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pop_roi_connectplot.m
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pop_roi_connectplot.m
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% pop_roi_connectplot - plot results of connectivity analysis computed
% by roi_connect.
% Usage:
% pop_roi_connectplot(EEG, 'key', 'val', ...);
%
% Inputs:
% EEG - EEGLAB dataset
%
% Required inputs:
% 'headmodel' - [string] head model file in MNI space
% 'sourcemodel' - [string] source model file
%
% Optional inputs:
% 'measure' - ['psd'|'roipsd'|'trgc'|'crossspecimag'|'crossspecpow'|'mic'|'mim']
% 'psd' : Source power spectrum
% 'psdroi': ROI based power spectrum
% 'trgc' : Time-reversed granger causality
% 'gc' : Granger causality
% 'crossspecimag': Imaginary part of coherence from cross-spectrum
% 'crossspecpow' : Average cross-spectrum power for each ROI
% 'mic' : Maximized Imaginary Coherency for each ROI
% 'mim' : Multivariate Interaction Measure for each ROI
% 'pac' : Phase-amplitude coupling for a certain frequency (band) combination based on bicoherence
% 'pac_anti': Phase-amplitude coupling for a certain frequency (band) combination based on the antisymmetrized bicoherence
% 'freqrange' - [min max] frequency range or [integer] single frequency in Hz. Default is to plot broadband power.
% 'smooth' - [float] smoothing factor for cortex surface plotting
% 'plotcortex' - ['on'|'off'] plot results on smooth cortex. Default is 'on'
% 'plotcortexparams' - [cell] ...
% 'plotcortexseedregion' - [string] plot seed voxel on cortex. Takes name of seed region as input.
% 'plot3d' - ['on'|'off'] ... Default is 'off'
% 'plot3dparams' - [cell] ...
% 'plotmatrix' - ['on'|'off'] plot results as ROI to ROI matrix. Default is 'off'
% 'plotbarplot' - ['on'|'off'] plot ROI based power spectrum as barplot. Default is 'off'
% 'hemisphere' - ['all'|'left'|'right'] hemisphere options for ROI to ROI matrix. Default is 'all'
% 'grouphemispheres' - ['on'|'off'] group ROIs by hemispheres (left hemisphere, then right hemisphere). Default is 'off'
% 'region' - ['all'|'cingulate'|'prefrontal'|'frontal'|'temporal'|'parietal'|'central'|'occipital'] region selection for ROI to ROI matrix. Default is 'all'
% 'largeplot' - ['on'|'off'] plot MIM, TRGC and Power in a single large plot. Default is 'off'
% 'plotpsd' - ['on'|'off'] plot PSD (for 'crossspecpow' only). Default is 'off'
% 'noplot' - ['on'|'off'] when 'on', disable all plotting. Default is 'off'
%
% Output
% matnet - connectivity matrix (nROI x nROI)
%
% Author: Stefan Haufe and Arnaud Delorme, 2019
%
% Example:
% % Requires prior call to pop_roi_connect
% matnet = pop_roi_connectplot(EEG, 'measure', 'ROIPSD');
%
% See also: STD_ROI_CONNECTPLOT, ROI_ACTIVITY, POP_ROI_ACTIVITY, POP_LEADFIELD
% 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 [matrix, com] = pop_roi_connectplot(EEG, varargin)
matrix = [];
com = '';
if nargin < 1
help pop_roi_connectplot;
return
end
if ~isfield(EEG, 'roi')
error('Compute connectivity first');
end
if ~exist('roi_plotbrainmovie')
fprintf(2, 'To plot connectivity in 3-D, install the brainmovie plugin\n');
plot3dFlag = -1;
else
plot3dFlag = 0;
end
% if ~isfield(EEG.dipfit, 'hdmfile')
% error('You need to select a head model file using DIPFIT settings first');
% end
%
% if ~isequal(EEG.dipfit.coordformat, 'MNI')
% error('You can only use this function with MNI coordinates - change head model');
% end
cortexFlag = isfield(EEG.roi.cortex, 'Faces')*2-1;
areaNames = [ 'All' { EEG.roi.atlas(1).Scouts.Label } ];
splot = [];
% splot(end+1).label = 'Source power spectrum'; % we do not save that information anymore
% splot(end ).acronym = 'PSD';
% splot(end ).unit = '?'; % not used yet
% splot(end ).cortex = cortexFlag;
% splot(end ).matrix = -1;
% splot(end ).psd = -1;
