Stress-related hypofrontality in depression and its relation to altered activation prior to the stress response.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches

[1] § Methods › Neural measurements ↔ 2026_04_09_fNIRSpreprocessing_TSST_Anticipation.m, lines 1612–1641 · score 0.65 · 0–40 s, Event related, baseline corrected, exported, anticipatory, TSST
[2] § Methods › Data analysis ↔ Plots.R, lines 294–362 · score 0.60 · multivariate outliers, Mahalanobis distance, fNIRS, window, anticipation
[3] § Methods › Neural measurements ↔ 2026_04_09_fNIRSpreprocessing_TSST_Anticipation.m, lines 568–587 · score 0.55 · signal improvement, TDDR, bandpass, CBSI, Correlation

Paper

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The authors' code

MATLAB · 2,838 lines · 130 KB · no license · 2 matches

%% Skript zur Auswertung von TSST Anticipation
% data: TSST session of CPT-TSST study (study 1) and first TSST of ERT study (study 2)
% last edited 2026_04_09 by Isabell Int-Veen
% add paths with subfolders:
% Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Matlab Code und Workspaces\EigeneFunktionen
% Q:\NAS\Ehlis_Auswertung\Laufwerk_S\routines\2017-04-27
clear all
clc
pfade = { ...
'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Matlab Code und Workspaces\EigeneFunktionen', ...
'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\routines\2017-04-27',...
'\\nacl2svm1.ukt.ad.local\psint1i1\UserProfile\Documents\MATLAB'};
for i = 1:numel(pfade)
if isfolder(pfade{i})
addpath(genpath(pfade{i}));
fprintf('Pfad hinzugefuegt: %s\n', pfade{i});
else
warning('Pfad nicht gefunden: %s', pfade{i});
end
end
clear i pfade
initroutines('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\routines\2017-04-27')
%% read data
S = NirsSubjectData();
S = S.setProperty('read_directory','Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Preprocessing_Matlab\Daten_gesamt');
S = S.setProperty('subject_keyword','VP');
S = S.setProperty('read_type','etg4000');
% CTRL1
S = S.setProperty('read_type','etg4000');
S = S.setProperty('file_name_filter',{'VP','_ctrl1_Probe1_Oxy','.csv'});
S = S.readSubjectData('CTL1.Oxy1');
S = S.setProperty('file_name_filter',{'VP','_ctrl1_Probe1_Deoxy','.csv'});
S = S.readSubjectData('CTL1.Deoxy1');
S = S.setProperty('read_type','etg4000_trigger');
S = S.readSubjectData('CTL1.trigger');
S = S.setProperty('read_type','etg4000');
S = S.setProperty('file_name_filter',{'VP','_ctrl1_Probe2_Oxy','.csv'});
S = S.readSubjectData('CTL1.Oxy2');
S = S.setProperty('file_name_filter',{'VP','_ctrl1_Probe2_Deoxy','.csv'});
S = S.readSubjectData('CTL1.Deoxy2');
% CTRL2
S = S.setProperty('read_type','etg4000');
S = S.setProperty('file_name_filter',{'VP','_ctrl2_Probe1_Oxy','.csv'});
S = S.readSubjectData('CTL2.Oxy1');
S = S.setProperty('file_name_filter',{'VP','_ctrl2_Probe1_Deoxy','.csv'});
S = S.readSubjectData('CTL2.Deoxy1');
S = S.setProperty('read_type','etg4000_trigger');
S = S.readSubjectData('CTL2.trigger');
S = S.setProperty('read_type','etg4000');
S = S.setProperty('file_name_filter',{'VP','_ctrl2_Probe2_Oxy','.csv'});
S = S.readSubjectData('CTL2.Oxy2');
S = S.setProperty('file_name_filter',{'VP','_ctrl2_Probe2_Deoxy','.csv'});
S = S.readSubjectData('CTL2.Deoxy2');
% Arith
S = S.setProperty('read_type','etg4000');
S = S.setProperty('file_name_filter',{'VP','_arit_Probe1_Oxy','.csv'});
S = S.readSubjectData('Arith.Oxy1');
S = S.setProperty('file_name_filter',{'VP','_arit_Probe1_Deoxy','.csv'});
S = S.readSubjectData('Arith.Deoxy1');
S = S.setProperty('read_type','etg4000_trigger');
S = S.readSubjectData('Stress.trigger');
S = S.setProperty('read_type','etg4000');
S = S.setProperty('file_name_filter',{'VP','_arit_Probe2_Oxy','.csv'});
S = S.readSubjectData('Arith.Oxy2');
S = S.setProperty('file_name_filter',{'VP','_arit_Probe2_Deoxy','.csv'});
S = S.readSubjectData('Arith.Deoxy2');
% probeset configuration
% CTRL1
F = NirsDataFunctor();
F = F.setProperty('function_handle',@separateProbeset34Stress);
F = F.setProperty('input_names', {'CTL1.Oxy1','CTL1.Deoxy1'});
F = F.setProperty('output_names',{'CTL1.Oxy3','CTL1.Deoxy3';...
'CTL1.Oxy4','CTL1.Deoxy4'});
S = S.processData(F);
F = NirsDataFunctor();
F = F.setProperty('function_handle',@separateProbeset56Stress);
F = F.setProperty('input_names', {'CTL1.Oxy2','CTL1.Deoxy2'});
F = F.setProperty('output_names',{'CTL1.Oxy5','CTL1.Deoxy5';...
'CTL1.Oxy6','CTL1.Deoxy6'});
S = S.processData(F);
% CTRL2
F = NirsDataFunctor();
F = F.setProperty('function_handle',@separateProbeset34Stress);
F = F.setProperty('input_names', {'CTL2.Oxy1','CTL2.Deoxy1'});
F = F.setProperty('output_names',{'CTL2.Oxy3','CTL2.Deoxy3';...
'CTL2.Oxy4','CTL2.Deoxy4'});
S = S.processData(F);
F = NirsDataFunctor();
F = F.setProperty('function_handle',@separateProbeset56Stress);
F = F.setProperty('input_names', {'CTL2.Oxy2','CTL2.Deoxy2'});
F = F.setProperty('output_names',{'CTL2.Oxy5','CTL2.Deoxy5';...
'CTL2.Oxy6','CTL2.Deoxy6'});
S = S.processData(F);
% Arith
F = NirsDataFunctor();
F = F.setProperty('function_handle',@separateProbeset34Stress);
F = F.setProperty('input_names', {'Arith.Oxy1','Arith.Deoxy1'});
F = F.setProperty('output_names',{'Arith.Oxy3','Arith.Deoxy3';...
'Arith.Oxy4','Arith.Deoxy4'});
S = S.processData(F);
F = NirsDataFunctor();
F = F.setProperty('function_handle',@separateProbeset56Stress);
F = F.setProperty('input_names', {'Arith.Oxy2','Arith.Deoxy2'});
F = F.setProperty('output_names',{'Arith.Oxy5','Arith.Deoxy5';...
'Arith.Oxy6','Arith.Deoxy6'});
S = S.processData(F);
% merge probesets
S = S.processData(NirsDataFunctor('function_handle',@ merge, 'input_names',{'CTL1.Oxy3';'CTL1.Oxy4';'CTL1.Oxy5';'CTL1.Oxy6'},'output_names',{'CTL1.Oxy'}));
S = S.processData(NirsDataFunctor('function_handle',@ merge, 'input_names',{'CTL1.Deoxy3';'CTL1.Deoxy4';'CTL1.Deoxy5';'CTL1.Deoxy6'},'output_names',{'CTL1.Deoxy'}));
S = S.processData(NirsDataFunctor('function_handle',@ merge, 'input_names',{'CTL2.Oxy3';'CTL2.Oxy4';'CTL2.Oxy5';'CTL2.Oxy6'},'output_names',{'CTL2.Oxy'}));
S = S.processData(NirsDataFunctor('function_handle',@ merge, 'input_names',{'CTL2.Deoxy3';'CTL2.Deoxy4';'CTL2.Deoxy5';'CTL2.Deoxy6'},'output_names',{'CTL2.Deoxy'}));
S = S.processData(NirsDataFunctor('function_handle',@ merge, 'input_names',{'Arith.Oxy3';'Arith.Oxy4';'Arith.Oxy5';'Arith.Oxy6'},'output_names',{'Arith.Oxy'}));
S = S.processData(NirsDataFunctor('function_handle',@ merge, 'input_names',{'Arith.Deoxy3';'Arith.Deoxy4';'Arith.Deoxy5';'Arith.Deoxy6'},'output_names',{'Arith.Deoxy'}));
% settings
clear settings P PS;
P = NirsPlotTool();
settings.experiment.time_series{1} = {'name','CTL1.cui','sample_rate',10,'trigger_name','CTL1.trigger'};
settings.experiment.time_series{2} = {'name','CTL2.cui','sample_rate',10,'trigger_name','CTL2.trigger'};
settings.experiment.time_series{3} = {'name','Arith.cui','sample_rate',10,'trigger_name','Stress.trigger'};
settings.experiment.time_series{4} = {'name','CTL1.Oxy','sample_rate',10,'trigger_name','CTL1.trigger'};
settings.experiment.time_series{5} = {'name','CTL1.Deoxy','sample_rate',10,'trigger_name','CTL1.trigger'};
settings.experiment.time_series{6} = {'name','CTL2.Oxy','sample_rate',10,'trigger_name','CTL2.trigger'};
settings.experiment.time_series{7} = {'name','CTL2.Deoxy','sample_rate',10,'trigger_name','CTL2.trigger'};
settings.experiment.time_series{8} = {'name','Arith.Oxy','sample_rate',10,'trigger_name','Stress.trigger'};
settings.experiment.time_series{9} = {'name','Arith.Deoxy','sample_rate',10,'trigger_name','Stress.trigger'};
settings.experiment.category{1} = {'name','TriggerA','trigger_token',1}; % for analysis of window 3
% settings.experiment.category{1} = {'name','TriggerA','trigger_token',9}; % for analysis of window 1 and 2
P = P.setProperties(settings);
P = NirsPlotTool();
P = P.setProperty('show_probeset','on');
P = P.setProperties(settings);
PS = NirsProbeset('brain_coord_file','Q:\NAS\Ehlis_Auswertung\Laufwerk_S\\AG-Mitglieder\David\Studie Stress Rumination\Koords\NIRS-probesetXYZ_Alle2.txt');
settings.plot_tool.probesets{1} = {'name','Gesamt','probeset',PS};
P = P.setProperties(settings);
P = P.setProperty('probesets',{'name','Gesamt','probeset',PS});
P = P.setProperties(settings);
P = P.setProperty('show_probeset','on');
P = P.setProperties(settings);
Subject=S.tags(1);
Bedingungen={'CTL1.Oxy';'CTL2.Oxy';'Arith.Oxy'};
Bedingungen2={'CTL1cui';'CTL2cui';'Arithcui'};
%% find NaNs
clear NaNOut
for x=1:size(Bedingungen,1)
clear dCui DataRS DataVar Out
Subject=S.tags(1);
for k=1:length(Subject)
dCui = S.getSubjectData(Subject{k},Bedingungen{x});
DataRS{k,1}(:,:)=dCui(:,:);
end
clear dCui
for k=1:length(Subject)
for i=1:size(DataRS{k,1}(:,:),2)
DataVar(k,i) = var(DataRS{k,1}(:,i));
end
end
for k=1:size(DataVar,1)
for i=1:size(DataVar,2)
if isnan(DataVar(k,i))==1
Out(k,i)=1;
else
Out(k,i)=0;
end
end
end
NaNOut.(Bedingungen2{x}).percent=mean(Out,2)*100;
for k=1:size(Out,1)
a=Out(k,:)'
NaNOut.(Bedingungen2{x}).Channel{k,1}(:)=find(a);
sid=Subject{k};
NaNOut.(Bedingungen2{x}).Channel{k,2}(:)=sid;
clear a sid
end
end
%% interpolate NaN
% template:
% F = NirsDataFunctor(); % clears output
% F = F.setProperty('parameters',{*Channel*,{[* * * *]}});S = S.processData(F,*ID*);
% ctrl1
F = NirsDataFunctor(); % clears output
F = F.setProperty('function_handle',@NAfilt.interpolateChannel);
F = F.setProperty('input_names',{'CTL1.Oxy','CTL1.Deoxy'});
F = F.setProperty('parameters',{8,{[3 6 9]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{11,{[6 9 12]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{30,{[25 26 35]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{31,{[26 27 35]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{32,{[27 28 37]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{34,{[29 38 39]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{36,{[35 37 40 41]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{25,{[26 29]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{30,{[26 34 35]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{29,{[25 34 38]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{37,{[32 33 41 42]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{45,{[40 41 44 46]}});S = S.processData(F,1018);
F = F.setProperty('parameters',{1,{[2 3 4]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{10,{[5 7 9]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{12,{[7 9 11]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{44,{[39 40 43 45]}});S = S.processData(F,1034);
F = F.setProperty('parameters',{29,{[25 34 38]}});S = S.processData(F,1046);
F = F.setProperty('parameters',{26,{[25 27 35]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{30,{[25 34 35]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{31,{[27 35 36]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{32,{[27 28 36]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{37,{[33 41 42]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1054);
F = F.setProperty('parameters',{30,{[25 26 29 31 35 34]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{2,{[4 1]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{5,{[4 10]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{7,{[4 6 9 10]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{22,{[17 19 24 21]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{32,{[27 28 31 33 36]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{37,{[33 36 42 41]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1111);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1112);
F = F.setProperty('parameters',{4,{[2 5 7 1 6 3]}});S = S.processData(F,1117);
F = F.setProperty('parameters',{4,{[2 5 7 1 6 3]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{40,{[35 36 39 41 44 45]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{11,{[6 8 9]}});S = S.processData(F,1121);
F = F.setProperty('parameters',{12,{[10 7 9]}});S = S.processData(F,1121);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{41,{[36 37 40 42 45 46]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{15,{[13 16]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{18,{[13 16 21]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{20,{[24 21 13]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{23,{[21 24]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{28,{[27 33 37]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{32,{[27 31 33 36 37]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{15,{[13 16]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{18,{[13 16 21]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{20,{[24 21 13]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{23,{[21 24]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{36,{[27 31 32 40 41 45]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{18,{[20 15 23 13 21 16]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{22,{[24 21 19]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{8,{[3 6 9]}});S = S.processData(F,2017);
F = F.setProperty('parameters',{11,{[9 12]}});S = S.processData(F,2017);
F = F.setProperty('parameters',{30,{[35 34 25 26]}});S = S.processData(F,2024);
F = F.setProperty('parameters',{29,{[25 30 34 38]}});S = S.processData(F,2030);
F = F.setProperty('parameters',{32,{[27 28 31 33 36 37]}});S = S.processData(F,2021);
F = F.setProperty('parameters',{31,{[26 27 30 32]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{36,{[32 37 41 40]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{45,{[40 41 46]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{29,{[38 34 25]}});S = S.processData(F,2032);
F = F.setProperty('parameters',{21,{[18 16 19]}});S = S.processData(F,2033);
F = F.setProperty('parameters',{23,{[20 18]}});S = S.processData(F,2033);
F = F.setProperty('parameters',{24,{[22 19]}});S = S.processData(F,2033);
F = F.setProperty('parameters',{29,{[38 34 ]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{31,{[30 26 27 32]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{35,{[30 34 39]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{36,{[27 32 41 45]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{40,{[39 44 45 41]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{42,{[46 41 37 33]}});S = S.processData(F,2034);
% ctrl2
F = NirsDataFunctor(); % clears output
F = F.setProperty('function_handle',@NAfilt.interpolateChannel);
F = F.setProperty('input_names',{'CTL2.Oxy','CTL2.Deoxy'});
F = F.setProperty('parameters',{8,{[3 6 9]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{11,{[6 9 12]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{30,{[25 26 35]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{34,{[29 38 39]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{25,{[26 29]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{30,{[26 34 35]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{33,{[28 37 42]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{45,{[40 41 44 46]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{29,{[25 34 38]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{37,{[32 33 41 42]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{45,{[40 41 44 46]}});S = S.processData(F,1018);
