diff --git a/algorithms/@LCModel_Caller/FitSpectrum.m b/algorithms/@LCModel_Caller/FitSpectrum.m
index bda877afdcdf65057394781cee9fdaa81c206a12..4a56b95861cf1898e68f117feb932003cc46910e 100644
--- a/algorithms/@LCModel_Caller/FitSpectrum.m
+++ b/algorithms/@LCModel_Caller/FitSpectrum.m
@@ -28,6 +28,9 @@ this.currentOutputFiles = currentOutputFiles;
this.currentControlFilePath = [this.homePath this.controlFilesPathRemote this.currentControlFile];
+remoteFilePath = [this.homePath this.outputFilesPathRemote];
+[this.connection, res] = sshfrommatlabissue(this.connection, ['mkdir ' remoteFilePath]);
+
[this.connection, res] = sshfrommatlabissue(this.connection, [this.homePath this.LCModelPath ' < ' this.currentControlFilePath]);
if (copyFileLocally)
diff --git a/algorithms/@MR_spectroS/getPpmVector.m b/algorithms/@MR_spectroS/getPpmVector.m
index 118f1302604744651ee8b6f79bf2bfe6928baf0f..46ac40a1e3bb147bc527025f3cbcedce5ab23e89 100644
--- a/algorithms/@MR_spectroS/getPpmVector.m
+++ b/algorithms/@MR_spectroS/getPpmVector.m
@@ -10,7 +10,7 @@ bw = linspace(-bw/2,bw/2,size(this.Data{1},this.kx_dim) * zeroFillFactor);
if strcmp(this.Parameter.Headers.Nucleus,'31P')
ppm = bw/(txFreq*1e-6);
else
- ppm = bw/(txFreq*1e-6) + 4.7;
+ ppm = bw/(txFreq*1e-6) + 4.66;
end
end
\ No newline at end of file
diff --git a/algorithms/ImportLCModelBasis.m b/algorithms/ImportLCModelBasis.m
index 4efb06c8924b4daebc49cdbf190fc95e537d2212..4bd8cfd3522a65a09bce48e945d7f6af5f12450c 100644
--- a/algorithms/ImportLCModelBasis.m
+++ b/algorithms/ImportLCModelBasis.m
@@ -12,9 +12,9 @@ if ~(strcmp(filepath(end),'\') || strcmp(filepath(end),'/'))
filepath = [filepath '/'];
end
-if ~contains(filename,'.raw', 'IgnoreCase',true)
- filename = strcat(filename, '.RAW');
-end
+% if ~contains(filename,'.raw', 'IgnoreCase',true)
+% filename = strcat(filename, '.RAW');
+% end
%open the .RAW data
filename = strcat(filepath, filename);
%
diff --git a/fMRS_processing/BoldEffectfMRS.m b/fMRS_processing/BoldEffectfMRS.m
index 9c10336839d8ca02965caf7647e0a3b323373cfb..dba988b65df3d5ae6a2609940060a0f186ef3277 100644
--- a/fMRS_processing/BoldEffectfMRS.m
+++ b/fMRS_processing/BoldEffectfMRS.m
@@ -10,7 +10,7 @@ BlockNo = cell(1,nBlocks);
for k = 1:nBlocks
BlockNo{k} = strcat('_Block', num2str(k));
end
-woBaseline = false;
+woBaseline = false; %false, true
subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
'4085';'3333';'4012';'4085';'6524'};
@@ -56,21 +56,26 @@ OutputFilesPathLocal = strcat(localFilePathBase, 'Output/BOLD/');
% OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline)
% end
%
-% %correlation FWHM, Amplitude
+%correlation FWHM, Amplitude
% metabType = {'NAA', 'Cr'};
+% metabType = {'Water', 'NAA'};
+% metabType = {'Water', 'Cr'};
+% % metabType = {'Water'};
% if exist('excludeSubjects')
% CorrBOLDSpearman(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
% else
% CorrBOLDSpearman(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
% end
-% % CorrBOLDCategorical(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+% % % CorrBOLDCategorical(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
%correlation Water, Metabolite Concentration
-metabType = {'Lac'};
+% metabType = {'Lac'};
if exist('excludeSubjects')
- CorrBOLDSpearman_MetConc(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
+ CorrBOLDSpearman_MetConc({'Lac'},visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
+ CorrBOLDSpearman_MetConc({'Glu'},visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
else
- CorrBOLDSpearman_MetConc(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+ CorrBOLDSpearman_MetConc({'Lac'},visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+ CorrBOLDSpearman_MetConc({'Glu'},visualType,BOLDType,OutputFilesPathLocal,woBaseline)
end
end
@@ -90,12 +95,22 @@ end
impFileMat = strcat(OutputFilesPathLocal,impName,'.mat');
BOLDData = load(impFileMat);
fields = fieldnames(BOLDData);
-BOLDDataXY(1) = BOLDData.(fields{1});
+BOLDDataXY(1) = BOLDData.(fields{1}); %Subj x Block
+%convert to vector with correct order:
+%subj1_block1,subj1_block2,...,subj1_block10,subj2_block1,...
+N = length(BOLDDataXY.amplitude);
+BOLDWaterAmp = zeros(N*N,1); BOLDWaterFWHM = zeros(N*N,1);
+for i=1:N
+ for j=1:N
+ BOLDWaterAmp((i-1)*N + j) = BOLDDataXY.amplitude(i,j);
+ BOLDWaterFWHM((i-1)*N + j) = BOLDDataXY.fwhm(i,j);
+ end
+end
%import Met Conc
impNameMetConc = strcat('BlockConc_Stim_woBOLDCorr_MMBSummed',woBaseline)
impFileMatMetConc = strcat(OutputFilesPathLocal(1:end-5),impNameMetConc,'.mat');
-BOLDData = load(impNameMetConc);
+BOLDData = load(impFileMatMetConc);
fields = fieldnames(BOLDData);
BOLDData_temp = BOLDData.(fields{1});
if exist('excludeSubjects')
@@ -113,11 +128,11 @@ if exist('excludeSubjects')
end
BOLDData_temp([excludeIdx],:) = [];
end
-indexMet = strcmp(BOLDData_temp(1,2:end),metabType);
+indexMet = strcmp(BOLDData_temp(1,:),metabType);
idxMet = find(indexMet);
-BOLDData_temp = BOLDData_temp(2:end-4,2:end);
-for i = 1:length(BOLDData_temp)
- BOLDData_Met{i} = BOLDData_temp{i,idxMet};
+% BOLDData_temp = BOLDData_temp(2:end-4,2:end);
+for i = 1:length(BOLDData_temp)-5
+ BOLDData_Met{i} = BOLDData_temp{i+1,idxMet};
end
if exist('excludeSubjects')
@@ -132,45 +147,113 @@ for i = 1:subjLength
subjects((1+(i-1)*10):(10+(i-1)*10)) = subjects((1+(i-1)*10):(10+(i-1)*10)).*i;
end
-%for FWHM
-fwhmTab = table(BOLDDataXY(1).fwhm(:),cell2mat(BOLDData_Met'),subjects);
+%for FWHM (Water Hz vs Met a.u.)
+fwhmTab = table(BOLDWaterFWHM,cell2mat(BOLDData_Met'),subjects);
BOLDData_FWHM = table2array(fwhmTab);
%Spearman correlation
[RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Spearman');
% [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
%linear regression
-fit = fitlm(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2));
+fit = fitlm(BOLDData_FWHM(:,2),BOLDData_FWHM(:,1));
figure
hold on
% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
plot(fit)
-xlabel(['FWHM Water / Hz'])
-ylabel(['Concentration ',metabType, ' / a.u.'])
-title(['Correlation FWHM Water & concentration', metabType, woBaseline], 'Interpreter', 'none')
+xlabel(['Concentration ' , char(metabType), ' / a.u.'])
+ylabel(['FWHM Water / Hz'])
+title(['Correlation FWHM Water & concentration ', char(metabType), woBaseline], 'Interpreter', 'none')
txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
-text(max(BOLDData_FWHM(:,1)-0.5),max(BOLDData_FWHM(:,2)-1),txt)
+text(max(BOLDData_FWHM(:,2)-1),max(BOLDData_FWHM(:,1)-0.5),txt)
+set(gca, 'FontWeight','bold');
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhmConc.fig'));
-savefig(fig2saveFWHM)
-
-%for amplitude
-ampTab = table(BOLDDataXY(1).amplitude(:),cell2mat(BOLDData_Met'),subjects);
+% savefig(fig2saveFWHM)
+
+%for FWHM (Delta Water Hz vs Delta Met a.u.)
+subjectsDelta = subjects;
+subjectsDelta(1:10:end,:) = [];
+tempFWHM = []; tempAmp = []; temp_Met = [];MetForDiff = cell2mat(BOLDData_Met');
+for i = 1:length(subjects)/10
+% %for Delta a.u. / Delta Hz
+ tempFWHM = [tempFWHM; diff(BOLDWaterFWHM(1+(i-1)*10:10+(i-1)*10))];
+ tempAmp = [tempAmp ; diff(BOLDWaterAmp(1+(i-1)*10:10+(i-1)*10))];
+ temp_Met = [temp_Met ; diff(MetForDiff(1+(i-1)*10:10+(i-1)*10))];
+% %for Delta in %
+% tempBaseline_FWHM = BOLDWaterFWHM(2+(i-1)*10);
+% tempFWHM = [tempFWHM; BOLDWaterFWHM(1+(i-1)*10:10+(i-1)*10)./tempBaseline_FWHM*100-100];
+% tempBaseline_Amp = BOLDWaterAmp(2+(i-1)*10);
+% tempAmp = [tempAmp; BOLDWaterAmp(1+(i-1)*10:10+(i-1)*10)./tempBaseline_Amp*100-100];
+% % % tempBaseline_Met = MetForDiff(2+(i-1)*10);
+% % % temp_Met = [temp_Met; MetForDiff(1+(i-1)*10:10+(i-1)*10)./tempBaseline_Met*100-100];
+end
+% tempFWHM(1:10:end,:) = []; tempAmp(1:10:end,:) = [];
+BOLDDataXYDiff.fwhm = tempFWHM; BOLDDataXYDiff.amplitude = tempAmp; BOLDData_Met_Diff = temp_Met;
+fwhmTabDelta = table(BOLDDataXYDiff(1).fwhm(:),BOLDData_Met_Diff,subjectsDelta);
+BOLDData_FWHM_Delta = table2array(fwhmTabDelta);
+%Spearman correlation
+[RHODelta,PVALDelta] = corr(BOLDData_FWHM_Delta(:,2),BOLDData_FWHM_Delta(:,1),'Type','Spearman');
+% [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+%linear regression
+fitDelta = fitlm(BOLDData_FWHM_Delta(:,2),BOLDData_FWHM_Delta(:,1));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fitDelta)
+xlabel([char(916), ' Concentration ' , char(metabType), ' / a.u.'])
+ylabel([char(916), ' FWHM Water / Hz'])
+% ylabel([char(916), ' FWHM Water / %'])
+title(['Correlation ', char(916), 'FWHM Water & concentration ', char(metabType), woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+text(max(BOLDData_FWHM_Delta(:,2))-0.2,max(BOLDData_FWHM_Delta(:,1)),txt);
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+
+fig2saveFWHMDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_DeltafwhmConc.fig'));
+% fig2saveFWHMDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_DeltafwhmConc_Perc.fig'));
+savefig(fig2saveFWHMDelta)
+
+%for amplitude (Water a.u. vs Met a.u.)
+ampTab = table(BOLDWaterAmp,cell2mat(BOLDData_Met'),subjects);
BOLDData_amp = table2array(ampTab);
%Spearman correlation
-[RHO,PVAL] = corr(BOLDData_amp(:,1),BOLDData_amp(:,2),'Type','Spearman');
+[RHO,PVAL] = corr(BOLDData_amp(:,2),BOLDData_amp(:,1),'Type','Spearman');
%linear regression
-fit = fitlm(BOLDData_amp(:,1),BOLDData_amp(:,2));
+fit = fitlm(BOLDData_amp(:,2),BOLDData_amp(:,1));
figure
hold on
% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
plot(fit)
-xlabel(['Amplitude Water / a.u.'])
-ylabel(['Concentration ',metabType, ' / a.u.'])
-title(['Correlation Amplitude Water & concentration', metabType, woBaseline], 'Interpreter', 'none')
+xlabel(['Concentration ' , char(metabType), ' / a.u.'])
+ylabel(['Amplitude Water / a.u.'])
+title(['Correlation Amplitude Water & concentration ', char(metabType), woBaseline], 'Interpreter', 'none')
txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
-text((min(BOLDData_amp(:,1))+min(BOLDData_amp(:,1))*0.05),(max(BOLDData_amp(:,2))-max(BOLDData_amp(:,2))*0.05),txt)
+text((max(BOLDData_amp(:,2))-max(BOLDData_amp(:,2))*0.1),(min(BOLDData_amp(:,1))+min(BOLDData_amp(:,1))*0.05),txt)
+set(gca, 'FontWeight','bold');
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_ampConc.fig'));
-savefig(fig2saveAmplitude)
+% savefig(fig2saveAmplitude)
+
+%for amplitude (Delta Water a.u. vs Delta Met a.u.)
+ampTabDelta = table(BOLDDataXYDiff(1).amplitude(:),BOLDData_Met_Diff,subjectsDelta);
+BOLDData_amp_Delta = table2array(ampTabDelta);
+%Spearman correlation
+[RHODelta,PVALDelta] = corr(BOLDData_amp_Delta(:,2),BOLDData_amp_Delta(:,1),'Type','Spearman');
+%linear regression
+fitDelta = fitlm(BOLDData_amp_Delta(:,2),BOLDData_amp_Delta(:,1));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fitDelta)
+xlabel([char(916), ' Concentration ' , char(metabType), ' / a.u.'])
+ylabel([char(916), ' Amplitude Water / a.u.'])
+% ylabel([char(916), ' Amplitude Water / %'])
+title(['Correlation ', char(916), 'Amplitude Water & concentration ', char(metabType), woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+text((max(BOLDData_amp_Delta(:,2))-max(BOLDData_amp_Delta(:,2))*0.3),(min(BOLDData_amp_Delta(:,1))-min(BOLDData_amp_Delta(:,1))*0.1),txt);
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+fig2saveAmplitudeDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_DeltaampConc.fig'));
+% fig2saveAmplitudeDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_DeltaampConc_Perc.fig'));
+savefig(fig2saveAmplitudeDelta)
end
function CorrBOLDSpearman(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
@@ -197,59 +280,192 @@ for idxmetab = 1:length(metabType)
impFileMat = strcat(OutputFilesPathLocal,impName,'.mat');
BOLDData = load(impFileMat);
fields = fieldnames(BOLDData);
- BOLDDataXY(idxmetab) = BOLDData.(fields{1});
+ BOLDDataXY(idxmetab) = BOLDData.(fields{1});%Subj x Block
+ %convert to vector with correct order:
+ %subj1_block1,subj1_block2,...,subj1_block10,subj2_block1,...
+ N = length(BOLDDataXY(idxmetab).amplitude);
+ BOLDWaterAmp = zeros(N*N,1); BOLDWaterFWHM = zeros(N*N,1);
+ for i=1:N
+ for j=1:N
+ BOLDWaterAmp((i-1)*N + j) = BOLDDataXY(idxmetab).amplitude(i,j);
+ BOLDWaterFWHM((i-1)*N + j) = BOLDDataXY(idxmetab).fwhm(i,j);
+ end
+ end
+ BOLDDataXY(idxmetab).amplitude = BOLDWaterAmp;
+ BOLDDataXY(idxmetab).fwhm = BOLDWaterFWHM;
end
if exist('excludeSubjects')
- file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline,'_Excluded');
+ if length(metabType) == 2
+ file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline,'_Excluded');
+ elseif length(metabType) == 1
+ file2save = strcat('Corr_',metabType{1},'_FWHMAmp_',visualType,BOLDType,woBaseline,'_Excluded');
+ end
else
- file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline);
+ if length(metabType) == 2
+ file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline);
+ elseif length(metabType) == 1
+ file2save = strcat('Corr_',metabType{1},'_FWHMAmp_',visualType,BOLDType,woBaseline);
+ end
end
-subjLength = length(BOLDDataXY(1).fwhm);
+subjLength = length(BOLDDataXY(1).fwhm)/10;
subjects = ones(subjLength*10,1);
for i = 1:subjLength
subjects((1+(i-1)*10):(10+(i-1)*10)) = subjects((1+(i-1)*10):(10+(i-1)*10)).*i;
end
-%for FWHM
-fwhmTab = table(BOLDDataXY(1).fwhm(:),BOLDDataXY(2).fwhm(:),subjects);
-BOLDData_FWHM = table2array(fwhmTab);
-%Spearman correlation
-[RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Spearman');
-% [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
-%linear regression
-fit = fitlm(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2));
-figure
-hold on
-% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
-plot(fit)
-xlabel(['FWHM ',num2str(metabType{1}), ' / Hz'])
-ylabel(['FWHM ',num2str(metabType{2}), ' / Hz'])
-title(['Correlation FWHM ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
-txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
-text(min(BOLDData_FWHM(:,1)+1),max(BOLDData_FWHM(:,2)-1),txt)
-
-fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
-savefig(fig2saveFWHM)
-
-%for amplitude
-ampTab = table(BOLDDataXY(1).amplitude(:),BOLDDataXY(2).amplitude(:),subjects);
-BOLDData_amp = table2array(ampTab);
-%Spearman correlation
-[RHO,PVAL] = corr(BOLDData_amp(:,1),BOLDData_amp(:,2),'Type','Spearman');
-%linear regression
-fit = fitlm(BOLDData_amp(:,1),BOLDData_amp(:,2));
-figure
-hold on
-% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
-plot(fit)
-xlabel(['Amplitude ',num2str(metabType{1}), ' / a.u.'])
-ylabel(['Amplitude ',num2str(metabType{2}), ' / a.u.'])
-title(['Correlation Amplitude ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
-txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
-text((min(BOLDData_amp(:,1))+min(BOLDData_amp(:,1))*0.05),(max(BOLDData_amp(:,2))-max(BOLDData_amp(:,2))*0.05),txt)
-fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_amp.fig'));
-savefig(fig2saveAmplitude)
+if length(metabType) == 2
+ %for FWHM (Hz)
+ fwhmTab = table(BOLDDataXY(1).fwhm(:),BOLDDataXY(2).fwhm(:),subjects);
+ BOLDData_FWHM = table2array(fwhmTab);
+ %Spearman correlation
+ [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Spearman');
+ % [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+ %linear regression
+ fit = fitlm(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fit)
+ xlabel(['FWHM ',num2str(metabType{1}), ' / Hz'])
+ ylabel(['FWHM ',num2str(metabType{2}), ' / Hz'])
+ title(['Correlation FWHM ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+ text(min(BOLDData_FWHM(:,1)+1),max(BOLDData_FWHM(:,2)-1),txt)
+ set(gca, 'FontWeight','bold');
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+
+ fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
+ savefig(fig2saveFWHM)
+
+ %for FWHM (Delta Hz)
+ subjectsDelta = subjects;
+ subjectsDelta(1:10:end,:) = [];
+ for idxmetab = 1:length(metabType)
+ tempFWHM = []; tempAmp = [];
+ for i = 1:length(subjects)/10
+ tempFWHM = [tempFWHM; diff(BOLDDataXY(idxmetab).fwhm(1+(i-1)*10:10+(i-1)*10))];
+ tempAmp = [tempAmp ; diff(BOLDDataXY(idxmetab).amplitude(1+(i-1)*10:10+(i-1)*10))];
+ end
+ BOLDDataXYDiff(idxmetab).fwhm = tempFWHM; BOLDDataXYDiff(idxmetab).amplitude = tempAmp;
+ end
+ fwhmTabDelta = table(BOLDDataXYDiff(1).fwhm(:),BOLDDataXYDiff(2).fwhm(:),subjectsDelta);
+ BOLDData_FWHM_Delta = table2array(fwhmTabDelta);
+ %Spearman correlation
+ [RHODelta,PVALDelta] = corr(BOLDData_FWHM_Delta(:,1),BOLDData_FWHM_Delta(:,2),'Type','Spearman');
+ % [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+ %linear regression
+ fitDelta = fitlm(BOLDData_FWHM_Delta(:,1),BOLDData_FWHM_Delta(:,2));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fitDelta)
+ xlabel([char(916) 'FWHM ',num2str(metabType{1}), ' / Hz'])
+ ylabel([char(916) 'FWHM ',num2str(metabType{2}), ' / Hz'])
+ title(['Correlation ' char(916) 'FWHM ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+ text(min(BOLDData_FWHM_Delta(:,1))+min(BOLDData_FWHM_Delta(:,1)*0.01),max(BOLDData_FWHM_Delta(:,2))-max(BOLDData_FWHM_Delta(:,2))*0.01,txt)
+ set(gca, 'FontWeight','bold');
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+
+ fig2saveFWHMDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_Deltafwhm.fig'));
+ savefig(fig2saveFWHMDelta)
+
+ %for amplitude (a.u.)
+ ampTab = table(BOLDDataXY(1).amplitude(:),BOLDDataXY(2).amplitude(:),subjects);
+ BOLDData_amp = table2array(ampTab);
+ %Spearman correlation
+ [RHO,PVAL] = corr(BOLDData_amp(:,1),BOLDData_amp(:,2),'Type','Spearman');
+ %linear regression
+ fit = fitlm(BOLDData_amp(:,1),BOLDData_amp(:,2));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fit)
+ xlabel(['Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ ylabel(['Amplitude ',num2str(metabType{2}), ' / a.u.'])
+ title(['Correlation Amplitude ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+ text((min(BOLDData_amp(:,1))+min(BOLDData_amp(:,1))*0.05),(max(BOLDData_amp(:,2))-max(BOLDData_amp(:,2))*0.05),txt)
+ set(gca, 'FontWeight','bold');
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+ fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_amp.fig'));
+ savefig(fig2saveAmplitude)
+
+ %for amplitude (Delta a.u.)
+ ampTabDelta = table(BOLDDataXYDiff(1).amplitude(:),BOLDDataXYDiff(2).amplitude(:),subjectsDelta);
+ BOLDData_amp_Delta = table2array(ampTabDelta);
+ %Spearman correlation
+ [RHODelta,PVALDelta] = corr(BOLDData_amp_Delta(:,1),BOLDData_amp_Delta(:,2),'Type','Spearman');
+ %linear regression
+ fitDelta = fitlm(BOLDData_amp_Delta(:,1),BOLDData_amp_Delta(:,2));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fitDelta)
+ xlabel([char(916) 'Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ ylabel([char(916) 'Amplitude ',num2str(metabType{2}), ' / a.u.'])
+ title(['Correlation ' char(916) 'Amplitude ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+ text((max(BOLDData_amp_Delta(:,1))-max(BOLDData_amp_Delta(:,1))*0.25),(min(BOLDData_amp_Delta(:,2))-min(BOLDData_amp_Delta(:,2))*0.25),txt)
+ set(gca, 'FontWeight','bold');
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+ fig2saveAmplitudeDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_Deltaamp.fig'));
+ savefig(fig2saveAmplitudeDelta)
+
+elseif length(metabType) == 1
+ %for FWHM (Hz) / Amp (a.u.)
+ fwhmAmpTab = table(BOLDDataXY(1).fwhm(:),BOLDDataXY(1).amplitude(:),subjects);
+ BOLDData = table2array(fwhmAmpTab);
+ %Spearman correlation
+ [RHO,PVAL] = corr(BOLDData(:,1),BOLDData(:,2),'Type','Spearman');
+ % [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+ %linear regression
+ fit = fitlm(BOLDData(:,1),BOLDData(:,2));
+ figure
+ hold on
+ plot(fit)
+ xlabel(['FWHM ',num2str(metabType{1}), ' / Hz'])
+ ylabel(['Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ title(['Correlation FWHM & Amplitude ', num2str(metabType{1}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+ text(min(BOLDData(:,1))+min(BOLDData(:,1))*0.001,min(BOLDData(:,2))+min(BOLDData(:,2))*0.001,txt)
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+
+ fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'.fig'));
+ savefig(fig2save)
+
+ %for FWHM (Delta Hz) / Amp (Delta a.u.)
+ subjectsDelta = subjects;
+ subjectsDelta(1:10:end,:) = [];
+ for idxmetab = 1:length(metabType)
+ tempFWHM = []; tempAmp = [];
+ for i = 1:length(subjects)/10
+ tempFWHM = [tempFWHM; diff(BOLDDataXY(idxmetab).fwhm(1+(i-1)*10:10+(i-1)*10))];
+ tempAmp = [tempAmp ; diff(BOLDDataXY(idxmetab).amplitude(1+(i-1)*10:10+(i-1)*10))];
+ end
+ BOLDDataXYDiff(idxmetab).fwhm = tempFWHM; BOLDDataXYDiff(idxmetab).amplitude = tempAmp;
+ end
+ fwhmAmpTabDelta = table(BOLDDataXYDiff(1).fwhm(:),BOLDDataXYDiff(1).amplitude(:),subjectsDelta);
+ BOLDData_Delta = table2array(fwhmAmpTabDelta);
+ %Spearman correlation
+ [RHODelta,PVALDelta] = corr(BOLDData_Delta(:,1),BOLDData_Delta(:,2),'Type','Spearman');
+ %linear regression
+ fitDelta = fitlm(BOLDData_Delta(:,1),BOLDData_Delta(:,2));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fitDelta)
+ xlabel([char(916) 'FWHM ',num2str(metabType{1}), ' / Hz'])
+ ylabel([char(916) 'Amplitude ',num2str(metabType{1}), ' / Hz'])
+ title(['Correlation ' char(916) 'FWHM & Amplitude ', num2str(metabType{1}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+ text(min(BOLDData_Delta(:,1)),min(BOLDData_Delta(:,2)),txt)
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+
+ fig2saveDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_Delta.fig'));
+ savefig(fig2saveDelta)
+end
end
@@ -320,7 +536,7 @@ end
BOLD.fwhm = fwhmArray; BOLD.amplitude = amplitudeArray;
eval([file2save '= BOLD']);
exportFileMat = [OutputFilesPathLocal, strcat(file2save,'.mat')];
-save(exportFileMat,file2save)
+% save(exportFileMat,file2save)
x = linspace(320/60/4,(320*5/60-320/60/4),10);
figure
@@ -346,9 +562,10 @@ end
patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
ylim([yLimits(1) yLimits(2)]);
-xlim([0 320*5/60]);
+xlim([0 320*5/60]); set(gca, 'FontWeight','bold');
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
-savefig(fig2save)
+% savefig(fig2save)
figure
hold on
@@ -379,9 +596,10 @@ end
patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
ylim([yLimits(1) yLimits(2)]);
-xlim([0 320*5/60]);
+xlim([0 320*5/60]); set(gca, 'FontWeight','bold');
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'_amplitude.fig'));
-savefig(fig2save)
+% savefig(fig2save)
close all
%plot mean, std of BOLD
@@ -440,10 +658,10 @@ end
patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
ylim([yLimits(1) yLimits(2)]);
-xlim([0 320*5/60]);
+xlim([0 320*5/60]); set(gca, 'FontWeight','bold');
set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 12, 18]);
fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'_Mean.fig'));
-savefig(fig2save)
+% savefig(fig2save)
end
function CorrBOLDCategorical(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
diff --git a/fMRS_processing/BoldEffectfMRS_SlidingAverage.m b/fMRS_processing/BoldEffectfMRS_SlidingAverage.m
new file mode 100644
index 0000000000000000000000000000000000000000..2f17605e7781b43e0e58eb934a3e79b46d9b831c
--- /dev/null
+++ b/fMRS_processing/BoldEffectfMRS_SlidingAverage.m
@@ -0,0 +1,506 @@
+function BoldEffectfMRS_SlidingAverage()
+clear; clc; close all;
+
+pathName = 'fMRS SlidingAverage data path';
+[localFilePathBase] = pathToDataFolder(pathName);
+visualType = 'Stim'; %'Stim', 'Rest'
+BOLDType = '_woBOLDCorr';
+nBlocks = 10;
+nAv = 37;
+AvNo = cell(1,nAv);
+for k = 1:nAv
+ AvNo{k} = strcat('_Av', num2str(k));
+end
+woBaseline = false;
+
+subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
+ '4085';'3333';'4012';'4085';'6524'};
+% prefixes = {'2020-02-18_';};
+prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
+ '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
+ '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
+suffixFolder = '_fMRS';
+excludeSubjects = {'5166';'9810';'5269'};
+
+subjectsPath = strcat(prefixes, subjects, suffixFolder);
+preprocessedFilesPathLocal = strcat(localFilePathBase, subjectsPath, '/');
+OutputFilesPathLocal = strcat(localFilePathBase, 'Output/BOLD/');
+
+% % %for Water
+% PeakRange = [4 5]; %ppm where to find the peak
+% metabType = 'Water';
+% if exist('excludeSubjects')
+% CalcBOLDEffect(subjects,AvNo,visualType,BOLDType,preprocessedFilesPathLocal,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,'',excludeSubjects)
+% else
+% CalcBOLDEffect(subjects,AvNo,visualType,BOLDType,preprocessedFilesPathLocal,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType)
+% end
+% %for NAA (2.008 ppm)
+% PeakRange = [1.8 2.3]; %ppm where to find the peak
+% metabType = 'NAA';
+% if exist('excludeSubjects')
+% CalcBOLDEffect(subjects,AvNo,visualType,BOLDType,preprocessedFilesPathLocal,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline,excludeSubjects)
+% else
+% CalcBOLDEffect(subjects,AvNo,visualType,BOLDType,preprocessedFilesPathLocal,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline)
+% end
+% %for Cr (methyl signal of tCr: 3.027ppm)
+% PeakRange = [2.9 3.15]; %ppm where to find the peak
+% metabType = 'Cr';
+% if exist('excludeSubjects')
+% CalcBOLDEffect(subjects,AvNo,visualType,BOLDType,preprocessedFilesPathLocal,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline,excludeSubjects)
+% else
+% CalcBOLDEffect(subjects,AvNo,visualType,BOLDType,preprocessedFilesPathLocal,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline)
+% end
+%
+% %correlation FWHM, Amplitude
+% metabType = {'NAA', 'Cr'};
+% if exist('excludeSubjects')
+% CorrBOLDSpearman(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
+% else
+% CorrBOLDSpearman(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+% end
+% % CorrBOLDCategorical(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+
+%correlation Water, Metabolite Concentration
+%%%%% didn't check this for metabolites from sliding average %%%%%
+metabType = {'Lac'};
+if exist('excludeSubjects')
+ CorrBOLDSpearman_MetConc(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
+else
+ CorrBOLDSpearman_MetConc(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+end
+
+end
+
+function CorrBOLDSpearman_MetConc(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+
+if exist('excludeSubjects')
+ impName = strcat('BOLD_Water_',visualType,BOLDType,'_SlidingAverage_Excluded');
+else
+ impName = strcat('BOLD_Water_',visualType,BOLDType,'_SlidingAverage');
+end
+impFileMat = strcat(OutputFilesPathLocal,impName,'.mat');
+BOLDData = load(impFileMat);
+fields = fieldnames(BOLDData);
+BOLDDataXY(1) = BOLDData.(fields{1});
+
+%import Met Conc
+impNameMetConc = strcat('BlockConc_Stim_woBOLDCorr_MMBSummed',woBaseline)
+impFileMatMetConc = strcat(OutputFilesPathLocal(1:end-5),impNameMetConc,'.mat');
+BOLDData = load(impNameMetConc);
+fields = fieldnames(BOLDData);
+BOLDData_temp = BOLDData.(fields{1});
+if exist('excludeSubjects')
+ n = length(BOLDData_temp);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(BOLDData_temp(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ BOLDData_temp([excludeIdx],:) = [];
+end
+indexMet = strcmp(BOLDData_temp(1,2:end),metabType);
+idxMet = find(indexMet);
+BOLDData_temp = BOLDData_temp(2:end-4,2:end);
+for i = 1:length(BOLDData_temp)
+ BOLDData_Met{i} = BOLDData_temp{i,idxMet};
+end
+
+if exist('excludeSubjects')
+ file2save = strcat('Corr_Water_',metabType{1},'_Conc_',visualType,BOLDType,woBaseline,'_Excluded');
+else
+ file2save = strcat('Corr_Water_',metabType{1},'_Conc_',visualType,BOLDType,woBaseline);
+end
+
+subjLength = length(BOLDDataXY(1).fwhm);
+subjects = ones(subjLength*10,1);
+for i = 1:subjLength
+ subjects((1+(i-1)*10):(10+(i-1)*10)) = subjects((1+(i-1)*10):(10+(i-1)*10)).*i;
+end
+
+%for FWHM
+fwhmTab = table(BOLDDataXY(1).fwhm(:),cell2mat(BOLDData_Met'),subjects);
+BOLDData_FWHM = table2array(fwhmTab);
+%Spearman correlation
+[RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Spearman');
+% [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+%linear regression
+fit = fitlm(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fit)
+xlabel(['FWHM Water / Hz'])
+ylabel(['Concentration ',metabType, ' / a.u.'])
+title(['Correlation FWHM Water & concentration', metabType, woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+text(max(BOLDData_FWHM(:,1)-0.5),max(BOLDData_FWHM(:,2)-1),txt)
+
+fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhmConc.fig'));
+savefig(fig2saveFWHM)
+
+%for amplitude
+ampTab = table(BOLDDataXY(1).amplitude(:),cell2mat(BOLDData_Met'),subjects);
+BOLDData_amp = table2array(ampTab);
+%Spearman correlation
+[RHO,PVAL] = corr(BOLDData_amp(:,1),BOLDData_amp(:,2),'Type','Spearman');
+%linear regression
+fit = fitlm(BOLDData_amp(:,1),BOLDData_amp(:,2));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fit)
+xlabel(['Amplitude Water / a.u.'])
+ylabel(['Concentration ',metabType, ' / a.u.'])
+title(['Correlation Amplitude Water & concentration', metabType, woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+text((min(BOLDData_amp(:,1))+min(BOLDData_amp(:,1))*0.05),(max(BOLDData_amp(:,2))-max(BOLDData_amp(:,2))*0.05),txt)
+fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_ampConc.fig'));
+savefig(fig2saveAmplitude)
+end
+
+function CorrBOLDSpearman(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+
+for idxmetab = 1:length(metabType)
+ if strcmp(metabType{idxmetab}(1:2),'Wa')
+ if exist('excludeSubjects')
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType,'_Excluded');
+ else
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType);
+ end
+ else
+ if exist('excludeSubjects')
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType,woBaseline,'_Excluded');
+ else
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType,woBaseline);
+ end
+ end
+ impFileMat = strcat(OutputFilesPathLocal,impName,'.mat');
+ BOLDData = load(impFileMat);
+ fields = fieldnames(BOLDData);
+ BOLDDataXY(idxmetab) = BOLDData.(fields{1});
+end
+if exist('excludeSubjects')
+ file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline,'_Excluded');
+else
+ file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline);
+end
+
+subjLength = length(BOLDDataXY(1).fwhm);
+subjects = ones(subjLength*10,1);
+for i = 1:subjLength
+ subjects((1+(i-1)*10):(10+(i-1)*10)) = subjects((1+(i-1)*10):(10+(i-1)*10)).*i;
+end
+
+%for FWHM
+fwhmTab = table(BOLDDataXY(1).fwhm(:),BOLDDataXY(2).fwhm(:),subjects);
+BOLDData_FWHM = table2array(fwhmTab);
+%Spearman correlation
+[RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Spearman');
+% [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+%linear regression
+fit = fitlm(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fit)
+xlabel(['FWHM ',num2str(metabType{1}), ' / Hz'])
+ylabel(['FWHM ',num2str(metabType{2}), ' / Hz'])
+title(['Correlation FWHM ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+text(min(BOLDData_FWHM(:,1)+1),max(BOLDData_FWHM(:,2)-1),txt)
+
+fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
+savefig(fig2saveFWHM)
+
+%for amplitude
+ampTab = table(BOLDDataXY(1).amplitude(:),BOLDDataXY(2).amplitude(:),subjects);
+BOLDData_amp = table2array(ampTab);
+%Spearman correlation
+[RHO,PVAL] = corr(BOLDData_amp(:,1),BOLDData_amp(:,2),'Type','Spearman');
+%linear regression
+fit = fitlm(BOLDData_amp(:,1),BOLDData_amp(:,2));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fit)
+xlabel(['Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ylabel(['Amplitude ',num2str(metabType{2}), ' / a.u.'])