if isfield(EEG.roi, 'source_roi_power')
splot(end+1).label = 'ROI based power spectrum';
splot(end ).labelshort = 'ROI PSD';
splot(end ).acronym = 'ROIPSD';
splot(end ).unit = 'Power (dB)'; % not used yet
splot(end ).cortex = cortexFlag;
splot(end ).matrix = -1;
splot(end ).psd = -1;
splot(end ).plot3d = -1;
end
if isfield(EEG.roi, 'CS')
splot(end+1).label = 'ROI to ROI cross-spectrum';
splot(end ).labelshort = 'Cross-spectrum';
splot(end ).acronym = 'crossspecpow';
splot(end ).unit = 'Power (dB)';
splot(end ).cortex = cortexFlag;
splot(end ).matrix = -1;
splot(end ).psd = 0;
splot(end ).plot3d = plot3dFlag;
end
if isfield(EEG.roi, 'CS')
splot(end+1).label = 'ROI to ROI imaginary part of cross-spectrum';
splot(end ).labelshort = 'Img. part of cross-spectrum';
splot(end ).acronym = 'crossspecimag';
splot(end ).unit = 'net |iCOH|';
splot(end ).cortex = cortexFlag;
splot(end ).matrix = 1;
splot(end ).psd = -1;
splot(end ).plot3d = plot3dFlag;
end
if isfield(EEG.roi, 'COH')
splot(end+1).label = 'ROI to ROI coherence';
splot(end ).labelshort = 'Coherence';
splot(end ).acronym = 'Coh';
splot(end ).unit = 'Coh';
splot(end ).cortex = cortexFlag;
splot(end ).matrix = 1;
splot(end ).psd = -1;
splot(end ).plot3d = plot3dFlag;
end
if isfield(EEG.roi, 'GC')
splot(end+1).label = 'ROI to ROI granger causality';
splot(end ).labelshort = 'Granger Causality';
splot(end ).acronym = 'GC';
splot(end ).unit = 'GC';
splot(end ).cortex = cortexFlag;
splot(end ).matrix = 1;
splot(end ).psd = -1;
splot(end ).plot3d = plot3dFlag;
end
if isfield(EEG.roi, 'TRGC')
splot(end+1).label = 'ROI to ROI time-reversed granger causality';
splot(end ).labelshort = 'Time-rev. Granger Causality';
splot(end ).acronym = 'TRGC';
splot(end ).unit = 'TRGC'; % not used yet
splot(end ).cortex = cortexFlag;
splot(end ).matrix = 1;
splot(end ).psd = -1;
splot(end ).plot3d = plot3dFlag;
end
if isfield(EEG.roi, 'MIC')
splot(end+1).label = 'ROI to ROI Maximized Imag. Coh.';
splot(end ).labelshort = 'Maximized Imag. Coh.';
splot(end ).acronym = 'MIC';
splot(end ).unit = 'MIC'; % not used yet
splot(end ).cortex = cortexFlag;
splot(end ).matrix = -1;
splot(end ).psd = 0;
splot(end ).plot3d = plot3dFlag;
end
if isfield(EEG.roi, 'MIM')
splot(end+1).label = 'ROI to ROI Multivariate Interaction Measure';
splot(end ).labelshort = 'Multivariate Interaction Measure';
splot(end ).acronym = 'MIM';
splot(end ).unit = 'MIM'; % not used yet
splot(end ).cortex = cortexFlag;
splot(end ).matrix = 1;
splot(end ).psd = 0;
splot(end ).plot3d = plot3dFlag;
end
if isfield(EEG.roi, 'PAC')
splot(end+1).label = 'ROI to ROI Phase-amplitude coupling';
splot(end ).labelshort = 'Phase-amplitude coupling';
splot(end ).acronym = 'PAC'; % PAC based on bicoherence
splot(end ).unit = 'PAC'; % not used yet
splot(end ).cortex = cortexFlag;
splot(end ).matrix = 1;
splot(end ).psd = 0;
splot(end ).plot3d = plot3dFlag;
end
if isfield(EEG.roi, 'PAC')
splot(end+1).label = 'ROI to ROI Phase-amplitude coupling';
splot(end ).labelshort = 'Phase-amplitude coupling';
splot(end ).acronym = 'PAC_anti'; % PAC based on antisymmetrized bicoherence
splot(end ).unit = 'PAC'; % not used yet
splot(end ).cortex = cortexFlag;
splot(end ).matrix = 1;
splot(end ).psd = 0;
splot(end ).plot3d = plot3dFlag;
end
if nargin < 2
cb_select = [ 'usrdat = get(gcf, ''userdata'');' ...
'usrdat = usrdat(get(findobj(gcf, ''tag'', ''selection''), ''value''));' ...
'fieldTmp = { ''cortex'' ''matrix'' ''psd'' ''plot3d'' };' ...
'for iField = 1:length(fieldTmp),' ...
' if usrdat.(fieldTmp{iField}) == 1,' ...
' set(findobj(gcf, ''tag'', fieldTmp{iField}), ''enable'', ''on'', ''value'', 1);' ...
' elseif usrdat.(fieldTmp{iField}) == 0,' ...
' set(findobj(gcf, ''tag'', fieldTmp{iField}), ''enable'', ''on'', ''value'', 0);' ...
' else,' ...