F = F.setProperty('parameters',{28,{[27 32 37]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{33,{[32 37 42]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{46,{[41 42 45]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{10,{[5 7 9]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{12,{[7 9 11]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{44,{[39 40 43 45]}});S = S.processData(F,1034);
F = F.setProperty('parameters',{39,{[34 35 44]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{43,{[34 38 44]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{26,{[25 27 35]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{30,{[25 34 35]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{31,{[27 35 36]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{32,{[27 28 36]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{37,{[33 41 42]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1054);
F = F.setProperty('parameters',{30,{[25 26 29 31 34]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{35,{[26 31 40 44]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{39,{[38 34 35 40 43 44]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{45,{[40 36 41]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{2,{[4 7]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{5,{[4 7]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{3,{[1 2 6 7]}});S = S.processData(F,1108);
F = F.setProperty('parameters',{22,{[24 21 19]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{29,{[25 34]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{30,{[25 26 31 34 35]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{32,{[27 28 31 36]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{33,{[28 42]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{37,{[36 41 42]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{46,{[41 42 45]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1111);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1112);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{40,{[35 36 39 41 44 45]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{11,{[6 8 9 12]}});S = S.processData(F,1121);
F = F.setProperty('parameters',{6,{[4 9 1 11 3 8]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{41,{[36 37 40 42 45 46]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{28,{[27 33 37]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{32,{[27 31 33 36 37]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{36,{[27 31 32 40 41 45]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{32,{[27 28 31 33 37]}});S = S.processData(F,1140);
F = F.setProperty('parameters',{36,{[27 31 41 45]}});S = S.processData(F,1140);
F = F.setProperty('parameters',{40,{[35 39 41 44 45]}});S = S.processData(F,1140);
F = F.setProperty('parameters',{15,{[13 16]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{18,{[13 16 21]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{20,{[24 21 13]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{22,{[21 24 19]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{23,{[21 24]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{38,{[34 39 43]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{13,{[15 18]}});S = S.processData(F,2011);
F = F.setProperty('parameters',{14,{[17 19]}});S = S.processData(F,2011);
F = F.setProperty('parameters',{16,{[18 19 ]}});S = S.processData(F,2011);
F = F.setProperty('parameters',{37,{[36 41 32 33 42]}});S = S.processData(F,2013);
F = F.setProperty('parameters',{8,{[3 6 9]}});S = S.processData(F,2017);
F = F.setProperty('parameters',{11,{[9 12]}});S = S.processData(F,2017);
F = F.setProperty('parameters',{32,{[27 28 31 33 36 37]}});S = S.processData(F,2021);
F = F.setProperty('parameters',{30,{[35 34 25 26]}});S = S.processData(F,2024);
F = F.setProperty('parameters',{29,{[25 30 34 38]}});S = S.processData(F,2030);
F = F.setProperty('parameters',{32,{[27 28 31 33 36 37]}});S = S.processData(F,2021);
F = F.setProperty('parameters',{4,{[1 3 2 5]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{6,{[1 3 8 11]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{7,{[2 5 10 12]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{9,{[8 11 12 10]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{31,{[26 27 30 32]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{39,{[38 34 35 40]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{43,{[38 34]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{44,{[35 40]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{45,{[40 41 46]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{29,{[38 34 25]}});S = S.processData(F,2032);
F = F.setProperty('parameters',{21,{[18 16 19]}});S = S.processData(F,2033);
F = F.setProperty('parameters',{23,{[20 18]}});S = S.processData(F,2033);
F = F.setProperty('parameters',{24,{[22 19]}});S = S.processData(F,2033);
F = F.setProperty('parameters',{29,{[38 34 ]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{31,{[30 26 27 32]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{35,{[30 34 39]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{36,{[27 32 41 45]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{40,{[39 44 45 41]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{42,{[46 41 37 33]}});S = S.processData(F,2034);
% arith
F = NirsDataFunctor(); % clears output
F = F.setProperty('function_handle',@NAfilt.interpolateChannel);
F = F.setProperty('input_names',{'Arith.Oxy','Arith.Deoxy'});
F = F.setProperty('parameters',{30,{[25 26 35]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{34,{[29 38 39]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{39,{[34 35 38 40]}});S = S.processData(F,1013);
F = F.setProperty('parameters',{43,{[34 38]}});S = S.processData(F,1013);
F = F.setProperty('parameters',{44,{[35 40 45]}});S = S.processData(F,1013);
F = F.setProperty('parameters',{34,{[29 30 39]}});S = S.processData(F,1014);
F = F.setProperty('parameters',{37,{[32 33 41 42]}});S = S.processData(F,1014);
F = F.setProperty('parameters',{38,{[29 43]}});S = S.processData(F,1014);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{29,{[25 34 38]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{45,{[40 41 44 46]}});S = S.processData(F,1018);
F = F.setProperty('parameters',{1,{[2 3 4]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{12,{[9 10 11]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{44,{[39 40 43 45]}});S = S.processData(F,1034);
F = F.setProperty('parameters',{26,{[25 27 35]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{30,{[25 34 35]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{31,{[27 35 36]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1054);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{2,{[1 4]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{5,{[4 10]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{7,{[4 9 10 12]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{22,{[19 21 24]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{32,{[27 28 31 33 36]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{37,{[33 36 41 42 46]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{39,{[34 35 38 40 43 44]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{40,{[35 36 39 41 44 45]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{11,{[6 8 9]}});S = S.processData(F,1121);
F = F.setProperty('parameters',{12,{[7 9 10]}});S = S.processData(F,1121);
F = F.setProperty('parameters',{4,{[1 2 3 5 7]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{6,{[1 3 8 9 11]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{32,{[27 28 31 33]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{36,{[31 40 45]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{37,{[33 42 46]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{41,{[40 42 45 46]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{28,{[27 33 37]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{32,{[27 31 33 36 37]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{15,{[13 16]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{18,{[13 16 21 23]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{20,{[21 23]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{26,{[25 30 35]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{27,{[28 36]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{31,{[30 35 36]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{32,{[28 33 36]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{37,{[33 36 42 46]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{41,{[36 40 42 45 46]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{20,{[15 18 21 23]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{32,{[27 28 31 33 36 37]}});S = S.processData(F,1140);
F = F.setProperty('parameters',{29,{[25 30 34 38]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{4,{[2 1]}});S = S.processData(F,2002);
F = F.setProperty('parameters',{6,{[3 8]}});S = S.processData(F,2002);
F = F.setProperty('parameters',{7,{[2 5 10 12]}});S = S.processData(F,2002);
F = F.setProperty('parameters',{9,{[8 11 12 10]}});S = S.processData(F,2002);
F = F.setProperty('parameters',{14,{[17 16 13]}});S = S.processData(F,2011);
F = F.setProperty('parameters',{8,{[3 6 9]}});S = S.processData(F,2017);
F = F.setProperty('parameters',{11,{[9 12]}});S = S.processData(F,2017);
F = F.setProperty('parameters',{25,{[30 26]}});S = S.processData(F,2017);
F = F.setProperty('parameters',{29,{[38 34 30]}});S = S.processData(F,2017);
F = F.setProperty('parameters',{30,{[35 34 25 26]}});S = S.processData(F,2024);
F = F.setProperty('parameters',{20,{[21 18 15 23]}});S = S.processData(F,2026);
F = F.setProperty('parameters',{29,{[25 30 34 38]}});S = S.processData(F,2030);
F = F.setProperty('parameters',{4,{[1 6 2 7]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{5,{[2 7 4 10]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{45,{[44 40 41 46]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{29,{[38 34 25]}});S = S.processData(F,2032);
F = F.setProperty('parameters',{25,{[26 30]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{29,{[38 34 ]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{31,{[30 26 27 32]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{35,{[30 34 39]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{36,{[27 32 41 45]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{40,{[39 44 45 41]}});S = S.processData(F,2034);
F = F.setProperty('parameters',{42,{[46 41 37 33]}});S = S.processData(F,2034);
%% corrections
%T DDR
S = S.processData(NirsDataFunctor('function_handle',@(X) TDDR(X,10),'input_names',{'CTL1.Oxy','CTL1.Deoxy','CTL2.Oxy','CTL2.Deoxy','Arith.Oxy','Arith.Deoxy'}));
% Bandpass
F = NirsDataFunctor('function_handle',@bandpass,...
'parameters',{[0.01 0.1],10,'new'},...
'input_names', {'CTL1.Oxy','CTL1.Deoxy','CTL2.Oxy','CTL2.Deoxy','Arith.Oxy','Arith.Deoxy'});
S = S.processData(F);
% Correlation Based Signal Improvement (CBSI)
F = NirsDataFunctor();
F = F.setProperty('function_handle',@NAfilt.correlationBasedSignalImprovement);
F = F.setProperty('input_names', {'CTL1.Oxy','CTL2.Oxy','Arith.Oxy';...
'CTL1.Deoxy','CTL2.Deoxy','Arith.Deoxy'});
F = F.setProperty('output_names',{'CTL1.cui','CTL2.cui','Arith.cui'});
S = S.processData(F);
%% OXY conditions
Subject=S.tags(1)
Bedingungen={'CTL1.cui';'CTL2.cui';'Arith.cui'}
Bedingungen2={'CTL1cui';'CTL2cui';'Arithcui'}
%% DEOXY conditions
Subject=S.tags(1)
Bedingungen={'CTL1.Deoxy';'CTL2.Deoxy';'Arith.Deoxy'}
Bedingungen2={'CTL1deoxy';'CTL2deoxy';'Arithdeoxy'}
%% find NaNs (in case they are created by CBSI)
clear NaNOut
for x=1:size(Bedingungen,1)
clear dCui DataRS DataVar Out
Subject=S.tags(1);
for k=1:length(Subject)
dCui = S.getSubjectData(Subject{k},Bedingungen{x});
DataRS{k,1}(:,:)=dCui(:,:);
end
clear dCui
for k=1:length(Subject)
for i=1:size(DataRS{k,1}(:,:),2)
DataVar(k,i) = var(DataRS{k,1}(:,i));
end
end
for k=1:size(DataVar,1)
for i=1:size(DataVar,2)
if isnan(DataVar(k,i))==1
Out(k,i)=1;
else
Out(k,i)=0;
end
end
end
NaNOut.(Bedingungen2{x}).percent=mean(Out,2)*100;
for k=1:size(Out,1)
a=Out(k,:)'
NaNOut.(Bedingungen2{x}).Channel{k,1}(:)=find(a);
sid=Subject{k};
NaNOut.(Bedingungen2{x}).Channel{k,2}(:)=sid;
clear a sid
end
end
%% NaN Interpolieren (nochmal)
% hier weichen die VPN ab von ERT Skript
% Vorlage:
% F = NirsDataFunctor(); % macht den output wieder leer
% F = F.setProperty('parameters',{*Channel*,{[* * * *]}});S = S.processData(F,*ID*);
% control task 1
F = NirsDataFunctor(); % macht den output wieder leer
F = F.setProperty('function_handle',@NAfilt.interpolateChannel);
F = F.setProperty('input_names',{'CTL1.cui','CTL1.Deoxy'});
F = F.setProperty('parameters',{11,{[8 9 12]}});S = S.processData(F,1050);
F = F.setProperty('parameters',{1,{[3 4 6]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{20,{[18 15 ]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{23,{[24 21]}});S = S.processData(F,2029);
% control task 2
F = NirsDataFunctor(); % macht den output wieder leer
F = F.setProperty('function_handle',@NAfilt.interpolateChannel);
F = F.setProperty('input_names',{'CTL2.cui', 'CTL2.Deoxy'});
F = F.setProperty('parameters',{28,{[27 32 37]}});S = S.processData(F,1009);
F = F.setProperty('parameters',{33,{[32 37 42]}});S = S.processData(F,1009);
F = F.setProperty('parameters',{11,{[8 9 12]}});S = S.processData(F,1050);
F = F.setProperty('parameters',{3,{[1 4 6]}});S = S.processData(F,1108);
F = F.setProperty('parameters',{32,{[27 28 31 33 36 37]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{1,{[2 3 6]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{4,{[2 3 6 7]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{16,{[13 14 15 17 18 19]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{23,{[18 20 21]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{14,{[13 16 19]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{17,{[16 19 22]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{21,{[18 19]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{23,{[24 20]}});S = S.processData(F,2029);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,2053);
% arith
F = NirsDataFunctor(); % macht den output wieder leer
F = F.setProperty('function_handle',@NAfilt.interpolateChannel);
F = F.setProperty('input_names',{'Arith.cui', 'Arith.Deoxy'});
F = F.setProperty('parameters',{11,{[8 9 12]}});S = S.processData(F,1050);
F = F.setProperty('parameters',{1,{[2 4 6]}});S = S.processData(F,1108);
F = F.setProperty('parameters',{3,{[4 6 8]}});S = S.processData(F,1108);
F = F.setProperty('parameters',{14,{[16 17 19]}});S = S.processData(F,1112);