+title(['Correlation Amplitude ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+text((min(BOLDData_amp(:,1))+min(BOLDData_amp(:,1))*0.05),(max(BOLDData_amp(:,2))-max(BOLDData_amp(:,2))*0.05),txt)
+fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_amp.fig'));
+savefig(fig2saveAmplitude)
+end
+
+
+
+
+function CalcBOLDEffect(subjects,AvNo,visualType,BOLDType,preprocessedFilesPathLocal,...
+ OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline,excludeSubjects)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+SelProcValues = SelectedProcessingValues();
+fwhmArray = zeros(length(subjects),length(AvNo));
+amplitudeArray = zeros(length(subjects),length(AvNo));
+for idxSubject = 1:length(subjects)
+ for nBl = 1:length(AvNo)
+ impName = strcat(subjects{idxSubject},'_',visualType,BOLDType,AvNo{nBl},woBaseline);
+ impFileMat = strcat(preprocessedFilesPathLocal{idxSubject},impName,'.mat');
+ dataPerBlock = load(impFileMat);
+ fields = fieldnames(dataPerBlock);
+ dataPerBlock = dataPerBlock.(fields{1});
+ SelProcValues.PeakRange = PeakRange; %ppm
+ currentValue = SelProcValues.PeakRange;
+ currentValue = convertPPMtoIndex(dataPerBlock, currentValue,1);
+ currentValue =[find(currentValue==1,1,'first') find(currentValue==1,1,'last')];
+ SelProcValues.PeakRange = [currentValue(1) currentValue(2)];
+ bandwidth = dataPerBlock.Parameter.Headers.Bandwidth_Hz;
+ datafid = squeeze(dataPerBlock.Data{1});
+ peakRange = get(SelProcValues, 'PeakRange');
+
+ if strcmp(metabType(1:2),'Wa')
+ datafid = datafid(:,2);
+ dataSpecReal = real(fftshift(fft(datafid)));
+ else
+ datafid = datafid(:,1);
+ dataSpecReal = real(fftshift(fft(datafid)));
+ end
+
+ fwhmArray(idxSubject,nBl) = linewidth(datafid, SelProcValues, bandwidth);
+ amplitudeArray(idxSubject,nBl) = max(abs(dataSpecReal(peakRange(1):peakRange(2))));
+ end
+end
+
+if exist('excludeSubjects')
+ n = length(subjects);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(subjects(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ fwhmArray([excludeIdx],:) = [];
+ amplitudeArray([excludeIdx],:) = [];
+ subjects([excludeIdx],:) = [];
+end
+
+if exist('excludeSubjects')
+ file2save = strcat('BOLD_',metabType,'_',visualType,BOLDType,woBaseline,'_SlidingAverage_Excluded');
+else
+ file2save = strcat('BOLD_',metabType,'_',visualType,BOLDType,woBaseline,'_SlidingAverage');
+end
+BOLD.fwhm = fwhmArray; BOLD.amplitude = amplitudeArray;
+eval([file2save '= BOLD']);
+exportFileMat = [OutputFilesPathLocal, strcat(file2save,'.mat')];
+save(exportFileMat,file2save)
+
+x = linspace(16*5,(320*5-16*5),37)/60;
+figure
+hold on
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 15, 30]);
+for idxSubject = 1:length(subjects)
+ plot(x,(fwhmArray(idxSubject,:)+(idxSubject-1)*1.3),'o-')
+end
+title ([num2str(file2save),' fwhm'], 'Interpreter', 'none');
+xlabel 't / min';
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:nBlocks/2
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < nBlocks/2
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:nBlocks/4
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+xlim([0 320*5/60]);
+fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
+savefig(fig2save)
+
+figure
+hold on
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 15, 30]);
+for idxSubject = 1:length(subjects)
+ if strcmp(metabType(1:2),'Cr')
+ plot(x,(amplitudeArray(idxSubject,:)+(idxSubject-1)*0.001),'o-')
+ elseif strcmp(metabType(1:3),'NAA')
+ plot(x,(amplitudeArray(idxSubject,:)+(idxSubject-1)*0.002),'o-')
+ else strcmp(metabType(1:3),'Wat')
+ plot(x,(amplitudeArray(idxSubject,:)+(idxSubject-1)*2),'o-')
+ end
+end
+title ([num2str(file2save),' amplitude'], 'Interpreter', 'none');
+xlabel 't / min';
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:nBlocks/2
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < nBlocks/2
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:nBlocks/4
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+xlim([0 320*5/60]);
+fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'_amplitude.fig'));
+savefig(fig2save)
+close all
+
+%plot mean, std of BOLD
+n = length(fwhmArray);
+for indexOfBlock = 1:length(AvNo)
+ fwhmArray_mean{1,indexOfBlock} = mean(fwhmArray(:,indexOfBlock));
+ fwhmArray_std{1,indexOfBlock} = std(fwhmArray(:,indexOfBlock));
+ amplitudeArray_mean{1,indexOfBlock} = mean(amplitudeArray(:,indexOfBlock));
+ amplitudeArray_std{1,indexOfBlock} = std(amplitudeArray(:,indexOfBlock));
+end
+
+figure
+subplot(2,1,1)
+hold on
+x = linspace(16*5,(320*5-16*5),37)/60;
+errorbar(x,cell2mat(fwhmArray_mean),cell2mat(fwhmArray_std))
+title ([num2str(file2save),' fwhm, mean'], 'Interpreter', 'none');
+ylabel 'FWHM / Hz';
+
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:nBlocks/2
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < nBlocks/2
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:nBlocks/4
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+xlim([0 320*5/60]);
+
+subplot(2,1,2)
+hold on
+errorbar(x,cell2mat(amplitudeArray_mean),cell2mat(amplitudeArray_std))
+title ([num2str(file2save),' amplitude, mean'], 'Interpreter', 'none');
+xlabel 't / min';ylabel 'Amplitude / a.u.';
+
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:nBlocks/2
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < nBlocks/2
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:nBlocks/4
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+xlim([0 320*5/60]);
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 12, 18]);
+fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'_Mean.fig'));
+savefig(fig2save)
+end
+
+function CorrBOLDCategorical(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+
+for idxmetab = 1:length(metabType)
+ if strcmp(metabType{idxmetab}(1:2),'Wa')
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType);
+ else
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType,woBaseline);
+ end
+ impFileMat = strcat(OutputFilesPathLocal,impName,'.mat');
+ BOLDData = load(impFileMat);
+ fields = fieldnames(BOLDData);
+ BOLDDataXY(idxmetab) = BOLDData.(fields{1});
+end
+file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline);
+figure
+subjects = ones(130,1);
+for i = 1:13
+ subjects((1+(i-1)*10):(10+(i-1)*10)) = subjects((1+(i-1)*10):(10+(i-1)*10)).*i;
+end
+fwhmTab = table(BOLDDataXY(1).fwhm(:),BOLDDataXY(2).fwhm(:),subjects);
+fwhmTab.subjects = categorical(fwhmTab.subjects);
+fit = fitlm(fwhmTab,'Var2~Var1*subjects');
+w = linspace(min(BOLDDataXY(1).fwhm(:)),max(BOLDDataXY(1).fwhm(:)));
+
+plot(BOLDDataXY(1).fwhm,BOLDDataXY(2).fwhm,'o')
+xlabel(['FWHM ',num2str(metabType{1}), ' / Hz'])
+ylabel(['FWHM ',num2str(metabType{2}), ' / Hz'])
+title(['Correlation FWHM ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+for i = 1:size(BOLDDataXY(1).fwhm,1)
+ line(w,feval(fit,w,num2str(i)))
+end
+fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
+savefig(fig2saveFWHM)
+
+figure
+fwhmAmp = table(BOLDDataXY(1).amplitude(:),BOLDDataXY(2).amplitude(:),subjects);
+fwhmAmp.subjects = categorical(fwhmAmp.subjects);
+fit = fitlm(fwhmAmp,'Var2~Var1*subjects');
+w = linspace(min(BOLDDataXY(1).amplitude(:)),max(BOLDDataXY(1).amplitude(:)));
+
+plot(BOLDDataXY(1).amplitude,BOLDDataXY(2).amplitude,'o')
+xlabel(['Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ylabel(['Amplitude ',num2str(metabType{2}), ' / a.u.'])
+title(['Correlation Amplitude ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+for i = 1:size(BOLDDataXY(1).fwhm,1)
+ line(w,feval(fit,w,num2str(i)))
+end
+fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_amp.fig'));
+savefig(fig2saveAmplitude)
+
+% close all
+end
\ No newline at end of file
diff --git a/fMRS_processing/BoldEffectfMRS_summedSpectra.m b/fMRS_processing/BoldEffectfMRS_summedSpectra.m
new file mode 100644
index 0000000000000000000000000000000000000000..6bfffd91b49d925f76b6f01403dc0a15cd2269d7
--- /dev/null
+++ b/fMRS_processing/BoldEffectfMRS_summedSpectra.m
@@ -0,0 +1,654 @@
+function BoldEffectfMRS_summedSpectra()
+clear; clc; close all;
+
+pathName = 'fMRS TimeCourse data path';
+[localFilePathBase] = pathToDataFolder(pathName);
+visualType = 'Stim'; %'Stim', 'Rest'
+BOLDType = '_woBOLDCorr';
+nBlocks = 10;
+BlockNo = cell(1,nBlocks);
+for k = 1:nBlocks
+ BlockNo{k} = strcat('_Block', num2str(k));
+end
+woBaseline = false; %false, false
+
+subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
+ '4085';'3333';'4012';'4085';'6524'};
+% prefixes = {'2020-02-18_';};
+prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
+ '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
+ '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
+suffixFolder = '_fMRS';
+excludeSubjects = {'5166';'9810';'5269'}; %doesn't work for not excludeSubjects
+
+if exist('excludeSubjects')
+ n = length(subjects);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(subjects(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ subjects([excludeIdx],:) = [];
+ prefixes([excludeIdx],:) = [];
+end
+
+numberOfSubjects = length(subjects);
+defaultSubject = 'XXXX';
+subjectsPath = strcat(prefixes, subjects, suffixFolder);
+preprocessedFilesPathLocal = strcat(localFilePathBase, subjectsPath, '/');
+OutputFilesPathLocal = strcat(localFilePathBase, 'Output_v2_MMBSummed/BOLD/');
+outputFileNameBase = strcat(defaultSubject, '_', num2str(visualType), BOLDType);
+extensionMat = '.mat';
+
+%% sum fMRS spectra of all volunteers, weighted with water fit (need to run doFitting_fMRS_Water.m first)
+% WaterReferenceTable = (readtable(strcat(OutputFilesPathLocal,'WaterReference_Fit.xlsx')));
+% WaterReferenceID = table2cell(WaterReferenceTable(2:end,1));
+% WaterReference = str2double(table2array(WaterReferenceTable(2:end,2:end)));
+% fMRSDataScaled = zeros(4096,numberOfSubjects,2,length(BlockNo)); %samples, subjects, data/water
+% for indexCurrentSubject = 1:numberOfSubjects
+% for indexBlock = 1:length(BlockNo)
+% idx = find(ismember(WaterReferenceID, strcat(prefixes{indexCurrentSubject}, subjects{indexCurrentSubject})));
+% currentScalingFactor = WaterReference(idx,1);
+% currentfMRSDataPath = strcat(localFilePathBase,subjectsPath{indexCurrentSubject});
+% load(strrep(strcat(currentfMRSDataPath, '\',outputFileNameBase,BlockNo{indexBlock},extensionMat),defaultSubject,subjects{indexCurrentSubject}));
+% currentfMRSData = squeeze(this.Data{1});
+% currentfMRSDataScaled = currentfMRSData./currentScalingFactor;
+% fMRSDataScaled(:,indexCurrentSubject,:,indexBlock) = currentfMRSDataScaled;
+% end
+% end
+% cd ('D:\PAPER\1H_fMRS\Data_TimeCourse\SummedData')
+% fMRSData_Summed_Path = strcat(localFilePathBase,'SummedData\');
+% this.Parameter.Filepath = fMRSData_Summed_Path;
+% fMRSData_Summed = squeeze(sum(fMRSDataScaled,2));
+% this.Parameter.Filename = 'fMRSData_Summed_Block1_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block1 = fMRSData_Summed(:,:,1); fMRSData_Summed_Block1 = permute(fMRSData_Summed_Block1,[1 3 4 5 6 7 8 9 2]);
+% % this.Data{1} = fMRSData_Summed_Block1; save('fMRSData_Summed_Block1','this'); this.ExportLcmRaw(fMRSData_Summed_Path,'fMRSData_Summed_Block1','Yes', 'Yes');
+% this.Data{1} = fMRSData_Summed_Block1; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block1'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block1'),'Yes', 'Yes');
+% this.Parameter.Filename = 'fMRSData_Summed_Block2_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block2 = fMRSData_Summed(:,:,2); fMRSData_Summed_Block2 = permute(fMRSData_Summed_Block2,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = fMRSData_Summed_Block2; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block2'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block2'),'Yes', 'Yes');
+% this.Parameter.Filename = 'fMRSData_Summed_Block3_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block3 = fMRSData_Summed(:,:,3); fMRSData_Summed_Block3 = permute(fMRSData_Summed_Block3,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = fMRSData_Summed_Block3; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block3'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block3'),'Yes', 'Yes');
+% this.Parameter.Filename = 'fMRSData_Summed_Block4_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block4 = fMRSData_Summed(:,:,4); fMRSData_Summed_Block4 = permute(fMRSData_Summed_Block4,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = fMRSData_Summed_Block4; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block4'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block4'),'Yes', 'Yes');
+% this.Parameter.Filename = 'fMRSData_Summed_Block5_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block5 = fMRSData_Summed(:,:,5); fMRSData_Summed_Block5 = permute(fMRSData_Summed_Block5,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = fMRSData_Summed_Block5; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block5'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block5'),'Yes', 'Yes');
+% this.Parameter.Filename = 'fMRSData_Summed_Block6_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block6 = fMRSData_Summed(:,:,6); fMRSData_Summed_Block6 = permute(fMRSData_Summed_Block6,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = fMRSData_Summed_Block6; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block6'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block6'),'Yes', 'Yes');
+% this.Parameter.Filename = 'fMRSData_Summed_Block7_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block7 = fMRSData_Summed(:,:,7); fMRSData_Summed_Block7 = permute(fMRSData_Summed_Block7,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = fMRSData_Summed_Block7; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block7'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block7'),'Yes', 'Yes');
+% this.Parameter.Filename = 'fMRSData_Summed_Block8_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block8 = fMRSData_Summed(:,:,8); fMRSData_Summed_Block8 = permute(fMRSData_Summed_Block8,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = fMRSData_Summed_Block8; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block8'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block8'),'Yes', 'Yes');
+% this.Parameter.Filename = 'fMRSData_Summed_Block9_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block9 = fMRSData_Summed(:,:,9); fMRSData_Summed_Block9 = permute(fMRSData_Summed_Block9,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = fMRSData_Summed_Block9; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block9'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block9'),'Yes', 'Yes');
+% this.Parameter.Filename = 'fMRSData_Summed_Block10_v2_MMBSummed_Excluded';
+% fMRSData_Summed_Block10 = fMRSData_Summed(:,:,10); fMRSData_Summed_Block10 = permute(fMRSData_Summed_Block10,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = fMRSData_Summed_Block10; save(strcat('fMRSData_Summed_',visualType,BOLDType,'_Block10'),'this'); this.ExportLcmRaw(fMRSData_Summed_Path,strcat('fMRSData_Summed_',visualType,BOLDType,'_Block10'),'Yes', 'Yes');
+
+%% BOLD FWHM / Amplitude
+% % for Water (not useful w/o baseline, since it doesn't have a baseline)
+% PeakRange = [4 5]; %ppm where to find the peak
+% metabType = 'Water';
+% if exist('excludeSubjects')
+% CalcBOLDEffect(BlockNo,visualType,BOLDType,localFilePathBase,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,'')
+% else
+% CalcBOLDEffect(BlockNo,visualType,BOLDType,localFilePathBase,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType)
+% end
+%
+% %for NAA (2.008 ppm)
+% PeakRange = [1.8 2.3]; %ppm where to find the peak
+% metabType = 'NAA';
+% if exist('excludeSubjects')
+% CalcBOLDEffect(BlockNo,visualType,BOLDType,localFilePathBase,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline)
+% else
+% CalcBOLDEffect(BlockNo,visualType,BOLDType,localFilePathBase,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline)
+% end
+%
+% %for Cr (methyl signal of tCr: 3.027ppm)
+% PeakRange = [2.9 3.15]; %ppm where to find the peak
+% metabType = 'Cr';
+% if exist('excludeSubjects')
+% CalcBOLDEffect(BlockNo,visualType,BOLDType,localFilePathBase,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline)
+% else
+% CalcBOLDEffect(BlockNo,visualType,BOLDType,localFilePathBase,...
+% OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline)
+% end
+%
+% % correlation FWHM, Amplitude
+metabType = {'NAA', 'Cr'};
+metabType = {'Water', 'NAA'};
+metabType = {'Water', 'Cr'};
+% metabType = {'Water'};
+if exist('excludeSubjects')
+ CorrBOLDSpearman(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
+else
+ CorrBOLDSpearman(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+end
+% % % % didn't check this for summed data
+% % % % CorrBOLDCategorical(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+
+%% correlation Water, Metabolite Concentration
+% metabType = {'Glu'};
+if exist('excludeSubjects')
+ CorrBOLDSpearman_MetConc({'Cr'},visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects); close all
+ CorrBOLDSpearman_MetConc({'PCr'},visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects); close all
+ CorrBOLDSpearman_MetConc({'Lac'},visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects); close all
+ CorrBOLDSpearman_MetConc({'Glu'},visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects); close all
+else
+ CorrBOLDSpearman_MetConc({'Cr'},visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+ CorrBOLDSpearman_MetConc({'PCr'},visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+% CorrBOLDSpearman_MetConc({'Lac'},visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+% CorrBOLDSpearman_MetConc({'Glu'},visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+end
+
+end
+
+function CorrBOLDSpearman_MetConc(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+
+if exist('excludeSubjects')
+ impName = strcat('BOLD_Water_',visualType,BOLDType,'_Excluded_SummedData');
+else
+ impName = strcat('BOLD_Water_',visualType,BOLDType,'_SummedData');
+end
+impFileMat = strcat(OutputFilesPathLocal,impName,'.mat');
+BOLDData = load(impFileMat);
+fields = fieldnames(BOLDData);
+BOLDDataXY(1) = BOLDData.(fields{1});
+
+%import Met Conc
+impNameMetConc = strcat('BlockConc_Summed_Stim_woBOLDCorr_MMBSummed_v2',woBaseline);
+impFileMatMetConc = strcat(OutputFilesPathLocal(1:end-5),impNameMetConc,'.mat');
+BOLDData = load(impFileMatMetConc);
+fields = fieldnames(BOLDData);
+BOLDData_temp = BOLDData.(fields{1});
+indexMet = strcmp(BOLDData_temp(1,:),metabType);
+idxMet = find(indexMet);
+for i = 1:10
+ BOLDData_Met(i) = BOLDData_temp{i+1,idxMet}; %Met conc per block
+end
+
+if exist('excludeSubjects')
+ file2save = strcat('Corr_Water_',metabType{1},'_Conc_Summed_',visualType,BOLDType,woBaseline,'_Excluded');
+else
+ file2save = strcat('Corr_Water_',metabType{1},'_Conc_Summed_',visualType,BOLDType,woBaseline);
+end
+
+% subjLength = length(BOLDDataXY(1).fwhm);
+subjLength = 1;
+subjects = ones(subjLength*10,1);
+for i = 1:subjLength
+ subjects((1+(i-1)*10):(10+(i-1)*10)) = subjects((1+(i-1)*10):(10+(i-1)*10)).*i;
+end
+
+%for FWHM (Water Hz vs Met a.u.)
+fwhmTab = table(BOLDDataXY(1).fwhm(:),BOLDData_Met',subjects);
+BOLDData_FWHM = table2array(fwhmTab);
+%Spearman correlation
+[RHO,PVAL] = corr(BOLDData_FWHM(:,2),BOLDData_FWHM(:,1),'Type','Spearman');
+% [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+%linear regression
+fit = fitlm(BOLDData_FWHM(:,2),BOLDData_FWHM(:,1));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fit); box on
+xlabel(['Concentration ' , char(metabType), ' / a.u.'])
+ylabel(['FWHM Water / Hz'])
+title(['Correlation FWHM Water & concentration ', char(metabType), woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+text(max(BOLDData_FWHM(:,2))-0.2,max(BOLDData_FWHM(:,1)),txt);
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+
+fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhmConc.fig'));
+% savefig(fig2saveFWHM)
+
+%for FWHM (Delta Water Hz vs Delta Met a.u.)
+subjectsDelta = subjects(1:9);
+fwhmTabDelta = table(diff(BOLDDataXY(1).fwhm(:)),diff(BOLDData_Met'),subjectsDelta);
+BOLDData_FWHM_Delta = table2array(fwhmTabDelta);
+%Spearman correlation
+[RHODelta,PVALDelta] = corr(BOLDData_FWHM_Delta(:,2),BOLDData_FWHM_Delta(:,1),'Type','Spearman');
+% [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+%linear regression
+fitDelta = fitlm(BOLDData_FWHM_Delta(:,2),BOLDData_FWHM_Delta(:,1));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fitDelta); box on
+xlabel([char(916), ' Concentration ' , char(metabType), ' / a.u.'])
+ylabel([char(916), ' FWHM Water / Hz'])
+title(['Correlation ', char(916), 'FWHM Water & concentration ', char(metabType), woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+text(max(BOLDData_FWHM_Delta(:,2))-0.2,max(BOLDData_FWHM_Delta(:,1)),txt);
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+
+fig2saveFWHMDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_DeltafwhmConc.fig'));
+% savefig(fig2saveFWHMDelta)
+
+%for amplitude (Water a.u. vs Met a.u.)
+ampTab = table(BOLDDataXY(1).amplitude(:),BOLDData_Met',subjects);
+BOLDData_amp = table2array(ampTab);
+%Spearman correlation
+[RHO,PVAL] = corr(BOLDData_amp(:,2),BOLDData_amp(:,1),'Type','Spearman');
+%linear regression
+fit = fitlm(BOLDData_amp(:,2),BOLDData_amp(:,1));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fit)
+ylim([1430 1500]); box on
+xlabel(['Concentration ' , char(metabType), ' / a.u.'])
+ylabel(['Amplitude Water / a.u.'])
+title(['Correlation Amplitude Water & concentration ', char(metabType), woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+text((max(BOLDData_amp(:,2))-max(BOLDData_amp(:,2))*0.06),(min(BOLDData_amp(:,1))+min(BOLDData_amp(:,1))*0.01),txt);
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+lines = findobj(gcf,'Type','Line');
+for i = 1:numel(lines)
+lines(i).LineWidth = 1.5;
+end
+fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_ampConc.fig'));
+savefig(fig2saveAmplitude)
+
+%for amplitude (Delta Water a.u. vs Delta Met a.u.)
+ampTabDelta = table(diff(BOLDDataXY(1).amplitude(:)),diff(BOLDData_Met'),subjectsDelta);
+BOLDData_amp_Delta = table2array(ampTabDelta);
+%Spearman correlation
+[RHODelta,PVALDelta] = corr(BOLDData_amp_Delta(:,2),BOLDData_amp_Delta(:,1),'Type','Spearman');
+%linear regression
+fitDelta = fitlm(BOLDData_amp_Delta(:,2),BOLDData_amp_Delta(:,1));
+figure
+hold on
+% plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+plot(fitDelta); box on
+xlabel([char(916), ' Concentration ' , char(metabType), ' / a.u.'])
+ylabel([char(916), ' Amplitude Water / a.u.'])
+title(['Correlation ', char(916), 'Amplitude Water & concentration ', char(metabType), woBaseline], 'Interpreter', 'none')
+txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+text((max(BOLDData_amp_Delta(:,2))-max(BOLDData_amp_Delta(:,2))*0.2),(min(BOLDData_amp_Delta(:,1))+min(BOLDData_amp_Delta(:,1))*0.01),txt);
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+fig2saveAmplitudeDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_DeltaampConc.fig'));
+% savefig(fig2saveAmplitudeDelta)
+end
+
+function CorrBOLDSpearman(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline,excludeSubjects)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+
+for idxmetab = 1:length(metabType)
+ if strcmp(metabType{idxmetab}(1:2),'Wa')
+ if exist('excludeSubjects')
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType,'_Excluded_SummedData');
+ else
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType,'_SummedData');
+ end
+ else
+ if exist('excludeSubjects')
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType,woBaseline,'_Excluded_SummedData');
+ else
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType,woBaseline,'_SummedData');
+ end
+ end
+ impFileMat = strcat(OutputFilesPathLocal,impName,'.mat');
+ BOLDData = load(impFileMat);
+ fields = fieldnames(BOLDData);
+ BOLDDataXY(idxmetab) = BOLDData.(fields{1});
+end
+
+if exist('excludeSubjects')
+ if length(metabType) == 2
+ file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline,'_Excluded_SummedData');
+ elseif length(metabType) == 1
+ file2save = strcat('Corr_',metabType{1},'_FWHMAmp_',visualType,BOLDType,woBaseline,'_Excluded_SummedData');
+ end
+else
+ if length(metabType) == 2
+ file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline,'_SummedData');
+ elseif length(metabType) == 1
+ file2save = strcat('Corr_',metabType{1},'_FWHMAmp_',visualType,BOLDType,woBaseline,'_SummedData');
+ end
+end
+
+% subjLength = length(BOLDDataXY(1).fwhm);
+subjLength = 1;
+subjects = ones(subjLength*10,1);
+for i = 1:subjLength
+ subjects((1+(i-1)*10):(10+(i-1)*10)) = subjects((1+(i-1)*10):(10+(i-1)*10)).*i;
+end
+
+if length(metabType) == 2
+ %for FWHM (Hz)
+ fwhmTab = table(BOLDDataXY(1).fwhm(:),BOLDDataXY(2).fwhm(:),subjects);
+ BOLDData_FWHM = table2array(fwhmTab);
+ %Spearman correlation
+ [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Spearman');
+ % [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+ %linear regression
+ fit = fitlm(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fit); box on;
+ xlabel(['FWHM ',num2str(metabType{1}), ' / Hz'])
+ ylabel(['FWHM ',num2str(metabType{2}), ' / Hz'])
+ title(['Correlation FWHM ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+ text(min(BOLDData_FWHM(:,1)),max(BOLDData_FWHM(:,2)),txt)
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+ lines = findobj(gcf,'Type','Line');
+ for i = 1:numel(lines)
+ lines(i).LineWidth = 1.5;
+ end
+ fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
+% savefig(fig2saveFWHM)
+
+ %for FWHM (Delta Hz)
+ subjectsDelta = subjects(1:9);
+ fwhmTabDelta = table(diff(BOLDDataXY(1).fwhm(:)),diff(BOLDDataXY(2).fwhm(:)),subjectsDelta);
+ BOLDData_FWHM_Delta = table2array(fwhmTabDelta);
+ %Spearman correlation
+ [RHODelta,PVALDelta] = corr(BOLDData_FWHM_Delta(:,1),BOLDData_FWHM_Delta(:,2),'Type','Spearman');
+ %linear regression
+ fitDelta = fitlm(BOLDData_FWHM_Delta(:,1),BOLDData_FWHM_Delta(:,2));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fitDelta); box on;
+ xlabel([char(916) 'FWHM ',num2str(metabType{1}), ' / Hz'])
+ ylabel([char(916) 'FWHM ',num2str(metabType{2}), ' / Hz'])
+ title(['Correlation ' char(916) 'FWHM ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+ text(min(BOLDData_FWHM_Delta(:,1)),max(BOLDData_FWHM_Delta(:,2)),txt)
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+ lines = findobj(gcf,'Type','Line');
+ for i = 1:numel(lines)
+ lines(i).LineWidth = 1.5;
+ end
+ fig2saveFWHMDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_Deltafwhm.fig'));
+% savefig(fig2saveFWHMDelta)
+
+ %for amplitude (a.u.)
+ ampTab = table(BOLDDataXY(1).amplitude(:),BOLDDataXY(2).amplitude(:),subjects);
+ BOLDData_amp = table2array(ampTab);
+ %Spearman correlation
+ [RHO,PVAL] = corr(BOLDData_amp(:,1),BOLDData_amp(:,2),'Type','Spearman');
+ %linear regression
+ fit = fitlm(BOLDData_amp(:,1),BOLDData_amp(:,2));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fit); box on;
+ xlabel(['Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ ylabel(['Amplitude ',num2str(metabType{2}), ' / a.u.'])
+ title(['Correlation Amplitude ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+ text(min(BOLDData_amp(:,1)),max(BOLDData_amp(:,2)),txt)
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+ lines = findobj(gcf,'Type','Line');
+ for i = 1:numel(lines)
+ lines(i).LineWidth = 1.5;
+ end
+ fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_amp.fig'));
+% savefig(fig2saveAmplitude)
+
+ %for amplitude (Delta a.u.)
+ ampTabDelta = table(diff(BOLDDataXY(1).amplitude(:)),diff(BOLDDataXY(2).amplitude(:)),subjectsDelta);
+ BOLDData_amp_Delta = table2array(ampTabDelta);
+ %Spearman correlation
+ [RHODelta,PVALDelta] = corr(BOLDData_amp_Delta(:,1),BOLDData_amp_Delta(:,2),'Type','Spearman');
+ %linear regression
+ fitDelta = fitlm(BOLDData_amp_Delta(:,1),BOLDData_amp_Delta(:,2));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fitDelta); box on;
+ xlabel([char(916) 'Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ ylabel([char(916) 'Amplitude ',num2str(metabType{2}), ' / a.u.'])
+ title(['Correlation ' char(916) 'Amplitude ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+ text(min(BOLDData_amp_Delta(:,1)),max(BOLDData_amp_Delta(:,2)),txt)
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+ lines = findobj(gcf,'Type','Line');
+ for i = 1:numel(lines)
+ lines(i).LineWidth = 1.5;
+ end
+ fig2saveAmplitudeDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_Deltaamp.fig'));
+% savefig(fig2saveAmplitudeDelta)
+
+elseif length(metabType) == 1
+ %for FWHM (Hz) / Amp (a.u.)
+ fwhmAmpTab = table(BOLDDataXY(1).fwhm(:),BOLDDataXY(1).amplitude(:),subjects);
+ BOLDData = table2array(fwhmAmpTab);
+ %Spearman correlation
+ [RHO,PVAL] = corr(BOLDData(:,1),BOLDData(:,2),'Type','Spearman');
+ % [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+ %linear regression
+ fit = fitlm(BOLDData(:,2),BOLDData(:,1));
+ figure
+ hold on
+ plot(fit); box on;
+ xlabel(['Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ ylabel(['FWHM ',num2str(metabType{1}), ' / Hz'])
+ title(['Correlation FWHM & Amplitude ', num2str(metabType{1}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+ text(min(BOLDData(:,2))+min(BOLDData(:,2))*0.001,min(BOLDData(:,1))+min(BOLDData(:,1))*0.001,txt)
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+ lines = findobj(gcf,'Type','Line');
+ for i = 1:numel(lines)
+ lines(i).LineWidth = 1.5;
+ end
+ fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'.fig'));
+ savefig(fig2save)
+
+ %for FWHM (Delta Hz) / Amp (Delta a.u.)
+ subjectsDelta = subjects(1:9);
+ fwhmAmpTabDelta = table(diff(BOLDDataXY(1).fwhm(:)),diff(BOLDDataXY(1).amplitude(:)),subjectsDelta);
+ BOLDData_Delta = table2array(fwhmAmpTabDelta);
+ %Spearman correlation
+ [RHODelta,PVALDelta] = corr(BOLDData_Delta(:,1),BOLDData_Delta(:,2),'Type','Spearman');
+ %linear regression
+ fitDelta = fitlm(BOLDData_Delta(:,2),BOLDData_Delta(:,1));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fitDelta); box on;
+ xlabel([char(916) 'Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ ylabel([char(916) 'FWHM ',num2str(metabType{1}), ' / Hz'])
+ title(['Correlation ' char(916) 'FWHM & Amplitude ', num2str(metabType{1}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHODelta)],['p < ' num2str(PVALDelta)]};
+ text(min(BOLDData_Delta(:,2)),min(BOLDData_Delta(:,1))+0.05,txt)
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]); set(gca, 'FontWeight','bold');
+ lines = findobj(gcf,'Type','Line');
+ for i = 1:numel(lines)
+ lines(i).LineWidth = 1.5;
+ end
+ fig2saveDelta = fullfile(OutputFilesPathLocal, strcat(file2save,'_Delta.fig'));
+% savefig(fig2saveDelta)
+end
+end
+
+
+
+
+function CalcBOLDEffect(BlockNo,visualType,BOLDType,preprocessedFilesPathLocal,...