' set(findobj(gcf, ''tag'', fieldTmp{iField}), ''enable'', ''off'', ''value'', 0);' ...
' end;' ...
'end;' ...
'clear iField fieldTmp usrdat;' ];
fcregions = {'all', 'cingulate', 'prefrontal', 'frontal', 'temporal', 'parietal', 'central', 'occipital'};
plotrow = [1 1];
uigeom = { [1 1] [1 1] [1 0.5 0.2] 1 [1 1] plotrow [1] [3.3 1.5 1.1 1.2] plotrow };
uilist = {{ 'style' 'text' 'string' 'Select a measure to plot' 'fontweight' 'bold'} ...
{ 'style' 'popupmenu' 'string' {splot.label} 'callback' cb_select 'value' 1 'tag' 'selection' } ...
{ 'style' 'text' 'string' 'Only from/to region' } ...
{ 'style' 'popupmenu' 'string' areaNames 'tag' 'seed_region'} ...
{ 'style' 'text' 'string' 'Frequency range in Hz [min max]:'} ...
{ 'style' 'edit' 'string' '' 'tag' 'freqs'} {} ...
{} ...
{ 'style' 'text' 'string' 'Measure to plot' 'fontweight' 'bold' } ...
{ 'style' 'text' 'string' 'Measure parameters' 'fontweight' 'bold' } ...
...
{ 'style' 'checkbox' 'string' '3d static brainmovie' 'tag' 'plot3d' 'value' 1 } ...
{ 'style' 'edit' 'string' '''thresholdper'', 0.8' 'tag' 'plot3dparams' } ...
...
{ 'style' 'checkbox' 'string' 'Connectivity on cortex (requires surface atlas)' 'tag' 'cortex' 'value' 1 } ...
...
{ 'style' 'checkbox' 'string' 'Matrix representation' 'tag' 'matrix' 'enable' 'off' } ...
{ 'style' 'popupmenu' 'string' fcregions 'callback' cb_select 'value' 1 'tag' 'region' } ....
{ 'style' 'checkbox' 'string' 'left' 'tag' 'hemisphere_left' 'value' 1 } ...
{ 'style' 'checkbox' 'string' 'right' 'tag' 'hemisphere_right' 'value' 1 } ...
...
{ 'style' 'checkbox' 'string' 'Power spectral density' 'tag' 'psd' 'enable' 'off' } {} ...
};
[result,~,~,outs] = inputgui('geometry', uigeom, 'uilist', uilist, 'helpcom', 'pophelp(''pop_loadbv'')', ...
'title', 'ROI connectivity', 'userdata', splot, 'eval', cb_select);
if isempty(result), return, end
options = {};
options = { options{:} 'measure' splot(result{1}).acronym };
options = { options{:} 'freqrange' eval( [ '[' outs.freqs ']' ] ) };
options = { options{:} 'plotcortex' fastif(outs.cortex, 'on', 'off') };
options = { options{:} 'plotcortexparams' {} };
options = { options{:} 'plotcortexseedregion' outs.seed_region-1 };
options = { options{:} 'plotmatrix' fastif(outs.matrix, 'on', 'off') };
options = { options{:} 'plotpsd' fastif(outs.psd , 'on', 'off') };
options = { options{:} 'plot3d' fastif(outs.plot3d, 'on', 'off') };
options = { options{:} 'plot3dparams' eval( [ '{' outs.plot3dparams '}' ] ) };
options = { options{:} 'region' fcregions{outs.region} };
% choose which hemisphere to plot
if outs.hemisphere_left == 1 && outs.hemisphere_right == 0
options = { options{:} 'hemisphere' 'left' };
elseif outs.hemisphere_left == 0 && outs.hemisphere_right == 1
options = { options{:} 'hemisphere' 'right' };
else
options = { options{:} 'hemisphere' 'all' };
end
else
options = varargin;
end
% decode input parameters
% -----------------------
g = finputcheck(options, { 'measure' 'string' {splot.acronym} '';
'freqrange' 'real' { } [];
'smooth' 'real' { } 0.35;
'plotcortex' 'string' { 'on' 'off' } 'on';
'plotcortexparams' 'cell' { } {};
'plotcortexseedregion' 'integer' { } [];
'plot3d' 'string' { 'on' 'off' } 'off';
'plot3dparams' 'cell' { } {};
'plotmatrix' 'string' { 'on' 'off' } 'off';
'noplot' 'string' { 'on' 'off' } 'off';
'plotbarplot' 'string' { 'on' 'off'} 'off';
'hemisphere' 'string' {'all' 'left' 'right'} 'all';
'grouphemispheres' 'string' { 'on' 'off'} 'off';
'region' 'string' { 'all', 'cingulate', 'prefrontal', 'frontal', 'temporal', 'parietal', 'central', 'occipital' } 'all';
'largeplot', 'string' { 'on' 'off' } 'off';
'plotpsd', 'string' { 'on' 'off' } 'off' }, 'pop_roi_connectplot');
if ischar(g), error(g); end
if isequal(g.plotcortexseedregion, 0)
g.plotcortexseedregion = [];
end
S = EEG.roi;
if isempty(g.measure)
error('You must define a measure to plot');
end
if isfield(S, 'roi_selection')
if ~isempty(S.roi_selection)
warning("Plotting options ('region', 'hemisphere', 'grouphemispheres') are disabled when ROIs were explicitely selected.")