F = F.setProperty('parameters',{1,{[2 3 6]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{4,{[2 3 6 7]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{30,{[25 26 29 31 34]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{35,{[31 39 40]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{16,{[13 14 18 19]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{13,{[14 15]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{15,{[13 16 18]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{20,{[18 21 23]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,2001);
%% define ROIs
lIFG=[7 9 6]
lDLPFC=[10 12 11]
rIFG=[18 21 19]
rDLPFC=[20 23 24]
SAC=[27 26 25 28 30 31 32 35 36]
close all
%% Plot ROI channels for visual inspection for each subjects: control task 1
close all
s = Subject{2};
P = P.plotChannels(S,s,[rIFG,lIFG,rDLPFC,lDLPFC,SAC],{'CTL1.Deoxy'},{'b'},{'-'});
P = P.showTrigger(S,s,'CTL1.Deoxy',4);
%% Plot ROI channels for visual inspection for each subjects: control task 2
close all
s = Subject{97};
P = P.plotChannels(S,s,[rIFG,lIFG,rDLPFC,lDLPFC,SAC],{'CTL2.cui'},{'b'},{'-'});
P = P.showTrigger(S,s,'CTL2.cui',4);
%% Plot ROI channels for visual inspection for each subjects: artihmetic task of the TSST
close all
s = Subject{97};
P = P.plotChannels(S,s,[rIFG,lIFG,rDLPFC,lDLPFC,SAC],{'Arith.cui'},{'b'},{'-'});
P = P.showTrigger(S,s,'Arith.cui',4);
%% show Brodman areas
% showBrodmann([5 7 9 19 44 45 46 47],'show_legend',true,'color_map',lines,'channel_style','ids', 'probeset',[PS]);
%% create new triggers 9 for window 2 and window 1 export
cd('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Matlab Code und Workspaces\EigeneFunktionen')
fs = 10; % sampling frequency in Hz
marker = 1; % trigger before new trigger should be created
newTrigger = 9; % new trigger
for i = 1:size(Subject, 1)
trigger_CTL1 = S.getSubjectData(Subject{i}, 'CTL1.trigger');
trigger_CTL2 = S.getSubjectData(Subject{i}, 'CTL2.trigger');
trigger_Stress = S.getSubjectData(Subject{i}, 'Stress.trigger');
trigger_CTL1 = NewTrigger_Isa(trigger_CTL1, marker, newTrigger, fs);
trigger_CTL2 = NewTrigger_Isa(trigger_CTL2, marker, newTrigger, fs);
trigger_Stress = NewTrigger_Isa(trigger_Stress, marker, newTrigger, fs);
S = S.addSubjectData(Subject{i}, 'CTL1.trigger', trigger_CTL1);
S = S.addSubjectData(Subject{i}, 'CTL2.trigger', trigger_CTL2);
S = S.addSubjectData(Subject{i}, 'Stress.trigger', trigger_Stress);
fprintf('Subject %d: New trigger created for CTL1: %d, CTL2: %d, Stress: %d\n', ...
Subject{i}, sum(trigger_CTL1==newTrigger), sum(trigger_CTL2==newTrigger), sum(trigger_Stress==newTrigger));
end
%% check how many triggers there are for each subject and condition
tasks = {'CTL1','CTL2','Stress'};
for t = 1:numel(tasks)
taskName = tasks{t};
trigger_new = S.getSubjectData(Subject{1}, [taskName '.trigger']);
uniqueTriggers = unique(trigger_new(trigger_new~=0));
fprintf('%s neue Triggerwerte: ', taskName);
fprintf('%d ', uniqueTriggers);
fprintf('\n');
end
%% check whether new trigger 9 is actrually 15 s before trigger 1
fs = 10; % sampling frequency in Hz
for i = 1:size(Subject,1)
tasks = {'CTL1','CTL2','Stress'};
for t = 1:numel(tasks)
test = S.getSubjectData(Subject{i}, [tasks{t} '.trigger']);
trigger_idx = find(test);
TriggerDaten.trigger{t,i} = test(trigger_idx);
TriggerDaten.AnzahlTrigger{t,i} = numel(trigger_idx); % number of triggers
TriggerDaten.Zeitpunkt{t,i} = trigger_idx / fs; % time point in seconds
end
end
%% delete all trigger "4" = endtrigger of the trials
cd('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Matlab Code und Workspaces\EigeneFunktionen')
deleteTrigger = 4; % trigger to be deleted
for i = 1:size(Subject, 1)
trigger_CTL1 = S.getSubjectData(Subject{i}, 'CTL1.trigger');
trigger_CTL2 = S.getSubjectData(Subject{i}, 'CTL2.trigger');
trigger_Stress = S.getSubjectData(Subject{i}, 'Stress.trigger');
trigger_CTL1(trigger_CTL1 == deleteTrigger) = 0;
trigger_CTL2(trigger_CTL2 == deleteTrigger) = 0;
trigger_Stress(trigger_Stress == deleteTrigger) = 0;
S = S.addSubjectData(Subject{i}, 'CTL1.trigger', trigger_CTL1);
S = S.addSubjectData(Subject{i}, 'CTL2.trigger', trigger_CTL2);
S = S.addSubjectData(Subject{i}, 'Stress.trigger', trigger_Stress);
fprintf('Subject %d: Number of trigger 4 → CTL1: %d, CTL2: %d, Stress: %d\n', ...
Subject{i}, ...
sum(trigger_CTL1 == deleteTrigger), ...
sum(trigger_CTL2 == deleteTrigger), ...
sum(trigger_Stress == deleteTrigger));
end
%% delete all trigger "1" = trigger indicating the beginning of each trial
cd('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Matlab Code und Workspaces\EigeneFunktionen')
deleteTrigger = 1; % trigger to be deleted
for i = 1:size(Subject, 1)
trigger_CTL1 = S.getSubjectData(Subject{i}, 'CTL1.trigger');
trigger_CTL2 = S.getSubjectData(Subject{i}, 'CTL2.trigger');
trigger_Stress = S.getSubjectData(Subject{i}, 'Stress.trigger');
trigger_CTL1(trigger_CTL1 == deleteTrigger) = 0;
trigger_CTL2(trigger_CTL2 == deleteTrigger) = 0;
trigger_Stress(trigger_Stress == deleteTrigger) = 0;
S = S.addSubjectData(Subject{i}, 'CTL1.trigger', trigger_CTL1);
S = S.addSubjectData(Subject{i}, 'CTL2.trigger', trigger_CTL2);
S = S.addSubjectData(Subject{i}, 'Stress.trigger', trigger_Stress);
fprintf('Subject %d: Number of trigger 1 → CTL1: %d, CTL2: %d, Stress: %d\n', ...
Subject{i}, ...
sum(trigger_CTL1 == deleteTrigger), ...
sum(trigger_CTL2 == deleteTrigger), ...
sum(trigger_Stress == deleteTrigger));
end
%% interpolation of single channels identified by visual inspection
% ctrl1
F = NirsDataFunctor(); % clear output
F = F.setProperty('function_handle',@NAfilt.interpolateChannel);
F = F.setProperty('input_names',{'CTL1.cui', 'CTL1.Deoxy'});
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1002);
F = F.setProperty('parameters',{21,{[18 19 23 24]}});S = S.processData(F,1002);
F = F.setProperty('parameters',{6,{[3 4 9]}});S = S.processData(F,1003);
F = F.setProperty('parameters',{8,{[3 11]}});S = S.processData(F,1003);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1005);
F = F.setProperty('parameters',{10,{[5 7 12]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1007);
F = F.setProperty('parameters',{26,{[25 30 31]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{27,{[28 31 32]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1011);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1013);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1014);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1014);
F = F.setProperty('parameters',{26,{[25 27 30 31]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1017);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1017);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1017);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1018);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1019);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1019);
F = F.setProperty('parameters',{9,{[6 7 11]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{12,{[10 11]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{28,{[27 32 33]}});S = S.processData(F,1021);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1021);
F = F.setProperty('parameters',{11,{[8 9 12]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{24,{[21 22 23]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{28,{[27 33 37]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{32,{[27 36 37]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{21,{[18 19 23 24]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{27,{[26 28 31]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{32,{[28 36 37]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{32,{[27 28 36 32]}});S = S.processData(F,1026);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{32,{[27 28 37]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{36,{[35 37 40 41]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1030);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1030);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1030);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1030);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1032);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1033);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1034);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1034);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1035);
F = F.setProperty('parameters',{11,{[8 9 12]}});S = S.processData(F,1037);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1038);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1039);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1039);
F = F.setProperty('parameters',{18,{[15 16 21]}});S = S.processData(F,1041);
F = F.setProperty('parameters',{18,{[15 16 21]}});S = S.processData(F,1042);
F = F.setProperty('parameters',{20,{[15 21 23]}});S = S.processData(F,1042);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1042);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1043);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1043);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1043);
F = F.setProperty('parameters',{6,{[3 4 8]}});S = S.processData(F,1044);
F = F.setProperty('parameters',{7,{[4 5 10]}});S = S.processData(F,1044);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1044);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1046);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1046);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{28,{[27 32 33]}});S = S.processData(F,1050);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1052);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1052);
F = F.setProperty('parameters',{36,{[31 32 40 41]}});S = S.processData(F,1053);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1056);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1056);
F = F.setProperty('parameters',{20,{[15 18 21 23]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{35,{[30 31 39 40]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1103);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1103);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1103);
F = F.setProperty('parameters',{18,{[15 16 21]}});S = S.processData(F,1107);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1107);
F = F.setProperty('parameters',{20,{[15 21]}});S = S.processData(F,1107);
F = F.setProperty('parameters',{23,{[21 24]}});S = S.processData(F,1107);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{24,{[21 22 23]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1112);
F = F.setProperty('parameters',{10,{[5 7 9 12]}});S = S.processData(F,1113);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1113);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1113);
F = F.setProperty('parameters',{23,{[18 20 21 24]}});S = S.processData(F,1114);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1115);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1115);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1116);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1116);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1117);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1117);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{28,{[27 32 33 37]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{36,{[31 32 40 41]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{18,{[15 16 21 23]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{20,{[15 21 23]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{24,{[19 21 22 23]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1125);
F = F.setProperty('parameters',{28,{[27 32 33 37]}});S = S.processData(F,1127);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1128);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1129);
F = F.setProperty('parameters',{28,{[27 33 37]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{32,{[27 31 33 36 37]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1132);
F = F.setProperty('parameters',{21,{[18 19 23 24]}});S = S.processData(F,1133);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1133);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1136);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1136);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1136);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1136);
F = F.setProperty('parameters',{6,{[1 3 4 8 11]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{7,{[2 4 5 10 12]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{9,{[8 10 11 12]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{24,{[19 21 22 23]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1138);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1139);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1139);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1140);
F = F.setProperty('parameters',{32,{[27 28 31 33 36 37]}});S = S.processData(F,1140);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1142);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1142);
F = F.setProperty('parameters',{20,{[15 18 21 23]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{23,{[19 20 21 24]}});S = S.processData(F,1143);
% ctrl2
F = NirsDataFunctor(); % clear output
F = F.setProperty('function_handle',@NAfilt.interpolateChannel);
F = F.setProperty('input_names',{'CTL2.cui', 'CTL2.Deoxy'});
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1001);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1001);
F = F.setProperty('parameters',{23,{[20 21 24]}});S = S.processData(F,1002);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1002);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1005);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1007);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{26,{[25 30 31]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{27,{[28 31 32]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{36,{[31 32 40 41]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{20,{[15 18 21]}});S = S.processData(F,1009);
F = F.setProperty('parameters',{23,{[18 21 24]}});S = S.processData(F,1009);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1011);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1013);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1013);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1014);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{25,{[29 30 34]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{26,{[27 30]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{31,{[27 35 36]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1017);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1018);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1018);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1019);
F = F.setProperty('parameters',{9,{[6 7 11]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1021);