+ OutputFilesPathLocal,nBlocks,PeakRange,metabType,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+ fitVersion = '_v2';
+else
+ woBaseline = '';
+ fitVersion = '';
+end
+SelProcValues = SelectedProcessingValues();
+fwhmArray = zeros(1,length(BlockNo));
+amplitudeArray = zeros(1,length(BlockNo));
+for nBl = 1:length(BlockNo)
+ impName = strcat('fMRSData_Summed_',visualType,BOLDType,fitVersion, BlockNo{nBl},woBaseline);
+ impFileMat = strcat(preprocessedFilesPathLocal,'SummedData\',impName,'.mat');
+ dataPerBlock = load(impFileMat);
+ fields = fieldnames(dataPerBlock);
+ dataPerBlock = dataPerBlock.(fields{1});
+ SelProcValues.PeakRange = PeakRange; %ppm
+ currentValue = SelProcValues.PeakRange;
+ currentValue = convertPPMtoIndex(dataPerBlock, currentValue,1);
+ currentValue =[find(currentValue==1,1,'first') find(currentValue==1,1,'last')];
+ SelProcValues.PeakRange = [currentValue(1) currentValue(2)];
+ bandwidth = dataPerBlock.Parameter.Headers.Bandwidth_Hz;
+ datafid = squeeze(dataPerBlock.Data{1});
+ peakRange = get(SelProcValues, 'PeakRange');
+
+ if strcmp(metabType(1:2),'Wa')
+ datafid = datafid(:,2);
+ dataSpecReal = real(fftshift(fft(datafid)));
+ else
+ datafid = datafid(:,1);
+ dataSpecReal = real(fftshift(fft(datafid)));
+ end
+
+ fwhmArray(1,nBl) = linewidth(datafid, SelProcValues, bandwidth);
+ amplitudeArray(1,nBl) = max(abs(dataSpecReal(peakRange(1):peakRange(2))));
+end
+
+file2save = strcat('BOLD_',metabType,'_',visualType,BOLDType,woBaseline,'_Excluded_SummedData');
+
+BOLD.fwhm = fwhmArray; BOLD.amplitude = amplitudeArray;
+eval([file2save '= BOLD']);
+exportFileMat = [OutputFilesPathLocal, strcat(file2save,'.mat')];
+% save(exportFileMat,file2save)
+
+x = linspace(320/60/4,(320*5/60-320/60/4),10);
+figure
+plot(x,fwhmArray,'o-')
+
+title ([num2str(file2save),' fwhm'], 'Interpreter', 'none');
+xlabel 't / min';
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:nBlocks/2
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < nBlocks/2
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:nBlocks/4
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+xlim([0 320*5/60]);set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+set(gca, 'FontWeight','bold');
+fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
+% savefig(fig2save)
+
+figure
+plot(x,amplitudeArray./10,'o-')
+
+title ([num2str(file2save),' amplitude'], 'Interpreter', 'none');
+xlabel 't / min';
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:nBlocks/2
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < nBlocks/2
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:nBlocks/4
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+xlim([0 320*5/60]);set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+set(gca, 'FontWeight','bold');
+fig2save = fullfile(OutputFilesPathLocal, strcat(file2save,'_amplitude.fig'));
+% savefig(fig2save)
+close all
+end
+
+function CorrBOLDCategorical(metabType,visualType,BOLDType,OutputFilesPathLocal,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+
+for idxmetab = 1:length(metabType)
+ if strcmp(metabType{idxmetab}(1:2),'Wa')
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType);
+ else
+ impName = strcat('BOLD_',metabType{idxmetab},'_',visualType,BOLDType,woBaseline);
+ end
+ impFileMat = strcat(OutputFilesPathLocal,impName,'.mat');
+ BOLDData = load(impFileMat);
+ fields = fieldnames(BOLDData);
+ BOLDDataXY(idxmetab) = BOLDData.(fields{1});
+end
+file2save = strcat('Corr_',metabType{1},'_',metabType{2},'_',visualType,BOLDType,woBaseline);
+figure
+subjects = ones(130,1);
+for i = 1:13
+ subjects((1+(i-1)*10):(10+(i-1)*10)) = subjects((1+(i-1)*10):(10+(i-1)*10)).*i;
+end
+fwhmTab = table(BOLDDataXY(1).fwhm(:),BOLDDataXY(2).fwhm(:),subjects);
+fwhmTab.subjects = categorical(fwhmTab.subjects);
+fit = fitlm(fwhmTab,'Var2~Var1*subjects');
+w = linspace(min(BOLDDataXY(1).fwhm(:)),max(BOLDDataXY(1).fwhm(:)));
+
+plot(BOLDDataXY(1).fwhm,BOLDDataXY(2).fwhm,'o')
+xlabel(['FWHM ',num2str(metabType{1}), ' / Hz'])
+ylabel(['FWHM ',num2str(metabType{2}), ' / Hz'])
+title(['Correlation FWHM ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+for i = 1:size(BOLDDataXY(1).fwhm,1)
+ line(w,feval(fit,w,num2str(i)))
+end
+fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
+savefig(fig2saveFWHM)
+
+figure
+fwhmAmp = table(BOLDDataXY(1).amplitude(:),BOLDDataXY(2).amplitude(:),subjects);
+fwhmAmp.subjects = categorical(fwhmAmp.subjects);
+fit = fitlm(fwhmAmp,'Var2~Var1*subjects');
+w = linspace(min(BOLDDataXY(1).amplitude(:)),max(BOLDDataXY(1).amplitude(:)));
+
+plot(BOLDDataXY(1).amplitude,BOLDDataXY(2).amplitude,'o')
+xlabel(['Amplitude ',num2str(metabType{1}), ' / a.u.'])
+ylabel(['Amplitude ',num2str(metabType{2}), ' / a.u.'])
+title(['Correlation Amplitude ', num2str(metabType{1}), ' & ', num2str(metabType{2}), woBaseline], 'Interpreter', 'none')
+for i = 1:size(BOLDDataXY(1).fwhm,1)
+ line(w,feval(fit,w,num2str(i)))
+end
+fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_amp.fig'));
+savefig(fig2saveAmplitude)
+
+% close all
+end
\ No newline at end of file
diff --git a/fMRS_processing/Corr_BOLD_Met.m b/fMRS_processing/Corr_BOLD_Met.m
new file mode 100644
index 0000000000000000000000000000000000000000..5636b7d04ed32d992f5a68a23cabcc027a0011d3
--- /dev/null
+++ b/fMRS_processing/Corr_BOLD_Met.m
@@ -0,0 +1,239 @@
+function Corr_BOLD_Met()
+clear; clc; close all;
+
+pathName = 'fMRS data path';
+[localFilePathBase] = pathToDataFolder(pathName);
+StimType = {'RestOFF', 'RestON'}; %'StimOFF', 'StimON', 'RestOFF', 'RestON'
+if ~isempty(strfind(StimType{1},'Stim'))
+ visualType = 'Stim';
+elseif ~isempty(strfind(StimType{1},'Rest'))
+ visualType = 'Rest';
+else
+ error('StimType needs to be Stim or Rest');
+end
+BOLDTypes = {'_woBOLDCorr128', '_withBOLDCorr128','_woBOLDCorr64', '_woBOLDCorr64#1', '_withBOLDCorr64', '_withBOLDCorr64#1'};
+MMBType = '_MMBSummed_v2';
+woBaseline = false; %false, true
+
+subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
+ '4085';'3333';'4012';'4085';'6524'};
+prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
+ '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
+ '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
+suffixFolder = '_fMRS';
+excludeSubjects = {'5166';'9810';'5269'};
+
+subjectsPath = strcat(prefixes, subjects, suffixFolder);
+preprocessedFilesPathLocal = strcat(localFilePathBase, subjectsPath, '/');
+OutputFilesPathLocal = strcat(localFilePathBase, 'Output_v2_MMBSummed/BOLD/');
+OutputConcDiffPathLocal = strcat('D:\PAPER\1H_fMRS\Statistics\');
+
+% for idxBOLDType = 1:length(BOLDTypes)
+% BOLDType = BOLDTypes{idxBOLDType};
+% %for Water
+% PeakRange = [4 5]; %ppm where to find the peak
+% metabType = 'Water';
+% CalcBOLDEffect(subjects,subjectsPath,StimType,visualType,BOLDType,preprocessedFilesPathLocal,...
+% OutputFilesPathLocal,PeakRange,metabType,'')
+% end
+% % %for NAA (2.008 ppm)
+% % PeakRange = [1.8 2.3]; %ppm where to find the peak
+% % metabType = 'NAA';
+% % CalcBOLDEffect(subjects,subjectsPath,StimType,visualType,BOLDType,preprocessedFilesPathLocal,...
+% % OutputFilesPathLocal,PeakRange,metabType,'')
+% % %for Cr (methyl signal of tCr: 3.027ppm)
+% % PeakRange = [2.9 3.15]; %ppm where to find the peak
+% % metabType = 'Cr';
+% % CalcBOLDEffect(subjects,subjectsPath,StimType,visualType,BOLDType,preprocessedFilesPathLocal,...
+% % OutputFilesPathLocal,PeakRange,metabType,'')
+% %
+
+% correlation Water, Metabolite Concentration
+metabType = {'Lac', 'Glu', 'Cr', 'PCr'};
+for idxBOLDType = 1:length(BOLDTypes)
+ BOLDType = BOLDTypes{idxBOLDType};
+ if exist('excludeSubjects')
+ CorrBOLDSpearman_MetConc(metabType,visualType,BOLDType,MMBType,OutputFilesPathLocal,woBaseline,subjects,prefixes,excludeSubjects,OutputConcDiffPathLocal)
+ else
+ CorrBOLDSpearman_MetConc(metabType,visualType,BOLDType,MMBType,OutputFilesPathLocal,woBaseline)
+ end
+end
+
+end
+
+function CorrBOLDSpearman_MetConc(metabType,visualType,BOLDType,MMBType,OutputFilesPathLocal,woBaseline,subjects,prefixes,excludeSubjects,OutputConcDiffPathLocal)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+
+if exist('excludeSubjects','var')
+ n = length(subjects);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(subjects(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ subjects([excludeIdx],:) = [];
+ prefixes([excludeIdx],:) = [];
+end
+subjectsPath = strcat(prefixes, subjects, '_fMRS/');
+
+for idxOfSubj = 1:length(subjects)
+ %import BOLD Water data, absolute change in amplitude (a.u.) and FWHM
+ %(Hz), not percent change
+ impWaterName = strcat(visualType,BOLDType,'_WaterBOLD');
+ impWaterFileMat = strrep(strcat(OutputFilesPathLocal,subjectsPath{idxOfSubj},impWaterName,'.mat'),'#','_');
+ BOLDData = load(impWaterFileMat);
+ fields = fieldnames(BOLDData);
+ BOLDDataXY = BOLDData.(fields{1});
+ FWHMChange = 100/BOLDDataXY.fwhm(1)*BOLDDataXY.fwhm(2)-100;
+ AmpChange = 100/BOLDDataXY.amplitude(1)*BOLDDataXY.amplitude(2)-100;
+ %import Metabolite data, concentration differences in percent per
+ %metabolite and subject (all subjects in one table)
+ %%%%D:\PAPER\1H_fMRS\Statistics\ConcDiff_Stim_woBOLDCorr64_1_MMBSummed_v2_Excluded.mat
+
+
+ if exist('excludeSubjects','var')
+ impMetName = strcat('ConcDiff_',visualType,BOLDType,MMBType,'_Excluded');
+ else
+ impMetName = strcat('ConcDiff_',visualType,BOLDType,MMBType);
+ end
+ impWaterFileMat = strrep(strcat(OutputConcDiffPathLocal,impMetName,'.mat'),'#','_');
+ MetData = load(impWaterFileMat);
+ fields = fieldnames(MetData);
+ MetData = MetData.(fields{1});
+ metaboliteNames = MetData.Properties.VariableNames;
+ subjectNames = MetData.Properties.RowNames;
+ for idxMet = 1:length(metabType)
+ indexMetChange = strcmp(metaboliteNames,metabType{idxMet});
+ idxMetChange = find(indexMetChange);
+ idxSubjRow = find(~cellfun(@isempty,strfind(subjectNames,subjectsPath{idxOfSubj}(1:15))));
+ MetChange(idxMet) = MetData{idxSubjRow,idxMetChange};
+ end
+ Data_Corr(idxOfSubj,:) = [AmpChange FWHMChange MetChange];
+end
+rowNames = strcat(prefixes, subjects);
+columnNames = {'Amp_H2O' 'FWHM_H2O' metabType{1} metabType{2} metabType{3} metabType{4}};
+Data_Corr_PercChange = array2table(Data_Corr,'rowNames',rowNames,'VariableNames',columnNames);
+
+if exist('excludeSubjects', 'var') & ~isempty(excludeSubjects)
+ ExpNamePercChange = strcat('Diff_BOLD_Met_', num2str(visualType), BOLDType, MMBType, '_Excluded');
+else
+ ExpNamePercChange = strcat('ConcDiff_', num2str(visualType), BOLDType, MMBType);
+end
+ExpNamePercChange = strrep(ExpNamePercChange,'#1','_1');
+eval([ExpNamePercChange '=Data_Corr_PercChange']);
+exportFileMatDiff = [OutputFilesPathLocal ,strcat(ExpNamePercChange,'.mat')];
+save(exportFileMatDiff,ExpNamePercChange)
+
+subjLength = 1;
+subjectsCorr = ones(subjLength*10,1);
+for i = 1:subjLength
+ subjectsCorr((1+(i-1)*10):(10+(i-1)*10)) = subjectsCorr((1+(i-1)*10):(10+(i-1)*10)).*i;
+end
+
+for idxMet = 1:length(metabType)
+ if exist('excludeSubjects')
+ file2save = strcat('Corr_BOLD_',metabType{idxMet},'_',visualType,BOLDType,MMBType,'_Excluded');
+ else
+ file2save = strcat('Corr_BOLD_',metabType{idxMet},'_',visualType,BOLDType,MMBType);
+ end
+ %for FWHM (Water Hz % vs Met a.u. %)
+ fwhmTab = table(Data_Corr(:,2),Data_Corr(:,idxMet+2),subjectsCorr);
+ BOLDData_FWHM = table2array(fwhmTab);
+ %Spearman correlation
+ [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Spearman');
+ % [RHO,PVAL] = corr(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'Type','Pearson');
+ %linear regression
+ fit = fitlm(BOLDData_FWHM(:,2),BOLDData_FWHM(:,1));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fit)
+ xlabel([char(916), char(metabType{idxMet}), ' / %'])
+ ylabel([char(916), 'FWHM Water / %'])
+ title(['Correlation ', char(916), 'FWHM Water & ', char(916), char(metabType{idxMet}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+ text(max(BOLDData_FWHM(:,2))-max(BOLDData_FWHM(:,2))*0.5,min(BOLDData_FWHM(:,1))-min(BOLDData_FWHM(:,1))*0.1,txt)
+ set(gca, 'FontWeight','bold');
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+
+ fig2saveFWHM = fullfile(OutputFilesPathLocal, strcat(file2save,'_fwhm.fig'));
+ savefig(fig2saveFWHM)
+
+ %for amplitude (Water a.u. vs Met a.u.)
+ ampTab = table(Data_Corr(:,1),Data_Corr(:,idxMet+2),subjectsCorr);
+ BOLDData_amp = table2array(ampTab);
+ %Spearman correlation
+ [RHO,PVAL] = corr(BOLDData_amp(:,2),BOLDData_amp(:,1),'Type','Spearman');
+ %linear regression
+ fit = fitlm(BOLDData_amp(:,2),BOLDData_amp(:,1));
+ figure
+ hold on
+ % plot(BOLDData_FWHM(:,1),BOLDData_FWHM(:,2),'o')
+ plot(fit)
+ xlabel([char(916), char(metabType{idxMet}), ' / %'])
+ ylabel([char(916), 'Amplitude Water / %'])
+ title(['Correlation ', char(916), 'Amplitude Water & ', char(916), char(metabType{idxMet}), woBaseline], 'Interpreter', 'none')
+ txt = {['R = ' num2str(RHO)],['p < ' num2str(PVAL)]};
+ text((max(BOLDData_amp(:,2))-max(BOLDData_amp(:,2))*0.5),(min(BOLDData_amp(:,1))+min(BOLDData_amp(:,1))*0.05),txt)
+ set(gca, 'FontWeight','bold');
+ set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+ fig2saveAmplitude = fullfile(OutputFilesPathLocal, strcat(file2save,'_amp.fig'));
+ savefig(fig2saveAmplitude)
+end
+end
+
+
+function CalcBOLDEffect(subjects,subjectsPath,StimType,visualType,BOLDType,preprocessedFilesPathLocal,...
+ OutputFilesPathLocal,PeakRange,metabType,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+SelProcValues = SelectedProcessingValues();
+fwhmArray = zeros(1,length(StimType));
+amplitudeArray = zeros(1,length(StimType));
+for idxSubject = 1:length(subjects)
+ for nStimType = 1:length(StimType)
+ impName = strcat(subjects{idxSubject},'_',StimType{nStimType},BOLDType,woBaseline);
+ impFileMat = strcat(preprocessedFilesPathLocal{idxSubject},impName,'.mat');
+ dataPerBlock = load(impFileMat);
+ fields = fieldnames(dataPerBlock);
+ dataPerBlock = dataPerBlock.(fields{1});
+ SelProcValues.PeakRange = PeakRange; %ppm
+ currentValue = SelProcValues.PeakRange;
+ currentValue = convertPPMtoIndex(dataPerBlock, currentValue,1);
+ currentValue =[find(currentValue==1,1,'first') find(currentValue==1,1,'last')];
+ SelProcValues.PeakRange = [currentValue(1) currentValue(2)];
+ bandwidth = dataPerBlock.Parameter.Headers.Bandwidth_Hz;
+ datafid = squeeze(dataPerBlock.Data{1});
+ peakRange = get(SelProcValues, 'PeakRange');
+
+ if strcmp(metabType(1:2),'Wa')
+ datafid = datafid(:,2);
+ dataSpecReal = real(fftshift(fft(datafid)));
+ else
+ datafid = datafid(:,1);
+ dataSpecReal = real(fftshift(fft(datafid)));
+ end
+
+ fwhmArray(1,nStimType) = linewidth(datafid, SelProcValues, bandwidth);
+ amplitudeArray(1,nStimType) = max(abs(dataSpecReal(peakRange(1):peakRange(2))));
+ end
+ file2save = strrep(strcat(visualType,BOLDType,woBaseline,'_WaterBOLD'),'#','_');
+ BOLD.fwhm = fwhmArray; BOLD.amplitude = amplitudeArray;
+ eval([file2save '= BOLD']);
+ exportFileMat = [OutputFilesPathLocal, strcat(subjectsPath{idxSubject},'/',file2save,'.mat')];
+ save(exportFileMat,file2save)
+end
+end
\ No newline at end of file
diff --git a/fMRS_processing/DoMARSSMetabolitesLineBroadening.m b/fMRS_processing/DoMARSSMetabolitesLineBroadening.m
index ebb02e4a78585e820f857527d421f122f73c5616..b812224aaff25cf07ed059b7e21682a8d297c661 100644
--- a/fMRS_processing/DoMARSSMetabolitesLineBroadening.m
+++ b/fMRS_processing/DoMARSSMetabolitesLineBroadening.m
@@ -1,9 +1,9 @@
function DoMARSSMetabolitesLineBroadening()
-% filepath = 'D:\CODE\MARSS\2020-02-18\sLASER_15x18x20_120V_Tx2.4ppm_Water\RawBasis_for_MARSSinput';
-filepath = 'D:\PAPER\1H_fMRS\Data\Basis_sets\MARSS_Output\sLASER_15x18x20_120V_Tx2.4ppm\RawBasis_for_MARSSinput';
+filepath = 'D:\PAPER\1H_fMRS\Data\Basis_sets\Basis_sets_15x18x20_TE24ms_URef120V_Ref24ppm_LineBroadened';
+% filepath = 'D:\PAPER\1H_fMRS\Data\Basis_sets\MARSS_Output\sLASER_15x18x20_120V_Tx2.4ppm\RawBasis_for_MARSSinput';
-metaboliteFileNames = {'sI'};
+metaboliteFileNames = {'GradEcho'};
plotBasis = true;
@@ -34,19 +34,19 @@ for indexMetabolite = 1 : length(metaboliteFileNames)
L = -1; %linewidth in Hz, L positive: line broadening, L negative: resolution enhancement
W = exp(-t'*pi*L).*exp(- t'.^2*(Fgau)^2);
newData = oldData.*W;
-
+newData = oldData;
addSinglet = true;
dwellTimeMs = dwellTime * 1e3;
% filePathExport = [filepath '\Basis_sets_LineBroadened\'];
- filePathExport = [filepath '\Basis_sets_LineBroadened_negative\'];
+ filePathExport = [filepath '\Basis_sets_LineBroadened\'];
mkdir(filePathExport)
ExportLCModelBasis((newData/100)', dwellTimeMs, scanFrequency, filePathExport, ...
filtered_metabolite_name, metaboliteFileNames{indexMetabolite}, addSinglet, ...
- false, 4.7, 0.5,'Yes',24,'jd_semiLASER');
+ false, 4.7, 3000,'Yes',24,'jd_semiLASER');
bw = linspace(-bandwidth/2,bandwidth/2,length(newData));
ppmVector = bw/(scanFrequency) + 4.7;
diff --git a/fMRS_processing/Statistics.m b/fMRS_processing/Statistics.m
index d075a5fb2491d0d71d8c0b4f98513c16ac9a1059..796ba6068e4805712a81f4aed82a0853b263b9e7 100644
--- a/fMRS_processing/Statistics.m
+++ b/fMRS_processing/Statistics.m
@@ -7,7 +7,7 @@ clear; clc; close all;
pathName = 'fMRS data path';
sampleCriteria = {'Output'};
[localFilePathBase] = pathToDataFolder(pathName, sampleCriteria);
-StimType = {'RestON', 'RestOFF'}; %'StimON', 'StimOFF', 'RestON', 'RestOFF'
+StimType = {'StimON', 'StimOFF'}; %'StimON', 'StimOFF', 'RestON', 'RestOFF'
if ~isempty(strfind(StimType{1},'Stim'))
visualType = 'Stim';
elseif ~isempty(strfind(StimType{1},'Rest'))
@@ -15,8 +15,8 @@ elseif ~isempty(strfind(StimType{1},'Rest'))
else
error('StimType needs to be Stim or Rest');
end
-BOLDType = '_withBOLDCorr64_1'; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _woBOLDCorr64_1, _withBOLDCorr64, _withBOLDCorr64_1
-MMBType = '_MMBSummed'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated, _MMBSummed_v2
+BOLDType = '_woBOLDCorr128'; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _woBOLDCorr64_1, _withBOLDCorr64, _withBOLDCorr64_1
+MMBType = '_MMBSummed_v2'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated, _MMBSummed_v2
% subjects = {'2823';};
subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
'4085';'3333';'4012';'4085';'6524'};
@@ -26,12 +26,16 @@ prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
'2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
excludeSubjects = {'5166';'9810';'5269'};
if strcmp(MMBType,'_MMBSummed')
- excludeMet = {'Glc';'NAA_NAAG';'Glu_Gln';'Glyc';'mI_Glyc';'PCh'}; %for v1
+ excludeMet = {'NAA';'GABA';'Gln';'Glyc';'GSH';'mI';'NAAG';'PCh';'GPC';'PE';'Scyllo';'Tau';'Glc';'NAA_NAAG';'Glu_Gln';'Glyc';'mI_Glyc';'PCh'}; %for v1
elseif strcmp(MMBType,'_MMBSummed_v2')
- excludeMet = {'Glc';'NAA_NAAG';'Glu_Gln'}; %for v2
+ excludeMet = {'NAA';'Asc';'GABA';'Glc';'Gln';'GSH';'mI';'NAAG';'tCh';'PE';'Scyllo';'Tau';'NAA_NAAG';'Glu_Gln'}; %for v2
end
%% file paths setup
-LCModelOutputPath = [localFilePathBase, 'Output/'];
+if ~isempty(strfind(MMBType,'_v2'))
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed/'];
+else
+ LCModelOutputPath = [localFilePathBase, 'Output/'];
+end
ExpName = strcat('LCModelFit_', num2str(visualType), BOLDType, MMBType);
exportFileMat = [LCModelOutputPath ,strcat(ExpName,'.mat')];
@@ -64,32 +68,72 @@ PercDiff = 100./OFF.*ON-100;
PercDiffMean = mean(PercDiff);
PercDiffStd = std(PercDiff);
-% two-sided Wilcoxon signed rank test
-% for indexOfMetabolite = 1:length(metaboliteNames)
-% if isempty(find(~isnan(PercDiff(:,indexOfMetabolite))))
-% p(indexOfMetabolite) = NaN;
-% h(indexOfMetabolite) = 0;
-% else
-% [p(indexOfMetabolite),h(indexOfMetabolite)] = signrank(PercDiff(:,indexOfMetabolite));
-% end
-% end
-
-%paired two-tailed t-test
+%% two-sided Wilcoxon signed rank test
+for indexOfMetabolite = 1:length(metaboliteNames)
+ if isempty(find(~isnan(PercDiff(:,indexOfMetabolite))))
+ p_Wilc(indexOfMetabolite) = NaN;
+ h_Wilc(indexOfMetabolite) = 0;
+ else
+ [p_Wilc(indexOfMetabolite),h_Wilc(indexOfMetabolite)] = signrank(PercDiff(:,indexOfMetabolite));
+ end
+end
+
+%check histogram of p-values
+edges = linspace(0,1,length(p_Wilc));
+histogram(p_Wilc,edges)
+
+%Benjamini-Hochberg correction for multiple testing
+[h_BH_Wilc, crit_p_Wilc, adj_ci_cvrg_Wilc, adj_p_Wilc]=fdr_bh(p_Wilc,0.05,'pdep','yes');
+%h=1: reject H0, h=0: H0 not rejected
+%crit_p: max p-value that is significant
+%adj_ci_cvrg: BH adjusted confidence interval coverage
+%adj_p: N pj / j, all adjusted p-values less than or equal to q are significant
+
+
+if exist('excludeMet')
+ [~,n] = size(metaboliteNames);
+ m = length(excludeMet);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = strcmp(metaboliteNames(1,i),excludeMet{j});
+% xx = find(~cellfun(@isempty,strcmp(metaboliteNames(1,i),excludeMet{j})));
+% if ~isempty(xx)
+ if xx
+ excludeMetIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ pExclMet_Wilc = p_Wilc;
+ pExclMet_Wilc(excludeMetIdx) = [];
+ [h_BH_ExclMet_Wilc, crit_p_ExclMet_Wilc, adj_ci_cvrg_ExclMet_Wilc, adj_p_ExclMet_Wilc]=fdr_bh(pExclMet_Wilc,0.05,'pdep','yes');
+ temph = ones(1,length(h_Wilc))*NaN; temp_p = ones(1,length(p_Wilc))*NaN; tempidx = [0 sort(excludeMetIdx)]; nstep = 1;
+ for j = 1:length(tempidx)-1
+ temp_p((tempidx(j)+1):(tempidx(j+1)-1)) = adj_p_ExclMet_Wilc((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
+ temph((tempidx(j)+1):(tempidx(j+1)-1)) = h_BH_ExclMet_Wilc((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
+ nstep = nstep + 1;
+ end
+ h_BH_ExclMet_Wilc = temph;
+ adj_p_ExclMet_Wilc = temp_p;
+end
+
+%% paired two-tailed t-test (data need to be normally distributed, which they aren't here! -> use nonparametric Wilcoxon)
for indexOfMetabolite = 1:length(metaboliteNames)
if isempty(find(~isnan(PercDiff(:,indexOfMetabolite))))
- h(indexOfMetabolite) = NaN;
- p(indexOfMetabolite) = 0;
+ h_ttest(indexOfMetabolite) = NaN;
+ p_ttest(indexOfMetabolite) = 0;
else
- [h(indexOfMetabolite),p(indexOfMetabolite)] = ttest(PercDiff(:,indexOfMetabolite));
+ [h_ttest(indexOfMetabolite),p_ttest(indexOfMetabolite)] = ttest(PercDiff(:,indexOfMetabolite));
end
end
%check histogram of p-values
-edges = linspace(0,1,length(p));
-histogram(p,edges)
+edges = linspace(0,1,length(p_ttest));
+histogram(p_ttest,edges)
%Benjamini-Hochberg correction for multiple testing
-[h_BH, crit_p, adj_ci_cvrg, adj_p]=fdr_bh(p,0.05,'pdep','yes');
+[h_BH_ttest, crit_p_ttest, adj_ci_cvrg_ttest, adj_p_ttest]=fdr_bh(p_ttest,0.05,'pdep','yes');
%h=1: reject H0, h=0: H0 not rejected
%crit_p: max p-value that is significant
%adj_ci_cvrg: BH adjusted confidence interval coverage
@@ -111,20 +155,21 @@ if exist('excludeMet')
end
end
end
- pExclMet = p;
- pExclMet(excludeMetIdx) = [];
- [h_BH_ExclMet, crit_p_ExclMet, adj_ci_cvrg_ExclMet, adj_p_ExclMet]=fdr_bh(pExclMet,0.05,'pdep','yes');
- temph = ones(1,length(h))*NaN; temp_p = ones(1,length(p))*NaN; tempidx = [0 sort(excludeMetIdx)]; nstep = 1;
+ pExclMet_ttest = p_ttest;
+ pExclMet_ttest(excludeMetIdx) = [];
+ [h_BH_ExclMet_ttest, crit_p_ExclMet_ttest, adj_ci_cvrg_ExclMet_ttest, adj_p_ExclMet_ttest]=fdr_bh(pExclMet_ttest,0.05,'pdep','yes');
+ temph = ones(1,length(h_ttest))*NaN; temp_p = ones(1,length(p_ttest))*NaN; tempidx = [0 sort(excludeMetIdx)]; nstep = 1;
for j = 1:length(tempidx)-1
- temp_p((tempidx(j)+1):(tempidx(j+1)-1)) = adj_p_ExclMet((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
- temph((tempidx(j)+1):(tempidx(j+1)-1)) = h_BH_ExclMet((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
+ temp_p((tempidx(j)+1):(tempidx(j+1)-1)) = adj_p_ExclMet_ttest((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
+ temph((tempidx(j)+1):(tempidx(j+1)-1)) = h_BH_ExclMet_ttest((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
nstep = nstep + 1;
end
- h_BH_ExclMet = temph;
- adj_p_ExclMet = temp_p;
+ h_BH_ExclMet_ttest = temph;
+ adj_p_ExclMet_ttest = temp_p;
end
+%% save data in table
if exist('excludeSubjects')
m = length(excludeSubjects);
for k = 1:m
@@ -136,9 +181,9 @@ end
rowNames = strcat(prefixes, subjects);
% rowNames = [rowNames; 'Mean'; 'Std'; 'p-Value'; 'h-Value'];
if exist ('excludeMet')
- rowNames = [rowNames; 'Mean'; 'Std'; 'p-Value'; 'h-Value'; 'p_BH'; 'h_BH'; 'p_BH_Excl'; 'h_BH_Excl'];
+ rowNames = [rowNames; 'Mean'; 'Std'; 'p-Value_Wilc'; 'h-Value_Wilc'; 'p_BH_Wilc'; 'h_BH_Wilc'; 'p_BH_Excl_Wilc'; 'h_BH_Excl_Wilc'; 'p-Value_ttest'; 'h-Value_ttest'; 'p_BH_ttest'; 'h_BH_ttest'; 'p_BH_Excl_ttest'; 'h_BH_Excl_ttest'];
else
- rowNames = [rowNames; 'Mean'; 'Std'; 'p-Value'; 'h-Value'; 'p_BH'; 'h_BH'];
+ rowNames = [rowNames; 'Mean'; 'Std'; 'p-Value_Wilc'; 'h-Value_Wilc'; 'p_BH_Wilc'; 'h_BH_Wilc'; 'p-Value_ttest'; 'h-Value_ttest'; 'p_BH_ttest'; 'h_BH_ttest'];
end
if exist('excludeSubjects')
ExpNameDiff = strcat('ConcDiff_', num2str(visualType), BOLDType, MMBType, '_Excluded');
@@ -146,14 +191,15 @@ else
ExpNameDiff = strcat('ConcDiff_', num2str(visualType), BOLDType, MMBType);
end
if exist ('excludeMet')
- ConcentrationsDiff_xx = array2table([PercDiff; PercDiffMean; PercDiffStd; p; h; adj_p; h_BH; adj_p_ExclMet; h_BH_ExclMet],'rowNames',rowNames,'VariableNames',metaboliteNames);
+ ConcentrationsDiff_xx = array2table([PercDiff; PercDiffMean; PercDiffStd; p_Wilc; h_Wilc; adj_p_Wilc; h_BH_Wilc; adj_p_ExclMet_Wilc; h_BH_ExclMet_Wilc; p_ttest; h_ttest; adj_p_ttest; h_BH_ttest; adj_p_ExclMet_ttest; h_BH_ExclMet_ttest],'rowNames',rowNames,'VariableNames',metaboliteNames);
else
- ConcentrationsDiff_xx = array2table([PercDiff; PercDiffMean; PercDiffStd; p; h; adj_p; h_BH],'rowNames',rowNames,'VariableNames',metaboliteNames);
+ ConcentrationsDiff_xx = array2table([PercDiff; PercDiffMean; PercDiffStd; p_Wilc; h_Wilc; adj_p_Wilc; h_BH_Wilc; p_ttest; h_ttest; adj_p_ttest; h_BH_ttest],'rowNames',rowNames,'VariableNames',metaboliteNames);
end
eval([ExpNameDiff '=ConcentrationsDiff_xx']);
exportFileMatDiff = ['D:\PAPER\1H_fMRS\Statistics\' ,strcat(ExpNameDiff,'.mat')];
save(exportFileMatDiff,ExpNameDiff)
+close all
%% plot concentrations mean and std
% y = table2array(ConcDiff_Stim_withBOLDCorr64_MMBSummed(14,[1:4,6:7,9:12,14:17]));
% x = 1:14;
diff --git a/fMRS_processing/createLCModelConfigfMRS.m b/fMRS_processing/createLCModelConfigfMRS.m
index 37c9b5794406e45c1afc2d838526565581e9034f..c2195accd7709078fff3cc64c2db31dd5e1f095f 100644
--- a/fMRS_processing/createLCModelConfigfMRS.m
+++ b/fMRS_processing/createLCModelConfigfMRS.m
@@ -1,6 +1,7 @@
function outputFile = createLCModelConfigfMRS(downField_MM_Met, controlFilesPath, outputControlFilesPath, defaultControlFile, ...
currentfMRS, defaultfMRS, defaultLCModelUser, currentLCModelUser, spectrumFileName, defaultSpectrum,...
- waterRefFileName, defaultWaterRef, subjectsPath, defaultSubjectsPath, subjectID, defaultSubject, MMBType, defaultMMB, fMRSDataSuff)
+ waterRefFileName, defaultWaterRef, subjectsPath, defaultSubjectsPath, subjectID, defaultSubject, MMBType,...
+ defaultMMB, fMRSDataSuff)
% Intro comments need to be rewritten
% Creates the control files for the TE series, given a default control file.