g.region = 'all';
g.hemisphere = 'all';
g.grouphemispheres = 'all';
end
end
% colormap
load cm17;
load cm18;
% replace low-resolution with high-resolution cortex
load cortex;
% extract frequency indices
if ~isempty(g.freqrange)
if length(g.freqrange) == 1
frq_inds = find(S.freqs == g.freqrange(1));
titleStr = sprintf('%1.1f Hz', g.freqrange(1));
else
frq_inds = find(S.freqs >= g.freqrange(1) & S.freqs < g.freqrange(2));
titleStr = sprintf('%1.1f-%1.1f Hz frequency band', g.freqrange(1), g.freqrange(2));
end
else
frq_inds = 1:length(S.freqs);
titleStr = 'broadband';
end
% plotting options
allMeasures = { splot.acronym };
pos = strmatch( g.measure, allMeasures, 'exact');
plotOpt = splot(pos);
% either plot large plot with MIM, TRGC and power or only individual plots
if strcmpi(g.largeplot, 'on')
source_roi_power_norm_dB = 10*log10( mean(EEG.roi.source_roi_power(frq_inds,:)) ); % roipsd
% TRGCnet = S.TRGC;
% TRGCnet = TRGCnet - permute(TRGCnet, [1 3 2]);
% TRGCnet = TRGCnet(:,:);
TRGC_matrix = squeeze(mean(S.TRGC(frq_inds, :, :)));
MIM_matrix = squeeze(mean(S.MIM(frq_inds, :, :)));
roi_largeplot(EEG, MIM_matrix, TRGC_matrix, source_roi_power_norm_dB, titleStr)
else
matrix = [];
switch lower(g.measure)
case { 'psd' 'roipsd' }
if strcmpi(g.measure, 'psd')
% plot poower of individual voxels
% we would need to save the power in roi_activity. The function below can plot power
% allplots_cortex_BS(S.cortex, P_dB, [min(P_dB) max(P_dB)], cm17a, 'power [dB]', g.smooth);
error('This option is obsolete');
end
if strcmpi(g.plotcortex, 'on') && strcmpi(lower(g.measure), 'roipsd')
cortexPlot = 10*log10( mean(EEG.roi.source_roi_power(frq_inds,:)) );
end
if strcmpi(g.plotbarplot, 'on') && ~strcmpi(g.noplot, 'on')
source_roi_power_norm_dB = 10*log10( mean(EEG.roi.source_roi_power(frq_inds,:)) );
roi_plotpower(EEG, source_roi_power_norm_dB, titleStr);
end
case { 'trgc' 'gc' }
% calculation of net TRGC scores (i->j minus j->i), recommended procedure
% new way to compute net scores
if strcmpi(g.measure, 'GC')
% TRGCnet = S.GC(:, :, 1) - S.GC(:, :, 2);
TRGC = S.GC;
else
% TRGCnet = S.TRGC(:, :, 1) - S.TRGC(:, :, 2);
TRGC = S.TRGC;
end
matrix = squeeze(mean(TRGC(frq_inds, :, :)));
cortexPlot = mean(matrix, 2);
cortexTitle = [ upper(g.measure) ' (' titleStr '); Red = net sender; Blue = net receiver' ];
case { 'mim' 'mic' }
if strcmpi(g.measure, 'MIC')
% MI = S.MIC(:, :);
MI = S.MIC;
else
% MI = S.MIM(:, :);
MI = S.MIM;
end
matrix = squeeze(mean(MI(frq_inds, :, :),1));
cortexPlot = mean(matrix, 2);
case { 'crossspecpow' 'coh' 'crossspecimag' }
if strcmpi(g.measure, 'coh')
PS = abs(S.COH); % do not know what to do here
PSarea2area = squeeze(mean(PS(frq_inds, :, :)));
cortexPlot = mean(PSarea2area, 2);
elseif strcmpi(g.measure, 'crossspecimag')
PS = abs(imag(cs2coh(S.CS)));
PSarea2area = squeeze(mean(PS(frq_inds, :, :)));
cortexPlot = mean(PSarea2area, 2);
else
PS = cs2psd(S.CS);
apow = squeeze(sum(sum(reshape(PS(frq_inds, :), [], S.nROI), 1), 2)).*S.source_roi_power_norm';
cortexPlot = 10*log10(apow);
PSarea2area = [];
end
plotPSDFreq = S.freqs(frq_inds);
plotPSD = PS(frq_inds, :);
matrix = PSarea2area;
case {'pac'}
if isfield(S.PAC, 'b_orig_norm')
matrix = S.PAC.b_orig_norm;
elseif isfield(S.PAC, 'b_orig')
matrix = S.PAC.b_orig;
else
error('PAC (original bicoherence) cannot be plotted, field is missing.')