F = F.setProperty('parameters',{28,{[27 32 33]}});S = S.processData(F,1021);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1021);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1021);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1023);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1023);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{11,{[8 9 12]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{24,{[21 22 23]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{21,{[18 19 24]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{23,{[18 20 24]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{28,{[27 32 33]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{27,{[26 28 31 32]}});S = S.processData(F,1028);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{31,{[26 27 35]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{32,{[27 28 37]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{36,{[35 37 40 41]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1031);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1032);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1033);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1034);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1034);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1035);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1035);
F = F.setProperty('parameters',{31,{[26 27 40]}});S = S.processData(F,1036);
F = F.setProperty('parameters',{35,{[30 39 40]}});S = S.processData(F,1036);
F = F.setProperty('parameters',{36,{[32 40 41]}});S = S.processData(F,1036);
F = F.setProperty('parameters',{10,{[5 7 12]}});S = S.processData(F,1037);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1037);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1037);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1039);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1039);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1041);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1042);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1042);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1042);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1043);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1043);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1044);
F = F.setProperty('parameters',{28,{[27 32 33]}});S = S.processData(F,1044);
F = F.setProperty('parameters',{26,{[25 27 30]}});S = S.processData(F,1046);
F = F.setProperty('parameters',{31,{[27 35 36]}});S = S.processData(F,1046);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1047);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1047);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1049);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1049);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1049);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1050);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1052);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1052);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{25,{[26 29 30]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{18,{[15 16 20]}});S = S.processData(F,1056);
F = F.setProperty('parameters',{19,{[16 17 22]}});S = S.processData(F,1056);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1101);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1103);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1107);
F = F.setProperty('parameters',{28,{[27 32 33 37]}});S = S.processData(F,1107);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{24,{[19 21 22 23]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1111);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1111);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1113);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1113);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1117);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1117);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1118);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1119);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1119);
F = F.setProperty('parameters',{35,{[30 31 39 40]}});S = S.processData(F,1119);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{35,{[30 31 39 40]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{12,{[7 9 10 11]}});S = S.processData(F,1121);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{10,{[5 7 9 12]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1125);
F = F.setProperty('parameters',{28,{[27 32 33 37]}});S = S.processData(F,1127);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1128);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1129);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1129);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{11,{[6 8 9 12]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1132);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1133);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1136);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1136);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1136);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1136);
F = F.setProperty('parameters',{7,{[2 4 5 12]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{9,{[6 8 11 12]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{10,{[5 12]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{20,{[15 18 21 23]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1138);
F = F.setProperty('parameters',{25,{[26 29 30 34]}});S = S.processData(F,1139);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1140);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1140);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{32,{[13 15 16 20 21 23]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1142);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1142);
F = F.setProperty('parameters',{6,{[1 3 4 8 11]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{7,{[2 4 5 12]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{9,{[6 8 11 12]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{10,{[5 12]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1143);
% arith
F = NirsDataFunctor(); % clear output
F = F.setProperty('function_handle',@NAfilt.interpolateChannel);
F = F.setProperty('input_names',{'Arith.cui', 'Arith.Deoxy'});
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1001);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1001);
F = F.setProperty('parameters',{23,{[20 21 24]}});S = S.processData(F,1002);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1004);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1004);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1005);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1006);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1007);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1007);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{36,{[31 32 40 41]}});S = S.processData(F,1008);
F = F.setProperty('parameters',{10,{[5 7 12]}});S = S.processData(F,1010);
F = F.setProperty('parameters',{18,{[15 16 20]}});S = S.processData(F,1011);
F = F.setProperty('parameters',{21,{[19 23 24]}});S = S.processData(F,1011);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1012);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1013);
F = F.setProperty('parameters',{36,{[31 32 40 41]}});S = S.processData(F,1013);
F = F.setProperty('parameters',{10,{[5 7 9]}});S = S.processData(F,1014);
F = F.setProperty('parameters',{12,{[7 9 11]}});S = S.processData(F,1014);
F = F.setProperty('parameters',{25,{[29 30 34]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{26,{[27 30 31]}});S = S.processData(F,1015);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1016);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1017);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1017);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1017);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1018);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1019);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1019);
F = F.setProperty('parameters',{9,{[6 7 11 12]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1020);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1021);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1022);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1023);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1023);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1023);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{24,{[21 22 23]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{28,{[27 32 33]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1024);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1025);
F = F.setProperty('parameters',{27,{[26 28 31 32]}});S = S.processData(F,1028);
F = F.setProperty('parameters',{32,{[27 28 37]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{36,{[35 37 40 41]}});S = S.processData(F,1029);
F = F.setProperty('parameters',{18,{[15 16 20]}});S = S.processData(F,1030);
F = F.setProperty('parameters',{19,{[16 17 22]}});S = S.processData(F,1030);
F = F.setProperty('parameters',{21,{[20 22 23 24]}});S = S.processData(F,1030);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1032);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1033);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1033);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1034);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1034);
F = F.setProperty('parameters',{10,{[5 7 12]}});S = S.processData(F,1037);
F = F.setProperty('parameters',{18,{[15 16 21]}});S = S.processData(F,1037);
F = F.setProperty('parameters',{20,{[15 21]}});S = S.processData(F,1037);
F = F.setProperty('parameters',{23,{[21 24]}});S = S.processData(F,1037);
F = F.setProperty('parameters',{10,{[5 7 12]}});S = S.processData(F,1038);
F = F.setProperty('parameters',{20,{[15 18 23]}});S = S.processData(F,1038);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1039);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1041);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1041);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1042);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1042);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1042);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1043);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1043);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1044);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1044);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1044);
F = F.setProperty('parameters',{32,{[27 28 36 37]}});S = S.processData(F,1044);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1046);
F = F.setProperty('parameters',{31,{[26 27 35 36]}});S = S.processData(F,1046);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{19,{[16 17 21 22]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{30,{[25 26 34 35]}});S = S.processData(F,1048);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1051);
F = F.setProperty('parameters',{6,{[3 4 8 9]}});S = S.processData(F,1052);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1052);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1055);
F = F.setProperty('parameters',{18,{[15 16 20]}});S = S.processData(F,1056);
F = F.setProperty('parameters',{19,{[16 17 22]}});S = S.processData(F,1056);
F = F.setProperty('parameters',{21,{[20 22 23 24]}});S = S.processData(F,1056);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1101);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1101);
F = F.setProperty('parameters',{7,{[2 4 5 9 12]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{10,{[5 9 12]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{21,{[18 19 20 22 23 24]}});S = S.processData(F,1102);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1103);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1103);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1104);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1106);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1106);
F = F.setProperty('parameters',{20,{[15 18 21 23]}});S = S.processData(F,1107);
F = F.setProperty('parameters',{30,{[25 26 29 35 36]}});S = S.processData(F,1107);
F = F.setProperty('parameters',{31,{[26 27 32 35 36]}});S = S.processData(F,1107);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1108);
F = F.setProperty('parameters',{18,{[15 16 20 21]}});S = S.processData(F,1109);
F = F.setProperty('parameters',{30,{[25 26 29 35 36]}});S = S.processData(F,1111);
F = F.setProperty('parameters',{31,{[26 27 32 35 36]}});S = S.processData(F,1111);
F = F.setProperty('parameters',{30,{[25 26 29 35 36]}});S = S.processData(F,1112);
F = F.setProperty('parameters',{31,{[26 27 32 35 36]}});S = S.processData(F,1112);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1113);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1113);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1114);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1116);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1119);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1119);
F = F.setProperty('parameters',{32,{[27 28 31 33 36 37]}});S = S.processData(F,1119);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1120);
F = F.setProperty('parameters',{9,{[6 7 8 10 11 12]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{26,{[25 27 30 31]}});S = S.processData(F,1122);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1123);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1124);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1125);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1128);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1130);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{36,{[31 32 35 37 40 41]}});S = S.processData(F,1131);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1132);
F = F.setProperty('parameters',{31,{[26 27 30 32 35 36]}});S = S.processData(F,1132);
F = F.setProperty('parameters',{18,{[13 15 16 20 23]}});S = S.processData(F,1133);
F = F.setProperty('parameters',{19,{[14 16 17 22 24]}});S = S.processData(F,1133);
F = F.setProperty('parameters',{21,{[20 22 23 24]}});S = S.processData(F,1133);
F = F.setProperty('parameters',{30,{[25 26 29 31 34 35]}});S = S.processData(F,1133);
F = F.setProperty('parameters',{18,{[13 15 16 20 23]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{31,{[26 27 30 32 35]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{36,{[32 35 37 40 41]}});S = S.processData(F,1135);
F = F.setProperty('parameters',{7,{[4 5 9 10]}});S = S.processData(F,1136);
F = F.setProperty('parameters',{24,{[19 21 22 23]}});S = S.processData(F,1137);
F = F.setProperty('parameters',{18,{[13 15 16 20 23]}});S = S.processData(F,1140);
F = F.setProperty('parameters',{6,{[1 3 4 8 9 11]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{7,{[2 4 5 9 10 12]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{18,{[13 15 16 20 23]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{31,{[26 27 30 35 36]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{32,{[27 28 33 36 37]}});S = S.processData(F,1141);