% the newly generated files will be placed in the same path as indicated by
@@ -17,7 +18,7 @@ fitSettingsDefaultFileID = fopen([controlFilesPath defaultControlFile], 'r');
mkdir(outputControlFilesPath);
outputFile = strcat(outputControlFilesPath, 'fitsettings_fMRS_', spectrumFileName, controlFileSuffix);
-if exist('fMRSDataSuff', 'var')
+if exist('fMRSDataSuff', 'var') & ~isempty(fMRSDataSuff)
spectrumFileName = strcat(fMRSDataSuff, spectrumFileName);
end
@@ -27,10 +28,23 @@ display(outputFile);
while(~feof(fitSettingsDefaultFileID))
s = fgetl(fitSettingsDefaultFileID);
k = strfind(s,'FILRAW');
- currentfMRSextended = strcat(currentfMRS, MMBType);
+ currentfMRSextended = strcat(currentfMRS, MMBType);
+ if strfind(spectrumFileName,'_woBaseline')
+ if strfind(MMBType,'v2')
+ currentfMRSNew = strcat(currentfMRS,'_v2_woBaseline');
+ else
+ currentfMRSNew = strcat(currentfMRS,'_woBaseline');
+ end
+ else
+% if strfind(MMBType,'v2')
+% currentfMRSNew = strcat(currentfMRS,'_v2');
+% else
+ currentfMRSNew = currentfMRS;
+% end
+ end
if ~isempty(k)
s = strrep(s, defaultLCModelUser, currentLCModelUser);
- s = strrep(s, defaultfMRS, currentfMRS);
+ s = strrep(s, defaultfMRS, currentfMRSNew);
s = strrep(s, defaultSubjectsPath, subjectsPath);
s = strrep(s, defaultSpectrum, spectrumFileName);
s = strrep(s, defaultWaterRef, waterRefFileName);
diff --git a/fMRS_processing/createLCModelConfigfMRSDiff.m b/fMRS_processing/createLCModelConfigfMRSDiff.m
index fc7305821165c853ec0afffa7c3295b028379078..c540f9855eb24296ed217a0af4c36a2088520563 100644
--- a/fMRS_processing/createLCModelConfigfMRSDiff.m
+++ b/fMRS_processing/createLCModelConfigfMRSDiff.m
@@ -21,6 +21,12 @@ if defaultControlFile(1:29) == 'fitsettings_fMRS_XXXX_StimOFF'
% else
outputFile = strcat(outputControlFilesPath, 'fitsettings_fMRS_', spectrumFileName, controlFileSuffix);
% end
+elseif defaultControlFile(1:32) == 'fitsettings_fMRS_v2_XXXX_StimOFF'
+ % if ~isempty(strfind(MMBType,'_Excluded'))
+ % outputFile = strcat(outputControlFilesPath, 'fitsettings_fMRS_', spectrumFileName, '_Excluded', controlFileSuffix);
+ % else
+ outputFile = strcat(outputControlFilesPath, 'fitsettings_fMRS_', spectrumFileName, controlFileSuffix);
+ % end
else
outputFile = strcat(outputControlFilesPath, defaultControlFile);
outputFile = strrep(outputFile, 'Difference_XXXX_StimOFF', spectrumFileName);
diff --git a/fMRS_processing/doFitting_fMRS_Met.m b/fMRS_processing/doFitting_fMRS_Met.m
index 1869158360f1f0bce2b57ed896e068c4c444d20e..5fcfc555f1014fea1e23688cac2b1bf914fae203 100644
--- a/fMRS_processing/doFitting_fMRS_Met.m
+++ b/fMRS_processing/doFitting_fMRS_Met.m
@@ -16,14 +16,14 @@ elseif ~isempty(strfind(StimType{1},'Rest'))
else
error('StimType needs to be Stim or Rest');
end
-BOLDType = {'_withBOLDCorr64#1'}; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _woBOLDCorr64#1, _withBOLDCorr64, _withBOLDCorr64#1
+BOLDType = {'_withBOLDCorr128'}; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _woBOLDCorr64#1, _withBOLDCorr64, _withBOLDCorr64#1
orderFMRS = strcat(StimType,BOLDType);%
% orderFMRS = {'StimON_woBOLDCorr128', 'StimOFF_woBOLDCorr128'};
MMBType = '_MMBSummed'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated
-subjects = {'6524';};
+subjects = {'3333';};
% subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
% '4085';'3333';'4012';'4085';'6524'};
-prefixes = {'2020-03-13_';};
+prefixes = {'2020-03-10_';};
% prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
% '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
% '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
@@ -61,23 +61,23 @@ subjectsLCModelOutputPath = strcat(LCModelOutputPath, subjectsPath, '/');
%% process fMRS data and save .RAW file
dataPath = strcat(localFilePathBase,subjectsPath, '/');
-% for i = 1:length(dataPath)
-% currentFolder = num2str(cell2mat(dataPath(i)));
-% cd (currentFolder);
-% subFolderName = dir(dataPath{i});
-% if ~isempty(strfind(StimType{1},'Stim'))
-% dataFile = dir('*Stim*.dat');
-% elseif ~isempty(strfind(StimType{1},'Rest'))
-% dataFile = dir('*Rest*.dat');
-% else
-% error('StimType needs to be Stim or Rest');
-% end
-% % nBlocks = 5;
+for i = 1:length(dataPath)
+ currentFolder = num2str(cell2mat(dataPath(i)));
+ cd (currentFolder);
+ subFolderName = dir(dataPath{i});
+ if ~isempty(strfind(StimType{1},'Stim'))
+ dataFile = dir('*Stim*.dat');
+ elseif ~isempty(strfind(StimType{1},'Rest'))
+ dataFile = dir('*Rest*.dat');
+ else
+ error('StimType needs to be Stim or Rest');
+ end
+ nBlocks = 5;
% nBlocks = 10;
-% PCSteps = 16;
-% reconstruct_1H_fMRS(currentFolder,dataFile.name,1,1,nBlocks,false,PCSteps,[],1,visualType); %currentFolder, fid_id, isMC, isInVivo, nBlocks, nOnAverages, PCSteps, weights, saveResults
-% close all; cd ..;
-% end
+ PCSteps = 16;
+ reconstruct_1H_fMRS(currentFolder,dataFile.name,1,1,nBlocks,false,PCSteps,[],1,visualType); %currentFolder, fid_id, isMC, isInVivo, nBlocks, nOnAverages, PCSteps, weights, saveResults
+ close all; cd ..;
+end
%% basic configurations
% LCModelTableFits = strcat(outputFileNameBase, orderFMRS, extensionTable);
diff --git a/fMRS_processing/doFitting_fMRS_Met_Summed.m b/fMRS_processing/doFitting_fMRS_Met_Summed.m
index 4a97b8c1b3c42f666f35a02afd0b0e7da2a15d72..6796666358ffbbff6201024cbe706e4c9e9d113a 100644
--- a/fMRS_processing/doFitting_fMRS_Met_Summed.m
+++ b/fMRS_processing/doFitting_fMRS_Met_Summed.m
@@ -1,10 +1,13 @@
function doFitting_fMRS_Met_Summed()
+%check StimType, BOLDType, MMBType
+
clear; clc; close all;
-pathName = 'fMRS data path';
+% pathName = 'fMRS data path'; %first version, no modulus correction for gradient sideband removal
+pathName = 'fMRS data path modulus'; %second version, with modulus correction for gradient sideband, only UF can be processed
sampleCriteria = {'Summed_fMRSData'};
[localFilePathBase] = pathToDataFolder(pathName, sampleCriteria);
-StimType = {'RestON', 'RestOFF'}; %'RestON', 'RestOFF', 'StimON', 'StimOFF'
+StimType = {'StimON', 'StimOFF'}; %'RestON', 'RestOFF', 'StimON', 'StimOFF'
if ~isempty(strfind(StimType{1},'Stim'))
visualType = 'Stim';
elseif ~isempty(strfind(StimType{1},'Rest'))
@@ -12,9 +15,9 @@ elseif ~isempty(strfind(StimType{1},'Rest'))
else
error('StimType needs to be Stim or Rest');
end
-BOLDType = {'_woBOLDCorr64#1'}; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _withBOLDCorr64, _woBOLDCorr64#1
+BOLDType = {'_woBOLDCorr128'}; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _withBOLDCorr64, _woBOLDCorr64#1
orderFMRS = strcat(StimType,BOLDType);
-MMBType = '_MMBSummed'; %only usefull with summed MMB
+MMBType = '_MMBSummed_v2'; %_MMBSummed_v2, only useful with summed MMB
subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
'4085';'3333';'4012';'4085';'6524'};
prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
@@ -50,13 +53,21 @@ extensionMat = '.mat';
defaultfMRS = 'StimOFF';
defaultSubject = 'XXXX';
defaultSubjectsPath = 'YYYY';
-defaultMMB = '_MMBSummed';
+if ~isempty(strfind(MMBType,'_v2'))
+ defaultMMB = '_MMBSummed_v2';
+else
+ defaultMMB = '_MMBSummed';
+end
defaultLCModelUser = 'jdorst';
outputFileNameBase_OFF = strcat(defaultSubject, '_', num2str(visualType), 'OFF', BOLDType{1});
outputFileNameBase_ON = strcat(defaultSubject, '_', num2str(visualType), 'ON', BOLDType{1});
outputFileNameBase_WRef = strcat(defaultSubject, '_WRef');
-controlFilesBase = 'fitsettings_fMRS_XXXX_';
+if ~isempty(strfind(MMBType,'_v2'))
+ controlFilesBase = 'fitsettings_fMRS_v2_XXXX_';
+else
+ controlFilesBase = 'fitsettings_fMRS_XXXX_';
+end
% controlFilesBaseDiff = 'fitsettings_fMRS_Difference_XXXX_';
% if visualType == 'Rest'
% controlFilesBaseDiff = 'fitsettings+_fMRS_Difference_XXXX_';
@@ -78,7 +89,15 @@ numberOfSubjects = length(subjects);
controlFilesPathRemote = '/Desktop/1H_fMRS/LCModelConfig/';
controlFilesPathLocal = [localFilePathBase, 'LCModelConfig/'];
LCModelOutputFilesPathRemote = '/Desktop/1H_fMRS/Output/';
-LCModelOutputPath = [localFilePathBase, 'Output/'];
+if ~isempty(strfind(MMBType,'_v2'))
+ if ~isempty(strfind(localFilePathBase,'Modulus'))
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed_Modulus/'];
+ else
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed/'];
+ end
+else
+ LCModelOutputPath = [localFilePathBase, 'Output/'];
+end
defaultControlFile = strcat(controlFilesBase, defaultfMRS, controlFilesBaseSuffix);
defaultControlFileDiff = strcat(controlFilesBaseDiff, defaultfMRS, controlFilesBaseSuffix);
@@ -92,7 +111,7 @@ summedLCModelOutputFilesRemote = strcat(LCModelOutputFilesPathRemote, summedPath
summedLCModelOutputPath = strcat(LCModelOutputPath, summedPath, '/');
%% do the LCModel fitting
-LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
%% sum fMRS spectra of all volunteers, weighted with water fit (need to run doFitting_fMRS_Water.m first)
@@ -141,15 +160,23 @@ exportFileMatOFF = [fMRSData_Summed_Path, strcat(fMRSData_Summed_OFF_BOLD,'.mat'
load(strrep(strcat(currentfMRSDataPath, '\',outputFileNameBase_OFF,extensionMat),defaultSubject,subjects{indexCurrentSubject}));
fMRSData_Summed_OFF_MR_spectroS = aOFF;
fMRSData_Summed_OFF_MR_spectroS.Data{1} = fMRSData_Summed_OFF;
-fMRSData_Summed_OFF_MR_spectroS.ExportLcmRaw(fMRSData_Summed_Path, fMRSData_Summed_OFF_BOLD, 'Yes', 'Yes');
save(exportFileMatOFF,'fMRSData_Summed_OFF_MR_spectroS')
-% % % % LCModelCallerInstance.CopyDataToRemote(fMRSData_Summed_Path, LCModelfMRSDataPathRemote, strcat(fMRSData_Summed_OFF_BOLD,extensionRAW))
+if ~isempty(strfind(localFilePathBase,'Modulus'))
+ fMRSData_Summed_OFF = real(fMRSData_Summed_OFF).*2;
+ fMRSData_Summed_OFF_MR_spectroS.Data{1} = fMRSData_Summed_OFF;
+end
+fMRSData_Summed_OFF_MR_spectroS.ExportLcmRaw(fMRSData_Summed_Path, fMRSData_Summed_OFF_BOLD, 'Yes', 'Yes');
+
load(strrep(strcat(currentfMRSDataPath, '\',outputFileNameBase_ON,extensionMat),defaultSubject,subjects{indexCurrentSubject}));
fMRSData_Summed_ON_MR_spectroS = aON;
fMRSData_Summed_ON_MR_spectroS.Data{1} = fMRSData_Summed_ON;
-fMRSData_Summed_ON_MR_spectroS.ExportLcmRaw(fMRSData_Summed_Path, fMRSData_Summed_ON_BOLD, 'Yes', 'Yes');
save(exportFileMatON,'fMRSData_Summed_ON_MR_spectroS')
-% % % % LCModelCallerInstance.CopyDataToRemote(fMRSData_Summed_Path, LCModelfMRSDataPathRemote, strcat(fMRSData_Summed_ON_BOLD,extensionRAW))
+if ~isempty(strfind(localFilePathBase,'Modulus'))
+ fMRSData_Summed_ON = real(fMRSData_Summed_ON).*2;
+ fMRSData_Summed_ON_MR_spectroS.Data{1} = fMRSData_Summed_ON;
+end
+fMRSData_Summed_ON_MR_spectroS.ExportLcmRaw(fMRSData_Summed_Path, fMRSData_Summed_ON_BOLD, 'Yes', 'Yes');
+
%% create and save difference spectrum from summed indiviually BOLD corrected spectra
fMRSData_Summed_ON = squeeze(fMRSData_Summed_ON);
@@ -229,44 +256,6 @@ if ~isempty(strfind(BOLDType{1},'woBOLDCorr'))
fMRSData_Summed_noBOLD.Parameter.ReconFlags.isfMRSDifference = 0;
fMRSData_Summed_noBOLD = fMRSData_Summed_noBOLD.fMRSDifference;
- % timePoints = size(fMRSData_Summed_noBOLD.Data{1},1);
- % dwellTime = fMRSData_Summed_noBOLD.Parameter.Headers.DwellTimeSig_ns * 1e-9; %in secs
- % timeVector = linspace(0,(timePoints-1)*dwellTime,timePoints)';
- % plot(real(fftshift(fft(data(:,1,1).*exp(-pi*0.3*timeVector).*exp(1i*2*pi*0.09*timeVector))))-real(fftshift(fft(data(:,1,2)))))
-
- figure; hold on;
- data = squeeze(fMRSData_Summed_noBOLD.Data{1});
- ppmVector = fMRSData_Summed_noBOLD.getPpmVector;
- plot(ppmVector,real(fftshift(fft(data(:,1,1)))))
- plot(ppmVector,real(fftshift(fft(data(:,1,2)))))
- plot(ppmVector,real(fftshift(fft(data(:,1,3)))))
- set(gca, 'XDir','reverse')
- xlim([0.5 4.5]); legend('On','Off','Residual')
-
- if exist('excludeSubjects')
- fig2save_fMRSData_Summed_noBOLD = fullfile(fMRSData_Summed_Path, strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_Excluded_BOLDafterSum.fig'));
- else
- fig2save_fMRSData_Summed_noBOLD = fullfile(fMRSData_Summed_Path, strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_BOLDafterSum.fig'));
- end
- savefig(fig2save_fMRSData_Summed_noBOLD)
- close all;
-
- %save only residual
- temp = squeeze(fMRSData_Summed_noBOLD.Data{1});
- temp = temp(:,:,3);
- fMRSData_Difference_BOLDafterSummation_MRSpectro = fMRSData_Summed_noBOLD;
- fMRSData_Difference_BOLDafterSummation_MRSpectro.Data{1} = permute(temp,[1 3 4 5 6 7 8 9 2]);
- if exist('excludeSubjects')
- fMRSData_Difference_BOLDafterSummation = strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_Excluded_BOLDafterSum');
- else
- fMRSData_Difference_BOLDafterSummation = strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_BOLDafterSum');
- end
- eval([fMRSData_Difference_BOLDafterSummation '=fig2save_fMRSData_Summed_noBOLD']);
- exportFileMatDifferenceBOLDafterSummation = [fMRSData_Summed_Path, strcat(fMRSData_Difference_BOLDafterSummation, '.mat')];
- save(exportFileMatDifferenceBOLDafterSummation,'fMRSData_Difference_BOLDafterSummation_MRSpectro')
- fMRSData_Difference_BOLDafterSummation_MRSpectro.ExportLcmRaw(fMRSData_Summed_Path, fMRSData_Difference_BOLDafterSummation, 'Yes', 'Yes');
- clear temp
-
%save ON OFF
%ON
temp = squeeze(fMRSData_Summed_noBOLD.Data{1});
@@ -280,7 +269,11 @@ if ~isempty(strfind(BOLDType{1},'woBOLDCorr'))
end
eval([fMRSData_Summed_ON_BOLDafterSummation '=fMRSData_Summed_ON_afterSummation']);
exportFileMatON = [fMRSData_Summed_Path, strcat(fMRSData_Summed_ON_BOLDafterSummation,'.mat')];
- save(exportFileMatON,'fMRSData_Summed_ON_BOLDafterSummation')
+ save(exportFileMatON,'fMRSData_Summed_ON_afterSummation')
+ if ~isempty(strfind(localFilePathBase,'Modulus'))
+ temp = real(temp).*2;
+ fMRSData_Summed_ON_afterSummation.Data{1} = temp;
+ end
fMRSData_Summed_ON_afterSummation.ExportLcmRaw(fMRSData_Summed_Path, fMRSData_Summed_ON_BOLDafterSummation, 'Yes', 'Yes');
%OFF
temp = squeeze(fMRSData_Summed_noBOLD.Data{1});
@@ -294,9 +287,60 @@ if ~isempty(strfind(BOLDType{1},'woBOLDCorr'))
end
eval([fMRSData_Summed_OFF_BOLDafterSummation '=fMRSData_Summed_OFF_afterSummation']);
exportFileMatON = [fMRSData_Summed_Path, strcat(fMRSData_Summed_OFF_BOLDafterSummation,'.mat')];
- save(exportFileMatON,'fMRSData_Summed_OFF_BOLDafterSummation')
+ save(exportFileMatON,'fMRSData_Summed_OFF_afterSummation')
+ if ~isempty(strfind(localFilePathBase,'Modulus'))
+ temp = real(temp).*2;
+ fMRSData_Summed_OFF_afterSummation.Data{1} = temp;
+ end
fMRSData_Summed_OFF_afterSummation.ExportLcmRaw(fMRSData_Summed_Path, fMRSData_Summed_OFF_BOLDafterSummation, 'Yes', 'Yes');
+ % create and save difference spectrum
+ %save only residual
+ temp = squeeze(fMRSData_Summed_noBOLD.Data{1});
+ temp = temp(:,:,3);
+ fMRSData_Difference_BOLDafterSummation_MRSpectro = fMRSData_Summed_noBOLD;
+ fMRSData_Difference_BOLDafterSummation_MRSpectro.Data{1} = permute(temp,[1 3 4 5 6 7 8 9 2]);
+ if exist('excludeSubjects')
+ fMRSData_Difference_BOLDafterSummation = strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_Excluded_BOLDafterSum');
+ fig2save_fMRSData_Summed_noBOLD = fullfile(fMRSData_Summed_Path, strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_Excluded_BOLDafterSum.fig'));
+ else
+ fMRSData_Difference_BOLDafterSummation = strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_BOLDafterSum');
+ fig2save_fMRSData_Summed_noBOLD = fullfile(fMRSData_Summed_Path, strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_BOLDafterSum.fig'));
+ end
+ eval([fMRSData_Difference_BOLDafterSummation '=fig2save_fMRSData_Summed_noBOLD']);
+ exportFileMatDifferenceBOLDafterSummation = [fMRSData_Summed_Path, strcat(fMRSData_Difference_BOLDafterSummation, '.mat')];
+ save(exportFileMatDifferenceBOLDafterSummation,'fMRSData_Difference_BOLDafterSummation_MRSpectro')
+ if ~isempty(strfind(localFilePathBase,'Modulus'))
+ temp = real(temp).*2;
+ fMRSData_Difference_BOLDafterSummation_MRSpectro.Data{1} = temp;
+ end
+ fMRSData_Difference_BOLDafterSummation_MRSpectro.ExportLcmRaw(fMRSData_Summed_Path, fMRSData_Difference_BOLDafterSummation, 'Yes', 'Yes');
+ clear temp
+
+ % create and save figure
+ % timePoints = size(fMRSData_Summed_noBOLD.Data{1},1);
+ % dwellTime = fMRSData_Summed_noBOLD.Parameter.Headers.DwellTimeSig_ns * 1e-9; %in secs
+ % timeVector = linspace(0,(timePoints-1)*dwellTime,timePoints)';
+ % plot(real(fftshift(fft(data(:,1,1).*exp(-pi*0.3*timeVector).*exp(1i*2*pi*0.09*timeVector))))-real(fftshift(fft(data(:,1,2)))))
+
+ figure; hold on;
+ data = squeeze(fMRSData_Summed_noBOLD.Data{1});
+ ppmVector = fMRSData_Summed_noBOLD.getPpmVector;
+ plot(ppmVector,real(fftshift(fft(real(data(:,1,1))))))
+ plot(ppmVector,real(fftshift(fft(real(data(:,1,2))))))
+ plot(ppmVector,real(fftshift(fft(real(data(:,1,3))))))
+ set(gca, 'XDir','reverse')
+ xlim([0.5 4.5]); legend('On','Off','Residual')
+
+ if exist('excludeSubjects')
+ fig2save_fMRSData_Summed_noBOLD = fullfile(fMRSData_Summed_Path, strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_Excluded_BOLDafterSum.fig'));
+ else
+ fig2save_fMRSData_Summed_noBOLD = fullfile(fMRSData_Summed_Path, strcat('Diff_',visualType,strrep(BOLDType{1},'#','_'),MMBType,'_BOLDafterSum.fig'));
+ end
+ savefig(fig2save_fMRSData_Summed_noBOLD)
+ close all;
+
+
end
%% sum Water reference spectra of all volunteers, weighted with water fit (need to run doFitting_fMRS_Water.m first)
WaterReferenceTable = (readtable(strcat(LCModelOutputPath,'WaterReference_Fit.xlsx')));
@@ -344,11 +388,13 @@ if exist('excludeSubjects')
waterRefFileName = strcat(waterRefFileName,'_Excluded');
currentControlFiles = strrep(LCModelControlFiles, defaultSubject, 'Summed');
currentControlFiles = strrep(currentControlFiles,'.control','_Excluded.control');
+ currentControlFiles = strrep(currentControlFiles,'fMRS_v2_','fMRS_');
currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, 'Summed');
currentOutputFiles = strcat(currentOutputFiles,'_Excluded');
else
waterRefFileName = strrep(defaultWaterRef, defaultSubject, 'fMRSData_Summed');
currentControlFiles = strrep(LCModelControlFiles, defaultSubject, 'Summed');
+ currentControlFiles = strrep(currentControlFiles,'fMRS_v2_','fMRS_');
currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, 'Summed');
end
@@ -484,12 +530,14 @@ if ~isempty(strfind(BOLDType{1},'woBOLDCorr'))
waterRefFileName = strcat(waterRefFileName,'_Excluded');
currentControlFiles = strrep(LCModelControlFiles, defaultSubject, 'Summed');
currentControlFiles = strrep(currentControlFiles,'.control','_Excluded_BOLDafterSum.control');
+ currentControlFiles = strrep(currentControlFiles,'fMRS_v2_','fMRS_');
currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, 'Summed');
currentOutputFiles = strcat(currentOutputFiles,'_Excluded_BOLDafterSum');
else
waterRefFileName = strrep(defaultWaterRef, defaultSubject, 'fMRSData_Summed');
currentControlFiles = strrep(LCModelControlFiles, defaultSubject, 'Summed');
currentControlFiles = strrep(currentControlFiles,'.control','_BOLDafterSum.control');
+ currentControlFiles = strrep(currentControlFiles,'fMRS_v2_','fMRS_');
currentOutputFiles = strrep(strcat(LCModelOutputFiles,'_BOLDafterSum'), defaultSubject, 'Summed');
end
@@ -741,9 +789,9 @@ if ~isempty(strfind(BOLDType{1},'woBOLDCorr'))
% save table to file
if exist('excludeSubjects')
- ExpName = strrep(strcat('LCModelFit_Summed_', num2str(visualType),BOLDType, MMBType, '_Exc_BOLDafterSu'),'#','_');
+ ExpName = strrep(strcat('LCModelFit_Summed_', num2str(visualType),BOLDType, MMBType, '_Exc_BOLDaftS'),'#','_');
else
- ExpName = strrep(strcat('LCModelFit_Summed_', num2str(visualType),BOLDType, MMBType, '_BOLDafterSum'),'#','_');
+ ExpName = strrep(strcat('LCModelFit_Summed_', num2str(visualType),BOLDType, MMBType, '_BOLDaftS'),'#','_');
end
ExpName = ExpName{1}; %convert 1x1cell to string
exportFileXlsx = [LCModelOutputPath ,strcat(ExpName,'.xlsx')];
@@ -770,9 +818,9 @@ if ~isempty(strfind(BOLDType{1},'woBOLDCorr'))
rowNames = {'PercDiff'};
if exist('excludeSubjects')
- ExpNameDiff = strrep(strcat('ConcDiff_Summed_', num2str(visualType),BOLDType, MMBType,'_Excl_BOLDafterSum'),'#','_');
+ ExpNameDiff = strrep(strcat('ConcDiff_Summed_', num2str(visualType),BOLDType, MMBType,'_Excl_BOLDaftS'),'#','_');
else
- ExpNameDiff = strrep(strcat('ConcDiff_Summed_', num2str(visualType),BOLDType, MMBType,'_BOLDafterSum'),'#','_');
+ ExpNameDiff = strrep(strcat('ConcDiff_Summed_', num2str(visualType),BOLDType, MMBType,'_BOLDaftS'),'#','_');
end
ExpNameDiff = ExpNameDiff{1}; %convert 1x1cell to string
ConcDiff_Summed = array2table(PercDiff,'rowNames',rowNames,'VariableNames',metaboliteNames);
diff --git a/fMRS_processing/doFitting_fMRS_Met_v2.m b/fMRS_processing/doFitting_fMRS_Met_v2.m
index 6ce5b6ab9960f7d60f42eab06aad96cae595399f..6d702cc13bb62f51c4541a71035b3332ea1ddc3d 100644
--- a/fMRS_processing/doFitting_fMRS_Met_v2.m
+++ b/fMRS_processing/doFitting_fMRS_Met_v2.m
@@ -1,14 +1,16 @@
function doFitting_fMRS_Met_v2()
clear; clc; close all;
-%check StimType, BOLDType, MMBType
+%check pathName, StimType, BOLDType, MMBType
%check nBlocks in reconstruct!
%for v2 (+Asc, -Glyc, PCh&GPC -> tCh), check MMBType, defaultMMB, currentControlFiles
-pathName = 'fMRS data path';
-sampleCriteria = {'Output', 'LCModelConfig'};
-[localFilePathBase] = pathToDataFolder(pathName, sampleCriteria);
+% pathName = 'fMRS data path'; %first version, no modulus correction for gradient sideband removal
+pathName = 'fMRS data path modulus'; %second version, with modulus correction for gradient sideband, only UF can be processed
+% pathName = 'fMRS data path 06-38ppm'; %third version, LCModel Fit w/o Cr/PCr at 3.9ppm
+% sampleCriteria = {'Output', 'LCModelConfig'};
+[localFilePathBase] = pathToDataFolder(pathName);
StimType = {'StimON', 'StimOFF'}; %'StimON', 'StimOFF', 'RestON', 'RestOFF'
if ~isempty(strfind(StimType{1},'Stim'))
visualType = 'Stim';
@@ -17,19 +19,26 @@ elseif ~isempty(strfind(StimType{1},'Rest'))
else
error('StimType needs to be Stim or Rest');
end
-BOLDType = {'_woBOLDCorr64#1'}; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _woBOLDCorr64#1, _withBOLDCorr64, _withBOLDCorr64#1
+BOLDTypes = {'_woBOLDCorr128', '_withBOLDCorr128','_woBOLDCorr64', '_woBOLDCorr64#1', '_withBOLDCorr64', '_withBOLDCorr64#1'}; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _woBOLDCorr64#1, _withBOLDCorr64, _withBOLDCorr64#1
+% BOLDTypes = {'_woBOLDCorr64#1'}; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _woBOLDCorr64#1, _withBOLDCorr64, _withBOLDCorr64#1
+for idxBOLDType = 1:length(BOLDTypes)
+ BOLDType = BOLDTypes(idxBOLDType);
orderFMRS = strcat(StimType,BOLDType);%
-% orderFMRS = {'StimON_woBOLDCorr128', 'StimOFF_woBOLDCorr128'};
MMBType = '_MMBSummed_v2'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated
-% subjects = {'3333';};
-subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
- '4085';'3333';'4012';'4085';'6524'};
-% prefixes = {'2020-03-10_';};
-prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
- '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
- '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
-% excludeSubjects = {'5166';'9810';'5269'};
+subjects = {'3333';};
+% subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
+% '4085';'3333';'4012';'4085';'6524'};
+prefixes = {'2020-03-10_';};
+% prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
+% '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
+% '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
+excludeSubjects = {'5166';'9810';'5269'};
suffixFolder = '_fMRS';
+if strcmp(MMBType,'_MMBSummed')
+ excludeMet = {'NAA';'GABA';'Gln';'Glyc';'GSH';'mI';'NAAG';'PCh';'GPC';'PE';'Scyllo';'Tau';'Glc';'NAA_NAAG';'Glu_Gln';'Glyc';'mI_Glyc';'PCh'}; %for v1
+elseif strcmp(MMBType,'_MMBSummed_v2')
+ excludeMet = {'NAA';'Asc';'GABA';'Glc';'Gln';'GSH';'mI';'NAAG';'tCh';'PE';'Scyllo';'Tau';'NAA_NAAG';'Glu_Gln'}; %for v2
+end
extensionTable = '.table';
extensionCoord = '.coord';
@@ -39,8 +48,6 @@ defaultSubjectsPath = 'YYYY';
% defaultMMB = '_MMBSummed';
defaultMMB = '_MMBSummed_v2';
defaultLCModelUser = 'jdorst';
-outputFileNameBaseWithoutMetabolite_OFF = strcat(defaultSubject, '_StimOFF_woBOLDCorr128');
-outputFileNameBaseWithoutMetabolite_ON = strcat(defaultSubject, '_StimON_woBOLDCorr128');
% controlFilesBase = 'fitsettings_fMRS_XXXX_';
controlFilesBase = 'fitsettings_fMRS_v2_XXXX_';
@@ -51,7 +58,16 @@ outputFileNameBase = strcat(defaultSubject, '_');
controlFilesPathRemote = '/Desktop/1H_fMRS/LCModelConfig/';
controlFilesPathLocal = [localFilePathBase, 'LCModelConfig/'];
LCModelOutputFilesPathRemote = '/Desktop/1H_fMRS/Output/';
-LCModelOutputPath = [localFilePathBase, 'Output/'];
+if ~isempty(strfind(localFilePathBase,'Modulus'))
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed_Modulus/'];
+ modulus = true;
+elseif ~isempty(strfind(localFilePathBase,'06-38ppm'))
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed_06-38ppm/'];
+ modulus = false;
+else
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed/'];
+ modulus = false;
+end
defaultControlFile = strcat(controlFilesBase, defaultfMRS, controlFilesBaseSuffix);
subjectsPath = strcat(prefixes, subjects, suffixFolder);
@@ -65,29 +81,34 @@ subjectsLCModelOutputPath = strcat(LCModelOutputPath, subjectsPath, '/');
%% process fMRS data and save .RAW file
dataPath = strcat(localFilePathBase,subjectsPath, '/');
-% for i = 1:length(dataPath)
-% currentFolder = num2str(cell2mat(dataPath(i)));
-% cd (currentFolder);
-% subFolderName = dir(dataPath{i});
-% if ~isempty(strfind(StimType{1},'Stim'))
-% dataFile = dir('*Stim*.dat');
-% elseif ~isempty(strfind(StimType{1},'Rest'))
-% dataFile = dir('*Rest*.dat');
-% else
-% error('StimType needs to be Stim or Rest');
-% end
-% % nBlocks = 5;
-% nBlocks = 10;
-% PCSteps = 16;
-% reconstruct_1H_fMRS(currentFolder,dataFile.name,1,1,nBlocks,false,PCSteps,[],1,visualType); %currentFolder, fid_id, isMC, isInVivo, nBlocks, nOnAverages, PCSteps, weights, saveResults
-% close all; cd ..;
-% end
-
+if ~isempty(strfind(BOLDType{1},'wo'))
+ for i = 1:length(dataPath)
+ currentFolder = num2str(cell2mat(dataPath(i)));
+ cd (currentFolder);
+ subFolderName = dir(dataPath{i});
+ if ~isempty(strfind(StimType{1},'Stim'))
+ dataFile = dir('*Stim*.dat');
+ elseif ~isempty(strfind(StimType{1},'Rest'))
+ dataFile = dir('*Rest*.dat');
+ else
+ error('StimType needs to be Stim or Rest');
+ end
+ if ~isempty(strfind(BOLDType{1},'128'))
+ nBlocks = 5;
+ else
+ nBlocks = 10;
+ end
+ PCSteps = 16;
+ reconstruct_1H_fMRS(currentFolder,dataFile.name,1,1,nBlocks,false,PCSteps,[],1,visualType,BOLDType{1}, modulus); %currentFolder, fid_id, isMC, isInVivo, nBlocks, nOnAverages, PCSteps, weights, saveResults
+ close all; cd ..;
+ end
+end
+% % % % end
%% basic configurations
-% LCModelTableFits = strcat(outputFileNameBase, orderFMRS, extensionTable);
-% LCModelTableFitsCoord = strcat(outputFileNameBase, orderFMRS, extensionCoord);
LCModelOutputFiles = strcat(outputFileNameBase, orderFMRS, MMBType);
+LCModelOutputFileswoBaseline = strcat(outputFileNameBase, orderFMRS, MMBType, '_woBaseline');
LCModelControlFiles = strcat(controlFilesBase, orderFMRS, MMBType, controlFilesBaseSuffix);
+LCModelControlFileswoBaseline = strcat(controlFilesBase, orderFMRS, MMBType, '_woBaseline', controlFilesBaseSuffix);
defaultWaterRef = strcat(outputFileNameBase, 'WRef');
%
@@ -95,9 +116,9 @@ numberOfFMRS = length(orderFMRS);
numberOfSubjects = length(subjects);
%% do the LCModel fitting
-% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+% % % LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
-% do the actual LCModel fitting
+% % do the actual LCModel fitting
parfor indexCurrentSubject = 1:numberOfSubjects
%% do the LCModel fitting
LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
@@ -124,16 +145,84 @@ parfor indexCurrentSubject = 1:numberOfSubjects
fittingLCModel(LCModelCallerInstance, currentControlFiles, currentOutputFiles, subjectsControlFilesPathRemote{indexCurrentSubject}, ...
subjectsControlFilesPathLocal{indexCurrentSubject}, preprocessedFilesPathLocal{indexCurrentSubject});
end
+if exist('excludeSubjects')
+ createConcTable(numberOfSubjects,orderFMRS,LCModelOutputFiles,LCModelOutputPath,defaultSubject,prefixes,subjects,subjectsPath,...
+ visualType,BOLDType,MMBType,excludeSubjects,excludeMet)
+else
+ createConcTable(numberOfSubjects,orderFMRS,LCModelOutputFiles,LCModelOutputPath,defaultSubject,prefixes,subjects,subjectsPath,...
+ visualType,BOLDType,MMBType,'',excludeMet)
+end
+
+%% subtract baseline from difference spectrum and fit again
+% only useful for MMBSummed
+if strcmp(MMBType(1:10),'_MMBSummed')
+ % LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+ parfor indexCurrentSubject = 1:numberOfSubjects
+ %% do the LCModel fitting
+ LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+
+ LCModelCallerInstance = LCModelCallerInstance.ConfigurePaths(subjectsControlFilesPathRemote{indexCurrentSubject}, subjectsLCModelOutputFilesRemote{indexCurrentSubject}, ...