end
cortexPlot = mean(matrix, 2);
case {'pac_anti'}
if isfield(S.PAC, 'b_anti_norm')
matrix = S.PAC.b_anti_norm;
elseif isfield(S.PAC, 'b_anti')
matrix = S.PAC.b_anti;
else
error('PAC (antisymmetrized bicoherence) cannot be plotted, field is missing.')
end
cortexPlot = mean(matrix, 2);
end
% get seed
if ~isempty(g.plotcortexseedregion)
[coordinate, seed_idx] = get_seedregion_coordinate(EEG.roi.atlas.Scouts, g.plotcortexseedregion, EEG.roi.cortex.Vertices);
seedMask = zeros(size(matrix));
seedMask(seed_idx, :) = 1;
seedMask(:,seed_idx) = 1;
else
seedMask = ones(size(matrix));
end
% frequency plot
if strcmpi(g.plotpsd, 'on')
figure; semilogy(plotPSDFreq, plotPSD); grid on
h = textsc(plotOpt.label, 'title');
set(h, 'fontsize', 20);
end
% plot on matrix
if strcmpi(g.plotmatrix, 'on') && ~isempty(matrix)
matrix = matrix.*seedMask;
roi_plotcoloredlobes(EEG, matrix, titleStr, g.measure, g.hemisphere, g.grouphemispheres, g.region);
% try
% roi_plotcoloredlobes(EEG, matrix, titleStr, g.measure, g.hemisphere, g.grouphemispheres, g.region);
% catch
% warning('Functionalities only available for the Desikan-Killiany atlas (68 ROIs).')
% figure; imagesc(matrix);
% end
end
% plot on cortical surface
if strcmpi(g.plotcortex, 'on') && cortexFlag ~= -1
cortexTitle = [ plotOpt.labelshort ' (' titleStr ')' ];
if isempty(g.plotcortexseedregion)
allplots_cortex_BS(cortex_highres, cortexPlot, [min(cortexPlot) max(cortexPlot)], cm17a, upper(g.measure), g.smooth);
% allplots_cortex_BS(cortex_highres, cortexPlot, [min(cortexPlot) max(cortexPlot)], cm17a, upper(splot.unit), g.smooth);
% allplots_cortex_BS(S.cortex, cortexPlot, [min(cortexPlot) max(cortexPlot)], cm17a, plotOpt.unit, g.smooth);
else
cortexTitle = [ cortexTitle ' for area ' int2str(seed_idx)];
cortexPlot = squeeze(matrix(seed_idx,:));
allplots_cortex_BS(S.cortex, cortexPlot, [min(cortexPlot) max(cortexPlot)], cm17a, upper(g.measure), g.smooth, [], {coordinate});
% allplots_cortex_BS(cortex_highres, cortexPlot, [min(cortexPlot) max(cortexPlot)], cm17a, upper(g.measure), g.smooth, [], {coordinate});
% allplots_cortex_BS(S.cortex, cortexPlot, [min(cortexPlot) max(cortexPlot)], cm17a, plotOpt.unit, g.smooth, [], {coordinate});
end
h = textsc(cortexTitle, 'title');
set(h, 'fontsize', 20);
elseif strcmpi(g.plotcortex, 'on') && cortexFlag == -1
warning('EEG.roi.cortex does not contain the field "Faces" required to plot surface topographies.')