F = F.setProperty('parameters',{18,{[13 15 16 20 21 23]}});S = S.processData(F,1142);
F = F.setProperty('parameters',{19,{[14 16 17 21 22 24]}});S = S.processData(F,1142);
F = F.setProperty('parameters',{18,{[13 15 16 20 23]}});S = S.processData(F,1143);
F = F.setProperty('parameters',{21,{[20 22 23 24]}});S = S.processData(F,1143);
%% Kernelfilter to eliminate the global component (systemic physiological artifacts): PCAGLOBAL(X, sigma)
for x=1:size(Bedingungen,1)
S = S.processData(NirsDataFunctor('function_handle',@(X) pcaglobal2(X,46,[1:46]),'input_names',{(Bedingungen{x})}));
end
Bedingungen3={'CTL1.Deoxy';'CTL2.Deoxy';'Arith.Deoxy'};
for x=1:size(Bedingungen3,1)
S = S.processData(NirsDataFunctor('function_handle',@(X) pcaglobal2(X,46,[1:46]),'input_names',{(Bedingungen3{x})}));
end
% NIRS Data Z-Transform for comparison between subjects
for x=1:size(Bedingungen,1)
S = S.processData(NirsDataFunctor('function_handle',@(X)NIRSZStandard(X),'input_names',{(Bedingungen{x})}));
end
for x=1:size(Bedingungen3,1)
S = S.processData(NirsDataFunctor('function_handle',@(X)NIRSZStandard(X),'input_names',{(Bedingungen3{x})}));
end
%% settings
clear settings P PS;
P = NirsPlotTool();
settings.experiment.time_series{1} = {'name','CTL1.cui','sample_rate',10,'trigger_name','CTL1.trigger'};
settings.experiment.time_series{2} = {'name','CTL2.cui','sample_rate',10,'trigger_name','CTL2.trigger'};
settings.experiment.time_series{3} = {'name','Arith.cui','sample_rate',10,'trigger_name','Stress.trigger'};
settings.experiment.time_series{4} = {'name','CTL1.Deoxy','sample_rate',10,'trigger_name','CTL1.trigger'};
settings.experiment.time_series{5} = {'name','CTL2.Deoxy','sample_rate',10,'trigger_name','CTL2.trigger'};
settings.experiment.time_series{6} = {'name','Arith.Deoxy','sample_rate',10,'trigger_name','Stress.trigger'};
settings.experiment.category{1} = {'name','TriggerA','trigger_token',1}; % for analysis of window 3
% settings.experiment.category{1} = {'name','TriggerA','trigger_token',9}; % for analysis of window 1 and window 2
P = P.setProperties(settings);
P = NirsPlotTool();
P = P.setProperty('show_probeset','on');
P = P.setProperties(settings);
PS = NirsProbeset('brain_coord_file','Q:\NAS\Ehlis_Auswertung\Laufwerk_S\\AG-Mitglieder\David\Studie Stress Rumination\Koords\NIRS-probesetXYZ_Alle2.txt');
settings.plot_tool.probesets{1} = {'name','Gesamt','probeset',PS};
P = P.setProperties(settings);
P = P.setProperty('probesets',{'name','Gesamt','probeset',PS});
P = P.setProperties(settings);
P = P.setProperty('show_probeset','on');
P = P.setProperties(settings);
Subject=S.tags(1)
%% data export
% EXPORT 1: 15 Sekunden vor Trigger 1 Zeitraum
% clear ERA
% settings.event_related_average.pre_time = 5; % Sekunden Baseline Correction
% settings.event_related_average.linear_detrend = 'off';
% settings.event_related_average.interval = [0 55];
% settings.event_related_average.peak_window = [5 40];
% settings.event_related_average.average_window = [0 15];
% % EXPORT 2: von Trigger 1 bis Trigger 4 Zeitraum
% clear ERA
% settings.event_related_average.pre_time = 5; % Sekunden Baseline Correction
% settings.event_related_average.linear_detrend = 'off';
% settings.event_related_average.interval = [0 55];
% settings.event_related_average.peak_window = [5 40];
% settings.event_related_average.average_window = [15 55];
% EXPORT 3: in Einstellungen wieder Trigger 1 als relevanten Trigger setzen
clear ERA
settings.event_related_average.pre_time = 5; % Sekunden Baseline Correction
settings.event_related_average.linear_detrend = 'off';
settings.event_related_average.interval = [0 55];
settings.event_related_average.peak_window = [5 40]; % 5 40
settings.event_related_average.average_window = [0 40];
ERA = NirsEventRelatedAverage();
ERA = ERA.setProperties(settings);
ERA = ERA.createEra(S);
%%
clear Data Data2
for i=1:size(Subject,1)
Data.AvgArith(:,:,i) = ERA.get({'era.avg','Arith.cui','TriggerA',Subject{i}});
Data.AmpArith(:,i) = ERA.get({'amplitudes','Arith.cui','TriggerA',Subject{i}});
Data.AvgCTL1(:,:,i) = ERA.get({'era.avg','CTL1.cui','TriggerA',Subject{i}});
Data.AmpCTL1(:,i) = ERA.get({'amplitudes','CTL1.cui','TriggerA',Subject{i}});
Data.AvgCTL2(:,:,i) = ERA.get({'era.avg','CTL2.cui','TriggerA',Subject{i}});
Data.AmpCTL2(:,i) = ERA.get({'amplitudes','CTL2.cui','TriggerA',Subject{i}});
end
dat1=Data.AmpArith'
dat2= Data.AmpCTL1'
dat3=Data.AmpCTL2'
dat=[dat1,dat2,dat3]
for i=1:size(Subject,1)
Data2.AvgArith(:,:,i) = ERA.get({'era.avg','Arith.Deoxy','TriggerA',Subject{i}});
Data2.AmpArith(:,i) = ERA.get({'amplitudes','Arith.Deoxy','TriggerA',Subject{i}});
Data2.AvgCTL1(:,:,i) = ERA.get({'era.avg','CTL1.Deoxy','TriggerA',Subject{i}});
Data2.AmpCTL1(:,i) = ERA.get({'amplitudes','CTL1.Deoxy','TriggerA',Subject{i}});
Data2.AvgCTL2(:,:,i) = ERA.get({'era.avg','CTL2.Deoxy','TriggerA',Subject{i}});
Data2.AmpCTL2(:,i) = ERA.get({'amplitudes','CTL2.Deoxy','TriggerA',Subject{i}});
end
dat1=Data2.AmpArith'
dat2= Data2.AmpCTL1'
dat3=Data2.AmpCTL2'
dat=[dat1,dat2,dat3]
clear Daten
Array(:,1)= cell2mat(Subject)
Array(:,2)=nanmean(Data2.AmpArith(lIFG,:),1) %left IFG
Array(:,3)=nanmean(Data2.AmpArith(lDLPFC,:),1) % left DLPFC
Array(:,4)=nanmean(Data2.AmpArith(rIFG,:),1) %right IFG
Array(:,5)=nanmean(Data2.AmpArith(rDLPFC,:),1) %right DLPFC
Array(:,6)=nanmean(Data2.AmpArith(SAC,:),1)
Array(:,7)=nanmean(Data2.AmpCTL1(lIFG,:),1) %left IFG
Array(:,8)=nanmean(Data2.AmpCTL1(lDLPFC,:),1) % left DLPFC
Array(:,9)=nanmean(Data2.AmpCTL1(rIFG,:),1) %right IFG
Array(:,10)=nanmean(Data2.AmpCTL1(rDLPFC,:),1) %right DLPFC
Array(:,11)=nanmean(Data2.AmpCTL1(SAC,:),1)
Array(:,12)=nanmean(Data2.AmpCTL2(lIFG,:),1) %left IFG
Array(:,13)=nanmean(Data2.AmpCTL2(lDLPFC,:),1) % left DLPFC
Array(:,14)=nanmean(Data2.AmpCTL2(rIFG,:),1) %right IFG
Array(:,15)=nanmean(Data2.AmpCTL2(rDLPFC,:),1) %right DLPFC
Array(:,16)=nanmean(Data2.AmpCTL2(SAC,:),1)
Daten = array2table(Array, 'VariableNames',{'Subject',...
'Arith_lIFG_Deoxy', 'Arith_lDLPFC_Deoxy','Arith_rIFG_Deoxy', 'Arith_rDLPFC_Deoxy','Arith_SAC_Deoxy',...
'CTL1_lIFG_Deoxy', 'CTL1_lDLPFC_Deoxy','CTL1_rIFG_Deoxy', 'CTL1_rDLPFC_Deoxy','CTL1_SAC_Deoxy',...
'CTL2_lIFG_Deoxy', 'CTL2_lDLPFC_Deoxy','CTL2_rIFG_Deoxy', 'CTL2_rDLPFC_Deoxy','CTL2_SAC_Deoxy'});
% writetable(Daten,'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Preprocessing_Matlab\2026_01_27_NIRS_TSST-Anticipation_Export1_0bis15s_deoxy.xlsx')
% writetable(Daten,'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Preprocessing_Matlab\2026_01_27_NIRS_TSST-Anticipation_Export2_15bis55s_deoxy.xlsx')
writetable(Daten,'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Preprocessing_Matlab\2026_04_08_NIRS_TSST-Anticipation_Export3_0bis40s_deoxy.xlsx')
%%
lIFG=[7 9 6]
lDLPFC=[10 11 12]
rIFG=[ 18 21 19]
rDLPFC=[20 23 24]
SAC=[27 26 25 28 30 31 32 35 36]
%% OXY
clear Data Data2
for i=1:size(Subject,1)
Data.AvgArith(:,:,i) = ERA.get({'era.avg','Arith.cui','TriggerA',Subject{i}});
Data.AmpArith(:,i) = ERA.get({'amplitudes','Arith.cui','TriggerA',Subject{i}});
Data.AvgCTL1(:,:,i) = ERA.get({'era.avg','CTL1.cui','TriggerA',Subject{i}});
Data.AmpCTL1(:,i) = ERA.get({'amplitudes','CTL1.cui','TriggerA',Subject{i}});
Data.AvgCTL2(:,:,i) = ERA.get({'era.avg','CTL2.cui','TriggerA',Subject{i}});
Data.AmpCTL2(:,i) = ERA.get({'amplitudes','CTL2.cui','TriggerA',Subject{i}});
end
dat1=Data.AmpArith'
dat2= Data.AmpCTL1'
dat3=Data.AmpCTL2'
dat=[dat1,dat2,dat3]
clear Daten
Array(:,1)= cell2mat(Subject)
Array(:,2)=mean(Data.AmpArith(lIFG,:),1) %left IFG
Array(:,3)=mean(Data.AmpArith(lDLPFC,:),1) % left DLPFC
Array(:,4)=mean(Data.AmpArith(rIFG,:),1) %right IFG
Array(:,5)=mean(Data.AmpArith(rDLPFC,:),1) %right DLPFC
Array(:,6)=mean(Data.AmpArith(SAC,:),1)
Array(:,7)=mean(Data.AmpCTL1(lIFG,:),1) %left IFG
Array(:,8)=mean(Data.AmpCTL1(lDLPFC,:),1) % left DLPFC
Array(:,9)=mean(Data.AmpCTL1(rIFG,:),1) %right IFG
Array(:,10)=mean(Data.AmpCTL1(rDLPFC,:),1) %right DLPFC
Array(:,11)=mean(Data.AmpCTL1(SAC,:),1)
Array(:,12)=mean(Data.AmpCTL2(lIFG,:),1) %left IFG
Array(:,13)=mean(Data.AmpCTL2(lDLPFC,:),1) % left DLPFC
Array(:,14)=mean(Data.AmpCTL2(rIFG,:),1) %right IFG
Array(:,15)=mean(Data.AmpCTL2(rDLPFC,:),1) %right DLPFC
Array(:,16)=mean(Data.AmpCTL2(SAC,:),1)
Daten = array2table(Array, 'VariableNames',{'Subject',...
'Arith_lIFG_Cui', 'Arith_lDLPFC_Cui','Arith_rIFG_Cui', 'Arith_rDLPFC_Cui','Arith_SAC_Cui',...
'CTL1_lIFG_Cui', 'CTL1_lDLPFC_Cui','CTL1_rIFG_Cui', 'CTL1_rDLPFC_Cui','CTL1_SAC_Cui',...
'CTL2_lIFG_Cui', 'CTL2_lDLPFC_Cui','CTL2_rIFG_Cui', 'CTL2_rDLPFC_Cui','CTL2_SAC_Cui'});
% writetable(Daten,'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Preprocessing_Matlab\2026_04_08_NIRS_TSST-Anticipation_Export1_0bis15s_only1trial.xlsx')
% writetable(Daten,'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Preprocessing_Matlab\2026_04_08_NIRS_TSST-Anticipation_Export2_15bis55s_only1trial.xlsx')
writetable(Daten,'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Preprocessing_Matlab\2026_04_08_NIRS_TSST-Anticipation_Export3_0bis40s_only1trial.xlsx')
%% export for brainmaps
save 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\PlotsData_Export3_trial1.mat' Data
save 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\PlotsSubjects_Export3_trial1.mat' Subject
% %% Time series Illustration ERA: TSST
%
% YLIMITS = [-0.9 1.2];
% XLIMITS = [-25 62];
% LEG_POS = [0.05, 0.75, 0.3, 0.1];
% FONT_SZ = 14;
% LEG_FS = 12;
%
% % --- Subplot-Konfiguration (nur ein Kanal/Subplot) ---
% subplotList = {
% lIFG, ' '
% };
%
% % --- Farben für Gruppen --- hexColor1 = '#00676f'; rgbColor1 =
% % sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
% hexColor1 = '#9b7b00'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
% % hexColor1 = '#b43602'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x',
% % [1 3]) / 255;
%
% % --- Figure setup (Querformat DINA4) ---
% close all
% figure
% set(gcf,'Units','inches','Position',[1 1 11.69 8.27]) % Breite x Höhe in inches
%
% % --- Zeitachse --- t = (-199:(size(Data.AvgArith,1)-200))'/10; % 20
% % Sekunden baseline
% t = (-49:(size(Data.AvgArith,1)-50))'/10; % 5 sekunden baseline
%
% % --- Ein Subplot, volle Fläche ---
% ax = axes('Position',[0.07 0.15 0.9 0.75]); % links, unten, Breite, Höhe (normiert)
% hold(ax,'on')
%
% % --- Hintergrund-Patches ---
% trialPatch = patch([0 40 40 0], [-1 -1 2 2], [0.95 0.95 0.95], ...
% 'EdgeColor','none', 'DisplayName','trial');
% pausePatch = patch([40 60 60 40], [-1 -1 2 2], [0.85 0.85 0.85], ...
% 'EdgeColor','none', 'DisplayName','pause');
%
% % --- Daten plotten ---
% chn = subplotList{1,1};
% TITLE = subplotList{1,2};
%
% % Mittelung über alle Subjects
% dat = squeeze(mean(Data.AvgArith(:,chn,:),2)); % Mittelung über Kanäle
% SEM = std(dat,0,2) ./ sqrt(size(Data.AvgArith,3)); % SEM über Subjects
% timeseries = mean(dat,2); % Mittelwert über Subjects
%
% % SEM als transparenten Patch
% xPatch = [t; flipud(t)];
% yPatch = [timeseries-SEM; flipud(timeseries+SEM)];
% hPatch = fill(xPatch, yPatch, rgbColor1, 'FaceAlpha', 0.15, 'EdgeColor', 'none', 'DisplayName','fNIRS data');
%
% % Mittelwert-Zeitreihe
% hLine = plot(t, timeseries, '.-', 'Color', rgbColor1, 'LineWidth',1.5, 'DisplayName','fNIRS data window 2');
%
% xticks(XLIMITS(1):5:XLIMITS(2))
%
% % --- Achsen, Labels, Titel ---
% ylim(YLIMITS)
% xlim(XLIMITS)
% xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
% ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
% title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% set(ax, 'FontSize', 14) % z.B. 14 Punkt für Tick-Beschriftungen
%
% % --- Legende ---
% lh = legend([trialPatch pausePatch hLine], 'Location','northoutside'); % oder legend([trialPatch pausePatch hLine])
% legend boxoff
% set(lh,'FontSize',LEG_FS,'FontWeight','bold')
% set(lh,'Units','normalized','Position',LEG_POS)
% set(lh,'Color','none'); % Hintergrund
% set(lh,'EdgeColor','none'); % Rahmen
% set(lh,'Box','off'); % Kasten
%
% hold off
%
% %% nur fNIRS Zeitreihe, alles andere unsichtbar
%
% YLIMITS = [-0.9 1.2];
% XLIMITS = [-25 62];
% LEG_POS = [0.05 0.75 0.3 0.1];
% LEG_FS = 14;
%
% % --- Farbe ---
% hexColor1 = '#00676f';
% rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
%
% % --- Figure setup (alles gleich groß wie vorher) ---
% close all
% figure
% set(gcf,'Units','inches','Position',[1 1 11.69 8.27])
%
% % --- Zeitachse ---
% t = (-49:(size(Data.AvgArith,1)-50))'/10;
%
% % --- Achse ---
% ax = axes('Position',[0.07 0.15 0.9 0.75]);
% hold(ax,'on')
%
% % --- Daten ---
% dat = squeeze(mean(Data.AvgArith(:,lIFG,:),2));
% timeseries = mean(dat,2);
%
% % --- Zeitreihe (sichtbar im Plot) ---
% plot(t, timeseries, '.-', ...
% 'Color', rgbColor1, ...
% 'LineWidth', 1.5, ...
% 'MarkerSize', 10, ...
% 'HandleVisibility','off');
%
% % --- Proxy-Objekt NUR für Legende ---
% hLeg = plot(nan, nan, '.-', ...
% 'Color', rgbColor1, ...
% 'LineWidth', 1.5, ...
% 'MarkerSize', 16, ...
% 'DisplayName','fNIRS data');
%
% % --- Limits setzen (wirksam, aber unsichtbar) ---
% xlim(XLIMITS)
% ylim(YLIMITS)
%
% % --- ALLES ausblenden außer Linie ---
% axis off
% set(ax,'Color','none') % transparenter Hintergrund
%
% % --- Legende (nur Zeitreihe) ---
% lh = legend(hLeg);
% set(lh,'FontSize',LEG_FS,'FontWeight','bold')
% set(lh,'Units','normalized','Position',LEG_POS)
% set(lh,'Color','none','EdgeColor','none','Box','off')
%
% hold off
%
% %% Dateiname ohne Erweiterung
% filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Plots\FigureX_Illustration_window2';
%
% % Speichern als .svg
% print(gcf, filename, '-dsvg', '-r600');
%% Time series HC vs. DP all ROIs: TSST (Export 3)
clear all
clc
lIFG=[7 9 6];
lDLPFC=[10 12 11];
rIFG=[18 21 19];
rDLPFC=[20 23 24];
SAC=[27 26 25 28 30 31 32 35 36];
load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Data_Export3.mat');
load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Subjects_Export3.mat');
lineV = @(x,spec) plot(repmat(x(:),1,2)',repmat(ylim,numel(x),1)',spec{:});
toPatch = @(x1,x2,y1,y2,spec) patch([x1(:);flipud(repmat(x2(:),numel(x1)./numel(x2),1))],[y1(:);flipud(repmat(y2(:),numel(y1)./numel(y2),1))],spec{:});
colors = get(groot,'defaultAxesColorOrder');
test = Subject;
Group1 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group2 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
YLIMITS = [-0.9 1.2];
XLIMITS = [-25 42];
LEG_POS = [0.62, 0.05, 0.32, 0.18]; % unten rechts
FONT_SZ = 8;
LEG_FS = 6;
% --- Subplot-Konfiguration ---
subplotList = {
lIFG, 'left VLPFC', 1;
rIFG, 'right VLPFC', 2;
lDLPFC, 'left DLPFC', 3;
rDLPFC, 'right DLPFC', 4;
SAC, 'SAC', 5;
};
% --- Farben fuer Gruppen ---
hexColor1 = '#00676f'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor2 = '#9b7b00'; rgbColor2 = sscanf(hexColor2(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor3 = '#b43602'; rgbColor3 = sscanf(hexColor3(2:end), '%2x%2x%2x', [1 3]) / 255;