+ subjectsLCModelOutputPath{indexCurrentSubject});
+
+ currentControlFileswoBaseline = strrep(LCModelControlFileswoBaseline, defaultSubject, subjects{indexCurrentSubject});
+ currentControlFileswoBaseline = strrep(currentControlFileswoBaseline,'fMRS_v2_','fMRS_');
+ currentControlFileswoBaseline = strrep(currentControlFileswoBaseline,'#1','_1');
+ if ~isempty(strfind(MMBType,'_v2'))
+ currentDataFiles = strrep(strcat(outputFileNameBase, orderFMRS, '_v2'), defaultSubject, subjects{indexCurrentSubject});
+ else
+ currentDataFiles = strrep(strcat(outputFileNameBase, visualType, BOLDType), defaultSubject, subjects{indexCurrentSubject});
+ end
+
+ % currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, subjects{indexCurrentSubject});
+ waterRefFileName = strrep(defaultWaterRef, defaultSubject, subjects{indexCurrentSubject});
+ currentDataPath = strcat(localFilePathBase,subjectsPath{indexCurrentSubject}, '/');
+ for indexFMRS = 1:length(currentDataFiles)
+ % currentOutputFileswoBaseline = currentOutputFileswoBaseline{indexFMRS};
+ currentFMRS = orderFMRS{indexFMRS};
+ currentFMRS = strrep(currentFMRS,'#1','_1');
+ currentLCModelOutputFile = LCModelOutputFiles{indexFMRS};
+ currentLCModelOutputFile = strrep(currentLCModelOutputFile,'#1','_1');
+ %check save option for .mat file in subtractBaseline_fMRS
+ currentOutputFileswoBaseline = strrep(LCModelOutputFileswoBaseline, defaultSubject, subjects{indexCurrentSubject});
+ subtractBaseline_fMRS(currentDataPath,subjectsLCModelOutputPath{indexCurrentSubject},currentDataFiles{indexFMRS},currentOutputFileswoBaseline{indexFMRS},'NAA')
+ currentOutputFileswoBaseline = strrep(currentOutputFileswoBaseline,'#1','_1');
+ createLCModelConfigfMRS('fMRS', controlFilesPathLocal, subjectsControlFilesPathLocal{indexCurrentSubject}, defaultControlFile, ...
+ currentFMRS, defaultfMRS, defaultLCModelUser, LCModelCallerInstance.LCModelUser, currentOutputFileswoBaseline{indexFMRS}, currentLCModelOutputFile,...
+ waterRefFileName, defaultWaterRef, subjectsPath{indexCurrentSubject}, defaultSubjectsPath, subjects{indexCurrentSubject},...
+ defaultSubject, MMBType, defaultMMB,'');
+ end
+ fittingLCModel(LCModelCallerInstance, currentControlFileswoBaseline, currentOutputFileswoBaseline, subjectsControlFilesPathRemote{indexCurrentSubject}, ...
+ subjectsControlFilesPathLocal{indexCurrentSubject}, preprocessedFilesPathLocal{indexCurrentSubject});
+
+ end
+ if exist('excludeSubjects')
+ createConcTable(numberOfSubjects,orderFMRS,LCModelOutputFiles,LCModelOutputPath,defaultSubject,prefixes,subjects,subjectsPath,...
+ visualType,BOLDType,MMBType,excludeSubjects,excludeMet,1)
+ else
+ createConcTable(numberOfSubjects,orderFMRS,LCModelOutputFiles,LCModelOutputPath,defaultSubject,prefixes,subjects,subjectsPath,...
+ visualType,BOLDType,MMBType,'',excludeMet,1)
+ end
+end
+end
+end
%% create concentration table
+function createConcTable(numberOfSubjects,orderFMRS,LCModelOutputFiles,LCModelOutputPath,defaultSubject,prefixes,subjects,subjectsPath,...
+ visualType,BOLDType,MMBType,excludeSubjects,excludeMet,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+ LCModelOutputFiles = strrep(LCModelOutputFiles,'#1','_1');
+else
+ woBaseline = '';
+end
tableConcentrations = [];
isFirstIter = 1;
s = 1;
for indexCurrentSubject = 1:numberOfSubjects
for indexorderFMRS = 1:length(orderFMRS)
% retrieve parameters
- currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, subjects{indexCurrentSubject});
+ currentOutputFiles = strrep(strcat(LCModelOutputFiles,woBaseline), defaultSubject, subjects{indexCurrentSubject});
currentPath = strcat(LCModelOutputPath,subjectsPath{indexCurrentSubject},'/',currentOutputFiles,'.table');
[c1 c2 c3 c4] = textread(currentPath{indexorderFMRS},'%s %s %s %s');
@@ -184,7 +273,7 @@ for indexCurrentSubject = 1:numberOfSubjects
end
% save table to file
-ExpName = strcat('LCModelFit_', num2str(visualType), BOLDType{1}, MMBType);
+ExpName = strcat('LCModelFit_', num2str(visualType), BOLDType{1}, MMBType, woBaseline);
ExpName = strrep(ExpName,'#1','_1');
exportFileXlsx = [LCModelOutputPath ,strcat(ExpName,'.xlsx')];
xlswrite(exportFileXlsx, {LCModelOutputPath}, 1, 'A1');
@@ -202,7 +291,7 @@ LCModelFitTable = eval(rawFile);
metaboliteNames = LCModelFitTable(1,4:2:end-2);
metaboliteNames = strrep(metaboliteNames,'+','_');
-if exist('excludeSubjects')
+if exist('excludeSubjects', 'var') & ~isempty(excludeSubjects)
[n,~] = size(LCModelFitTable);
m = length(excludeSubjects);
nstep = 1;
@@ -224,59 +313,136 @@ PercDiff = 100./OFF.*ON-100;
PercDiffMean = mean(PercDiff);
PercDiffStd = std(PercDiff);
-%two-sided Wilcoxon signed rank test
-% for indexOfMetabolite = 1:length(metaboliteNames)
-% if isempty(find(~isnan(PercDiff(:,indexOfMetabolite))))
-% p(indexOfMetabolite) = NaN;
-% h(indexOfMetabolite) = 0;
-% else
-% [p(indexOfMetabolite),h(indexOfMetabolite)] = signrank(PercDiff(:,indexOfMetabolite));
-% end
-% end
-
-%paired two-tailed t-test
+%% two-sided Wilcoxon signed rank test
for indexOfMetabolite = 1:length(metaboliteNames)
if isempty(find(~isnan(PercDiff(:,indexOfMetabolite))))
- h(indexOfMetabolite) = NaN;
- p(indexOfMetabolite) = 0;
+ p_Wilc(indexOfMetabolite) = NaN;
+ h_Wilc(indexOfMetabolite) = 0;
else
- [h(indexOfMetabolite),p(indexOfMetabolite)] = ttest(PercDiff(:,indexOfMetabolite));
+ [p_Wilc(indexOfMetabolite),h_Wilc(indexOfMetabolite)] = signrank(PercDiff(:,indexOfMetabolite));
end
end
%check histogram of p-values
-edges = linspace(0,1,length(p));
-histogram(p,edges)
+edges = linspace(0,1,length(p_Wilc));
+histogram(p_Wilc,edges)
%Benjamini-Hochberg correction for multiple testing
-[h_BH, crit_p, adj_ci_cvrg, adj_p]=fdr_bh(p,0.05,'pdep','yes');
+[h_BH_Wilc, crit_p_Wilc, adj_ci_cvrg_Wilc, adj_p_Wilc]=fdr_bh(p_Wilc,0.05,'pdep','yes');
%h=1: reject H0, h=0: H0 not rejected
%crit_p: max p-value that is significant
%adj_ci_cvrg: BH adjusted confidence interval coverage
%adj_p: N pj / j, all adjusted p-values less than or equal to q are significant
-if exist('excludeSubjects')
+
+if exist('excludeMet')
+ [~,n] = size(metaboliteNames);
+ m = length(excludeMet);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = strcmp(metaboliteNames(1,i),excludeMet{j});
+% xx = find(~cellfun(@isempty,strcmp(metaboliteNames(1,i),excludeMet{j})));
+% if ~isempty(xx)
+ if xx
+ excludeMetIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ pExclMet_Wilc = p_Wilc;
+ pExclMet_Wilc(excludeMetIdx) = [];
+ [h_BH_ExclMet_Wilc, crit_p_ExclMet_Wilc, adj_ci_cvrg_ExclMet_Wilc, adj_p_ExclMet_Wilc]=fdr_bh(pExclMet_Wilc,0.05,'pdep','yes');
+ temph = ones(1,length(h_Wilc))*NaN; temp_p = ones(1,length(p_Wilc))*NaN; tempidx = [0 sort(excludeMetIdx)]; nstep = 1;
+ for j = 1:length(tempidx)-1
+ temp_p((tempidx(j)+1):(tempidx(j+1)-1)) = adj_p_ExclMet_Wilc((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
+ temph((tempidx(j)+1):(tempidx(j+1)-1)) = h_BH_ExclMet_Wilc((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
+ nstep = nstep + 1;
+ end
+ h_BH_ExclMet_Wilc = temph;
+ adj_p_ExclMet_Wilc = temp_p;
+end
+
+%% paired two-tailed t-test (data need to be normally distributed, which they aren't here! -> use nonparametric Wilcoxon)
+for indexOfMetabolite = 1:length(metaboliteNames)
+ if isempty(find(~isnan(PercDiff(:,indexOfMetabolite))))
+ h_ttest(indexOfMetabolite) = NaN;
+ p_ttest(indexOfMetabolite) = 0;
+ else
+ [h_ttest(indexOfMetabolite),p_ttest(indexOfMetabolite)] = ttest(PercDiff(:,indexOfMetabolite));
+ end
+end
+
+%check histogram of p-values
+edges = linspace(0,1,length(p_ttest));
+histogram(p_ttest,edges)
+
+%Benjamini-Hochberg correction for multiple testing
+[h_BH_ttest, crit_p_ttest, adj_ci_cvrg_ttest, adj_p_ttest]=fdr_bh(p_ttest,0.05,'pdep','yes');
+%h=1: reject H0, h=0: H0 not rejected
+%crit_p: max p-value that is significant
+%adj_ci_cvrg: BH adjusted confidence interval coverage
+%adj_p: N pj / j, all adjusted p-values less than or equal to q are significant
+
+if exist('excludeMet', 'var')
+ [~,n] = size(metaboliteNames);
+ m = length(excludeMet);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = strcmp(metaboliteNames(1,i),excludeMet{j});
+% xx = find(~cellfun(@isempty,strcmp(metaboliteNames(1,i),excludeMet{j})));
+% if ~isempty(xx)
+ if xx
+ excludeMetIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ pExclMet_ttest = p_ttest;
+ pExclMet_ttest(excludeMetIdx) = [];
+ [h_BH_ExclMet_ttest, crit_p_ExclMet_ttest, adj_ci_cvrg_ExclMet_ttest, adj_p_ExclMet_ttest]=fdr_bh(pExclMet_ttest,0.05,'pdep','yes');
+ temph = ones(1,length(h_ttest))*NaN; temp_p = ones(1,length(p_ttest))*NaN; tempidx = [0 sort(excludeMetIdx)]; nstep = 1;
+ for j = 1:length(tempidx)-1
+ temp_p((tempidx(j)+1):(tempidx(j+1)-1)) = adj_p_ExclMet_ttest((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
+ temph((tempidx(j)+1):(tempidx(j+1)-1)) = h_BH_ExclMet_ttest((tempidx(j)+2-nstep):(tempidx(j+1)-nstep));
+ nstep = nstep + 1;
+ end
+ h_BH_ExclMet_ttest = temph;
+ adj_p_ExclMet_ttest = temp_p;
+end
+if exist('excludeSubjects', 'var') & ~isempty(excludeSubjects)
m = length(excludeSubjects);
for k = 1:m
- [~, idx(k)] = ismember(excludeSubjects(k), subjects)
+ [~, idx(k)] = ismember(excludeSubjects(k), subjects);
end
prefixes([idx],:) = [];
subjects([idx],:) = [];
end
-
rowNames = strcat(prefixes, subjects);
-% rowNames = [rowNames; 'Mean'; 'Std'; 'p-Value'; 'h-Value'];
-rowNames = [rowNames; 'Mean'; 'Std'; 'p-Value'; 'h-Value'; 'p_BH'; 'h_BH'];
-if exist('excludeSubjects')
- ExpNameDiff = strcat('ConcDiff_', num2str(visualType), BOLDType, MMBType, '_Excluded');
+if exist('excludeMet', 'var')
+ rowNames = [rowNames; 'Mean'; 'Std'; 'p-Value_Wilc'; 'h-Value_Wilc'; 'p_BH_Wilc'; 'h_BH_Wilc'; 'p_BH_Excl_Wilc'; 'h_BH_Excl_Wilc'; 'p-Value_ttest'; 'h-Value_ttest'; 'p_BH_ttest'; 'h_BH_ttest'; 'p_BH_Excl_ttest'; 'h_BH_Excl_ttest'];
+else
+ rowNames = [rowNames; 'Mean'; 'Std'; 'p-Value_Wilc'; 'h-Value_Wilc'; 'p_BH_Wilc'; 'h_BH_Wilc'; 'p-Value_ttest'; 'h-Value_ttest'; 'p_BH_ttest'; 'h_BH_ttest'];
+end
+if exist('excludeSubjects', 'var') & ~isempty(excludeSubjects)
+ ExpNameDiff = strcat('ConcDiff_', num2str(visualType), BOLDType, MMBType, '_Excluded_woBaseline');
else
- ExpNameDiff = strcat('ConcDiff_', num2str(visualType), BOLDType, MMBType);
+ ExpNameDiff = strcat('ConcDiff_', num2str(visualType), BOLDType, MMBType, '_woBaseline');
end
ExpNameDiff = ExpNameDiff{1}; %convert 1x1cell to string
ExpNameDiff = strrep(ExpNameDiff,'#1','_1');
-ConcentrationsDiff_xx = array2table([PercDiff; PercDiffMean; PercDiffStd; p; h; adj_p; h_BH],'rowNames',rowNames,'VariableNames',metaboliteNames);
+if exist ('excludeMet')
+ ConcentrationsDiff_xx = array2table([PercDiff; PercDiffMean; PercDiffStd; p_Wilc; h_Wilc; adj_p_Wilc; h_BH_Wilc; adj_p_ExclMet_Wilc; h_BH_ExclMet_Wilc; p_ttest; h_ttest; adj_p_ttest; h_BH_ttest; adj_p_ExclMet_ttest; h_BH_ExclMet_ttest],'rowNames',rowNames,'VariableNames',metaboliteNames);
+else
+ ConcentrationsDiff_xx = array2table([PercDiff; PercDiffMean; PercDiffStd; p_Wilc; h_Wilc; adj_p_Wilc; h_BH_Wilc; p_ttest; h_ttest; adj_p_ttest; h_BH_ttest],'rowNames',rowNames,'VariableNames',metaboliteNames);
+end
+
+% ConcentrationsDiff_xx = array2table([PercDiff; PercDiffMean; PercDiffStd; p; h; adj_p; h_BH],'rowNames',rowNames,'VariableNames',metaboliteNames);
eval([ExpNameDiff '=ConcentrationsDiff_xx']);
exportFileMatDiff = [LCModelOutputPath ,strcat(ExpNameDiff,'.mat')];
save(exportFileMatDiff,ExpNameDiff)
-end
\ No newline at end of file
+close all
+end
+
diff --git a/fMRS_processing/doFitting_fMRS_SlidingAverage.m b/fMRS_processing/doFitting_fMRS_SlidingAverage.m
index 9db466fea0d8b048c7af841f8ed5dcbae7b0a2c8..2726558324a13b889a035be4c5cd61857e4fa80f 100644
--- a/fMRS_processing/doFitting_fMRS_SlidingAverage.m
+++ b/fMRS_processing/doFitting_fMRS_SlidingAverage.m
@@ -8,6 +8,7 @@ fitversion = 'v2';
pathName = 'fMRS SlidingAverage data path';
sampleCriteria = {'Output', 'LCModelConfig'};
[localFilePathBase] = pathToDataFolder(pathName, sampleCriteria);
+
visualType = 'Rest'; %'Stim', 'Rest'
BOLDType = '_woBOLDCorr'; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _withBOLDCorr64
if fitversion == 'v2'
@@ -25,15 +26,10 @@ for k = 1:nBlocks
end
% subjects = {'2823';};
-% subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
-% '4085';'3333';'4012';'4085';'6524'};
-subjects = {'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
+subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
'4085';'3333';'4012';'4085';'6524'};
% prefixes = {'2020-02-18_';};
-% prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
-% '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
-% '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
-prefixes = {'2020-02-21_';'2020-02-21_';'2020-02-26_';...
+prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
'2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
'2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
excludeSubjects = {'5166';'9810';'5269'};
@@ -61,6 +57,18 @@ end
controlFilesBaseSuffix = '.control';
outputFileNameBase = strcat(defaultSubject, '_');
+
+pathNameBaseline = 'fMRS TimeCourse data path';
+sampleCriteriaBaseline = {'Output_v2_MMBSummed', 'Output', 'LCModelConfig'};
+[localFilePathBaseBaseline] = pathToDataFolder(pathNameBaseline, sampleCriteriaBaseline);
+if fitversion == 'v2'
+ localBaselinePath = [localFilePathBaseBaseline, 'Output_v2_MMBSummed/'];
+ BaselineFiles = strcat('BaselineConc_',num2str(visualType), BOLDType, MMBType);
+else
+ localBaselinePath = [localFilePathBaseBaseline, 'Output/'];
+ BaselineFiles = strcat('BaselineConc_',num2str(visualType), BOLDType, MMBType);
+end
+
%% file paths setup
controlFilesPathRemote = '/Desktop/1H_fMRS/LCModelConfig/';
controlFilesPathLocal = [localFilePathBase, 'LCModelConfig/'];
@@ -108,118 +116,380 @@ numberOfSubjects = length(subjects);
% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
% do the actual LCModel fitting
-parfor indexCurrentSubject = 1:numberOfSubjects
- %% do the LCModel fitting
- LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
-
- LCModelCallerInstance = LCModelCallerInstance.ConfigurePaths(subjectsControlFilesPathRemote{indexCurrentSubject}, subjectsLCModelOutputFilesRemote{indexCurrentSubject}, ...
- subjectsLCModelOutputPath{indexCurrentSubject});
-
- if fitversion == 'v2'
- currentControlFiles = strrep(LCModelControlFiles, strcat('v2_',defaultSubject), subjects{indexCurrentSubject});
- else
- currentControlFiles = strrep(LCModelControlFiles, defaultSubject, subjects{indexCurrentSubject});
- end
- currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, subjects{indexCurrentSubject});
- waterRefFileName = strrep(defaultWaterRef, defaultSubject, subjects{indexCurrentSubject});
-
- %% create the control file series
- for nBl = 1:nBlocks
- currentOutputFile = currentOutputFiles{nBl};
- currentFMRS = strcat(visualType,BOLDType);
-% MMBType_temp = strrep(MMBType,'_v2','');
- createLCModelConfigfMRSTimeCourse(controlFilesPathLocal, subjectsControlFilesPathLocal{indexCurrentSubject}, defaultControlFile, ...
- currentFMRS, defaultVisual, currentOutputFile,...
- waterRefFileName, defaultWaterRef, subjectsPath{indexCurrentSubject}, defaultSubjectsPath, subjects{indexCurrentSubject},...
- defaultSubject, MMBType, defaultMMB, BlockNo{nBl});
- end
-
- fittingLCModel(LCModelCallerInstance, currentControlFiles, currentOutputFiles, subjectsControlFilesPathRemote{indexCurrentSubject}, ...
- subjectsControlFilesPathLocal{indexCurrentSubject}, preprocessedFilesPathLocal{indexCurrentSubject});
-end
+% for indexCurrentSubject = 1:numberOfSubjects
+% %% do the LCModel fitting
+% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+%
+% LCModelCallerInstance = LCModelCallerInstance.ConfigurePaths(subjectsControlFilesPathRemote{indexCurrentSubject}, subjectsLCModelOutputFilesRemote{indexCurrentSubject}, ...
+% subjectsLCModelOutputPath{indexCurrentSubject});
+%
+% if fitversion == 'v2'
+% currentControlFiles = strrep(LCModelControlFiles, strcat('v2_',defaultSubject), subjects{indexCurrentSubject});
+% else
+% currentControlFiles = strrep(LCModelControlFiles, defaultSubject, subjects{indexCurrentSubject});
+% end
+% currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, subjects{indexCurrentSubject});
+% waterRefFileName = strrep(defaultWaterRef, defaultSubject, subjects{indexCurrentSubject});
+%
+% %% create the control file series
+% for nBl = 1:nBlocks
+% currentOutputFile = currentOutputFiles{nBl};
+% currentFMRS = strcat(visualType,BOLDType);
+% % MMBType_temp = strrep(MMBType,'_v2','');
+% createLCModelConfigfMRSTimeCourse(controlFilesPathLocal, subjectsControlFilesPathLocal{indexCurrentSubject}, defaultControlFile, ...
+% currentFMRS, defaultVisual, currentOutputFile,...
+% waterRefFileName, defaultWaterRef, subjectsPath{indexCurrentSubject}, defaultSubjectsPath, subjects{indexCurrentSubject},...
+% defaultSubject, MMBType, defaultMMB, BlockNo{nBl});
+% end
+%
+% fittingLCModel(LCModelCallerInstance, currentControlFiles, currentOutputFiles, subjectsControlFilesPathRemote{indexCurrentSubject}, ...
+% subjectsControlFilesPathLocal{indexCurrentSubject}, preprocessedFilesPathLocal{indexCurrentSubject});
+% end
-% Create and save table with baseline concentrations fitted with baseline, taken from second block of first REST
-% createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
-% LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,0)
+%%% not useful here, take it from Data_TimeCourse
+% % % Create and save table with baseline concentrations fitted with baseline, taken from second block of first REST
+% % createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+% % LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,0)
-% Create and save table from fitted concentrations with baseline, taken from
-% all blocks
-for indexCurrentSubject = 1:numberOfSubjects
-createConcTableBlock(BlockNo,LCModelOutputFiles,defaultSubject,subjects{indexCurrentSubject},...
- LCModelOutputPath,subjectsPath{indexCurrentSubject},prefixes{indexCurrentSubject},visualType,BOLDType,MMBType,0)
-end
+% % Create and save table from fitted concentrations with baseline, taken from
+% % all blocks
+% for indexCurrentSubject = 1:numberOfSubjects
+% createConcTableBlock(BlockNo,LCModelOutputFiles,defaultSubject,subjects{indexCurrentSubject},...
+% LCModelOutputPath,subjectsPath{indexCurrentSubject},prefixes{indexCurrentSubject},visualType,BOLDType,MMBType,0)
+% end
%% subtract baseline from difference spectrum and fit again
%only useful for MMBSummed
if strcmp(MMBType(1:10),'_MMBSummed')
% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
- parfor indexCurrentSubject = 1:numberOfSubjects
- %% do the LCModel fitting
- LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
-
- LCModelCallerInstance = LCModelCallerInstance.ConfigurePaths(subjectsControlFilesPathRemote{indexCurrentSubject}, subjectsLCModelOutputFilesRemote{indexCurrentSubject}, ...
- subjectsLCModelOutputPath{indexCurrentSubject});
+% for indexCurrentSubject = 1:numberOfSubjects
+% %% do the LCModel fitting
+% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
%
- if fitversion == 'v2'
- currentControlFileswoBaseline = strrep(LCModelControlFileswoBaseline, strcat('v2_',defaultSubject), subjects{indexCurrentSubject});
- else
- currentControlFileswoBaseline = strrep(LCModelControlFileswoBaseline, defaultSubject, subjects{indexCurrentSubject});
- end
- currentDataFiles = strrep(strcat(outputFileNameBase, visualType, BOLDType, BlockNo), defaultSubject, subjects{indexCurrentSubject});
- currentOutputFileswoBaseline = strrep(LCModelOutputFileswoBaseline, defaultSubject, subjects{indexCurrentSubject});
- currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, subjects{indexCurrentSubject});
- waterRefFileName = strrep(defaultWaterRef, defaultSubject, subjects{indexCurrentSubject});
- currentDataPath = strcat(localFilePathBase,subjectsPath{indexCurrentSubject}, '/');
- for nBl = 1:nBlocks
- %check save option for .mat file in subtractBaseline_fMRS
- subtractBaseline_fMRS(currentDataPath,subjectsLCModelOutputPath{indexCurrentSubject},currentDataFiles{nBl},currentOutputFileswoBaseline{nBl},'NAA')
- currentFMRS = strcat(visualType,BOLDType);
- createLCModelConfigfMRSTimeCourse(controlFilesPathLocal, subjectsControlFilesPathLocal{indexCurrentSubject}, defaultControlFile, ...
- currentFMRS, defaultVisual, currentOutputFileswoBaseline{nBl},...
- waterRefFileName, defaultWaterRef, subjectsPath{indexCurrentSubject}, defaultSubjectsPath, subjects{indexCurrentSubject},...
- defaultSubject, MMBType, defaultMMB, strcat(BlockNo{nBl}, '_woBaseline'));
- end
- fittingLCModel(LCModelCallerInstance, currentControlFileswoBaseline, currentOutputFileswoBaseline, subjectsControlFilesPathRemote{indexCurrentSubject}, ...
- subjectsControlFilesPathLocal{indexCurrentSubject}, preprocessedFilesPathLocal{indexCurrentSubject});
-
- end
-
- % Create and save table with baseline concentrations fitted w/o baseline, taken from second block of first REST
- % createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
- % LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,1)
+% LCModelCallerInstance = LCModelCallerInstance.ConfigurePaths(subjectsControlFilesPathRemote{indexCurrentSubject}, subjectsLCModelOutputFilesRemote{indexCurrentSubject}, ...
+% subjectsLCModelOutputPath{indexCurrentSubject});
+% %
+% if fitversion == 'v2'
+% currentControlFileswoBaseline = strrep(LCModelControlFileswoBaseline, strcat('v2_',defaultSubject), subjects{indexCurrentSubject});
+% else
+% currentControlFileswoBaseline = strrep(LCModelControlFileswoBaseline, defaultSubject, subjects{indexCurrentSubject});
+% end
+% currentDataFiles = strrep(strcat(outputFileNameBase, visualType, BOLDType, BlockNo), defaultSubject, subjects{indexCurrentSubject});
+% currentOutputFileswoBaseline = strrep(LCModelOutputFileswoBaseline, defaultSubject, subjects{indexCurrentSubject});
+% currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, subjects{indexCurrentSubject});
+% waterRefFileName = strrep(defaultWaterRef, defaultSubject, subjects{indexCurrentSubject});
+% currentDataPath = strcat(localFilePathBase,subjectsPath{indexCurrentSubject}, '/');
+% for nBl = 1:nBlocks
+% %check save option for .mat file in subtractBaseline_fMRS
+% subtractBaseline_fMRS(currentDataPath,subjectsLCModelOutputPath{indexCurrentSubject},currentDataFiles{nBl},currentOutputFileswoBaseline{nBl},'NAA')
+% currentFMRS = strcat(visualType,BOLDType);
+% createLCModelConfigfMRSTimeCourse(controlFilesPathLocal, subjectsControlFilesPathLocal{indexCurrentSubject}, defaultControlFile, ...
+% currentFMRS, defaultVisual, currentOutputFileswoBaseline{nBl},...
+% waterRefFileName, defaultWaterRef, subjectsPath{indexCurrentSubject}, defaultSubjectsPath, subjects{indexCurrentSubject},...
+% defaultSubject, MMBType, defaultMMB, strcat(BlockNo{nBl}, '_woBaseline'));
+% end
+% fittingLCModel(LCModelCallerInstance, currentControlFileswoBaseline, currentOutputFileswoBaseline, subjectsControlFilesPathRemote{indexCurrentSubject}, ...
+% subjectsControlFilesPathLocal{indexCurrentSubject}, preprocessedFilesPathLocal{indexCurrentSubject});
+%
+% end
+ %%% not useful here, take it from Data_TimeCourse
+% % Create and save table with baseline concentrations fitted w/o baseline, taken from second block of first REST
+% % createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+% % LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,1)
% Create and save table from fitted concentrations w/o baseline, taken from all blocks
- for indexCurrentSubject = 1:numberOfSubjects
- createConcTableBlock(BlockNo,LCModelOutputFiles,defaultSubject,subjects{indexCurrentSubject},...
- LCModelOutputPath,subjectsPath{indexCurrentSubject},prefixes{indexCurrentSubject},visualType,BOLDType,MMBType,1)
- end
+% for indexCurrentSubject = 1:numberOfSubjects
+% createConcTableBlock(BlockNo,LCModelOutputFiles,defaultSubject,subjects{indexCurrentSubject},...
+% LCModelOutputPath,subjectsPath{indexCurrentSubject},prefixes{indexCurrentSubject},visualType,BOLDType,MMBType,1)
+% end
end
%% create concentration time course from fit with and w/o Baseline
ExpName = strcat('BlockConc_', subjects, '_', num2str(visualType), BOLDType, MMBType);
if exist('excludeSubjects')
-% createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'Lac',0)
-% createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'Glu',0)
-% createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'Cr',0)
-% createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'PCr',0)
+% createConcTimeCourseBoxplot(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'Lac',excludeSubjects,0)
+% createConcTimeCourseBoxplot(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'Glu',excludeSubjects,0)
+ % createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'Lac',0)
+ % createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'Glu',0)
+% createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'Cr',0)
+% createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'PCr',0)
+% createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'Glu',excludeSubjects,0)
+% createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'GSH',excludeSubjects,0)
+% createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'Cr',excludeSubjects,0)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'PCr',excludeSubjects,0)
if strcmp(MMBType(1:10),'_MMBSummed')
- createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'Glu',1)
+% createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,'Glu',1)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'Glu',excludeSubjects,1)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'Lac',excludeSubjects,1)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'Cr',excludeSubjects,1)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'PCr',excludeSubjects,1)
end
else
% createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,'Lac',0)
% createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,'Glu',0)
% createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,'Cr',0)
- createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,'PCr',0)
- if strcmp(MMBType(1:10),'_MMBSummed')
- createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,'Lac',1)
- createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,'Glu',1)
- end
+% createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,'PCr',0)
+createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,'Glu','',0)
+% if strcmp(MMBType(1:10),'_MMBSummed')
+% createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,'Lac',1)
+% createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,'Glu',1)
+% end
end
close all
end
+function createConcTimeCourseBoxplot(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,metabToFig,excludeSubjects,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+ExpName = strcat(ExpName, woBaseline);
+exportFileMat = strcat(subjectsLCModelOutputPath,ExpName,'.mat');
+
+if exist('excludeSubjects') & ~isempty(excludeSubjects)
+ n = length(ExpName);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(exportFileMat(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ ExpName([excludeIdx],:) = [];
+ exportFileMat([excludeIdx],:) = [];
+end
+
+for k = 1:numel(exportFileMat)
+ tmp = load(exportFileMat{k});
+ dataTimeCourse(:,:,k) = tmp.(ExpName{k});
+end
+
+metaboliteNames = dataTimeCourse(1,:,1);
+indexMet = strcmp(metaboliteNames,metabToFig);
+% idxMet = find(not(cellfun('isempty',indexMet)));
+idxMet = find(indexMet == true);
+
+%Baseline concentration
+if exist('excludeSubjects') & ~isempty(excludeSubjects)
+ BaselineFiles = strcat(BaselineFiles,'_Excluded');
+end
+baselineConcFile = strcat(localBaselinePath,BaselineFiles);
+
+baselineConcTemp = load(strcat(baselineConcFile,'.mat'));
+baselineConc = baselineConcTemp.(BaselineFiles);
+[o,~] = size(baselineConc);
+for q = 2:o-2
+ baselineConcIdxMet(1,q-1) = baselineConc{q,idxMet};
+end
+
+% MetConc = cell2mat(squeeze(dataTimeCourse(2:end,idxMet,:)));
+MetConc = squeeze(dataTimeCourse(2:end,idxMet,:));
+
+%calculate concentration change of data points with reference to baseline
+[a,b] = size(MetConc);
+for av = 1:a
+ for sub = 1:b
+ PercChange(av,sub) = (MetConc{av,sub}-baselineConcIdxMet(1,sub))/baselineConcIdxMet(1,sub)*100;
+ end
+end
+
+% for k = 1:size(MetConc,1)
+% PercChange_Mean(k) = mean(PercChange(k,:));
+% PercChange_Std(k) = std(PercChange(k,:));
+% end
+
+figure
+x = linspace(16*5,(320*5-16*5),37)/60;
+positions = 1:length(x)+0.5;
+hold on
+boxplot(PercChange','Labels',x,'positions',positions)
+hold on
+% plot([0,320*5/60],[0 0])
+plot([0,38],[0 0])
+xlabel 't / min'; ylabel 'Concentration change / %.'
+if ~isempty(find(~cellfun(@isempty,strfind(ExpName,'Rest'))))
+ visualType = 'Rest';
+else
+ visualType = 'Stim'
+end
+title ([metabToFig, ' ', visualType,' time course, subjects excluded'])
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+set(gca,'FontWeight','bold');
+% xlim([0 320*5/60]);
+xlim([0 38])
+
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:5
+% boxoff = [boxoff 320/60*i 320/60*i];
+ boxoff = [boxoff 38/5*i 38/5*i];
+ if i < 5
+% boxon = [boxon 320/60*i 320/60*i];
+ boxon = [boxon 38/5*i 38/5*i];
+ end
+end
+for ii = 1:2.5
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+set(gca,'XTick',[])
+set(gca, 'XTick', [0 320/60*1.34 320/60*1.34*2 320/60*1.34*3 320/60*1.34*4 320/60*1.34*5]);
+set(gca,'XTickLabel',['0 ';'5 ';'10';'15';'20';'25']);
+PathName = strcat(LCModelOutputPath,'SlAvTimeCourse/');
+if ~isempty(find(~cellfun(@isempty,strfind(ExpName,'Rest'))))
+ metabToFig = strcat(metabToFig,'_Rest');
+else
+ metabToFig = strcat(metabToFig,'_Stim');
+end
+if ~isempty(find(~cellfun(@isempty,strfind(ExpName,'_v2'))))
+ if exist('excludeSubjects') & ~isempty(excludeSubjects)
+ fig2save = fullfile(PathName, strcat(metabToFig, '_v2', woBaseline, '_PercChange_Excluded_Boxplot.fig'));
+ else
+ fig2save = fullfile(PathName, strcat(metabToFig, '_v2', woBaseline, '_PercChange_Boxplot.fig'));
+ end
+else
+ if exist('excludeSubjects') & ~isempty(excludeSubjects)
+ fig2save = fullfile(PathName, strcat(metabToFig, woBaseline, '_PercChange_Excluded_Boxplot.fig'));
+ else
+ fig2save = fullfile(PathName, strcat(metabToFig, woBaseline, '_PercChange_Boxplot.fig'));
+ end
+end
+savefig(fig2save)
+end
+
+function createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,localBaselinePath,BaselineFiles,subjects,metabToFig,excludeSubjects,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+ExpName = strcat(ExpName, woBaseline);
+exportFileMat = strcat(subjectsLCModelOutputPath,ExpName,'.mat');
+
+if exist('excludeSubjects') & ~isempty(excludeSubjects)
+ n = length(ExpName);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(exportFileMat(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ ExpName([excludeIdx],:) = [];
+ exportFileMat([excludeIdx],:) = [];
+end
+
+for k = 1:numel(exportFileMat)
+ tmp = load(exportFileMat{k});
+ dataTimeCourse(:,:,k) = tmp.(ExpName{k});
+end
+
+metaboliteNames = dataTimeCourse(1,:,1);
+indexMet = strcmp(metaboliteNames,metabToFig);
+% idxMet = find(not(cellfun('isempty',indexMet)));
+idxMet = find(indexMet == true);
+
+%Baseline concentration
+if exist('excludeSubjects') & ~isempty(excludeSubjects)
+ BaselineFiles = strcat(BaselineFiles,'_Excluded');
+end
+baselineConcFile = strcat(localBaselinePath,BaselineFiles);
+
+baselineConcTemp = load(strcat(baselineConcFile,'.mat'));
+baselineConc = baselineConcTemp.(BaselineFiles);
+[o,~] = size(baselineConc);
+for q = 2:o-2
+ baselineConcIdxMet(1,q-1) = baselineConc{q,idxMet};
+end
+
+[n,~,m] = size(dataTimeCourse);
+for k = 2:n
+ for l = 1:m
+ MetConc(k-1,l) = dataTimeCourse(k,idxMet(1),l);
+ end
+end
+
+%calculate concentration change of data points with reference to baseline
+[a,b] = size(MetConc);
+for av = 1:a
+ for sub = 1:b
+ PercChange(av,sub) = (MetConc{av,sub}-baselineConcIdxMet(1,sub))/baselineConcIdxMet(1,sub)*100;
+ end
+end
+
+for k = 1:size(MetConc,1)
+ PercChange_Mean(k) = mean(PercChange(k,:));
+ PercChange_Std(k) = std(PercChange(k,:));
+end
+
+figure
+x = linspace(16*5,(320*5-16*5),37)/60;
+hold on
+errorbar(x,PercChange_Mean,PercChange_Std)
+hold on
+plot([0,320*5/60],[0 0])
+xlabel 't / min'; ylabel 'Concentration change / %.'