end
% plot 3D
if strcmpi(g.plot3d, 'on') && ~isempty(matrix)
PSarea2area = matrix.*seedMask;
roi_plotbrainmovie(matrix, 'cortex', EEG.roi.cortex, 'atlas', EEG.roi.atlas, g.plot3dparams{:});
end
end
if nargin < 2
com = sprintf('pop_roi_connectplot(EEG, %s);', vararg2str( options ));
end
end
function [coordinate, seed_idx] = get_seedregion_coordinate(scouts, seed_idx, vc)
% determine voxel of selected seed region, if needed
% assign region index to selected seed region (passed as index)
if ~isempty(seed_idx)
% ball not visible for these regions when plotting the mean voxel
manual_region_idxs = [2, 16, 18, 25, 26, 31, 32, 45, 49, 50, 55, 56, 59, 60, 61, 64];
pos_idx = scouts(seed_idx).Vertices;
pos = vc(pos_idx,:);
if seed_idx == 1
coordinate = vc(736,:);
elseif ismember(seed_idx, manual_region_idxs)
pos_sorted = sortrows(pos, 3, 'descend'); % sort by descending Z-coordinate
coordinate = pos_sorted(1,:);
else
mid_point = mean(pos,1);
[~,closest_pos_idx] = min(eucl(mid_point, pos)); % determine mean voxel
coordinate = pos(closest_pos_idx,:);
end
else
error('Selected region not in cortex')
end
end
function labels = get_labels(EEG)
% retrieve labels from atlas
labels = strings(1,length(EEG.roi.atlas.Scouts));
for i = 1:length(labels)
scout = struct2cell(EEG.roi.atlas.Scouts(i));
labels(i) = char(scout(1));
end
labels = cellstr(labels);
% remove region labels that were not selected
if isfield(EEG.roi, 'roi_selection')
if ~isempty(EEG.roi.roi_selection)
labels = labels(cell2mat(EEG.roi.roi_selection));
end
end
end
function new_labels = replace_underscores(labels)
% remove underscores in label names to avoid bug
new_labels = strrep(labels, '_', ' ');
end
function [colors, color_idxx, roi_idxx, labels_sorted, roi_loc] = get_colored_labels(EEG)
labels = get_labels(EEG);
colors = {[0,0,0]/255, [163, 107, 64]/255, [171, 163, 71]/255, [217, 37, 88]/255, [113, 15, 82]/255,[35, 103, 81]/255,[2, 45, 126]/255,};
% assign labels to colors
roi_loc ={'LT';'RT';'LL';'RL';'LF';'RF';'LO';'RO';'LT';'RT';'LPF';'RPF';'LT';'RT';'LP';'RP';'LT';'RT';'LT';'RT';'LL';'RL';'LO';'RO';'LPF';'RPF';'LO';'RO';'LPF';'RPF';'LT';'RT';'LC';'RC';'LT';'RT';'LF';'RF';'LPF';'RPF';'LF';'RF';'LO';'RO';'LC';'RC';'LL';'RL';'LC';'RC';'LP';'RP';'LL';'RL';'LF';'RF';'LF';'RF';'LP';'RP';'LT';'RT';'LP';'RP';'LT';'RT';'LT';'RT'};
roi_loc = string(roi_loc);
roi_loc = strrep(roi_loc, 'PF', '2');
roi_loc = strrep(roi_loc, 'F', '3');
roi_loc = strrep(roi_loc, 'T', '4');
roi_loc = strrep(roi_loc, 'P', '5');
roi_loc = strrep(roi_loc, 'C', '6');
roi_loc = strrep(roi_loc, 'O', '7');
roi_loc = strrep(roi_loc, 'LL', 'L1');
roi_loc = strrep(roi_loc, 'RL', 'R1');
roi_loc = strrep(roi_loc, 'L', '');
roi_loc = strrep(roi_loc, 'R', '');
% remove regions that were not selected
if isfield(EEG.roi, 'roi_selection')
if ~isempty(EEG.roi.roi_selection)
roi_loc = roi_loc(cell2mat(EEG.roi.roi_selection));
end
end
try
[color_idxx,roi_idxx] = sort(str2double(roi_loc));
labels_sorted = labels(roi_idxx);
catch
roi_idxx = 1:length(labels);
color_idxx = mod(roi_idxx, length(colors))+1;
labels_sorted = labels;
end
end
function roi_plotpower(EEG, source_roi_power_norm_dB, titleStr)
[colors, color_idxx, roi_idxx, labels_sorted, ~] = get_colored_labels(EEG);
n_roi_labels = size(labels_sorted, 2);
barh(source_roi_power_norm_dB(roi_idxx));
set(gca, 'YDir', 'reverse');
set(gca,'ytick',[1:n_roi_labels],'yticklabel',labels_sorted(1:end), 'fontweight','bold','fontsize', 9, 'TickLength',[0.015, 0.02], 'LineWidth',0.7);
h = title([ 'ROI source power' ' (' titleStr ')' ]);
set(h, 'fontsize', 16);
ylabel('power [dB]')
ax = gca;
for i=1:numel(roi_idxx)
ax.YTickLabel{ceil(i)} = sprintf('\\color[rgb]{%f,%f,%f}%s', colors{color_idxx(i)}, ax.YTickLabel{ceil(i)});
end
pos = get(gcf, 'Position');