% --- Figure setup ---
close all
figure
set(gcf,'Units','inches','Position',[1 1 8.27 6])
% --- Zeitachse ---
timeOffset = 0; % kein offset bei Export3
t = (-49:(size(Data.AvgArith,1)-50))'/10 + timeOffset;
lh = cell(size(subplotList,1),1);
% --- Loop ueber alle Subplots ---
for iPlot = 1:size(subplotList,1)
chn = subplotList{iPlot,1};
TITLE = subplotList{iPlot,2};
spID = subplotList{iPlot,3};
subplot(3,2,spID)
% --- Hintergrund-Patches ---
p1 = patch([0 40 40 0], [-1 -1 2 2], [0.95 0.95 0.95], ...
'EdgeColor','none','DisplayName','trial');
hold on
p2 = patch([-5 0 0 -5], [-1 -1 2 2], [0.85 0.85 0.85], ...
'EdgeColor','none','DisplayName','baseline');
% --- Gruppe 1: HC ---
dat = squeeze(mean(Data.AvgArith(:,chn,Group1),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group1));
timeseries = mean(dat,2);
h1 = plot(t,timeseries,'.-','Color',rgbColor1,'DisplayName','HC');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor1,'facealpha',0.15})
% --- Gruppe 2: DP ---
dat = squeeze(mean(Data.AvgArith(:,chn,Group2),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group2));
timeseries = mean(dat,2);
h2 = plot(t,timeseries,'.-','Color',rgbColor2,'DisplayName','DP');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor2,'facealpha',0.15})
% --- Achsen, Labels, Titel ---
ylim(YLIMITS)
xlim(XLIMITS)
xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% --- Legende ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
legend boxoff
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
% --- weisser Hintergrund ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
set(lh{iPlot}, 'Color', 'w'); % Weisser Hintergrund
set(lh{iPlot}, 'EdgeColor', 'w'); % Rahmen
set(lh{iPlot}, 'Box', 'on'); % Kasten sichtbar
hold off
end
filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\SupplementaryMaterial\FigureS6.3_Timeseries_DPvsHC_Arith_window3';
print(gcf, [filename '.svg'], '-dsvg', '-r600');
print(gcf, [filename '.jpg'], '-djpeg', '-r600');
%% Time series HC vs. DP all ROIs: CTL1 (Export 3)
lineV = @(x,spec) plot(repmat(x(:),1,2)',repmat(ylim,numel(x),1)',spec{:});
toPatch = @(x1,x2,y1,y2,spec) patch([x1(:);flipud(repmat(x2(:),numel(x1)./numel(x2),1))],[y1(:);flipud(repmat(y2(:),numel(y1)./numel(y2),1))],spec{:});
colors = get(groot,'defaultAxesColorOrder');
test = Subject;
Group1 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group2 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
YLIMITS = [-0.9 1.2];
XLIMITS = [-25 42];
LEG_POS = [0.62, 0.05, 0.32, 0.18]; % unten rechts
FONT_SZ = 8;
LEG_FS = 6;
% --- Subplot-Konfiguration ---
subplotList = {
lIFG, 'left VLPFC', 1;
rIFG, 'right VLPFC', 2;
lDLPFC, 'left DLPFC', 3;
rDLPFC, 'right DLPFC', 4;
SAC, 'SAC', 5;
};
% --- Farben fuer Gruppen ---
hexColor1 = '#00676f'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor2 = '#9b7b00'; rgbColor2 = sscanf(hexColor2(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor3 = '#b43602'; rgbColor3 = sscanf(hexColor3(2:end), '%2x%2x%2x', [1 3]) / 255;
% --- Figure setup ---
close all
figure
set(gcf,'Units','inches','Position',[1 1 8.27 6])
% --- Zeitachse ---
timeOffset = 0; % kein offset bei Export3
t = (-49:(size(Data.AvgCTL1,1)-50))'/10 + timeOffset;
lh = cell(size(subplotList,1),1);
% --- Loop ueber alle Subplots ---
for iPlot = 1:size(subplotList,1)
chn = subplotList{iPlot,1};
TITLE = subplotList{iPlot,2};
spID = subplotList{iPlot,3};
subplot(3,2,spID)
% --- Hintergrund-Patches ---
p1 = patch([0 40 40 0], [-1 -1 2 2], [0.95 0.95 0.95], ...
'EdgeColor','none','DisplayName','trial');
hold on
p2 = patch([-5 0 0 -5], [-1 -1 2 2], [0.85 0.85 0.85], ...
'EdgeColor','none','DisplayName','baseline');
% --- Gruppe 1: HC ---
dat = squeeze(mean(Data.AvgCTL1(:,chn,Group1),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group1));
timeseries = mean(dat,2);
h1 = plot(t,timeseries,'.-','Color',rgbColor1,'DisplayName','HC');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor1,'facealpha',0.15})
% --- Gruppe 2: DP ---
dat = squeeze(mean(Data.AvgCTL1(:,chn,Group2),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group2));
timeseries = mean(dat,2);
h2 = plot(t,timeseries,'.-','Color',rgbColor2,'DisplayName','DP');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor2,'facealpha',0.15})
% --- Achsen, Labels, Titel ---
ylim(YLIMITS)
xlim(XLIMITS)
xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% --- Legende ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
legend boxoff
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
% --- weisser Hintergrund ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
set(lh{iPlot}, 'Color', 'w'); % Weisser Hintergrund
set(lh{iPlot}, 'EdgeColor', 'w'); % Rahmen
set(lh{iPlot}, 'Box', 'on'); % Kasten sichtbar
hold off
end
filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\FigureX_Timeseries_DPvsHC_CTL1_window3';
print(gcf, [filename '.svg'], '-dsvg', '-r600');
print(gcf, [filename '.jpg'], '-djpeg', '-r600');
%% Time series HC vs. DP all ROIs: CTL2 (Export 3)
lineV = @(x,spec) plot(repmat(x(:),1,2)',repmat(ylim,numel(x),1)',spec{:});
toPatch = @(x1,x2,y1,y2,spec) patch([x1(:);flipud(repmat(x2(:),numel(x1)./numel(x2),1))],[y1(:);flipud(repmat(y2(:),numel(y1)./numel(y2),1))],spec{:});
colors = get(groot,'defaultAxesColorOrder');
test = Subject;
Group1 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group2 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
YLIMITS = [-0.9 1.2];
XLIMITS = [-25 42];
LEG_POS = [0.62, 0.05, 0.32, 0.18]; % unten rechts
FONT_SZ = 8;
LEG_FS = 6;
% --- Subplot-Konfiguration ---
subplotList = {
lIFG, 'left VLPFC', 1;
rIFG, 'right VLPFC', 2;
lDLPFC, 'left DLPFC', 3;
rDLPFC, 'right DLPFC', 4;
SAC, 'SAC', 5;
};
% --- Farben fuer Gruppen ---
hexColor1 = '#00676f'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor2 = '#9b7b00'; rgbColor2 = sscanf(hexColor2(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor3 = '#b43602'; rgbColor3 = sscanf(hexColor3(2:end), '%2x%2x%2x', [1 3]) / 255;
% --- Figure setup ---
close all
figure
set(gcf,'Units','inches','Position',[1 1 8.27 6])
% --- Zeitachse ---
timeOffset = 0; % kein offset bei Export3
t = (-49:(size(Data.AvgCTL2,1)-50))'/10 + timeOffset;
lh = cell(size(subplotList,1),1);
% --- Loop ueber alle Subplots ---
for iPlot = 1:size(subplotList,1)
chn = subplotList{iPlot,1};
TITLE = subplotList{iPlot,2};
spID = subplotList{iPlot,3};
subplot(3,2,spID)
% --- Hintergrund-Patches ---
p1 = patch([0 40 40 0], [-1 -1 2 2], [0.95 0.95 0.95], ...
'EdgeColor','none','DisplayName','trial');
hold on
p2 = patch([-5 0 0 -5], [-1 -1 2 2], [0.85 0.85 0.85], ...
'EdgeColor','none','DisplayName','baseline');
% --- Gruppe 1: HC ---
dat = squeeze(mean(Data.AvgCTL2(:,chn,Group1),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group1));
timeseries = mean(dat,2);
h1 = plot(t,timeseries,'.-','Color',rgbColor1,'DisplayName','HC');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor1,'facealpha',0.15})
% --- Gruppe 2: DP ---
dat = squeeze(mean(Data.AvgCTL2(:,chn,Group2),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group2));
timeseries = mean(dat,2);
h2 = plot(t,timeseries,'.-','Color',rgbColor2,'DisplayName','DP');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor2,'facealpha',0.15})
% --- Achsen, Labels, Titel ---
ylim(YLIMITS)
xlim(XLIMITS)
xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% --- Legende ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
legend boxoff
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
% --- weisser Hintergrund ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
set(lh{iPlot}, 'Color', 'w'); % Weisser Hintergrund
set(lh{iPlot}, 'EdgeColor', 'w'); % Rahmen
set(lh{iPlot}, 'Box', 'on'); % Kasten sichtbar
hold off
end
filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\FigureX_Timeseries_DPvsHC_CTL2_window3';
print(gcf, [filename '.svg'], '-dsvg', '-r600');
print(gcf, [filename '.jpg'], '-djpeg', '-r600');
%% Time series HC vs. DP all ROIs: TSST (Export 2)
clear all
clc
lIFG=[7 9 6];
lDLPFC=[10 12 11];
rIFG=[18 21 19];
rDLPFC=[20 23 24];
SAC=[27 26 25 28 30 31 32 35 36];
load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Data_Export2.mat');
load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Subjects_Export2.mat');
lineV = @(x,spec) plot(repmat(x(:),1,2)',repmat(ylim,numel(x),1)',spec{:});
toPatch = @(x1,x2,y1,y2,spec) patch([x1(:);flipud(repmat(x2(:),numel(x1)./numel(x2),1))],[y1(:);flipud(repmat(y2(:),numel(y1)./numel(y2),1))],spec{:});
colors = get(groot,'defaultAxesColorOrder');
test = Subject;
Group1 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group2 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
YLIMITS = [-0.9 1.2];
XLIMITS = [-25 42];
LEG_POS = [0.62, 0.05, 0.32, 0.18]; % unten rechts
FONT_SZ = 8;
LEG_FS = 6;
% --- Subplot-Konfiguration ---
subplotList = {
lIFG, 'left VLPFC', 1;
rIFG, 'right VLPFC', 2;
lDLPFC, 'left DLPFC', 3;
rDLPFC, 'right DLPFC', 4;
SAC, 'SAC', 5;
};
% --- Farben fuer Gruppen ---
hexColor1 = '#00676f'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor2 = '#9b7b00'; rgbColor2 = sscanf(hexColor2(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor3 = '#b43602'; rgbColor3 = sscanf(hexColor3(2:end), '%2x%2x%2x', [1 3]) / 255;
% --- Figure setup ---
close all
figure
set(gcf,'Units','inches','Position',[1 1 8.27 6])
% --- Zeitachse ---
timeOffset = -15; % offset bei Export1 und Export2
t = (-49:(size(Data.AvgArith,1)-50))'/10 + timeOffset;
lh = cell(size(subplotList,1),1);
% --- Loop ueber alle Subplots ---
for iPlot = 1:size(subplotList,1)
chn = subplotList{iPlot,1};
TITLE = subplotList{iPlot,2};
spID = subplotList{iPlot,3};
subplot(3,2,spID)
% --- Hintergrund-Patches ---
p1 = patch([0 40 40 0], [-1 -1 2 2], [0.95 0.95 0.95], ...
'EdgeColor','none','DisplayName','trial');
hold on
p2 = patch([-20 -15 -15 -20], [-1 -1 2 2], [0.85 0.85 0.85], ...
'EdgeColor','none','DisplayName','baseline');
% --- Gruppe 1: HC ---
dat = squeeze(mean(Data.AvgArith(:,chn,Group1),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group1));
timeseries = mean(dat,2);
h1 = plot(t,timeseries,'.-','Color',rgbColor1,'DisplayName','HC');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor1,'facealpha',0.15})
% --- Gruppe 2: DP ---
dat = squeeze(mean(Data.AvgArith(:,chn,Group2),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group2));
timeseries = mean(dat,2);
h2 = plot(t,timeseries,'.-','Color',rgbColor2,'DisplayName','DP');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor2,'facealpha',0.15})
% --- Achsen, Labels, Titel ---
ylim(YLIMITS)
xlim(XLIMITS)
xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% --- Legende ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
legend boxoff
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
% --- weisser Hintergrund ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
set(lh{iPlot}, 'Color', 'w'); % Weisser Hintergrund
set(lh{iPlot}, 'EdgeColor', 'w'); % Rahmen
set(lh{iPlot}, 'Box', 'on'); % Kasten sichtbar
hold off
end
filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\SupplementaryMaterial\FigureS6.2_Timeseries_DPvsHC_Arith_window2';
print(gcf, [filename '.svg'], '-dsvg', '-r600');
print(gcf, [filename '.jpg'], '-djpeg', '-r600');
%% Time series HC vs. DP all ROIs: CLT1 (Export 2)
lineV = @(x,spec) plot(repmat(x(:),1,2)',repmat(ylim,numel(x),1)',spec{:});
toPatch = @(x1,x2,y1,y2,spec) patch([x1(:);flipud(repmat(x2(:),numel(x1)./numel(x2),1))],[y1(:);flipud(repmat(y2(:),numel(y1)./numel(y2),1))],spec{:});
colors = get(groot,'defaultAxesColorOrder');
test = Subject;
Group1 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group2 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
YLIMITS = [-0.9 1.2];
XLIMITS = [-25 42];
LEG_POS = [0.62, 0.05, 0.32, 0.18]; % unten rechts
FONT_SZ = 8;
LEG_FS = 6;
% --- Subplot-Konfiguration ---
subplotList = {
lIFG, 'left VLPFC', 1;
rIFG, 'right VLPFC', 2;
lDLPFC, 'left DLPFC', 3;
rDLPFC, 'right DLPFC', 4;
SAC, 'SAC', 5;
};
% --- Farben fuer Gruppen ---
hexColor1 = '#00676f'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor2 = '#9b7b00'; rgbColor2 = sscanf(hexColor2(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor3 = '#b43602'; rgbColor3 = sscanf(hexColor3(2:end), '%2x%2x%2x', [1 3]) / 255;
% --- Figure setup ---
close all
figure
set(gcf,'Units','inches','Position',[1 1 8.27 6])
% --- Zeitachse ---
timeOffset = -15; % offset bei Export1 und Export2
t = (-49:(size(Data.AvgCTL1,1)-50))'/10 + timeOffset;
lh = cell(size(subplotList,1),1);
% --- Loop ueber alle Subplots ---
for iPlot = 1:size(subplotList,1)
chn = subplotList{iPlot,1};
TITLE = subplotList{iPlot,2};
spID = subplotList{iPlot,3};
subplot(3,2,spID)
% --- Hintergrund-Patches ---
p1 = patch([0 40 40 0], [-1 -1 2 2], [0.95 0.95 0.95], ...
'EdgeColor','none','DisplayName','trial');
hold on
p2 = patch([-20 -15 -15 -20], [-1 -1 2 2], [0.85 0.85 0.85], ...
'EdgeColor','none','DisplayName','baseline');
% --- Gruppe 1: HC ---
dat = squeeze(mean(Data.AvgCTL1(:,chn,Group1),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group1));
timeseries = mean(dat,2);
h1 = plot(t,timeseries,'.-','Color',rgbColor1,'DisplayName','HC');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor1,'facealpha',0.15})
% --- Gruppe 2: DP ---
dat = squeeze(mean(Data.AvgCTL1(:,chn,Group2),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group2));
timeseries = mean(dat,2);
h2 = plot(t,timeseries,'.-','Color',rgbColor2,'DisplayName','DP');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor2,'facealpha',0.15})
% --- Achsen, Labels, Titel ---
ylim(YLIMITS)
xlim(XLIMITS)
xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% --- Legende ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
legend boxoff
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
% --- weisser Hintergrund ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
set(lh{iPlot}, 'Color', 'w'); % Weisser Hintergrund
set(lh{iPlot}, 'EdgeColor', 'w'); % Rahmen
set(lh{iPlot}, 'Box', 'on'); % Kasten sichtbar
hold off
end
filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\FigureX_Timeseries_DPvsHC_CTL1_window2';
print(gcf, [filename '.svg'], '-dsvg', '-r600');
print(gcf, [filename '.jpg'], '-djpeg', '-r600');
%% Time series HC vs. DP all ROIs: CLT2 (Export 2)
lineV = @(x,spec) plot(repmat(x(:),1,2)',repmat(ylim,numel(x),1)',spec{:});
toPatch = @(x1,x2,y1,y2,spec) patch([x1(:);flipud(repmat(x2(:),numel(x1)./numel(x2),1))],[y1(:);flipud(repmat(y2(:),numel(y1)./numel(y2),1))],spec{:});
colors = get(groot,'defaultAxesColorOrder');
test = Subject;
Group1 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group2 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
YLIMITS = [-0.9 1.2];
XLIMITS = [-25 42];
LEG_POS = [0.62, 0.05, 0.32, 0.18]; % unten rechts
FONT_SZ = 8;
LEG_FS = 6;
% --- Subplot-Konfiguration ---
subplotList = {
lIFG, 'left VLPFC', 1;
rIFG, 'right VLPFC', 2;
lDLPFC, 'left DLPFC', 3;
rDLPFC, 'right DLPFC', 4;
SAC, 'SAC', 5;
};
% --- Farben fuer Gruppen ---
