.addrois) && ~isempty(g.addrois)
if ~exist(g.addrois, 'file')
p = fileparts(which('roi_definenetwork'));
tmpTable = fullfile(p, g.addrois);
if ~exist(tmpTable, 'file')
error('File not found %s', g.addrois);
end
g.addrois = tmpTable;
end
g.addrois = readtable(g.addrois,'delimiter', char(9));
end
try
allLabels = { roi.atlas.Scouts.Label };
catch
error('Atlas not found. Use pop_leadfield to choose a source model which contains an Atlas.');
end
% add new ROIs
connectmat = g.connectmat;
if ~isempty(g.addrois)
colNames = fieldnames(g.addrois);
ROIinds = cell(1, size(g.addrois,2));
for iCol = 1:size(g.addrois,2) % scan columns
roi.atlas.Scouts(end+1).Label = colNames{iCol};
inds = [];
if isnumeric(g.addrois(1,iCol))
inds = g.addrois(:,iCol);
else
for iRow = 1:size(g.addrois,1)
val = g.addrois{iRow, iCol}{1};
if ~isempty(val)
indTmp1 = strmatch(val, allLabels, 'exact');
indTmp2 = strmatch([ 'Brodmann area ' val], allLabels, 'exact');
indTmp = [ indTmp1 indTmp2 ];
if length(indTmp) == 0
if strcmpi(g.ignoremissing, 'off')
error('Area %s not found ', val);
else
fprintf('Area %s not found, ignoring it\n', val);
indTmp = [];
end
elseif length(indTmp) > 1
if strcmpi(g.ignoremissing, 'off')
error('Area %s duplicate', val);
else
fprintf('Area %s duplicate, ignoring\n', val);
indTmp = [];
end
else
inds = [ inds;indTmp ];
end
end
end
end
ROIinds{iCol} = inds;
roi.atlas.Scouts(end).Vertices = vertcat(roi.atlas.Scouts(inds).Vertices);
end
% add ROIs to connectivity matrix by combining info from origin ROIs (not ideal, better compute it directly)
if ~isempty(connectmat)
if iscell(connectmat)
for iMat = 1:length(connectmat)
connectmat{iMat} = augmentConnectivity(connectmat{iMat}, ROIinds);
end
else
connectmat = augmentConnectivity(connectmat, ROIinds);
end
end
end
% only add ROIs - return
if isempty(roiTable)
networks = [];
return;
end
% define networks
networks = [];
colNames = fieldnames(roiTable);
allLabels = lower({ roi.atlas.Scouts.Label });
for iCol = 1:size(roiTable,2) % scan columns
networks(end+1).name = colNames{iCol};
inds = [];
if isnumeric(roiTable{1,iCol})
inds = [ roiTable{:,iCol} ]';
else
for iRow = 1:size(roiTable,1)
val = roiTable{iRow, iCol}{1};
if ~isempty(val)
indTmp1 = strmatch(lower(val), allLabels, 'exact');
indTmp2 = strmatch(lower([ 'Brodmann area ' val]), allLabels, 'exact');
indTmp = [ indTmp1 indTmp2 ];
if isempty(indTmp)
if strcmpi(g.ignoremissing, 'off')
error('Area %s not found', val);
else
fprintf('Area %s not found\n', val);
end
elseif length(indTmp) > 1
fprintf('Area %s duplicate, using the first one\n', val);
indTmp = indTmp(1);
end
inds = [ inds;indTmp ];
end
end
end
networks(end).ROI_inds = inds';
end
roi.atlas.networks = networks;
if flagEEG
EEG.roi = roi;
else
EEG = roi;
end
% % augment connectivity rows/cols A, B, C, D
% % new areas (A,B) and (C,D)
% % The connectivity between (A,B) and (C,D) is ( A->C + A->D + B->C + B->D )/4
% % equals (4 -2 +1 -1)/4 = 0.5 in the example below
% connectmat = [ 0 1 4 -2; 1 0 1 -1; 4 1 0 1; -2 -1 1 0];
% ROIinds = { [1 2] [ 3 4] }
% newconnectmat = augmentConnectivity(connectmat,ROIinds)
function newconnectmat = augmentConnectivity(connectmat,ROIinds)
sz = [size(connectmat) 1 1];
permFlag = false;
if sz(2) == sz(3) && sz(1) ~= sz(2)
connectmat = permute(connectmat, [2 3 1]);
permFlag = true;
end
% accross subjects if any
nVals = size(connectmat,1);
if size(connectmat,3) > 1
newconnectmat = zeros(nVals+length(ROIinds), nVals+length(ROIinds), size(connectmat,3));
for iSubject = 1:size(connectmat,3)
newconnectmat(:,:,iSubject) = augmentConnectivity(connectmat(:,:,iSubject),ROIinds);
end
if permFlag
newconnectmat = permute(newconnectmat, [3 1 2]);
end
return;
end
newconnectmat = zeros(nVals+length(ROIinds), nVals+length(ROIinds));
newconnectmat(1:nVals,1:nVals) = connectmat;
for iCol = 1:length(ROIinds)
newconnectmat(nVals+iCol,1:nVals) = mean(connectmat(ROIinds{iCol},:),1);
newconnectmat(1:nVals,nVals+iCol) = mean(connectmat(:,ROIinds{iCol}),2);
end
for iCol1 = 1:length(ROIinds)
for iCol2 = 1:length(ROIinds)
if iCol1 ~= iCol2
newconnectmat(nVals+iCol1,nVals+iCol2) = mean(newconnectmat(nVals+iCol1,ROIinds{iCol2}));
if 0
% brute force check (but slower)
tot = 0;
for iRoi1 = ROIinds{iCol1}(:)'
for iRoi2 = ROIinds{iCol2}(:)'
tot = tot + connectmat(iRoi1, iRoi2);
end
end
tot = tot/length(ROIinds{iCol1})/length(ROIinds{iCol2});
if abs(tot - newconnectmat(nVals+iCol1,nVals+iCol2)) > 1e-15
error('Non equal value');
end
end
end
end
end