set(gcf, 'Position', [pos(1) pos(2) pos(3) pos(4)*1.8])
movegui(gcf, 'south') % remove after
end
function roi_plotcoloredlobes( EEG, matrix, titleStr, measure, hemisphere, grouphems, region)
% check if Desikan-Killiany atlas is used
if EEG.roi.nROI == 68
isDKatlas = true;
else
isDKatlas = false;
end
if ~strcmpi(region, 'all') && isDKatlas == 0
error('Region plotting is only supported for the Desikan-Killiany atlas.');
end
% plot matrix with colored labels
load cm18
switch lower(measure)
case {'mim', 'mic', 'coh'}
cmap = cm18a;
otherwise
cmap = cm18;
end
clim_min = min(matrix, [], 'all');
clim_max = max(matrix, [], 'all');
% hemisphere parameters to determine which labels to use
last_char = EEG.roi.atlas.Scouts(1).Label(end);
if strcmpi(hemisphere, 'left')
if strcmpi(last_char, 'R')
hem_idx = {2 2 2}; % use labels 2:2:end (first two values), only use 1/2 of the labels (3rd value)
else
hem_idx = {1 2 2}; % use labels 1:2:end (first two values), only use 1/2 of the labels (3rd value)
end
elseif strcmpi(hemisphere, 'right')
if strcmpi(last_char, 'L')
hem_idx = {1 2 2};
else
hem_idx = {2 2 2};
end
else
hem_idx = {1 1 1};
end
% sort matrix according to color scheme
% reduce matrix to only keep components corresponding to selected region
if isDKatlas == 1
[colors, color_idxx, roi_idxx, labels_sorted, roi_loc] = get_colored_labels(EEG);
labels = labels_sorted;
% assign region input to an index
[GC, ~] = groupcounts(roi_loc);
switch lower(region)
case 'cingulate'
region_idx = 1;
case 'prefrontal'
region_idx = 2;
case 'frontal'
region_idx = 3;
case 'temporal'
region_idx = 4;
case 'parietal'
region_idx = 5;
case 'central'
region_idx = 6;
case 'occipital'
region_idx = 7;
otherwise
region_idx = 99;
end
matrix = matrix(roi_idxx, roi_idxx);
if not(region_idx == 99)
if region_idx == 1
start_idx = 1;
else
start_idx = 1 + sum(GC(1:region_idx-1));
end
end_idx = start_idx + GC(region_idx) - 1;
matrix = matrix(start_idx:end_idx, start_idx:end_idx);
labels = labels(start_idx:end_idx);
color_idxx = color_idxx(start_idx:end_idx);
end
else
labels = get_labels(EEG);
end
n_roi_labels = size(matrix, 1);
% remove underscores in labels to avoid plotting bug
labels = replace_underscores(labels);
% create dummy plot and add custom legend
f = figure();
%f.WindowState = 'maximized';
hold on
n_dummy_labels = 7;
x = 1:10;
% labels on dummy plot for positioning
xlim([0 n_roi_labels])
ylim([0 n_roi_labels])
set(gca,'xtick',1:n_roi_labels,'xticklabel',labels(hem_idx{1}:hem_idx{2}:n_roi_labels));%, 'TickLabelInterpreter','none');
ax = gca;
if isDKatlas == 1
for k=1:n_dummy_labels
plot(x, x*k, '-', 'LineWidth', 9, 'Color', colors{k});
end
for i=hem_idx{1}:hem_idx{2}:n_roi_labels
ax.XTickLabel{ceil(i/hem_idx{3})} = sprintf('\\color[rgb]{%f,%f,%f}%s', colors{color_idxx(i)}, ax.XTickLabel{ceil(i/2)});
end
legend('Cingulate', 'Prefrontal', 'Frontal', 'Temporal', 'Parietal', 'Central', 'Occipital', 'Location', 'southeastoutside'); % modify legend position
end
xtickangle(90)
pos = get(gca, 'Position');
set(gca, 'Position', pos, 'DataAspectRatio',[1 1 1], 'visible', 'off')
axes('pos', [pos(1) pos(2) pos(3) pos(4)]) % plot matrix over the dummy plot and keep the legend
% group by hemispheres (left first, then right)
if strcmp(grouphems, 'on')
% sort matrix
mat_left_row = matrix(1:2:end,:);
mat_right_row = matrix(2:2:end,:);
matrix = [mat_left_row; mat_right_row]; % sort rows
mat_left_col = matrix(:,1:2:end);
mat_right_col = matrix(:,2:2:end);
matrix = horzcat(mat_left_col, mat_right_col); % sort columns
% sort labels
try % if color can be assigned
lc = {labels; transpose(color_idxx)};
for i = 1:length(lc)
left = lc{i}(1:2:end);
right = lc{i}(2:2:end);
lc{i} = [left right];
end
labels = lc{1};
color_idxx = transpose(lc{2});