hexColor1 = '#00676f'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor2 = '#9b7b00'; rgbColor2 = sscanf(hexColor2(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor3 = '#b43602'; rgbColor3 = sscanf(hexColor3(2:end), '%2x%2x%2x', [1 3]) / 255;
% --- Figure setup ---
close all
figure
set(gcf,'Units','inches','Position',[1 1 8.27 6])
% --- Zeitachse ---
timeOffset = -15; % offset bei Export1 und Export2
t = (-49:(size(Data.AvgCTL2,1)-50))'/10 + timeOffset;
lh = cell(size(subplotList,1),1);
% --- Loop ueber alle Subplots ---
for iPlot = 1:size(subplotList,1)
chn = subplotList{iPlot,1};
TITLE = subplotList{iPlot,2};
spID = subplotList{iPlot,3};
subplot(3,2,spID)
% --- Hintergrund-Patches ---
p1 = patch([0 40 40 0], [-1 -1 2 2], [0.95 0.95 0.95], ...
'EdgeColor','none','DisplayName','trial');
hold on
p2 = patch([-20 -15 -15 -20], [-1 -1 2 2], [0.85 0.85 0.85], ...
'EdgeColor','none','DisplayName','baseline');
% --- Gruppe 1: HC ---
dat = squeeze(mean(Data.AvgCTL2(:,chn,Group1),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group1));
timeseries = mean(dat,2);
h1 = plot(t,timeseries,'.-','Color',rgbColor1,'DisplayName','HC');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor1,'facealpha',0.15})
% --- Gruppe 2: DP ---
dat = squeeze(mean(Data.AvgCTL2(:,chn,Group2),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group2));
timeseries = mean(dat,2);
h2 = plot(t,timeseries,'.-','Color',rgbColor2,'DisplayName','DP');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor2,'facealpha',0.15})
% --- Achsen, Labels, Titel ---
ylim(YLIMITS)
xlim(XLIMITS)
xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% --- Legende ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
legend boxoff
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
% --- weisser Hintergrund ---
lh{iPlot} = legend([p1 p2 h1 h2],'trial','baseline','HC','DP');
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
set(lh{iPlot}, 'Color', 'w'); % Weisser Hintergrund
set(lh{iPlot}, 'EdgeColor', 'w'); % Rahmen
set(lh{iPlot}, 'Box', 'on'); % Kasten sichtbar
hold off
end
filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\FigureX_Timeseries_DPvsHC_CTL2_window2';
print(gcf, [filename '.svg'], '-dsvg', '-r600');
print(gcf, [filename '.jpg'], '-djpeg', '-r600');
%% Time series HC vs. DP all ROIs: TSST (Export 1)
clear all
clc
lIFG=[7 9 6];
lDLPFC=[10 12 11];
rIFG=[18 21 19];
rDLPFC=[20 23 24];
SAC=[27 26 25 28 30 31 32 35 36];
load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Data_Export1.mat');
load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Subjects_Export1.mat');
lineV = @(x,spec) plot(repmat(x(:),1,2)',repmat(ylim,numel(x),1)',spec{:});
toPatch = @(x1,x2,y1,y2,spec) patch([x1(:);flipud(repmat(x2(:),numel(x1)./numel(x2),1))],[y1(:);flipud(repmat(y2(:),numel(y1)./numel(y2),1))],spec{:});
colors = get(groot,'defaultAxesColorOrder');
test = Subject;
Group1 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group2 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
YLIMITS = [-0.9 1.2];
XLIMITS = [-25 2];
LEG_POS = [0.62, 0.05, 0.32, 0.18]; % unten rechts
FONT_SZ = 8;
LEG_FS = 6;
% --- Subplot-Konfiguration ---
subplotList = {
lIFG, 'left VLPFC', 1;
rIFG, 'right VLPFC', 2;
lDLPFC, 'left DLPFC', 3;
rDLPFC, 'right DLPFC', 4;
SAC, 'SAC', 5;
};
% --- Farben fuer Gruppen ---
hexColor1 = '#00676f'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor2 = '#9b7b00'; rgbColor2 = sscanf(hexColor2(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor3 = '#b43602'; rgbColor3 = sscanf(hexColor3(2:end), '%2x%2x%2x', [1 3]) / 255;
% --- Figure setup ---
close all
figure
set(gcf,'Units','inches','Position',[1 1 8.27 6])
% --- Zeitachse ---
timeOffset = -15; % offset bei Export1 und Export2
t = (-49:(size(Data.AvgArith,1)-50))'/10 + timeOffset;
lh = cell(size(subplotList,1),1);
% --- Loop ueber alle Subplots ---
for iPlot = 1:size(subplotList,1)
chn = subplotList{iPlot,1};
TITLE = subplotList{iPlot,2};
spID = subplotList{iPlot,3};
subplot(3,2,spID)
% --- Hintergrund-Patches ---
hold on
p2 = patch([-20 -15 -15 -20], [-1 -1 2 2], [0.85 0.85 0.85], ...
'EdgeColor','none','DisplayName','baseline');
% --- Gruppe 1: HC ---
dat = squeeze(mean(Data.AvgArith(:,chn,Group1),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group1));
timeseries = mean(dat,2);
h1 = plot(t,timeseries,'.-','Color',rgbColor1,'DisplayName','HC');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor1,'facealpha',0.15})
% --- Gruppe 2: DP ---
dat = squeeze(mean(Data.AvgArith(:,chn,Group2),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group2));
timeseries = mean(dat,2);
h2 = plot(t,timeseries,'.-','Color',rgbColor2,'DisplayName','DP');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor2,'facealpha',0.15})
% --- Achsen, Labels, Titel ---
ylim(YLIMITS)
xlim(XLIMITS)
xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% --- Legende ---
lh{iPlot} = legend([p2 h1 h2],'baseline','HC','DP');
legend boxoff
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
% --- weisser Hintergrund ---
lh{iPlot} = legend([p2 h1 h2],'baseline','HC','DP');
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
set(lh{iPlot}, 'Color', 'w'); % Weisser Hintergrund
set(lh{iPlot}, 'EdgeColor', 'w'); % Rahmen
set(lh{iPlot}, 'Box', 'on'); % Kasten sichtbar
hold off
end
filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\SupplementaryMaterial\FigureS6.1_Timeseries_DPvsHC_Arith_window1';
print(gcf, [filename '.svg'], '-dsvg', '-r600');
print(gcf, [filename '.jpg'], '-djpeg', '-r600');
%% Time series HC vs. DP all ROIs: CTL1 (Export 1)
lineV = @(x,spec) plot(repmat(x(:),1,2)',repmat(ylim,numel(x),1)',spec{:});
toPatch = @(x1,x2,y1,y2,spec) patch([x1(:);flipud(repmat(x2(:),numel(x1)./numel(x2),1))],[y1(:);flipud(repmat(y2(:),numel(y1)./numel(y2),1))],spec{:});
colors = get(groot,'defaultAxesColorOrder');
test = Subject;
Group1 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group2 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
YLIMITS = [-0.9 1.2];
XLIMITS = [-25 2];
LEG_POS = [0.62, 0.05, 0.32, 0.18]; % unten rechts
FONT_SZ = 8;
LEG_FS = 6;
% --- Subplot-Konfiguration ---
subplotList = {
lIFG, 'left VLPFC', 1;
rIFG, 'right VLPFC', 2;
lDLPFC, 'left DLPFC', 3;
rDLPFC, 'right DLPFC', 4;
SAC, 'SAC', 5;
};
% --- Farben fuer Gruppen ---
hexColor1 = '#00676f'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor2 = '#9b7b00'; rgbColor2 = sscanf(hexColor2(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor3 = '#b43602'; rgbColor3 = sscanf(hexColor3(2:end), '%2x%2x%2x', [1 3]) / 255;
% --- Figure setup ---
close all
figure
set(gcf,'Units','inches','Position',[1 1 8.27 6])
% --- Zeitachse ---
timeOffset = -15; % offset bei Export1 und Export2
t = (-49:(size(Data.AvgCTL1,1)-50))'/10 + timeOffset;
lh = cell(size(subplotList,1),1);
% --- Loop ueber alle Subplots ---
for iPlot = 1:size(subplotList,1)
chn = subplotList{iPlot,1};
TITLE = subplotList{iPlot,2};
spID = subplotList{iPlot,3};
subplot(3,2,spID)
% --- Hintergrund-Patches ---
hold on
p2 = patch([-20 -15 -15 -20], [-1 -1 2 2], [0.85 0.85 0.85], ...
'EdgeColor','none','DisplayName','baseline');
% --- Gruppe 1: HC ---
dat = squeeze(mean(Data.AvgCTL1(:,chn,Group1),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group1));
timeseries = mean(dat,2);
h1 = plot(t,timeseries,'.-','Color',rgbColor1,'DisplayName','HC');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor1,'facealpha',0.15})
% --- Gruppe 2: DP ---
dat = squeeze(mean(Data.AvgCTL1(:,chn,Group2),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group2));
timeseries = mean(dat,2);
h2 = plot(t,timeseries,'.-','Color',rgbColor2,'DisplayName','DP');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor2,'facealpha',0.15})
% --- Achsen, Labels, Titel ---
ylim(YLIMITS)
xlim(XLIMITS)
xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% --- Legende ---
lh{iPlot} = legend([p2 h1 h2],'baseline','HC','DP');
legend boxoff
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
% --- weisser Hintergrund ---
lh{iPlot} = legend([p2 h1 h2],'baseline','HC','DP');
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
set(lh{iPlot}, 'Color', 'w'); % Weisser Hintergrund
set(lh{iPlot}, 'EdgeColor', 'w'); % Rahmen
set(lh{iPlot}, 'Box', 'on'); % Kasten sichtbar
hold off
end
filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\FigureX_Timeseries_DPvsHC_CTL1_window1';
print(gcf, [filename '.svg'], '-dsvg', '-r600');
print(gcf, [filename '.jpg'], '-djpeg', '-r600');
%% Time series HC vs. DP all ROIs: CTL2 (Export 1)
lineV = @(x,spec) plot(repmat(x(:),1,2)',repmat(ylim,numel(x),1)',spec{:});
toPatch = @(x1,x2,y1,y2,spec) patch([x1(:);flipud(repmat(x2(:),numel(x1)./numel(x2),1))],[y1(:);flipud(repmat(y2(:),numel(y1)./numel(y2),1))],spec{:});
colors = get(groot,'defaultAxesColorOrder');
test = Subject;
Group1 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group2 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
YLIMITS = [-0.9 1.2];
XLIMITS = [-25 2];
LEG_POS = [0.62, 0.05, 0.32, 0.18]; % unten rechts
FONT_SZ = 8;
LEG_FS = 6;
% --- Subplot-Konfiguration ---
subplotList = {
lIFG, 'left VLPFC', 1;
rIFG, 'right VLPFC', 2;
lDLPFC, 'left DLPFC', 3;
rDLPFC, 'right DLPFC', 4;
SAC, 'SAC', 5;
};
% --- Farben fuer Gruppen ---
hexColor1 = '#00676f'; rgbColor1 = sscanf(hexColor1(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor2 = '#9b7b00'; rgbColor2 = sscanf(hexColor2(2:end), '%2x%2x%2x', [1 3]) / 255;
hexColor3 = '#b43602'; rgbColor3 = sscanf(hexColor3(2:end), '%2x%2x%2x', [1 3]) / 255;
% --- Figure setup ---
close all
figure
set(gcf,'Units','inches','Position',[1 1 8.27 6])
% --- Zeitachse ---
timeOffset = -15; % offset bei Export1 und Export2
t = (-49:(size(Data.AvgCTL2,1)-50))'/10 + timeOffset;
lh = cell(size(subplotList,1),1);
% --- Loop ueber alle Subplots ---
for iPlot = 1:size(subplotList,1)
chn = subplotList{iPlot,1};
TITLE = subplotList{iPlot,2};
spID = subplotList{iPlot,3};
subplot(3,2,spID)
% --- Hintergrund-Patches ---
hold on
p2 = patch([-20 -15 -15 -20], [-1 -1 2 2], [0.85 0.85 0.85], ...
'EdgeColor','none','DisplayName','baseline');
% --- Gruppe 1: HC ---
dat = squeeze(mean(Data.AvgCTL2(:,chn,Group1),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group1));
timeseries = mean(dat,2);
h1 = plot(t,timeseries,'.-','Color',rgbColor1,'DisplayName','HC');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor1,'facealpha',0.15})
% --- Gruppe 2: DP ---
dat = squeeze(mean(Data.AvgCTL2(:,chn,Group2),2));
SEM = std(dat,0,2) ./ sqrt(numel(Group2));
timeseries = mean(dat,2);
h2 = plot(t,timeseries,'.-','Color',rgbColor2,'DisplayName','DP');
toPatch(t,t,timeseries-SEM,timeseries+SEM,{rgbColor2,'facealpha',0.15})
% --- Achsen, Labels, Titel ---
ylim(YLIMITS)
xlim(XLIMITS)
xlabel('time in s','FontSize',FONT_SZ,'FontWeight','bold')
ylabel('z','FontSize',FONT_SZ,'FontWeight','bold')
title(TITLE,'FontSize',FONT_SZ,'FontWeight','bold')
% --- Legende ---
lh{iPlot} = legend([p2 h1 h2],'baseline','HC','DP');
legend boxoff
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
% --- weisser Hintergrund ---
lh{iPlot} = legend([p2 h1 h2],'baseline','HC','DP');
set(lh{iPlot},'FontSize',LEG_FS,'FontWeight','bold')
set(lh{iPlot},'Units','normalized','Position',LEG_POS)
set(lh{iPlot}, 'Color', 'w'); % Weisser Hintergrund
set(lh{iPlot}, 'EdgeColor', 'w'); % Rahmen
set(lh{iPlot}, 'Box', 'on'); % Kasten sichtbar
hold off
end
filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\FigureX_Timeseries_DPvsHC_CTL2_window1';
print(gcf, [filename '.svg'], '-dsvg', '-r600');
print(gcf, [filename '.jpg'], '-djpeg', '-r600');
%% Brainmaps CTL1 CTL2 und TSST contrast
close all
clear all
clc
initroutines('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\routines\2017-04-27')
clear settings P PS;
P = NirsPlotTool();
settings.experiment.time_series{1} = {'name','CTL1.cui','sample_rate',10,'trigger_name','CTL1.trigger'};
settings.experiment.time_series{2} = {'name','CTL2.cui','sample_rate',10,'trigger_name','CTL2.trigger'};
settings.experiment.time_series{3} = {'name','Arith.cui','sample_rate',10,'trigger_name','Stress.trigger'};
% settings.experiment.category{1} = {'name','TriggerA','trigger_token',1}; % for analysis of window 3
settings.experiment.category{1} = {'name','TriggerA','trigger_token',9}; % for analysis of window 1 and window 2
P = P.setProperties(settings);
P = NirsPlotTool();
P = P.setProperty('show_probeset','on');
P = P.setProperties(settings);
PS = NirsProbeset('brain_coord_file','Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Studie Stress Rumination\Koords\NIRS-probesetXYZ_Alle2.txt');
settings.plot_tool.probesets{1} = {'name','Gesamt','probeset',PS};
P = P.setProperties(settings);
P = P.setProperty('probesets',{'name','Gesamt','probeset',PS});
P = P.setProperties(settings);
P = P.setProperty('show_probeset','on');
P = P.setProperties(settings);
lIFG=[7 9 6];
lDLPFC=[10 12 11];
rIFG=[18 21 19];
rDLPFC=[20 23 24];
SAC=[27 26 25 28 30 31 32 35 36];
% load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Data_Export1.mat');
% load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Subjects_Export1.mat');
load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Data_Export3.mat');
load('Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Subjects_Export3.mat');
Group2 = [56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 105 106 108 109 110 111 116 118 120 121 122 123 124 125 126 127 132 ]; % HC
Group1 = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 104 107 112 113 114 115 117 119 128 129 130 131 133 134 135 136 137 138 139 140 141 142 ]; % DP
T1=((mean(Data.AmpCTL1( :,Group1),2)')-(mean(Data.AmpCTL1( :,Group2),2)'))./(sqrt((var(Data.AmpCTL1( :,Group2)')+var(Data.AmpCTL1( :,Group1)'))/2))
T2=((mean(Data.AmpCTL2( :,Group1),2)')-(mean(Data.AmpCTL2( :,Group2),2)'))./(sqrt((var(Data.AmpCTL2( :,Group2)')+var(Data.AmpCTL2( :,Group1)'))/2))
T3=((mean(Data.AmpArith(:,Group1),2)')-(mean(Data.AmpArith(:,Group2),2)'))./(sqrt((var(Data.AmpArith(:,Group2)')+var(Data.AmpArith(:,Group1)'))/2))
P = P.setProperty('show_probeset','on'); % "Zahlen-Channelbezeichnungen" werden rausgenommen
P = P.setProperty('show_head','on','map_type','brain_map','color_map','braincmap','color_limit',[-0.5 0.5],'color_gap',[],'view_angles',{[180 30],[-90 0],[90 0],[0 30];[180 30],[-90 0],[90 0],[0 30];[180 30],[-90 0],[90 0],[0 30]},'column_names',{'frontal','left','right','parietal'},'row_names',{'CTL1','CTL2','TSST'},'show_colorbar','d');
P = P.setProperty('values2map',{{T1},{T1},{T1},{T1};{T2},{T2},{T2},{T2};{T3},{T3},{T3},{T3}}...
,'probesets2map',{{'Gesamt'},{'Gesamt'},{'Gesamt'},{'Gesamt'};{'Gesamt'},{'Gesamt'},{'Gesamt'},{'Gesamt'};{'Gesamt'},{'Gesamt'},{'Gesamt'},{'Gesamt'}});
P = P.map('values');
% filename = 'Q:\NAS\Ehlis_Auswertung\Laufwerk_S\AG-Mitglieder\David\Artikel\TSST Anticipation\Auswertung\Plots\Figure6_Brainmap_window1';
% print(gcf, [filename '.svg'], '-dsvg', '-r600');
% print(gcf, [filename '.jpg'], '-djpeg', '-r600');