+if ~isempty(find(~cellfun(@isempty,strfind(ExpName,'Rest'))))
+ visualType = 'Rest';
+else
+ visualType = 'Stim'
+end
+title ([metabToFig, ' ', visualType,' time course, subjects excluded'])
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+set(gca,'FontWeight','bold');
+xlim([0 320*5/60]);
+
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:5
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < 5
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:2.5
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+PathName = strcat(LCModelOutputPath,'SlAvTimeCourse/');
+if ~isempty(find(~cellfun(@isempty,strfind(ExpName,'Rest'))))
+ metabToFig = strcat(metabToFig,'_Rest');
+else
+ metabToFig = strcat(metabToFig,'_Stim');
+end
+if ~isempty(find(~cellfun(@isempty,strfind(ExpName,'_v2'))))
+ if exist('excludeSubjects') & ~isempty(excludeSubjects)
+ fig2save = fullfile(PathName, strcat(metabToFig, '_v2', woBaseline, '_PercChange_Excluded.fig'));
+ else
+ fig2save = fullfile(PathName, strcat(metabToFig, '_v2', woBaseline, '_PercChange.fig'));
+ end
+else
+ if exist('excludeSubjects') & ~isempty(excludeSubjects)
+ fig2save = fullfile(PathName, strcat(metabToFig, woBaseline, '_PercChange_Excluded.fig'));
+ else
+ fig2save = fullfile(PathName, strcat(metabToFig, woBaseline, '_PercChange.fig'));
+ end
+end
+savefig(fig2save)
+end
+
function createConcTimeCourseFigExcluded(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,excludeSubjects,metabToFig,woBaseline)
if exist('woBaseline', 'var') & woBaseline == true
woBaseline = '_woBaseline';
@@ -273,6 +543,7 @@ errorbar(x,MetConc_Mean,MetConc_Std)
xlabel 't / min'; ylabel 'Concentration / a.u.'
title ([metabToFig,' time course, subjects excluded'])
set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+set(gca,'FontWeight','bold');
xlim([0 320*5/60]);
yLimits = get(gca,'YLim');
@@ -341,6 +612,7 @@ errorbar(x,MetConc_Mean,MetConc_Std)
xlabel 't / min'; ylabel 'Concentration / a.u.'
title ([metabToFig,' time course'])
set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+set(gca,'FontWeight','bold');
xlim([0 320*5/60]);
yLimits = get(gca,'YLim');
diff --git a/fMRS_processing/doFitting_fMRS_SlidingAverage_Summed.m b/fMRS_processing/doFitting_fMRS_SlidingAverage_Summed.m
new file mode 100644
index 0000000000000000000000000000000000000000..679e37f120b65e852200810418886923d785acf2
--- /dev/null
+++ b/fMRS_processing/doFitting_fMRS_SlidingAverage_Summed.m
@@ -0,0 +1,700 @@
+function doFitting_fMRS_SlidingAverage_Summed()
+clear; clc; close all;
+
+fitversion = 'v2';
+pathName = 'fMRS SlidingAverage data path';
+sampleCriteria = {'Output', 'LCModelConfig'};
+[localFilePathBase] = pathToDataFolder(pathName, sampleCriteria);
+
+visualType = 'Stim'; %'Stim', 'Rest'
+BOLDType = '_woBOLDCorr'; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _withBOLDCorr64
+if strcmp(fitversion,'v2')
+ MMBType = '_MMBSummed_v2'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated
+else
+ MMBType = '_MMBSummed'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated
+end
+nOfSpectra = 32; %how many spectra should be averaged? Need full PC+MC
+nslidingAv = 8; %how many spectra should be skipped before next averaging
+nBlocks = (320 - nOfSpectra)/nslidingAv + 1; %here 320 averages per .dat file acquired. nBlocks = #spectra after sliding average
+
+BlockNo = cell(1,nBlocks);
+for k = 1:nBlocks
+ BlockNo{k} = strcat('_Av', num2str(k));
+end
+
+% subjects = {'2823';};
+subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
+ '4085';'3333';'4012';'4085';'6524'};
+% prefixes = {'2020-02-18_';};
+prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
+ '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
+ '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
+excludeSubjects = {'5166';'9810';'5269'};
+suffixFolder = '_fMRS';
+
+if exist('excludeSubjects','var')
+ n = length(subjects);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(subjects(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ subjects([excludeIdx],:) = [];
+ prefixes([excludeIdx],:) = [];
+end
+numberOfSubjects = length(subjects);
+
+extensionTable = '.table';
+extensionCoord = '.coord';
+defaultVisual = 'Stim';
+defaultSubject = 'XXXX';
+defaultSubjectsPath = 'YYYY';
+if fitversion == 'v2'
+ defaultMMB = '_MMBSummed_v2';
+else
+ defaultMMB = '_MMBSummed';
+end
+defaultLCModelUser = 'jdorst';
+defaultWaterRef = 'XXXX_WRef';
+
+if fitversion == 'v2'
+ controlFilesBase = 'fitsettings_v2_XXXX_';
+else
+ controlFilesBase = 'fitsettings_XXXX_';
+end
+controlFilesBaseSuffix = '.control';
+outputFileNameBase = strcat(defaultSubject, '_');
+outputFileNameBase_WRef = strcat(defaultSubject, '_WRef');
+
+pathNameBaseline = 'fMRS TimeCourse data path';
+sampleCriteriaBaseline = {'Output_v2_MMBSummed', 'Output', 'LCModelConfig'};
+[localFilePathBaseBaseline] = pathToDataFolder(pathNameBaseline, sampleCriteriaBaseline);
+if fitversion == 'v2'
+ localBaselinePath = [localFilePathBaseBaseline, 'Output_v2_MMBSummed/'];
+ BaselineFiles = strcat('BaselineConc_',num2str(visualType), BOLDType, MMBType);
+else
+ localBaselinePath = [localFilePathBaseBaseline, 'Output/'];
+ BaselineFiles = strcat('BaselineConc_',num2str(visualType), BOLDType, MMBType);
+end
+
+%% file paths setup
+controlFilesPathRemote = '/Desktop/1H_fMRS/LCModelConfig/';
+controlFilesPathLocal = [localFilePathBase, 'LCModelConfig/'];
+LCModelOutputFilesPathRemote = '/Desktop/1H_fMRS/Output/';
+LCModelOutputPath = [localFilePathBase, 'Output/'];
+defaultControlFile = strcat(controlFilesBase, defaultVisual, controlFilesBaseSuffix);
+
+subjectsPath = strcat(prefixes, subjects, suffixFolder);
+summedPath = 'Summed_fMRSData';
+
+subjectsControlFilesPathRemote = strcat(controlFilesPathRemote, summedPath, '/');
+subjectsControlFilesPathLocal = strcat(controlFilesPathLocal, summedPath, '/');
+preprocessedFilesPathLocal = strcat(localFilePathBase, summedPath, '/');
+subjectsLCModelOutputFilesRemote = strcat(LCModelOutputFilesPathRemote, summedPath, '/');
+subjectsLCModelOutputPath = strcat(LCModelOutputPath, summedPath, '/');
+
+
+%% sum fMRS spectra of all volunteers, weighted with water fit (need to run
+%doFitting_fMRS_Water.m & doFitting_fMRS_SlidingAverage.m first)
+% WaterReferenceTable = (readtable(strcat(LCModelOutputPath,'WaterReference_Fit.xlsx')));
+% WaterReferenceID = table2cell(WaterReferenceTable(2:end,1));
+% WaterReference = str2double(table2array(WaterReferenceTable(2:end,2:end)));
+% for indexCurrentSubject = 1:numberOfSubjects
+% for nBl = 1:nBlocks
+% idx = find(ismember(WaterReferenceID, strcat(prefixes{indexCurrentSubject}, subjects{indexCurrentSubject})));
+% currentScalingFactor = WaterReference(idx,1);
+% currentfMRSDataPath = strcat(localFilePathBase,subjectsPath{indexCurrentSubject},'\');
+% currentDataFile = strcat(subjects{indexCurrentSubject},'_',visualType,'_woBOLDCorr','_Av',num2str(nBl),'.mat');
+% load([currentfMRSDataPath,currentDataFile]);
+% currentfMRSData = squeeze(this.Data{1});
+% currentfMRSDataScaled = currentfMRSData./currentScalingFactor;
+% fMRSDataScaled(:,:,indexCurrentSubject,nBl) = currentfMRSDataScaled;
+% end
+% end
+% %sum over all subjects
+% fMRSData_Summed = squeeze(sum(fMRSDataScaled,3));
+% %save summed data
+% for nBl = 1:nBlocks
+% current_fMRSData_Summed = fMRSData_Summed(:,:,nBl);
+% current_fMRSData_Summed = permute(current_fMRSData_Summed,[1 3 4 5 6 7 8 9 2]);
+% this.Data{1} = current_fMRSData_Summed;
+% fMRSData_Summed_Path = strcat(localFilePathBase,summedPath);
+% this.Parameter.Filepath = fMRSData_Summed_Path;
+% this.Parameter.Filename = strcat('fMRSData_Summed_',visualType,BOLDType,'_SlidingAverages');
+% if exist('excludeSubjects','var')
+% this.Parameter.Filename = strcat('fMRSData_Summed_',visualType,BOLDType,'_SlidingAverages_Excluded_Av',num2str(nBl));
+% exportFile = strcat(fMRSData_Summed_Path,'\Summed_',visualType,BOLDType,'_Excluded_Av',num2str(nBl));
+% else
+% this.Parameter.Filename = strcat('fMRSData_Summed_',visualType,BOLDType,'_SlidingAverages_Av',num2str(nBl));
+% exportFile = strcat(fMRSData_Summed_Path,'\Summed_',visualType,BOLDType,'_Av',num2str(nBl));
+% end
+% save(strcat(exportFile,'.mat'),'this')
+% this.ExportLcmRaw('',exportFile, 'Yes', 'Yes');
+% end
+
+%% sum Water reference spectra of all volunteers, weighted with water fit (need to run doFitting_fMRS_Water.m first)
+% WaterReferenceTable = (readtable(strcat(LCModelOutputPath,'WaterReference_Fit.xlsx')));
+% WaterReferenceID = table2cell(WaterReferenceTable(2:end,1));
+% WaterReference = str2double(table2array(WaterReferenceTable(2:end,2:end)));
+% WRefDataScaled = zeros(4096,numberOfSubjects); %samples, subjects, data/water
+% for indexCurrentSubject = 1:numberOfSubjects
+% idx = find(ismember(WaterReferenceID, strcat(prefixes{indexCurrentSubject}, subjects{indexCurrentSubject})));
+% currentScalingFactor = WaterReference(idx,1);
+% currentWRefDataPath = strcat(localFilePathBase,subjectsPath{indexCurrentSubject});
+% load(strrep(strcat(currentWRefDataPath, '\',outputFileNameBase_WRef,'.mat'),defaultSubject,subjects{indexCurrentSubject}));
+% currentfMRSData_WRef = squeeze(a.Data{1});
+% currentfMRSDataScaled_WRef = currentfMRSData_WRef./currentScalingFactor;
+% WRefDataScaled(:,indexCurrentSubject) = currentfMRSDataScaled_WRef;
+% end
+% %sum over all subjects
+% fMRSData_Summed_WRef = squeeze(sum(WRefDataScaled,2));
+% %save summed data
+% this = a;
+% this.Data{1} = fMRSData_Summed_WRef;
+% this.Parameter.Filepath = fMRSData_Summed_Path;
+% this.Parameter.Filename = 'fMRSData_Summed_WRef';
+% if exist('excludeSubjects','var')
+% exportFileWRef = strcat(fMRSData_Summed_Path,'\WRef_Summed_Excluded');
+% else
+% exportFileWRef = strcat(fMRSData_Summed_Path,'\WRef_Summed');
+% end
+% save(strcat(exportFileWRef,'.mat'),'this')
+% this.ExportLcmRaw('',exportFileWRef, 'Yes', 'Yes');
+
+%% do the LCModel fitting
+if exist('excludeSubjects','var')
+ LCModelOutputFiles = strcat('Summed_', visualType, MMBType, '_Excluded', BlockNo);
+ LCModelOutputFileswoBaseline = strcat('Summed_', visualType, MMBType, '_Excluded', BlockNo, '_woBaseline');
+ LCModelControlFiles = strcat(controlFilesBase, 'Summed_', visualType, MMBType, '_Excluded', BlockNo, controlFilesBaseSuffix);
+ LCModelControlFileswoBaseline = strcat(controlFilesBase, 'Summed_', visualType, MMBType, '_Excluded', BlockNo, '_woBaseline', controlFilesBaseSuffix);
+ waterRefFileName = 'WRef_Summed_Excluded';
+else
+ LCModelOutputFiles = strcat('Summed_', visualType, MMBType, BlockNo);
+ LCModelOutputFileswoBaseline = strcat('Summed_', visualType, MMBType, BlockNo, '_woBaseline');
+ LCModelControlFiles = strcat(controlFilesBase, 'Summed_', visualType, MMBType, BlockNo, controlFilesBaseSuffix);
+ LCModelControlFileswoBaseline = strcat(controlFilesBase, 'Summed_', visualType, MMBType, BlockNo, '_woBaseline', controlFilesBaseSuffix);
+ waterRefFileName = 'WRef_Summed';
+end
+
+
+%% do the LCModel fitting
+% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+%
+% LCModelCallerInstance = LCModelCallerInstance.ConfigurePaths(subjectsControlFilesPathRemote, subjectsLCModelOutputFilesRemote, ...
+% subjectsLCModelOutputPath);
+%
+% if strcmp(fitversion,'v2')
+% currentControlFiles = strrep(LCModelControlFiles, strcat('_v2_',defaultSubject), '');
+% else
+% currentControlFiles = strrep(LCModelControlFiles, strcat('_',defaultSubject), '');
+% end
+% currentOutputFiles = strrep(LCModelOutputFiles,'Corr','Corr_v2');
+%
+%
+% % create the control file series
+% for nBl = 1:nBlocks
+% currentOutputFile = currentOutputFiles{nBl};
+% if exist('excludeSubjects','var')
+% currentFMRS = strcat(visualType,BOLDType,'_Excluded');
+% else
+% currentFMRS = strcat(visualType,BOLDType);
+% end
+% createLCModelConfigfMRSTimeCourse(controlFilesPathLocal, subjectsControlFilesPathLocal, defaultControlFile, ...
+% currentFMRS, defaultVisual, currentOutputFile,...
+% waterRefFileName, defaultWaterRef, 'Summed_fMRSData', defaultSubjectsPath, 'Summed',...
+% defaultSubject, MMBType, defaultMMB, BlockNo{nBl});
+% end
+% if exist('excludeSubjects','var')
+% currentOutputFilesLCModel = strcat('Summed_',visualType,BOLDType,'_Excluded',MMBType,BlockNo);
+% else
+% currentOutputFilesLCModel = strcat('Summed_',visualType,BOLDType,MMBType,BlockNo);
+% end
+% fittingLCModel(LCModelCallerInstance, currentControlFiles, currentOutputFilesLCModel, subjectsControlFilesPathRemote, ...
+% subjectsControlFilesPathLocal, preprocessedFilesPathLocal);
+
+%%
+%%% doesn't exist in Data_TimeCourse, but same as nBl = 5 (33-64 average)
+% % Create and save table with baseline concentrations fitted with baseline, taken from second block of first REST
+% createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+% LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,0)
+
+% % Create and save table from fitted concentrations with baseline, taken from
+% % all blocks
+% if exist('excludeSubjects','var')
+% createConcTableBlock(BlockNo,currentOutputFilesLCModel,defaultSubject,'Summed',...
+% LCModelOutputPath,'Summed_fMRSData',visualType,strcat(BOLDType,'_Excl'),MMBType,0)
+% else
+% createConcTableBlock(BlockNo,currentOutputFilesLCModel,defaultSubject,'Summed',...
+% LCModelOutputPath,'Summed_fMRSData',visualType,BOLDType,MMBType,0)
+% end
+
+%% subtract baseline from difference spectrum and fit again
+%only useful for MMBSummed
+if strcmp(MMBType(1:10),'_MMBSummed')
+ %% do the LCModel fitting
+% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+%
+% LCModelCallerInstance = LCModelCallerInstance.ConfigurePaths(subjectsControlFilesPathRemote, subjectsLCModelOutputFilesRemote, ...
+% subjectsLCModelOutputPath);
+% %
+% if fitversion == 'v2'
+% currentControlFileswoBaseline = strrep(LCModelControlFileswoBaseline, strcat('_v2_',defaultSubject), '');
+% else
+% currentControlFileswoBaseline = strrep(LCModelControlFileswoBaseline, strcat('_',defaultSubject), '');
+% end
+% if exist('excludeSubjects','var')
+% currentDataFiles = strrep(strcat(outputFileNameBase, visualType, BOLDType, '_Excluded', BlockNo), defaultSubject, 'Summed');
+% else
+% currentDataFiles = strrep(strcat(outputFileNameBase, visualType, BOLDType, BlockNo), defaultSubject, 'Summed');
+% end
+%
+% if exist('excludeSubjects','var')
+% currentOutputFileswoBaseline = strcat('Summed_',visualType,BOLDType,'_Excluded',MMBType,BlockNo);
+% else
+% currentOutputFileswoBaseline = strcat('Summed_',visualType,BOLDType,MMBType,BlockNo);
+% end
+% currentDataPath = strcat(localFilePathBase,'Summed_fMRSData\');
+% for nBl = 1:nBlocks
+% %check save option for .mat file in subtractBaseline_fMRS
+% subtractBaseline_fMRS(currentDataPath,subjectsLCModelOutputPath,currentDataFiles{nBl},currentOutputFileswoBaseline{nBl},'NAA')
+% currentFitsettingsOutputFileswoBaseline = LCModelOutputFileswoBaseline{nBl};
+% if exist('excludeSubjects','var')
+% currentFMRS = strcat(visualType,BOLDType,'_Excluded');
+% else
+% currentFMRS = strcat(visualType,BOLDType);
+% end
+% createLCModelConfigfMRSTimeCourse(controlFilesPathLocal, subjectsControlFilesPathLocal, defaultControlFile, ...
+% currentFMRS, defaultVisual, currentFitsettingsOutputFileswoBaseline,...
+% waterRefFileName, defaultWaterRef, 'Summed_fMRSData', defaultSubjectsPath, 'Summed',...
+% defaultSubject, MMBType, defaultMMB, strcat(BlockNo{nBl}, '_woBaseline'));
+% end
+% if exist('excludeSubjects','var')
+% currentOutputFilesLCModelwoBaseline = strcat('Summed_',visualType,BOLDType,'_Excluded',MMBType,BlockNo,'_woBaseline');
+% else
+% currentOutputFilesLCModelwoBaseline = strcat('Summed_',visualType,BOLDType,MMBType,BlockNo,'_woBaseline');
+% end
+% fittingLCModel(LCModelCallerInstance, currentControlFileswoBaseline, currentOutputFilesLCModelwoBaseline, subjectsControlFilesPathRemote, ...
+% subjectsControlFilesPathLocal, preprocessedFilesPathLocal);
+
+ %% not useful here, take it from Data_TimeCourse
+ % Create and save table with baseline concentrations fitted w/o baseline, taken from second block of first REST
+ % createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+ % LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,1)
+
+ % Create and save table from fitted concentrations w/o baseline, taken from all blocks
+% if exist('excludeSubjects','var')
+% createConcTableBlock(BlockNo,currentOutputFilesLCModelwoBaseline,defaultSubject,'Summed',...
+% LCModelOutputPath,'Summed_fMRSData',visualType,strcat(BOLDType,'_Excl'),MMBType,1)
+% else
+% createConcTableBlock(BlockNo,currentOutputFilesLCModelwoBaseline,defaultSubject,'Summed',...
+% LCModelOutputPath,'Summed_fMRSData',visualType,BOLDType,MMBType,1)
+% end
+end
+
+
+%% create concentration time course from fit with and w/o Baseline
+if exist('excludeSubjects','var')
+ ExpName = strcat('BlockConc_Summed_', num2str(visualType), strcat(BOLDType,'_Excl'), MMBType);
+else
+ ExpName = strcat('BlockConc_Summed_', num2str(visualType), BOLDType, MMBType);
+end
+
+% createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,'PCr',0)
+% createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,'Cr',0)
+
+createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,'Cr',0)
+createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,'PCr',0)
+if strcmp(MMBType(1:10),'_MMBSummed')
+ createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,'PCr',1)
+ createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,'Cr',1)
+
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,'Cr',1)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,'PCr',1)
+end
+
+close all
+
+end
+
+function createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,metabToFig,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+ExpName = strcat(ExpName, woBaseline);
+exportFileMat = strcat(subjectsLCModelOutputPath,ExpName,'.mat');
+
+tmp = load(exportFileMat);
+dataTimeCourse(:,:) = tmp.(ExpName);
+
+metaboliteNames = dataTimeCourse(1,:);
+indexMet = strcmp(metaboliteNames,metabToFig);
+idxMet = find(indexMet == true);
+
+%Baseline concentration, equals 5th sliding average
+baselineConcIdxMet = dataTimeCourse{6,idxMet};
+
+[n,~] = size(dataTimeCourse);
+for k = 2:n
+ MetConc(k-1) = dataTimeCourse(k,idxMet(1));
+end
+
+%calculate concentration change of data points with reference to baseline
+[~,b] = size(MetConc);
+for av = 1:b
+ PercChange(av) = (MetConc{1,av}-baselineConcIdxMet)/baselineConcIdxMet*100;
+end
+
+figure
+x = linspace(16*5,(320*5-16*5),37)/60;
+hold on
+plot(x,PercChange)
+hold on
+plot([0,320*5/60],[0 0])
+xlabel 't / min'; ylabel 'Concentration change / %.'
+if ~isempty(strfind(ExpName,'Rest'))
+ visualType = 'Rest';
+else
+ visualType = 'Stim';
+end
+title ([metabToFig,', ',ExpName], 'Interpreter', 'none')
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+xlim([0 320*5/60]);
+
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:5
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < 5
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:2.5
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+PathName = strcat(LCModelOutputPath,'SlAvTimeCourse/');
+
+if ~isempty(strfind(ExpName,'Rest'))
+ metabToFig = strcat(metabToFig,'_Rest_Summed');
+else
+ metabToFig = strcat(metabToFig,'_Stim_Summed');
+end
+if ~isempty(strfind(ExpName,'_v2'))
+ if ~isempty(strfind(ExpName,'Excl'))
+ fig2save = fullfile(PathName, strcat(metabToFig, '_v2', woBaseline, '_PercChange_Excluded.fig'));
+ else
+ fig2save = fullfile(PathName, strcat(metabToFig, '_v2', woBaseline, '_PercChange.fig'));
+ end
+else
+ if ~isempty(strfind(ExpName,'Excl'))
+ fig2save = fullfile(PathName, strcat(metabToFig, woBaseline, '_PercChange_Excluded.fig'));
+ else
+ fig2save = fullfile(PathName, strcat(metabToFig, woBaseline, '_PercChange.fig'));
+ end
+end
+savefig(fig2save)
+end
+
+function createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,metabToFig,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+ExpName = strcat(ExpName, woBaseline);
+exportFileMat = strcat(subjectsLCModelOutputPath,ExpName,'.mat');
+
+tmp = load(exportFileMat);
+dataTimeCourse(:,:) = tmp.(ExpName);
+
+metaboliteNames = dataTimeCourse(1,:,1);
+indexMet = strcmp(metaboliteNames,metabToFig);
+idxMet = find(indexMet == true);
+
+[n,~] = size(dataTimeCourse);
+for k = 2:n
+ MetConc(k-1) = dataTimeCourse(k,idxMet(1));
+end
+
+figure
+x = linspace(16*5,(320*5-16*5),37)/60;
+hold on
+% errorbar(x,MetConc_Mean,MetConc_Std)
+plot(x,cell2mat(MetConc))
+xlabel 't / min'; ylabel 'Concentration / a.u.'
+title ([metabToFig,', ',ExpName], 'Interpreter', 'none')
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+xlim([0 320*5/60]);
+
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:5
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < 5
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:2.5
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+PathName = strcat(LCModelOutputPath,'SlAvTimeCourse/');
+if ~isempty(strfind(ExpName,'Rest'))
+ metabToFig = strcat(metabToFig,'_Rest_Summed');
+else
+ metabToFig = strcat(metabToFig,'_Stim_Summed');
+end
+if ~isempty(strfind(ExpName,'_v2'))
+ if ~isempty(strfind(ExpName,'Excl'))
+ fig2save = fullfile(PathName, strcat(metabToFig, '_v2', woBaseline, '_Excluded.fig'));
+ else
+ fig2save = fullfile(PathName, strcat(metabToFig, '_v2', woBaseline, '.fig'));
+ end
+else
+ if ~isempty(strfind(ExpName,'Excl'))
+ fig2save = fullfile(PathName, strcat(metabToFig, woBaseline, '_Excluded.fig'));
+ else
+ fig2save = fullfile(PathName, strcat(metabToFig, woBaseline, '.fig'));
+ end
+end
+savefig(fig2save)
+end
+
+% function createConcTimeCourseFig(ExpName,LCModelOutputPath,subjectsLCModelOutputPath,subjects,metabToFig,woBaseline)
+% if exist('woBaseline', 'var') & woBaseline == true
+% woBaseline = '_woBaseline';
+% else
+% woBaseline = '';
+% end
+% ExpName = strcat(ExpName, woBaseline);
+%
+% exportFileMat = strcat(subjectsLCModelOutputPath,ExpName,'.mat');
+%
+% for k = 1:numel(exportFileMat)
+% tmp = load(exportFileMat{k});
+% dataTimeCourse(:,:,k) = tmp.(ExpName{k});
+% end
+%
+% metaboliteNames = dataTimeCourse(1,:,1);
+% indexMet = strcmp(metaboliteNames,metabToFig);
+% % idxMet = find(not(cellfun('isempty',indexMet)));
+% idxMet = find(indexMet == true);
+%
+% [n,~,m] = size(dataTimeCourse);
+% for k = 2:n
+% for l = 1:m
+% MetConc(k-1,l) = dataTimeCourse(k,idxMet(1),l);
+% end
+% end
+%
+% for k = 1:size(MetConc,1)
+% MetConc_Mean(k) = mean(cell2mat(MetConc(k,:)));
+% MetConc_Std(k) = std(cell2mat(MetConc(k,:)));
+% end
+%
+% figure
+% x = linspace(16*5,(320*5-16*5),37)/60;
+% hold on;
+% errorbar(x,MetConc_Mean,MetConc_Std)
+% xlabel 't / min'; ylabel 'Concentration / a.u.'