catch
left = labels(1:2:end);
right = labels(2:2:end);
labels = [left right];
end
end
% reduce matrix to keep only one hemisphere
if strcmp(hemisphere, 'left')
matrix = matrix(hem_idx{1}:hem_idx{2}:n_roi_labels,:);
matrix(:,2:2:end) = [];
imagesc(matrix); colormap(cmap);
elseif strcmp(hemisphere, 'right')
matrix = matrix(hem_idx{1}:hem_idx{2}:n_roi_labels,:);
matrix(:,1:2:end) = [];
imagesc(matrix); colormap(cmap);
else
imagesc(matrix); colormap(cmap);
end
cb = colorbar;
% tf = isMATLABReleaseOlderThan("R2022a");
% if tf
% caxis([clim_min clim_max])
% else
% clim([clim_min clim_max])
% end
try
caxis([clim_min clim_max])
catch
clim([clim_min clim_max])
end
if isDKatlas == 1
set(cb, 'Location', 'southoutside')
else
set(cb, 'Location', 'eastoutside')
end
set(gca, 'Position', pos, 'DataAspectRatio',[1 1 1], 'visible', 'on')
% add colored labels with display option
ax = gca;
set(gca,'xtick',1:n_roi_labels,'xticklabel',labels(hem_idx{1}:hem_idx{2}:n_roi_labels));
if isDKatlas == 1
set(gca,'ytick',1:n_roi_labels,'yticklabel',labels(hem_idx{1}:hem_idx{2}:n_roi_labels), 'fontsize', 9, 'TickLength',[0.015, 0.02], 'LineWidth',0.75);
for i=hem_idx{1}:hem_idx{2}:n_roi_labels
ax.XTickLabel{ceil(i/hem_idx{3})} = sprintf('\\color[rgb]{%f,%f,%f}%s', colors{color_idxx(i)}, ax.XTickLabel{ceil(i/hem_idx{3})});
ax.YTickLabel{ceil(i/hem_idx{3})} = sprintf('\\color[rgb]{%f,%f,%f}%s', colors{color_idxx(i)}, ax.YTickLabel{ceil(i/hem_idx{3})});
end
else
set(gca,'ytick',1:n_roi_labels,'yticklabel',labels(hem_idx{1}:hem_idx{2}:n_roi_labels), 'fontsize', 7, 'TickLength',[0.015, 0.02], 'LineWidth',0.75);
end
h = title([ 'ROI to ROI ' upper(replace_underscores(measure)) ' (' titleStr ')' ]);
set(h, 'fontsize', 16);
xtickangle(90)
end
function roi_largeplot(EEG, mim, trgc, roipsd, titleStr)
% plot MIM, TRGC and power (barplot) in a single large figure
load cm18
[colors, color_idxx, roi_idxx, labels_sorted, ~] = get_colored_labels(EEG);
n_roi_labels = size(labels_sorted, 2);
f = figure();
f.WindowState = 'maximized';
fc_matrices = cell(1,2);
fc_matrices{1,1} = mim;
fc_matrices{1,2} = trgc;
fc_names = ["MIM", "TRGC"];
for k = 1:2
plt(k) = subplot(1,3,k);
fc = fc_matrices{k};
img = squeeze(fc)';
img_sorted = img(roi_idxx, roi_idxx);
imagesc(img_sorted)
set(gca,'ytick',[1:n_roi_labels],'yticklabel',labels_sorted(1:end), 'fontsize', 5, 'TickLength',[0.015, 0.02], 'LineWidth',0.75);
set(gca,'xtick',[1:n_roi_labels],'xticklabel',labels_sorted(1:end));
h = title([ 'ROI to ROI ' fc_names{k} ' (' titleStr ')' ]);
set(h, 'fontsize', 16);
hcb = colorbar;
hcb.Label.FontSize = 10;
set(gca,'DataAspectRatio',[1 1 1])
xtickangle(90)
ax = gca;
for i=1:numel(roi_idxx)
ax.XTickLabel{ceil(i)} = sprintf('\\color[rgb]{%f,%f,%f}%s', colors{color_idxx(i)}, ax.XTickLabel{ceil(i)});
ax.YTickLabel{ceil(i)} = sprintf('\\color[rgb]{%f,%f,%f}%s', colors{color_idxx(i)}, ax.YTickLabel{ceil(i)});
end
end
colormap(plt(1), cm18a)
colormap(plt(2), cm18)
% power
subplot(1,3,3);
barh(roipsd(roi_idxx));
set(gca, 'YDir', 'reverse');
set(gca,'ytick',[1:n_roi_labels],'yticklabel',labels_sorted(1:end), 'fontsize', 9, 'TickLength',[0.015, 0.02], 'LineWidth',0.7);
h = title([ 'ROI source power' ' (' titleStr ')' ]);
set(h, 'fontsize', 16);
ylabel('power [dB]')
ax = gca;
for i=1:numel(roi_idxx)
ax.YTickLabel{ceil(i)} = sprintf('\\color[rgb]{%f,%f,%f}%s', colors{color_idxx(i)}, ax.YTickLabel{ceil(i)});
end
% invisible plot to add legend
hold on
n_labels = 7;
h = zeros(n_labels, 1);
for k=1:numel(h)
h(k) = plot(NaN, NaN, '-', 'LineWidth', 8, 'Color', colors{k});
end
if n_roi_labels == 68
lgd = legend(h, 'Cingulate', 'Prefrontal', 'Frontal', 'Temporal', 'Parietal', 'Central', 'Occipital');
lgd.FontSize = 10;
set(lgd, 'Position', [0.44 0.06 0.25 0.25]);
end
end