2026_04_09_fNIRSpreprocessing_TSST_Anticipation.m at commit 51b5b83, no license · at the source

Overview

Authors: Isabell Int-Veen^1,2, Ann-Christine Ehlis^1,2,3, Agnes Kroczek¹, Hendrik Laicher^1,2, Andreas J Fallgatter^1,2,3, David Rosenbaum¹

ORCID iDs: Isabell Int-Veen

Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany
German Center for Mental Health, partner site Tübingen, Germany
LEAD Graduate School & Research Network, University of Tuebingen, Tuebingen, Germany

Institutions: University of Tübingen (Germany)

Journal: NeuroImage. Clinical, volume 51, article 104019

Dates: received 2 March 2026; accepted 28 May 2026; published online 29 May 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.nicl.2026.104019 · PMID 42288110 · PMCID PMC13277536 · OpenAlex W7162795579

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), depression (population)

Methods: Spectral & time-frequency, Statistics, Preprocessing, Graphs, fMRI & imaging, Physiology & signal measures

Keywords: Hypofrontality, Prefrontal hypoactivation, Depression, Trier Social Stress Test (TSST), Anticipation

MeSH: Depression*, Prefrontal Cortex*, Stress, Psychological*, Adult, Anticipation, Psychological, Brain Mapping, Female, Humans, Male, Spectroscopy, Near-Infrared, Young Adult (* major topic)

Topic: Stress Responses and Cortisol (Behavioral Neuroscience, Neuroscience), according to OpenAlex

Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

Stress responses unfold across distinct phases, including anticipation, stressor confrontation, and recovery, yet research in depression has predominantly focused on neural processes during stress exposure itself. A robust finding in this context is reduced prefrontal activation under stress in patients with depression (DP). However, neural processes preceding stress exposure may critically shape task-related activation and bias group comparisons. To date, no study has directly examined the impact of this anticipatory neural activation on prefrontal hypoactivation under stress. Here, data from two functional near-infrared spectroscopy (fNIRS) studies employing the Trier Social Stress Test (TSST) in DP and healthy controls (HC) were pooled. Results revealed pronounced group differences prior to task onset, namely increased activation in the DP group compared to HC. With respect to stress-related brain activation, we replicated the well-established finding of reduced stress-related prefrontal activation in DP compared to HC when activation prior to the beginning of each trial was not accounted for. However, when it was accounted for, group differences in stress-related activation were no longer evident. Together, these findings suggest that apparent prefrontal hypoactivation may partly reflect heightened anticipatory recruitment of regulatory brain regions rather than insufficient engagement during task execution, underscoring the importance of (re-)considering the baseline problem in future studies.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

isabellintveen/TSSTAnticipation

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 51b5b832be2772f7d8efef4ce43a6dc6061297da, 21 April 2026

Languages: SPSS (2), MATLAB (1), R (1)

Size: 5 files, 4 scripts

Software Heritage: not archived

Found in: the text, “Data analysis”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: cowplot (1 file), Statistics and Machine Learning Toolbox (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

5 files

2026_04_09_fNIRSpreprocessing_TSST_Anticipation.m, MATLAB, 2,838 lines, 2 matches
Plots.R, R, 505 lines, 1 match
Syntax.sps, SPSS, 623 lines
Syntax_age.sps, SPSS, 335 lines
README.md, Text, 5 lines

At the source: github.com/isabellintveen/TSSTAnticipation.git

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
4 scripts, each with its path and the digest of its content;
3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Data availability

Data will be made available on request.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 11 MeSH terms, 57 references.

Cite

This paper

Int-Veen, I., Ehlis, A.-C., Kroczek, A., Laicher, H., Fallgatter, A. J., & Rosenbaum, D. (2026). Stress-related hypofrontality in depression and its relation to altered activation prior to the stress response. NeuroImage. Clinical, 51, 104019. https://doi.org/10.1016/j.nicl.2026.104019

BibTeX

@article{intveen2026stress, author = {Int-Veen, Isabell and Ehlis, Ann-Christine and Kroczek, Agnes and Laicher, Hendrik and Fallgatter, Andreas J and Rosenbaum, David}, title = {{Stress-related hypofrontality in depression and its relation to altered activation prior to the stress response}}, journal = {NeuroImage. Clinical}, year = {2026}, month = may, volume = {51}, pages = {104019}, publisher = {Elsevier}, issn = {2213-1582}, doi = {10.1016/j.nicl.2026.104019}, url = {https://doi.org/10.1016/j.nicl.2026.104019}, pmid = {42288110}, pmcid = {PMC13277536} }

RIS

TY - JOUR AU - Int-Veen, Isabell AU - Ehlis, Ann-Christine AU - Kroczek, Agnes AU - Laicher, Hendrik AU - Fallgatter, Andreas J AU - Rosenbaum, David TI - Stress-related hypofrontality in depression and its relation to altered activation prior to the stress response T2 - NeuroImage. Clinical J2 - Neuroimage Clin PY - 2026 DA - 2026/05/29 VL - 51 SP - 104019 SN - 2213-1582 PB - Elsevier DO - 10.1016/j.nicl.2026.104019 UR - https://doi.org/10.1016/j.nicl.2026.104019 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.nicl.2026.104019", "type": "article-journal", "title": "Stress-related hypofrontality in depression and its relation to altered activation prior to the stress response", "container-title": "NeuroImage. Clinical", "author": [ { "family": "Int-Veen", "given": "Isabell" }, { "family": "Ehlis", "given": "Ann-Christine" }, { "family": "Kroczek", "given": "Agnes" }, { "family": "Laicher", "given": "Hendrik" }, { "family": "Fallgatter", "given": "Andreas J" }, { "family": "Rosenbaum", "given": "David" } ], "container-title-short": "Neuroimage Clin", "volume": "51", "page": "104019", "DOI": "10.1016/j.nicl.2026.104019", "PMID": "42288110", "PMCID": "PMC13277536", "ISSN": "2213-1582", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.nicl.2026.104019", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 29 ] ] } }

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