+% title ([metabToFig,' time course'])
+% set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+% xlim([0 320*5/60]);
+%
+% yLimits = get(gca,'YLim');
+% boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+% for i = 1:5
+% boxoff = [boxoff 320/60*i 320/60*i];
+% if i < 5
+% boxon = [boxon 320/60*i 320/60*i];
+% end
+% end
+% for ii = 1:2.5
+% boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+% boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+% end
+% patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+% patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+% ylim([yLimits(1) yLimits(2)]);
+% PathName = strcat(LCModelOutputPath,'SlAvTimeCourse/');
+% if ~isempty(find(~cellfun(@isempty,strfind(ExpName,'v2'))))
+% fig2save = fullfile(PathName, strcat(metabToFig, '_v2', woBaseline, '.fig'));
+% else
+% fig2save = fullfile(PathName, strcat(metabToFig, woBaseline, '.fig'));
+% end
+% savefig(fig2save)
+% end
+
+function createConcTableBlock(BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+ LCModelOutputPath,subjectsPath,visualType,BOLDType,MMBType,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+tableConcentrations = [];
+isFirstIter = 1;
+s = 1;
+ for nBl = 1:length(BlockNo)
+ % retrieve parameters
+ currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, subjects);
+ currentPath = strcat(LCModelOutputPath,subjectsPath,'/',currentOutputFiles{nBl},'.table');
+ [c1 c2 c3 c4] = textread(currentPath,'%s %s %s %s');
+
+ if isFirstIter
+ start = find(strcmp(c1,'Conc.'));
+ start = start + 1;
+ finish = find(strcmpi(c1,'$$MISC')) - 1;
+ tableConcentrations{1,1} = 'ID';
+
+ s = 2;
+ for j=start:finish
+ if(strcmp(c4{j},'')==1)
+ c3_split = strsplit(c3{j},'+');
+ if length(c3_split) == 1
+ c3_split = strsplit(c3{j},'-');
+ end
+ tableConcentrations{1,s} = c3_split{2};
+ else
+ tableConcentrations{1,s} = c4{j};
+ if length(tableConcentrations{1,s}) == 3 & tableConcentrations{1,s} == 'Leu'
+ tableConcentrations{1,s} = 'MMB';
+ end
+ end
+ tableConcentrations{1,s+1} = 'CRLB %';
+ s = s + 2;
+ end
+ tableConcentrations{1,s} = 'water concentration';
+ end
+
+ % add ID and Water conc to table
+ tableConcentrations{nBl+1,1} = strcat(subjects, '_', visualType, BlockNo{nBl});
+ index_wconc = find(strcmp(c1,'wconc='));
+ wconc_LCModel = str2num(c2{index_wconc});
+ tableConcentrations{nBl+1,end} = wconc_LCModel;
+ % add metabolites to table
+ s = 2;
+ for j=start:finish
+ % add quantified metabolite
+ tableConcentrations{nBl+1,s} = str2num( c1{j});
+ % add CRLB
+ tableConcentrations{nBl+1,s+1} = str2num( c2{j}(1:end-1));
+ s = s+2;
+ end
+
+ isFirstIter = false;
+
+ end
+
+% save table to file
+ExpName = strcat('BlockConc_', subjects, '_', num2str(visualType), BOLDType, MMBType, woBaseline);
+exportFileXlsx = [LCModelOutputPath,subjectsPath,'/',strcat(ExpName,'.xlsx')];
+xlswrite(exportFileXlsx, {LCModelOutputPath}, 1, 'A1');
+cellPosition = 'A2';
+xlswrite(exportFileXlsx, tableConcentrations, 1, cellPosition);
+
+exportFileMat = [LCModelOutputPath,subjectsPath,'/',strcat(ExpName,'.mat')];
+eval([ExpName '= tableConcentrations']); %use of eval not recommended, change it
+save(exportFileMat, ExpName)
+end
+
+function createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+ LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+tableConcentrations = [];
+isFirstIter = 1;
+s = 1;
+for indexCurrentSubject = 1:numberOfSubjects
+ for nBl = 2 %take baseline from second block of first REST
+ % retrieve parameters
+ currentOutputFiles = strrep(strcat(LCModelOutputFiles,woBaseline), defaultSubject, subjects{indexCurrentSubject});
+ currentPath = strcat(LCModelOutputPath,subjectsPath{indexCurrentSubject},'/',currentOutputFiles{nBl},'.table');
+ [c1 c2 c3 c4] = textread(currentPath,'%s %s %s %s');
+
+ if isFirstIter
+ start = find(strcmp(c1,'Conc.'));
+ start = start + 1;
+ finish = find(strcmpi(c1,'$$MISC')) - 1;
+ tableConcentrations{1,1} = 'ID';
+
+ s = 2;
+ for j=start:finish
+ if(strcmp(c4{j},'')==1)
+ c3_split = strsplit(c3{j},'+');
+ if length(c3_split) == 1
+ c3_split = strsplit(c3{j},'-');
+ end
+ tableConcentrations{1,s} = c3_split{2};
+ else
+ tableConcentrations{1,s} = c4{j};
+ if length(tableConcentrations{1,s}) == 3 & tableConcentrations{1,s} == 'Leu'
+ tableConcentrations{1,s} = 'MMB';
+ end
+ end
+ tableConcentrations{1,s+1} = 'CRLB %';
+ s = s + 2;
+ end
+ tableConcentrations{1,s} = 'water concentration';
+ end
+
+ % add ID and Water conc to table
+ tableConcentrations{indexCurrentSubject+1,1} = strcat(prefixes{indexCurrentSubject}, subjects{indexCurrentSubject}, BlockNo{nBl});
+ index_wconc = find(strcmp(c1,'wconc='));
+ wconc_LCModel = str2num(c2{index_wconc});
+ tableConcentrations{indexCurrentSubject+1,end} = wconc_LCModel;
+ % add metabolites to table
+ s = 2;
+ for j=start:finish
+ %add quantified metabolite
+ tableConcentrations{indexCurrentSubject+1,s} = str2num( c1{j});
+ %add CRLB
+ tableConcentrations{indexCurrentSubject+1,s+1} = str2num( c2{j}(1:end-1));
+ s = s+2;
+ end
+
+ isFirstIter = false;
+
+ end
+end
+
+%calculate mean, std
+[n,m] = size(tableConcentrations);
+subArray = cell2mat(tableConcentrations(2:end,2:end));
+meanValue = mean(subArray);
+stdValue = std(subArray);
+tableConcentrations{n+1,1} = 'mean';
+tableConcentrations{n+2,1} = 'std';
+for indexOfMetab = 1:m-1
+ tableConcentrations{n+1,indexOfMetab+1} = meanValue(1,indexOfMetab);
+ tableConcentrations{n+2,indexOfMetab+1} = stdValue(1,indexOfMetab);
+end
+
+% save table to file
+ExpName = strcat('BaselineConc_', num2str(visualType), BOLDType, MMBType, woBaseline);
+exportFileXlsx = [LCModelOutputPath ,strcat(ExpName,'.xlsx')];
+xlswrite(exportFileXlsx, {LCModelOutputPath}, 1, 'A1');
+cellPosition = 'A2';
+xlswrite(exportFileXlsx, tableConcentrations, 1, cellPosition);
+
+exportFileMat = [LCModelOutputPath ,strcat(ExpName,'.mat')];
+eval([ExpName '= tableConcentrations']); %use of eval not recommended, change it
+save(exportFileMat, ExpName)
+end
\ No newline at end of file
diff --git a/fMRS_processing/doFitting_fMRS_TimeCourse.m b/fMRS_processing/doFitting_fMRS_TimeCourse.m
index 8e60958d51991bc4339df7d39ca3c6a55ec67a75..ef79288649255625953855f5fdadd45b1f3db8a8 100644
--- a/fMRS_processing/doFitting_fMRS_TimeCourse.m
+++ b/fMRS_processing/doFitting_fMRS_TimeCourse.m
@@ -1,12 +1,15 @@
function doFitting_fMRS_TimeCourse()
clear; clc; close all;
+allVisTypes = {'Stim', 'Rest'}; %'Stim', 'Rest'
+
+for i = 1:length(allVisTypes)
pathName = 'fMRS TimeCourse data path';
sampleCriteria = {'Output', 'LCModelConfig'};
[localFilePathBase] = pathToDataFolder(pathName, sampleCriteria);
-visualType = 'Rest'; %'Stim', 'Rest'
-BOLDType = '_woBOLDCorr'; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _withBOLDCorr64
-MMBType = '_MMBSummed'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated
+visualType = allVisTypes{i}; %'Stim', 'Rest'
+BOLDType = '_woBOLDCorr'; %only _woBOLDCorr reasonable since block wise fitting for metabolite time courses
+MMBType = '_MMBSummed_v2'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated
% nBlocks = 5;
nBlocks = 10;
@@ -30,12 +33,20 @@ extensionCoord = '.coord';
defaultVisual = 'Stim';
defaultSubject = 'XXXX';
defaultSubjectsPath = 'YYYY';
-defaultMMB = '_MMBSummed';
+if ~isempty(strfind(MMBType,'_v2'))
+ defaultMMB = '_MMBSummed_v2';
+else
+ defaultMMB = '_MMBSummed';
+end
defaultLCModelUser = 'jdorst';
outputFileNameBaseWithoutMetabolite_OFF = strcat(defaultSubject, '_StimOFF_woBOLDCorr128');
outputFileNameBaseWithoutMetabolite_ON = strcat(defaultSubject, '_StimON_woBOLDCorr128');
-controlFilesBase = 'fitsettings_XXXX_';
+if ~isempty(strfind(MMBType,'_v2'))
+ controlFilesBase = 'fitsettings_v2_XXXX_';
+else
+ controlFilesBase = 'fitsettings_XXXX_';
+end
controlFilesBaseSuffix = '.control';
outputFileNameBase = strcat(defaultSubject, '_');
@@ -43,7 +54,11 @@ outputFileNameBase = strcat(defaultSubject, '_');
controlFilesPathRemote = '/Desktop/1H_fMRS/LCModelConfig/';
controlFilesPathLocal = [localFilePathBase, 'LCModelConfig/'];
LCModelOutputFilesPathRemote = '/Desktop/1H_fMRS/Output/';
-LCModelOutputPath = [localFilePathBase, 'Output/'];
+if ~isempty(strfind(MMBType,'_v2'))
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed/'];
+else
+ LCModelOutputPath = [localFilePathBase, 'Output/'];
+end
defaultControlFile = strcat(controlFilesBase, defaultVisual, controlFilesBaseSuffix);
subjectsPath = strcat(prefixes, subjects, suffixFolder);
@@ -94,6 +109,7 @@ numberOfSubjects = length(subjects);
% subjectsLCModelOutputPath{indexCurrentSubject});
%
% currentControlFiles = strrep(LCModelControlFiles, defaultSubject, subjects{indexCurrentSubject});
+% currentControlFiles = strrep(currentControlFiles,'fitsettings_v2_','fitsettings_');
% currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, subjects{indexCurrentSubject});
% waterRefFileName = strrep(defaultWaterRef, defaultSubject, subjects{indexCurrentSubject});
%
@@ -113,17 +129,22 @@ numberOfSubjects = length(subjects);
% end
%
% % Create and save table with baseline concentrations fitted with baseline, taken from second block of first REST
-% createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
-% LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,0)
+% if exist('excludeSubjects')
+% createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+% LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,0,excludeSubjects)
+% else
+% createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+% LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,0)
+% end
%
% % Create and save table from fitted concentrations with baseline, take from blocks 3-10
% createConcTableBlock(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
% LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,0)
%% subtract baseline from difference spectrum and fit again
-%only useful for MMBSummed
+% only useful for MMBSummed
% if strcmp(MMBType(1:10),'_MMBSummed')
-% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+% % LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
% parfor indexCurrentSubject = 1:numberOfSubjects
% %% do the LCModel fitting
% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
@@ -132,7 +153,12 @@ numberOfSubjects = length(subjects);
% subjectsLCModelOutputPath{indexCurrentSubject});
%
% currentControlFileswoBaseline = strrep(LCModelControlFileswoBaseline, defaultSubject, subjects{indexCurrentSubject});
-% currentDataFiles = strrep(strcat(outputFileNameBase, visualType, BOLDType, BlockNo), defaultSubject, subjects{indexCurrentSubject});
+% currentControlFileswoBaseline = strrep(currentControlFileswoBaseline,'fitsettings_v2_','fitsettings_');
+% if ~isempty(strfind(MMBType,'_v2'))
+% currentDataFiles = strrep(strcat(outputFileNameBase, visualType, BOLDType, '_v2', BlockNo), defaultSubject, subjects{indexCurrentSubject});
+% else
+% currentDataFiles = strrep(strcat(outputFileNameBase, visualType, BOLDType, BlockNo), defaultSubject, subjects{indexCurrentSubject});
+% end
% currentOutputFileswoBaseline = strrep(LCModelOutputFileswoBaseline, defaultSubject, subjects{indexCurrentSubject});
% currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, subjects{indexCurrentSubject});
% waterRefFileName = strrep(defaultWaterRef, defaultSubject, subjects{indexCurrentSubject});
@@ -152,8 +178,13 @@ numberOfSubjects = length(subjects);
% end
%
% % Create and save table with baseline concentrations fitted w/o baseline, taken from second block of first REST
-% createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+% if exist('excludeSubjects')
+% createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
+% LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,1,excludeSubjects)
+% else
+% createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
% LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,1)
+% end
%
% % Create and save table from fitted concentrations w/o baseline, taken from blocks 3-10
% createConcTableBlock(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
@@ -166,21 +197,188 @@ ExpName = strcat('BlockConc_', num2str(visualType), BOLDType, MMBType);
if exist('excludeSubjects')
createConcTimeCourseFig(ExpName,LCModelOutputPath,subjects,nBlocks,'Lac',0,excludeSubjects)
createConcTimeCourseFig(ExpName,LCModelOutputPath,subjects,nBlocks,'Glu',0,excludeSubjects)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjects,nBlocks,'Lac',0,excludeSubjects)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjects,nBlocks,'Glu',0,excludeSubjects)
if strcmp(MMBType(1:10),'_MMBSummed')
createConcTimeCourseFig(ExpName,LCModelOutputPath,subjects,nBlocks,'Lac',1,excludeSubjects)
createConcTimeCourseFig(ExpName,LCModelOutputPath,subjects,nBlocks,'Glu',1,excludeSubjects)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjects,nBlocks,'Lac',1,excludeSubjects)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjects,nBlocks,'Glu',1,excludeSubjects)
end
else
createConcTimeCourseFig(ExpName,LCModelOutputPath,subjects,nBlocks,'Lac',0)
createConcTimeCourseFig(ExpName,LCModelOutputPath,subjects,nBlocks,'Glu',0)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjects,nBlocks,'Lac',0)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjects,nBlocks,'Glu',0)
if strcmp(MMBType(1:10),'_MMBSummed')
createConcTimeCourseFig(ExpName,LCModelOutputPath,subjects,nBlocks,'Lac',1)
createConcTimeCourseFig(ExpName,LCModelOutputPath,subjects,nBlocks,'Glu',1)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjects,nBlocks,'Lac',1)
+ createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjects,nBlocks,'Glu',1)
end
end
close all
end
+end
+
+function createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,subjects,nBlocks,metabToFig,woBaseline,excludeSubjects)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+ExpName = strcat(ExpName, woBaseline);
+
+exportFileMat = [LCModelOutputPath ,strcat(ExpName,'.mat')];
+dataTimeCourse = load(exportFileMat);
+fields = fieldnames(dataTimeCourse);
+dataTimeCourse = dataTimeCourse.(fields{1});
+metaboliteNames = (dataTimeCourse(1,:));
+indexMet = strfind(metaboliteNames,metabToFig);
+idxMet = find(not(cellfun('isempty',indexMet)));
+
+if exist('excludeSubjects')
+ n = length(dataTimeCourse);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(dataTimeCourse(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ dataTimeCourse([excludeIdx],:) = [];
+end
+
+%Baseline concentration, equals 2nd block (second 64 averages of first
+%REST)
+nsteps = 1;
+for i = 3:floor(length(dataTimeCourse)/10):length(dataTimeCourse)
+ baselineConcIdxMet(nsteps,1) = dataTimeCourse{i,idxMet};
+ nsteps = nsteps + 1;
+end
+
+[n,~] = size(dataTimeCourse);
+for k = 2:n
+ MetConc(k-1) = dataTimeCourse(k,idxMet(1));
+end
+
+%calculate concentration change of data points with reference to baseline
+[~,b] = size(MetConc);
+for av = 1:b-4
+ PercChange(av) = (MetConc{1,av}-baselineConcIdxMet(ceil(av/10)))/baselineConcIdxMet(ceil(av/10))*100;
+end
+
+% figure
+% x = linspace(320/60/4,(320*5/60-320/60/4),10);
+% hold on
+% [ss,~] = size(dataTimeCourse);
+% for idxSubject = 1:floor(ss/nBlocks)
+% if metabToFig == 'Lac'
+% plot(x,cell2mat(dataTimeCourse(((idxSubject-1)*10+2):((idxSubject-1)*10+11),idxMet(1)))+(idxSubject-1)*1.5,'d-')
+% elseif metabToFig == 'Glu'
+% plot(x,cell2mat(dataTimeCourse(((idxSubject-1)*10+2):((idxSubject-1)*10+11),idxMet(1)))+(idxSubject-1)*5,'d-')
+% else
+% error('can only plot Lac and Glu so far');
+% end
+% end
+% xlabel 't / min'; ylabel 'Concentration Change / %'
+% if exist('excludeSubjects')
+% if ~isempty(strfind(ExpName,'Rest'))
+% title ([metabToFig,' time course, subjects excluded, Rest'])
+% else
+% title ([metabToFig,' time course, subjects excluded, Stim'])
+% end
+% else
+% if ~isempty(strfind(ExpName,'Rest'))
+% title ([metabToFig,' time course, Rest'])
+% else
+% title ([metabToFig,' time course, Stim'])
+% end
+% end
+%
+% yLimits = get(gca,'YLim');
+% boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+% for i = 1:nBlocks/2
+% boxoff = [boxoff 320/60*i 320/60*i];
+% if i < nBlocks/2
+% boxon = [boxon 320/60*i 320/60*i];
+% end
+% end
+% for ii = 1:nBlocks/4
+% boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+% boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+% end
+% patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+% patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+% ylim([yLimits(1) yLimits(2)]);
+% xlim([0 320*5/60]); set(gca, 'FontWeight','bold');
+% set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 12, 20]);
+% PathName = strcat(LCModelOutputPath,'BlockConcTimeCourse/');
+% if exist('excludeSubjects')
+% fig2save = fullfile(PathName, strcat(ExpName, '_', metabToFig, '_Excluded_PercChange.fig'));
+% else
+% fig2save = fullfile(PathName, strcat(ExpName, '_', metabToFig, '_PercChange.fig'));
+% end
+% savefig(fig2save)
+
+
+% PercChange_temp = cell2mat(dataTimeCourse(2:end-4,2:end));
+for indexOfBlock = 1:nBlocks
+ PercChange_mean{indexOfBlock,1} = mean(PercChange(1,indexOfBlock:nBlocks:end));
+ PercChange_std{indexOfBlock,1} = std(PercChange(1,indexOfBlock:nBlocks:end));
+end
+
+figure
+x = linspace(320/60/4,(320*5/60-320/60/4),10);
+hold on
+errorbar(x,cell2mat(PercChange_mean),cell2mat(PercChange_std),'-d')
+xlabel 't / min'; ylabel 'Mean Concentration Change / %'
+if exist('excludeSubjects')
+ if ~isempty(strfind(ExpName,'Rest'))
+ title ([metabToFig,' time course, subjects excluded, Rest'])
+ else
+ title ([metabToFig,' time course, subjects excluded, Stim'])
+ end
+else
+ if ~isempty(strfind(ExpName,'Rest'))
+ title ([metabToFig,' time course, Rest'])
+ else
+ title ([metabToFig,' time course, Stim'])
+ end
+end
+
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:nBlocks/2
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < nBlocks/2
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:nBlocks/4
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+xlim([0 320*5/60]); set(gca, 'FontWeight','bold')
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+PathName = strcat(LCModelOutputPath,'BlockConcTimeCourse/');
+if exist('excludeSubjects')
+ fig2save = fullfile(PathName, strcat(ExpName, '_', metabToFig, '_Mean_Excluded_PercChange.fig'));
+else
+ fig2save = fullfile(PathName, strcat(ExpName, '_', metabToFig, '_Mean_PercChange.fig'));
+end
+savefig(fig2save)
+
+end
+
function createConcTimeCourseFig(ExpName,LCModelOutputPath,subjects,nBlocks,metabToFig,woBaseline,excludeSubjects)
if exist('woBaseline', 'var') & woBaseline == true
@@ -257,7 +455,7 @@ end
patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
ylim([yLimits(1) yLimits(2)]);
-xlim([0 320*5/60]);
+xlim([0 320*5/60]); set(gca, 'FontWeight','bold');
set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 12, 20]);
PathName = strcat(LCModelOutputPath,'BlockConcTimeCourse/');
if exist('excludeSubjects')
@@ -311,7 +509,7 @@ end
patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
ylim([yLimits(1) yLimits(2)]);
-xlim([0 320*5/60]);
+xlim([0 320*5/60]); set(gca, 'FontWeight','bold');
set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
PathName = strcat(LCModelOutputPath,'BlockConcTimeCourse/');
if exist('excludeSubjects')
@@ -430,12 +628,30 @@ save(exportFileMat, ExpName)
end
function createConcTableBaseline(numberOfSubjects,BlockNo,LCModelOutputFiles,defaultSubject,subjects,...
- LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,woBaseline)
+ LCModelOutputPath,subjectsPath,prefixes,visualType,BOLDType,MMBType,woBaseline,excludeSubjects)
if exist('woBaseline', 'var') & woBaseline == true
woBaseline = '_woBaseline';
else
woBaseline = '';
end
+if exist('excludeSubjects')
+ n = length(subjects);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(subjects(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ subjects([excludeIdx],:) = [];
+ prefixes([excludeIdx],:) = [];
+ subjectsPath([excludeIdx],:) = [];
+ numberOfSubjects = length(subjects);
+end
tableConcentrations = [];
isFirstIter = 1;
s = 1;
@@ -505,7 +721,11 @@ for indexOfMetab = 1:m-1
end
% save table to file
-ExpName = strcat('BaselineConc_', num2str(visualType), BOLDType, MMBType, woBaseline);
+if exist('excludeSubjects')
+ ExpName = strcat('BaselineConc_', num2str(visualType), BOLDType, MMBType, woBaseline, '_Excluded');
+else
+ ExpName = strcat('BaselineConc_', num2str(visualType), BOLDType, MMBType, woBaseline);
+end
exportFileXlsx = [LCModelOutputPath ,strcat(ExpName,'.xlsx')];
xlswrite(exportFileXlsx, {LCModelOutputPath}, 1, 'A1');
cellPosition = 'A2';
diff --git a/fMRS_processing/doFitting_fMRS_TimeCourse_Summed.m b/fMRS_processing/doFitting_fMRS_TimeCourse_Summed.m
new file mode 100644
index 0000000000000000000000000000000000000000..84955dc5f171e1034efa882fcfc3f9626b78826e
--- /dev/null
+++ b/fMRS_processing/doFitting_fMRS_TimeCourse_Summed.m
@@ -0,0 +1,546 @@
+function doFitting_fMRS_TimeCourse_Summed()
+clear; clc; close all;
+
+%%%%%
+% summed data created with
+% D:\MR_spectroS\fMRS_processing\BOLDEffectfMRS_summedSpectra.m
+% saved in D:\PAPER\1H_fMRS\Data_TimeCourse\SummedData
+%%%%%
+
+allVisTypes = {'Stim'}; %'Stim', 'Rest'
+
+for i = 1:length(allVisTypes)
+pathName = 'fMRS TimeCourse data path';
+sampleCriteria = {'Output', 'LCModelConfig'};
+[localFilePathBase] = pathToDataFolder(pathName, sampleCriteria);
+visualType = allVisTypes{i}; %'Stim', 'Rest'
+BOLDType = '_woBOLDCorr'; %only _woBOLDCorr reasonable since block wise fitting for metabolite time courses
+MMBType = '_MMBSummed_v2'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated
+% nBlocks = 5;
+nBlocks = 10;
+
+BlockNo = cell(1,nBlocks);
+for k = 1:nBlocks
+ BlockNo{k} = strcat('_Block', num2str(k));
+end
+
+% subjects = {'2823';};
+subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
+ '4085';'3333';'4012';'4085';'6524'};
+% prefixes = {'2020-02-18_';};
+prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
+ '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
+ '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
+suffixFolder = '_fMRS';
+excludeSubjects = {'5166';'9810';'5269'};
+
+% if exist('excludeSubjects')
+% n = length(subjects);
+% m = length(excludeSubjects);
+% nstep = 1;
+% for j = 1:m
+% for i = 1:n
+% xx = find(~cellfun(@isempty,strfind(subjects(i,1),excludeSubjects{j})));
+% if ~isempty(xx)
+% excludeIdx(nstep) = i;
+% nstep = nstep + 1;
+% end
+% end
+% end
+% subjects([excludeIdx],:) = [];
+% prefixes([excludeIdx],:) = [];
+% end
+
+
+extensionTable = '.table';
+extensionCoord = '.coord';
+defaultVisual = 'Stim';
+defaultSubject = 'XXXX';
+defaultSubjectsPath = 'YYYY';
+if ~isempty(strfind(MMBType,'_v2'))
+ defaultMMB = '_MMBSummed_v2';
+else
+ defaultMMB = '_MMBSummed';
+end
+defaultLCModelUser = 'jdorst';
+outputFileNameBaseWithoutMetabolite_OFF = strcat(defaultSubject, '_StimOFF_woBOLDCorr128');
+outputFileNameBaseWithoutMetabolite_ON = strcat(defaultSubject, '_StimON_woBOLDCorr128');
+
+if ~isempty(strfind(MMBType,'_v2'))
+ controlFilesBase = 'fitsettings_v2_XXXX_';
+else
+ controlFilesBase = 'fitsettings_XXXX_';
+end
+controlFilesBaseSuffix = '.control';
+outputFileNameBase = strcat(defaultSubject, '_');
+
+%% file paths setup
+controlFilesPathRemote = '/Desktop/1H_fMRS/LCModelConfig/';
+controlFilesPathLocal = [localFilePathBase, 'LCModelConfig/'];
+LCModelOutputFilesPathRemote = '/Desktop/1H_fMRS/Output/';
+if ~isempty(strfind(MMBType,'_v2'))
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed/'];
+else
+ LCModelOutputPath = [localFilePathBase, 'Output/'];
+end
+defaultControlFile = strcat(controlFilesBase, defaultVisual, controlFilesBaseSuffix);
+
+% subjectsPath = strcat(prefixes, subjects, suffixFolder);
+summedPath = 'Summed_fMRSData';
+
+summedControlFilesPathRemote = strcat(controlFilesPathRemote, summedPath, '/');
+summedControlFilesPathLocal = strcat(controlFilesPathLocal, summedPath, '/');
+preprocessedFilesPathLocal = strcat(localFilePathBase, 'SummedData/');
+summedLCModelOutputFilesRemote = strcat(LCModelOutputFilesPathRemote, summedPath, '/');
+summedLCModelOutputPath = strcat(LCModelOutputPath, summedPath, '/');
+
+
+%% basic configurations
+LCModelOutputFiles = strcat(outputFileNameBase, visualType, BOLDType, MMBType, BlockNo);
+LCModelOutputFileswoBaseline = strcat(outputFileNameBase, visualType, BOLDType, MMBType, BlockNo, '_woBaseline');
+LCModelControlFiles = strcat(controlFilesBase, visualType, BOLDType, MMBType, BlockNo, controlFilesBaseSuffix);
+LCModelControlFileswoBaseline = strcat(controlFilesBase, visualType, BOLDType, MMBType, BlockNo, '_woBaseline', controlFilesBaseSuffix);
+defaultWaterRef = strcat(outputFileNameBase, 'WRef');
+
+numberOfSubjects = length(subjects);
+
+%% do the LCModel fitting
+% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+%
+% LCModelCallerInstance = LCModelCallerInstance.ConfigurePaths(summedControlFilesPathRemote, summedLCModelOutputFilesRemote, ...
+% summedLCModelOutputPath);
+%
+% currentControlFiles = strrep(LCModelControlFiles, defaultSubject, 'Summed');
+% currentControlFiles = strrep(currentControlFiles,'.control','_Excluded.control');
+% currentControlFiles = strrep(currentControlFiles,'fitsettings_v2_','fitsettings_');
+% currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, 'Summed');
+% currentOutputFiles = strcat(currentOutputFiles,'_Excluded');
+% waterRefFileName = strrep(defaultWaterRef, defaultSubject, 'fMRSData_Summed');
+% waterRefFileName = strcat(waterRefFileName,'_Excluded');
+%
+% %%create the control file series
+% for nBl = 1:nBlocks
+% currentOutputFile = currentOutputFiles{nBl};
+% currentFMRS = strcat(visualType,BOLDType);
+% % % % currentLCModelOutputFile = LCModelOutputFiles{nBl};
+% createLCModelConfigfMRSTimeCourse(controlFilesPathLocal, summedControlFilesPathLocal, defaultControlFile, ...
+% currentFMRS, defaultVisual, currentOutputFile,...
+% waterRefFileName, defaultWaterRef, summedPath, defaultSubjectsPath, 'fMRSData_Summed',...
+% defaultSubject, MMBType, defaultMMB, BlockNo{nBl});
+% end
+%
+% outputFiles = strcat('fMRSData_',strrep(LCModelOutputFiles, defaultSubject, 'Summed'));
+% fittingLCModel(LCModelCallerInstance, currentControlFiles, outputFiles, summedControlFilesPathRemote, ...
+% summedControlFilesPathLocal, preprocessedFilesPathLocal);
+% %%
+% % % always excludeSubjects, only 10 subjects in summed RAW file, see D:\MR_spectroS\fMRS_processing\BoldEffectfMRS_summedSpectra.m
+% createConcTableBaseline(BlockNo,outputFiles,...
+% LCModelOutputPath,summedPath,visualType,BOLDType,MMBType,0)
+% %
+% % % Create and save table from fitted concentrations with baseline, take from blocks 3-10
+% createConcTableBlock(BlockNo,outputFiles,defaultSubject,...
+% LCModelOutputPath,summedPath,visualType,BOLDType,MMBType,0)
+%
+% %% subtract baseline from difference spectrum and fit again
+% % only useful for MMBSummed
+% if strcmp(MMBType(1:10),'_MMBSummed')
+% %% do the LCModel fitting
+% LCModelCallerInstance = startLCModel(controlFilesPathRemote, LCModelOutputFilesPathRemote, LCModelOutputPath);
+%
+% LCModelCallerInstance = LCModelCallerInstance.ConfigurePaths(summedControlFilesPathRemote, summedLCModelOutputFilesRemote, ...
+% summedLCModelOutputPath);
+%
+% currentControlFileswoBaseline = strrep(LCModelControlFileswoBaseline, defaultSubject, 'Summed');
+% currentControlFileswoBaseline = strrep(currentControlFileswoBaseline,'.control','_Excluded.control');
+% currentControlFileswoBaseline = strrep(currentControlFileswoBaseline,'fitsettings_v2_','fitsettings_');
+% if ~isempty(strfind(MMBType,'_v2'))
+% currentDataFiles = strcat('fMRSData_Summed_', visualType, BOLDType, '_v2', BlockNo);
+% else
+% currentDataFiles = strcat('fMRSData_Summed_', visualType, BOLDType, BlockNo);
+% end
+% currentOutputFileswoBaseline = strrep(LCModelOutputFileswoBaseline, defaultSubject, 'Summed');
+% currentOutputFileswoBaseline = strcat(currentOutputFileswoBaseline,'_Excluded');
+% currentOutputFiles = strrep(LCModelOutputFiles, defaultSubject, 'Summed');
+% currentOutputFiles = strcat(currentOutputFiles,'_Excluded');
+% waterRefFileName = strrep(defaultWaterRef, defaultSubject, 'fMRSData_Summed');
+% waterRefFileName = strcat(waterRefFileName,'_Excluded');
+% currentDataPath = strcat(localFilePathBase,'SummedData', '/');
+% for nBl = 1:nBlocks
+% %check save option for .mat file in subtractBaseline_fMRS
+% subtractBaseline_fMRS(currentDataPath,summedLCModelOutputPath,currentDataFiles{nBl},strcat('fMRSData_',currentOutputFileswoBaseline{nBl}),'NAA')
+% currentFMRS = strcat(visualType,BOLDType);
+% createLCModelConfigfMRSTimeCourse(controlFilesPathLocal, summedControlFilesPathLocal, defaultControlFile, ...
+% currentFMRS, defaultVisual, currentOutputFileswoBaseline{nBl},...
+% waterRefFileName, defaultWaterRef, summedPath, defaultSubjectsPath, 'fMRSData_Summed',...
+% defaultSubject, MMBType, defaultMMB, strcat(BlockNo{nBl}, '_woBaseline'));
+% end
+% outputFileswoBaseline = strcat('fMRSData_',strrep(LCModelOutputFileswoBaseline, defaultSubject, 'Summed'));
+% fittingLCModel(LCModelCallerInstance, currentControlFileswoBaseline, outputFileswoBaseline, summedControlFilesPathRemote, ...
+% summedControlFilesPathLocal, preprocessedFilesPathLocal);
+%
+% %
+% % % Create and save table with baseline concentrations fitted w/o baseline, taken from second block of first REST
+% % always excludeSubjects, only 10 subjects in summed RAW file, see D:\MR_spectroS\fMRS_processing\BoldEffectfMRS_summedSpectra.m
+% createConcTableBaseline(BlockNo,outputFiles,...
+% LCModelOutputPath,summedPath,visualType,BOLDType,MMBType,1)
+% %
+% % % Create and save table from fitted concentrations w/o baseline, taken from blocks 3-10
+% createConcTableBlock(BlockNo,outputFiles,defaultSubject,...
+% LCModelOutputPath,summedPath,visualType,BOLDType,MMBType,1)
+% end
+
+
+%% create concentration time course from fit with and w/o Baseline
+ExpName = strcat('BlockConc_Summed_', num2str(visualType), BOLDType, MMBType);
+% always excludeSubjects, only 10 subjects in summed RAW file, see D:\MR_spectroS\fMRS_processing\BoldEffectfMRS_summedSpectra.m
+createConcTimeCourseFig(ExpName,LCModelOutputPath,nBlocks,'Lac',0)
+createConcTimeCourseFig(ExpName,LCModelOutputPath,nBlocks,'Glu',0)
+createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,nBlocks,'Lac',0)
+createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,nBlocks,'Glu',0)
+createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,nBlocks,'Cr',0)
+if strcmp(MMBType(1:10),'_MMBSummed')
+ createConcTimeCourseFig(ExpName,LCModelOutputPath,nBlocks,'Lac',1)
+ createConcTimeCourseFig(ExpName,LCModelOutputPath,nBlocks,'Glu',1)
+createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,nBlocks,'Lac',1)
+createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,nBlocks,'Glu',1)
+end
+close all
+
+end
+end
+
+function createConcTimeCourseFig(ExpName,LCModelOutputPath,nBlocks,metabToFig,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+ExpName = strcat(ExpName, woBaseline);
+
+exportFileMat = [LCModelOutputPath ,strcat(ExpName,'.mat')];
+dataTimeCourse = load(exportFileMat);
+fields = fieldnames(dataTimeCourse);
+dataTimeCourse = dataTimeCourse.(fields{1});
+metaboliteNames = (dataTimeCourse(1,:));
+indexMet = strfind(metaboliteNames,metabToFig);
+idxMet = find(not(cellfun('isempty',indexMet)));
+
+figure
+x = linspace(320/60/4,(320*5/60-320/60/4),10);
+hold on
+[ss,~] = size(dataTimeCourse);
+for idxSubject = 1:floor(ss/nBlocks)
+% if metabToFig == 'Lac'
+ plot(x,cell2mat(dataTimeCourse(((idxSubject-1)*10+2):((idxSubject-1)*10+11),idxMet(1)))+(idxSubject-1)*1.5,'d-')
+% elseif metabToFig == 'Glu'
+% plot(x,cell2mat(dataTimeCourse(((idxSubject-1)*10+2):((idxSubject-1)*10+11),idxMet(1)))+(idxSubject-1)*5,'d-')
+% else
+% error('can only plot Lac and Glu so far');
+% end
+end
+xlabel 't / min'; ylabel 'Concentration / a.u.'
+if ~isempty(strfind(ExpName,'Rest'))
+ title ([metabToFig,' time course, fit from summed data, subjects excluded, Rest'])
+else
+ title ([metabToFig,' time course, fit from summed data, subjects excluded, Stim'])
+end
+
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:nBlocks/2
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < nBlocks/2
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:nBlocks/4
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+xlim([0 320*5/60]); set(gca, 'FontWeight','bold')
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+PathName = strcat(LCModelOutputPath,'BlockConcTimeCourse/');
+fig2save = fullfile(PathName, strcat(ExpName, '_', metabToFig, '_Excluded.fig'));
+savefig(fig2save)
+
+end
+
+function createConcTimeCourseFigPercChange(ExpName,LCModelOutputPath,nBlocks,metabToFig,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+ExpName = strcat(ExpName, woBaseline);
+
+exportFileMat = [LCModelOutputPath ,strcat(ExpName,'.mat')];
+dataTimeCourse = load(exportFileMat);
+fields = fieldnames(dataTimeCourse);
+dataTimeCourse = dataTimeCourse.(fields{1});
+metaboliteNames = (dataTimeCourse(1,:));
+indexMet = strfind(metaboliteNames,metabToFig);
+idxMet = find(not(cellfun('isempty',indexMet)));
+
+%Baseline concentration, equals 2nd block (second 64 averages of first
+%REST)
+baselineConcIdxMet = dataTimeCourse{3,idxMet};
+
+[n,~] = size(dataTimeCourse);
+for k = 2:n
+ MetConc(k-1) = dataTimeCourse(k,idxMet(1));
+end
+
+%calculate concentration change of data points with reference to baseline
+[~,b] = size(MetConc);
+for av = 1:b-4
+ PercChange(av) = (MetConc{1,av}-baselineConcIdxMet)/baselineConcIdxMet*100;
+end
+
+figure
+x = linspace(320/60/4,(320*5/60-320/60/4),10);
+hold on
+plot(x,PercChange,'d-')
+plot([0,320*5/60],[0 0])
+xlabel 't / min'; ylabel 'Concentration change / %'
+if ~isempty(strfind(ExpName,'Rest'))
+ title ([metabToFig,' time course, fit from summed data, subjects excluded, Rest'])
+else
+ title ([metabToFig,' time course, fit from summed data, subjects excluded, Stim'])
+end
+
+yLimits = get(gca,'YLim');
+boxoff = [0 0]; boxon = []; boxyoff = [yLimits(1) yLimits(2) yLimits(2) yLimits(1)]; boxyon = []; n = 1;
+for i = 1:nBlocks/2
+ boxoff = [boxoff 320/60*i 320/60*i];
+ if i < nBlocks/2
+ boxon = [boxon 320/60*i 320/60*i];
+ end
+end
+for ii = 1:nBlocks/4
+ boxyoff = [boxyoff yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+ boxyon = [boxyon yLimits(1) yLimits(2) yLimits(2) yLimits(1)];
+end
+patch(boxoff,boxyoff,[0 0 1],'FaceAlpha',0.2)
+patch(boxon,boxyon,[1 0 0],'FaceAlpha',0.2)
+ylim([yLimits(1) yLimits(2)]);
+xlim([0 320*5/60]); set(gca, 'FontWeight','bold')
+set(gcf, 'Units', ' centimeters', 'OuterPosition', [0, 0, 18, 12.6]);
+PathName = strcat(LCModelOutputPath,'BlockConcTimeCourse/');
+fig2save = fullfile(PathName, strcat(ExpName, '_', metabToFig, '_Excluded_PercChange.fig'));
+savefig(fig2save)
+
+end
+
+function createConcTableBlock(BlockNo,LCModelOutputFiles,defaultSubject,...
+ LCModelOutputPath,summedPath,visualType,BOLDType,MMBType,woBaseline)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+tableConcentrations = [];
+isFirstIter = 1;
+s = 1;
+for nBl = 1:length(BlockNo)
+ % retrieve parameters
+ currentOutputFiles = strrep(strcat(LCModelOutputFiles,woBaseline), defaultSubject, 'fMRSData_Summed');
+ currentPath = strcat(LCModelOutputPath,summedPath,'/',currentOutputFiles{nBl},'.table');
+ [c1 c2 c3 c4] = textread(currentPath,'%s %s %s %s');
+
+ if isFirstIter
+ start = find(strcmp(c1,'Conc.'));
+ start = start + 1;
+ finish = find(strcmpi(c1,'$$MISC')) - 1;
+ tableConcentrations{1,1} = 'ID';
+
+ s = 2;
+ for j=start:finish
+ if(strcmp(c4{j},'')==1)
+ c3_split = strsplit(c3{j},'+');
+ if length(c3_split) == 1
+ c3_split = strsplit(c3{j},'-');
+ end
+ tableConcentrations{1,s} = c3_split{2};
+ else
+ tableConcentrations{1,s} = c4{j};
+ if length(tableConcentrations{1,s}) == 3 & tableConcentrations{1,s} == 'Leu'
+ tableConcentrations{1,s} = 'MMB';
+ end
+ end
+ tableConcentrations{1,s+1} = 'CRLB %';
+ s = s + 2;
+ end
+ tableConcentrations{1,s} = 'water concentration';
+ end
+
+ % add ID and Water conc to table
+ % tableConcentrations{(length(BlockNo)-2)*(indexCurrentSubject-1)+nBl-1,1} = strcat(prefixes{indexCurrentSubject}, subjects{indexCurrentSubject}, BlockNo{nBl});
+ tableConcentrations{nBl+1,1} = strcat('fMRSData_Summed', BlockNo{nBl});
+ index_wconc = find(strcmp(c1,'wconc='));
+ wconc_LCModel = str2num(c2{index_wconc});
+ tableConcentrations{nBl+1,end} = wconc_LCModel;
+ % add metabolites to table
+ s = 2;
+ for j=start:finish
+ % add quantified metabolite
+ tableConcentrations{nBl+1,s} = str2num( c1{j});
+ % add CRLB
+ tableConcentrations{nBl+1,s+1} = str2num( c2{j}(1:end-1));
+ s = s+2;
+ end
+
+ isFirstIter = false;
+
+end
+
+% calculate mean, std
+[n,m] = size(tableConcentrations);
+subArray = cell2mat(tableConcentrations(2:end,2:end));
+subArrayON1 = subArray(3:10:end,:);
+subArrayON2 = subArray(4:10:end,:);
+subArrayON3 = subArray(7:10:end,:);
+subArrayON4 = subArray(8:10:end,:);
+subArrayON = [subArrayON1; subArrayON2; subArrayON3; subArrayON4];
+meanValueON = mean(subArrayON);
+stdValueON = std(subArrayON);
+tableConcentrations{n+1,1} = 'meanON';
+tableConcentrations{n+2,1} = 'stdON';
+for indexOfMetab = 1:m-1
+ tableConcentrations{n+1,indexOfMetab+1} = meanValueON(1,indexOfMetab);
+ tableConcentrations{n+2,indexOfMetab+1} = stdValueON(1,indexOfMetab);
+end
+subArrayOFF1 = subArray(5:10:end,:);
+subArrayOFF2 = subArray(6:10:end,:);
+subArrayOFF3 = subArray(9:10:end,:);
+subArrayOFF4 = subArray(10:10:end,:);
+subArrayOFF = [subArrayOFF1; subArrayOFF2; subArrayOFF3; subArrayOFF4];
+meanValueOFF = mean(subArrayOFF);
+stdValueOFF = std(subArrayOFF);
+tableConcentrations{n+3,1} = 'meanOFF';
+tableConcentrations{n+4,1} = 'stdOFF';
+for indexOfMetab = 1:m-1
+ tableConcentrations{n+3,indexOfMetab+1} = meanValueOFF(1,indexOfMetab);
+ tableConcentrations{n+4,indexOfMetab+1} = stdValueOFF(1,indexOfMetab);
+end
+
+% save table to file
+ExpName = strcat('BlockConc_Summed_', num2str(visualType), BOLDType, MMBType, woBaseline);
+exportFileXlsx = [LCModelOutputPath ,strcat(ExpName,'.xlsx')];
+xlswrite(exportFileXlsx, {LCModelOutputPath}, 1, 'A1');
+cellPosition = 'A2';
+xlswrite(exportFileXlsx, tableConcentrations, 1, cellPosition);
+
+exportFileMat = [LCModelOutputPath ,strcat(ExpName,'.mat')];
+eval([ExpName '= tableConcentrations']); %use of eval not recommended, change it
+save(exportFileMat, ExpName)
+end
+
+function createConcTableBaseline(BlockNo,LCModelOutputFiles,...
+ LCModelOutputPath,summedPath,visualType,BOLDType,MMBType,woBaseline,excludeSubjects)
+if exist('woBaseline', 'var') & woBaseline == true
+ woBaseline = '_woBaseline';
+else
+ woBaseline = '';
+end
+% if exist('excludeSubjects')
+% n = length(subjects);
+% m = length(excludeSubjects);
+% nstep = 1;
+% for j = 1:m
+% for i = 1:n
+% xx = find(~cellfun(@isempty,strfind(subjects(i,1),excludeSubjects{j})));
+% if ~isempty(xx)
+% excludeIdx(nstep) = i;
+% nstep = nstep + 1;
+% end
+% end
+% end
+% subjects([excludeIdx],:) = [];
+% prefixes([excludeIdx],:) = [];
+% subjectsPath([excludeIdx],:) = [];
+% numberOfSubjects = length(subjects);
+% end
+tableConcentrations = [];
+isFirstIter = 1;
+s = 1;
+for nBl = 2 %take baseline from second block of first REST
+ % retrieve parameters
+ currentOutputFiles = strcat(LCModelOutputFiles,woBaseline);
+ currentPath = strcat(LCModelOutputPath,summedPath,'/',currentOutputFiles{nBl},'.table');
+ [c1 c2 c3 c4] = textread(currentPath,'%s %s %s %s');
+
+ if isFirstIter
+ start = find(strcmp(c1,'Conc.'));
+ start = start + 1;
+ finish = find(strcmpi(c1,'$$MISC')) - 1;
+ tableConcentrations{1,1} = 'ID';
+
+ s = 2;
+ for j=start:finish
+ if(strcmp(c4{j},'')==1)
+ c3_split = strsplit(c3{j},'+');
+ if length(c3_split) == 1
+ c3_split = strsplit(c3{j},'-');
+ end
+ tableConcentrations{1,s} = c3_split{2};
+ else
+ tableConcentrations{1,s} = c4{j};
+ if length(tableConcentrations{1,s}) == 3 & tableConcentrations{1,s} == 'Leu'
+ tableConcentrations{1,s} = 'MMB';
+ end
+ end
+ tableConcentrations{1,s+1} = 'CRLB %';
+ s = s + 2;
+ end
+ tableConcentrations{1,s} = 'water concentration';
+ end
+
+ % add ID and Water conc to table
+ tableConcentrations{1,1} = strcat('fMRSData_Summed', BlockNo{nBl});
+ index_wconc = find(strcmp(c1,'wconc='));
+ wconc_LCModel = str2num(c2{index_wconc});
+ tableConcentrations{1,end} = wconc_LCModel;
+ % add metabolites to table
+ s = 2;
+ for j=start:finish
+ %add quantified metabolite
+ tableConcentrations{1,s} = str2num( c1{j});
+ %add CRLB
+ tableConcentrations{1,s+1} = str2num( c2{j}(1:end-1));
+ s = s+2;
+ end
+
+ isFirstIter = false;
+
+end
+
+% %calculate mean, std
+% [n,m] = size(tableConcentrations);
+% subArray = cell2mat(tableConcentrations(2:end,2:end));
+% meanValue = mean(subArray);
+% stdValue = std(subArray);
+% tableConcentrations{n+1,1} = 'mean';
+% tableConcentrations{n+2,1} = 'std';
+% for indexOfMetab = 1:m-1
+% tableConcentrations{n+1,indexOfMetab+1} = meanValue(1,indexOfMetab);
+% tableConcentrations{n+2,indexOfMetab+1} = stdValue(1,indexOfMetab);
+% end
+
+% save table to file
+% if exist('excludeSubjects')
+% ExpName = strcat('BaselineConc_Summed_', num2str(visualType), BOLDType, MMBType, woBaseline, '_Excluded');
+% else
+ ExpName = strcat('BaselineConc_Summed_', num2str(visualType), BOLDType, MMBType, woBaseline);
+% end
+exportFileXlsx = [LCModelOutputPath ,strcat(ExpName,'.xlsx')];
+xlswrite(exportFileXlsx, {LCModelOutputPath}, 1, 'A1');
+cellPosition = 'A2';
+xlswrite(exportFileXlsx, tableConcentrations, 1, cellPosition);
+
+exportFileMat = [LCModelOutputPath ,strcat(ExpName,'.mat')];
+eval([ExpName '= tableConcentrations']); %use of eval not recommended, change it
+save(exportFileMat, ExpName)
+end
\ No newline at end of file
diff --git a/fMRS_processing/plot_properties_Reproducibility.m b/fMRS_processing/plot_properties_Reproducibility.m
new file mode 100644
index 0000000000000000000000000000000000000000..bb6ec3882e16cd996ee820f7fd57458860921a10
--- /dev/null
+++ b/fMRS_processing/plot_properties_Reproducibility.m
@@ -0,0 +1,85 @@
+function plot_properties_Reproducibility()
+clear; clc; close all;
+
+pathName = 'fMRS TimeCourse data path';
+sampleCriteria = {'Output', 'LCModelConfig'};
+[localFilePathBase] = pathToDataFolder(pathName, sampleCriteria);
+visualType = 'Stim'; %'Stim', 'Rest'
+BOLDType = '_woBOLDCorr'; %only _woBOLDCorr reasonable since block wise fitting for metabolite time courses
+MMBType = '_MMBSummed_v2'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated
+% nBlocks = 5;
+nBlocks = 10;
+
+BlockNo = cell(1,nBlocks);
+for k = 1:nBlocks
+ BlockNo{k} = strcat('_Block', num2str(k));
+end
+
+% subjects = {'2823';};
+subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
+ '4085';'3333';'4012';'4085';'6524'};
+% prefixes = {'2020-02-18_';};
+prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
+ '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
+ '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
+suffixFolder = '_fMRS';
+excludeSubjects = {'5166';'9810';'5269'};
+
+if exist('excludeSubjects')
+ n = length(subjects);
+ m = length(excludeSubjects);
+ nstep = 1;
+ for j = 1:m
+ for i = 1:n
+ xx = find(~cellfun(@isempty,strfind(subjects(i,1),excludeSubjects{j})));
+ if ~isempty(xx)
+ excludeIdx(nstep) = i;
+ nstep = nstep + 1;
+ end
+ end
+ end
+ subjects([excludeIdx],:) = [];
+ prefixes([excludeIdx],:) = [];
+end
+
+subjectsPath = strcat(prefixes, subjects, suffixFolder);
+
+%load Rest data per block, then mean, std, plot
+for indexCurrentSubject = 1:length(subjects)
+ currentDataFilePath = [localFilePathBase,subjectsPath{indexCurrentSubject}];
+ for nBl = 1:nBlocks
+ % currentData = load(strcat(currentDataFilePath,'\',subjects{indexCurrentSubject},'_',visualType,BOLDType,BlockNo{nBl}));
+ % fields = fieldnames(currentData);
+ % currentData = currentData.(fields{1});
+ %hopefully saved as 'this'
+ load(strcat(currentDataFilePath,'\',subjects{indexCurrentSubject},'_',visualType,BOLDType,BlockNo{nBl}));
+ Data_temp = squeeze(real(fftshift(fft(this.Data{1}))));
+ Data(indexCurrentSubject,nBl,:,:) = squeeze(real(fftshift(fft(this.Data{1})))); %Subjects x Blocks x Data x Water/Met
+ end
+ DataBlockMean_Met(indexCurrentSubject,:) = squeeze(mean(Data(indexCurrentSubject,:,:,2),2));
+ DataBlockStd_Met(indexCurrentSubject,:) = squeeze(std(Data(indexCurrentSubject,:,:,2)));
+ DataBlockMean_H2O(indexCurrentSubject,:) = squeeze(mean(Data(indexCurrentSubject,:,:,1),2));
+ DataBlockStd_H2O(indexCurrentSubject,:) = squeeze(std(Data(indexCurrentSubject,:,:,1)));
+end
+ppmvector = this.getPpmVector();
+colors = {[0 0.7 0]; [0 0.4 0.8]; [0.8 0 0.8]; [0.8 0.4 0];[0 0.5 0.5]; [0.4 0 0.8]; [1 0 0]; [0 0.6 0.3]; [1 0 0.5]; [0 0 1];};
+
+for indexCurrentSubject = 1:length(subjects)
+ lo = (DataBlockMean_Met(indexCurrentSubject,:) - DataBlockStd_Met(indexCurrentSubject,:)) + 0.01 * (indexCurrentSubject-1); hi = DataBlockMean_Met(indexCurrentSubject,:) + DataBlockStd_Met(indexCurrentSubject,:) + 0.01 * (indexCurrentSubject-1);
+% figure
+ hp = patch([ppmvector ppmvector(end:-1:1) ppmvector(1)], [lo hi(end:-1:1) lo(1)], 'r');
+ hold on;
+ hl = line(ppmvector,DataBlockMean_Met(indexCurrentSubject,:) + 0.01 * (indexCurrentSubject-1));
+ set(hp, 'facecolor', colors{indexCurrentSubject},'FaceAlpha',.3, 'edgecolor', colors{indexCurrentSubject},'EdgeAlpha',.2);
+ set(hl, 'color', colors{indexCurrentSubject});
+ set(gca,'xdir','reverse')
+ xlim([0.6 8.5]); xlabel 'f / ppm'
+ title(['Reproducibility ',visualType])
+end
+figPath2save = fullfile(localFilePathBase, 'Output_v2_MMBSummed\Reproducibility');
+fig2save = [figPath2save, '\Reproducibility_all_',visualType,'.fig'];
+% fig2save = [figPath2save, '\Reproducibility_first3_',visualType,'.fig'];
+% fig2save = [figPath2save, '\Reproducibility_second3_',visualType,'.fig'];
+% fig2save = [figPath2save, '\Reproducibility_last4_',visualType,'.fig'];
+savefig(fig2save)
+end
\ No newline at end of file
diff --git a/fMRS_processing/reconstruct_1H_fMRS.m b/fMRS_processing/reconstruct_1H_fMRS.m
index 21fdc2087ba05bfc2a5be996f742d6076673bd63..bbf4e3d894cf27891a3392ef98479a1b715e8b76 100644
--- a/fMRS_processing/reconstruct_1H_fMRS.m
+++ b/fMRS_processing/reconstruct_1H_fMRS.m
@@ -1,4 +1,5 @@
-function [a] = reconstruct_1H_fMRS(currentFolder,fid_id, isMC, isInVivo, nBlocks, nOnAverages, PCSteps, weights, saveResults,visualType)
+function [a] = reconstruct_1H_fMRS(currentFolder,fid_id, isMC, isInVivo,...
+ nBlocks, nOnAverages, PCSteps, weights, saveResults, visualType,BOLDType,modulus)
nsamplesBeforeEcho = 4;
@@ -10,6 +11,10 @@ if ~exist('saveResults', 'var')
saveResults = true;
end
+if ~exist('modulus', 'var')
+ error('choose modulus type true or false');
+end
+
if nBlocks == 5
nAvg2save = '#64';
elseif nBlocks == 10
@@ -82,10 +87,14 @@ end
%%
if isInVivo
%% truncate before anything
- trunc_ms = 250;
+% if exist('modulus','var') && (modulus == 1)
+% trunc_ms = 200;
+% else
+ trunc_ms = 250;
+% end
truncPoint = floor( trunc_ms/(a.Parameter.Headers.DwellTimeSig_ns*1e-6));
a = a.Truncate(truncPoint);
-
+
a = a.FrequencyAlign;
end
if isMC
@@ -95,14 +104,17 @@ if isMC
a = a.ReconData;
end
-%average data block-wise
+
+%% %average data block-wise
if ~exist('nBlocks', 'var')
a.Parameter.AverageSettings.nBlocks = 1;
else
a.Parameter.AverageSettings.nBlocks = nBlocks;
end
-a = a.AverageData;
+if exist('modulus','var') && (modulus == 0)
+ a = a.AverageData;
+end
%eddy current correction
if isMC
@@ -127,34 +139,154 @@ else
a = a.CombineCoils;
end
-%% setup the settings for the FrequencyAlignFreqDomain
-a.Parameter.FreqAlignFreqDomainSettings.selectedCoil = 1; % check for max SNR coil
-a.Parameter.FreqAlignFreqDomainSettings.selectedMix = 1;
-
-%indices of Interest: given in ppm
-% water peak
-a.Parameter.FreqAlignFreqDomainSettings.peaksInPpm = 2.008;
-
-%set zeroFillingParameter to get smooth approximations
-a.Parameter.FreqAlignFreqDomainSettings.zeroFillFactor = 50;
+%%
+%prepare Lenkinski correction, correct only spectra, not water
+if exist('modulus','var') && (modulus == 1)
+ % setup the settings for the FrequencyAlignFreqDomain
+ a.Parameter.FreqAlignFreqDomainSettings.selectedCoil = 1; % check for max SNR coil
+ a.Parameter.FreqAlignFreqDomainSettings.selectedMix = 1;
+
+ %indices of Interest: given in ppm
+ % NAA peak
+ a.Parameter.FreqAlignFreqDomainSettings.peaksInPpm = 2.008;
+
+ %set zeroFillingParameter to get smooth approximations
+ a.Parameter.FreqAlignFreqDomainSettings.zeroFillFactor = 50;
+
+ %search area (+-) in ppm
+ if isInVivo
+ a.Parameter.FreqAlignFreqDomainSettings.searchArea = 0.1;
+ else
+ a.Parameter.FreqAlignFreqDomainSettings.searchArea = 0.2;
+ end
+ %spline filtering is not needed if the spectra are not too noisy.
+ %flag to do spline filtering
+ a.Parameter.FreqAlignFreqDomainSettings.doSplineSmoothing = false;
+ %spline filtering coefficient
+ a.Parameter.FreqAlignFreqDomainSettings.splineSmoothingCoeff = 0.01;
+ % do the actual Frequency Alignment
+ a = a.FrequencyAlignFreqDomain;
+
+ ppmvector = a.getPpmVector();
+ [~, idx5] = min(abs(ppmvector-5.55));
+ idxend = length(ppmvector);
+ for idxAv = 1:size(a.Data{1},12)
+ currentDataFID = squeeze(a.Data{1}(:,1,1,1,1,1,1,1,1,1,1,idxAv));
+ currentData_spec = fftshift(fft(currentDataFID(:,1)));
+
+ x = 0:50;
+ g = exp(-((x-0)./15) .^(2));
+ [~, idxHalf] = min(abs(g-0.5*max(g)));
+ g2 = g(idxHalf+2:end);
+ g2 = fliplr(g2)*(-1)+max(g);
+ filter = [ones(1,2241-45),g2,g(idxHalf:end)];
+ filter = [filter, zeros(1,length(currentData_spec)-length(filter),1)];
+ currentData_spec_mod = currentData_spec.*filter';
+% figure
+% plot(ppmvector,filter.*max(real(currentData_spec))); hold on
+% plot(ppmvector,real(currentData_spec_mod))
+
+% currentData_fid_mod = ifft(ifftshift(abs(currentData_spec_mod));
+ currentData_fid_mod = ifft(ifftshift(currentData_spec_mod));
+% currentData_fid_mod_trunc = currentData_fid_mod;
+% currentData_fid_mod_trunc(truncPoint:end) = 0;
+
+% figure
+% plot(ppmvector,real(fftshift(fft(currentDataFID))))
+% hold on
+% plot(ppmvector,real(fftshift(fft(currentData_fid_mod))))
+% plot(ppmvector,real(fftshift(fft(currentData_fid_mod_trunc))))
+% plot(ppmvector,real(fftshift(fft(currentDataFID-currentData_fid_mod_trunc))))
+%
+% figure
+% plot(ppmvector,real(fftshift(fft((currentDataFID./2)))))
+% hold on
+% plot(ppmvector,real(fftshift(fft(real(currentData_fid_mod)))))
+% plot(ppmvector,real(fftshift(fft(real(currentData_fid_mod_trunc)))))
+% plot(ppmvector,real(fftshift(fft(real(currentData_fid_mod)-real(currentData_fid_mod_trunc)))))
+% plot(ppmvector,real(fftshift(fft((currentDataFID./2)-real(currentData_fid_mod_trunc)))))
+% set(gca, 'XDir','reverse')
+% xlim([0.5 4.2]);
+%
+% figure
+% plot(ppmvector,real(fftshift(fft((currentDataFID./2)))))
+% hold on
+% plot(ppmvector,real(fftshift(fft(abs(currentData_fid_mod)))))
+% plot(ppmvector,real(fftshift(fft(abs(currentData_fid_mod_trunc)))))
+% plot(ppmvector,real(fftshift(fft(abs(currentData_fid_mod)-abs(currentData_fid_mod_trunc)))))
+% plot(ppmvector,real(fftshift(fft((currentDataFID./2)-abs(currentData_fid_mod_trunc)))))
+% set(gca, 'XDir','reverse')
+% xlim([0.5 4.2]);
+
+
+
+% val5 = currentData_spec(idx5);
+% valend = currentData_spec(idxend);
+% yval = linspace(val5,valend,(idxend-idx5));
+% currentData_spec_mod = [currentData_spec(1:idx5); yval'];
+% % currentData_spec_mod = currentData_spec_mod-valend;
+% currentData_fid_mod = ifft(ifftshift(currentData_spec_mod));
+ %flip tail
+ tail = flipud(currentData_fid_mod(truncPoint:end,1));
+ tail = [tail; zeros(4096-length(tail),1)];
+ summedFIDs = tail + currentData_fid_mod;
+ %truncate fid to remove tail (LCModel doesn't like the tail)
+ currentData_fid_mod_trunc = summedFIDs;
+ currentData_fid_mod_trunc(truncPoint:end) = 0;
+% currentData_fid_mod_trunc_Lenkinski = complex(real(currentData_fid_mod_trunc),zeros(size(currentData_fid_mod_trunc)));
+% currentData_fid_mod_Lenkinski = complex(real(currentData_fid_mod),zeros(size(currentData_fid_mod)));
+% %add tail of FID to FID, then truncate
+% tail = flipud(currentData_fid_mod_Lenkinski(1600:end,1));
+% tail = [tail; zeros(4096-length(tail),1)];
+% tail = complex(tail,zeros(size(currentData_fid_mod_Lenkinski)));
+% summedFIDs = tail + currentData_fid_mod_Lenkinski;
+% summedFIDsTrunc = summedFIDs;
+% summedFIDsTrunc(truncPoint:end) = 0;
+ Data_Mod(:,1,1,1,1,1,1,1,1,1,1,idxAv) = currentData_fid_mod_trunc;
+ end
+ a.Data{1}(:,1,1,1,1,1,1,1,1,1,1,:) = Data_Mod;
+
+
+ %average data block-wise
+ if ~exist('nBlocks', 'var')
+ a.Parameter.AverageSettings.nBlocks = 1;
+ else
+ a.Parameter.AverageSettings.nBlocks = nBlocks;
+ end
+
+ a = a.AverageData;
+end
-%search area (+-) in ppm
-if isInVivo
- a.Parameter.FreqAlignFreqDomainSettings.searchArea = 0.1;
-else
- a.Parameter.FreqAlignFreqDomainSettings.searchArea = 0.2;
+%% setup the settings for the FrequencyAlignFreqDomain
+if exist('modulus','var') && (modulus == 0)
+ a.Parameter.FreqAlignFreqDomainSettings.selectedCoil = 1; % check for max SNR coil
+ a.Parameter.FreqAlignFreqDomainSettings.selectedMix = 1;
+
+ %indices of Interest: given in ppm
+ % NAA peak
+ a.Parameter.FreqAlignFreqDomainSettings.peaksInPpm = 2.008;
+
+ %set zeroFillingParameter to get smooth approximations
+ a.Parameter.FreqAlignFreqDomainSettings.zeroFillFactor = 50;
+
+ %search area (+-) in ppm
+ if isInVivo
+ a.Parameter.FreqAlignFreqDomainSettings.searchArea = 0.1;
+ else
+ a.Parameter.FreqAlignFreqDomainSettings.searchArea = 0.2;
+ end
+ %spline filtering is not needed if the spectra are not too noisy.
+ %flag to do spline filtering
+ a.Parameter.FreqAlignFreqDomainSettings.doSplineSmoothing = false;
+ %spline filtering coefficient
+ a.Parameter.FreqAlignFreqDomainSettings.splineSmoothingCoeff = 0.01;
+ % do the actual Frequency Alignment
+ a = a.FrequencyAlignFreqDomain;
end
-%spline filtering is not needed if the spectra are not too noisy.
-%flag to do spline filtering
-a.Parameter.FreqAlignFreqDomainSettings.doSplineSmoothing = false;
-%spline filtering coefficient
-a.Parameter.FreqAlignFreqDomainSettings.splineSmoothingCoeff = 0.01;
-% do the actual Frequency Alignment
-a = a.FrequencyAlignFreqDomain;
%% water suppression
if isInVivo
- a.Parameter.HsvdSettings.bound = [-100 100];
+ a.Parameter.HsvdSettings.bound = [-80 80];
else
a.Parameter.HsvdSettings.bound = [-100 100];
end
@@ -171,12 +303,14 @@ if isMC
end
%truncate data
-if isInVivo
- trunc_ms = 150;
-% trunc_ms = 100; %MM
- truncPoint = floor( trunc_ms/(a.Parameter.Headers.DwellTimeSig_ns*1e-6));
- a = a.Truncate(truncPoint);
-end
+% if exist('modulus','var') && (modulus == 0)
+ if isInVivo
+ trunc_ms = 150;
+ % trunc_ms = 100; %MM
+ truncPoint = floor( trunc_ms/(a.Parameter.Headers.DwellTimeSig_ns*1e-6));
+ a = a.Truncate(truncPoint);
+ end
+% end
%% fMRS processing
@@ -426,26 +560,28 @@ end
%average on and off blocks
if nBlocks == 5 %size(a.Data{1},a.meas_dim)
paradigm(1) = -1;
-elseif nBlocks == 10 %take second 32 averages of each ON/OFF block
- paradigm = ones(1,2*nBlocks);
- for i = 1:2:2*nBlocks
- paradigm(i) = -1;
- if (i == 11) || (i == 19)
- paradigm(i) = 0;
- elseif (i == 7) || (i == 15)
- paradigm(i) = 1;
+elseif nBlocks == 10
+ if ~isempty(strfind(BOLDType,'64#1')) %take first 32 averages of each ON/OFF block
+ paradigm = ones(1,2*nBlocks);
+ for i = 1:2:2*nBlocks
+ paradigm(i) = -1;
+ if (i == 9) || (i == 17)
+ paradigm(i) = 0;
+ elseif (i == 5) || (i == 13)
+ paradigm(i) = 1;
+ end
+ end
+ else %take second 32 averages of each ON/OFF block
+ paradigm = ones(1,2*nBlocks);
+ for i = 1:2:2*nBlocks
+ paradigm(i) = -1;
+ if (i == 11) || (i == 19)
+ paradigm(i) = 0;
+ elseif (i == 7) || (i == 15)
+ paradigm(i) = 1;
+ end
end
end
-% elseif nBlocks == 10 %take first 32 averages of each ON/OFF block
-% paradigm = ones(1,2*nBlocks);
-% for i = 1:2:2*nBlocks
-% paradigm(i) = -1;
-% if (i == 9) || (i == 17)
-% paradigm(i) = 0;
-% elseif (i == 5) || (i == 13)
-% paradigm(i) = 1;
-% end
-% end
end
a.Parameter.fMRSAveSettings.paradigm = paradigm;
a = a.fMRSAverage; %now dim 1=ON, 2=OFF
@@ -454,14 +590,27 @@ data = squeeze(a.Data{1});
ppmVector = a.getPpmVector;
plot(ppmVector,real(fftshift(fft(data(:,1,1)))))
plot(ppmVector,real(fftshift(fft(data(:,1,2)))))
+plot(ppmVector,real(fftshift(fft(data(:,1,1)-data(:,1,2)))))
+if exist('modulus','var') && (modulus == 1)
+ plot(ppmVector,real(fftshift(fft(real(data(:,1,1)-data(:,1,2)).*2))))
+end
+% plot(ppmVector,real(fftshift(fft(abs(data(:,1,1)-data(:,1,2)).*2))))
set(gca, 'XDir','reverse')
-xlim([0.5 4.5]); legend('On','Off')
+if exist('modulus','var') && (modulus == 1)
+ xlim([0.5 4.5]); legend('On','Off','Residual')
+else
+ xlim([0.5 4.5]); legend('On','Off','Residual','Real Residual')
+end
if saveResults
if nBlocks == 5
fig2save_ONOFF = fullfile(currentFolder, [num2str(visualType) '_ONOFF_128.fig']);
elseif nBlocks == 10
- fig2save_ONOFF = fullfile(currentFolder, [num2str(visualType) '_ONOFF_64.fig']);
+ if ~isempty(strfind(BOLDType,'64#1'))
+ fig2save_ONOFF = fullfile(currentFolder, [num2str(visualType) '_ONOFF_64#1.fig']);
+ else
+ fig2save_ONOFF = fullfile(currentFolder, [num2str(visualType) '_ONOFF_64.fig']);
+ end
end
savefig(fig2save_ONOFF)
close all;
@@ -477,29 +626,57 @@ if saveResults
if nBlocks == 5
save([filename '_' num2str(visualType) 'ON_woBOLDCorr128.mat'], 'aON')
elseif nBlocks == 10
- save([filename '_' num2str(visualType) 'ON_woBOLDCorr64.mat'], 'aON')
+ if ~isempty(strfind(BOLDType,'64#1'))
+ data2save_ONOFF = [filename '_' num2str(visualType) 'ON_woBOLDCorr64#1.mat'];
+ else
+ data2save_ONOFF = [filename '_' num2str(visualType) 'ON_woBOLDCorr64.mat'];
+ end
+ save(data2save_ONOFF, 'aON')
end
%export to LCModel Stimulus ON
addSinglet0ppm = true;
+ if exist('modulus','var') && (modulus == 1)
+ aON = a;
+ aON.Data{1} = complex(real(data(:,:,:,:,:,:,:,:,:,:,:,1).*2),zeros(size(data(:,:,:,:,:,:,:,:,:,:,:,1))));
+ end
if nBlocks == 5
aON.ExportLcmRaw('',[filename '_' num2str(visualType) 'ON_woBOLDCorr128'], addSinglet0ppm);
elseif nBlocks == 10
- aON.ExportLcmRaw('',[filename '_' num2str(visualType) 'ON_woBOLDCorr64'], addSinglet0ppm);
+ if ~isempty(strfind(BOLDType,'64#1'))
+ aON.ExportLcmRaw('',[filename '_' num2str(visualType) 'ON_woBOLDCorr64#1'], addSinglet0ppm);
+ else
+ aON.ExportLcmRaw('',[filename '_' num2str(visualType) 'ON_woBOLDCorr64'], addSinglet0ppm);
+ end
end
+
%saving the file Stimulus OFF
aOFF = a;
aOFF.Data{1} = data(:,:,:,:,:,:,:,:,:,:,:,2);
if nBlocks == 5
save([filename '_' num2str(visualType) 'OFF_woBOLDCorr128.mat'], 'aOFF')
elseif nBlocks == 10
- save([filename '_' num2str(visualType) 'OFF_woBOLDCorr64.mat'], 'aOFF')
+ if ~isempty(strfind(BOLDType,'64#1'))
+ data2save_ONOFF = [filename '_' num2str(visualType) 'OFF_woBOLDCorr64#1.mat'];
+ else
+ data2save_ONOFF = [filename '_' num2str(visualType) 'OFF_woBOLDCorr64.mat'];
+ end
+ save(data2save_ONOFF, 'aOFF')
end
+
%export to LCModel Stimulus OFF
addSinglet0ppm = true;
+ if exist('modulus','var') && (modulus == 1)
+ aOFF = a;
+ aOFF.Data{1} = complex(real(data(:,:,:,:,:,:,:,:,:,:,:,2).*2),zeros(size(data(:,:,:,:,:,:,:,:,:,:,:,2))));
+ end
if nBlocks == 5
aOFF.ExportLcmRaw('',[filename '_' num2str(visualType) 'OFF_woBOLDCorr128'], addSinglet0ppm);
elseif nBlocks == 10
- aOFF.ExportLcmRaw('',[filename '_' num2str(visualType) 'OFF_woBOLDCorr64'], addSinglet0ppm);
+ if ~isempty(strfind(BOLDType,'64#1'))
+ aOFF.ExportLcmRaw('',[filename '_' num2str(visualType) 'OFF_woBOLDCorr64#1'], addSinglet0ppm);
+ else
+ aOFF.ExportLcmRaw('',[filename '_' num2str(visualType) 'OFF_woBOLDCorr64'], addSinglet0ppm);
+ end
end
end
@@ -518,14 +695,25 @@ ppmVector = a.getPpmVector;
plot(ppmVector,real(fftshift(fft(data(:,1,1)))))
plot(ppmVector,real(fftshift(fft(data(:,1,2)))))
plot(ppmVector,real(fftshift(fft(data(:,1,3)))))
+if exist('modulus','var') && (modulus == 1)
+ plot(ppmVector,real(fftshift(fft(real(data(:,1,3).*2)))))
+end
set(gca, 'XDir','reverse')
-xlim([0.5 4.5]); legend('On','Off','Residual')
+if exist('modulus','var') && (modulus == 1)
+ xlim([0.5 4.5]); legend('On','Off','Residual')
+else
+ xlim([0.5 4.5]); legend('On','Off','Residual','Real Residual')
+end
if saveResults
if nBlocks == 5
fig2save_ONOFF_BOLDCorr = fullfile(currentFolder, [num2str(visualType) '_ONOFF_BOLDCorr128.fig']);
elseif nBlocks == 10
- fig2save_ONOFF_BOLDCorr = fullfile(currentFolder, [num2str(visualType) '_ONOFF_BOLDCorr64.fig']);
+ if ~isempty(strfind(BOLDType,'64#1'))
+ fig2save_ONOFF_BOLDCorr = fullfile(currentFolder, [num2str(visualType) '_ONOFF_BOLDCorr64#1.fig']);
+ else
+ fig2save_ONOFF_BOLDCorr = fullfile(currentFolder, [num2str(visualType) '_ONOFF_BOLDCorr64.fig']);
+ end
end
savefig(fig2save_ONOFF_BOLDCorr)
close all;
@@ -540,15 +728,29 @@ if saveResults
if nBlocks == 5
save([filename '_' num2str(visualType) 'ON_withBOLDCorr128.mat'], 'aON')
elseif nBlocks == 10
- save([filename '_' num2str(visualType) 'ON_withBOLDCorr64.mat'], 'aON')
+ if ~isempty(strfind(BOLDType,'64#1'))
+ data2save_ONOFF = [filename '_' num2str(visualType) 'ON_withBOLDCorr64#1.mat'];
+ else
+ data2save_ONOFF = [filename '_' num2str(visualType) 'ON_withBOLDCorr64.mat'];
+ end
+ save(data2save_ONOFF, 'aON')
end
%export to LCModel Stimulus ON
addSinglet0ppm = true;
+ if exist('modulus','var') && (modulus == 1)
+ aON = a;
+ aON.Data{1} = complex(real(data(:,:,:,:,:,:,:,:,:,:,:,1).*2),zeros(size(data(:,:,:,:,:,:,:,:,:,:,:,1))));
+% aON.Data{1} = real(data(:,:,:,:,:,:,:,:,:,:,:,1).*2);
+ end
if nBlocks == 5
aON.ExportLcmRaw('',[filename '_' num2str(visualType) 'ON_withBOLDCorr128'], addSinglet0ppm);
elseif nBlocks == 10
- aON.ExportLcmRaw('',[filename '_' num2str(visualType) 'ON_withBOLDCorr64'], addSinglet0ppm);
+ if ~isempty(strfind(BOLDType,'64#1'))
+ aON.ExportLcmRaw('',[filename '_' num2str(visualType) 'ON_withBOLDCorr64#1'], addSinglet0ppm);
+ else
+ aON.ExportLcmRaw('',[filename '_' num2str(visualType) 'ON_withBOLDCorr64'], addSinglet0ppm);
+ end
end
%saving the file Stimulus OFF
@@ -557,15 +759,29 @@ if saveResults
if nBlocks == 5
save([filename '_' num2str(visualType) 'OFF_withBOLDCorr128.mat'], 'aOFF')
elseif nBlocks == 10
- save([filename '_' num2str(visualType) 'OFF_withBOLDCorr64.mat'], 'aOFF')
- end
+ if ~isempty(strfind(BOLDType,'64#1'))
+ data2save_ONOFF = [filename '_' num2str(visualType) 'OFF_withBOLDCorr64#1.mat'];
+ else
+ data2save_ONOFF = [filename '_' num2str(visualType) 'OFF_withBOLDCorr64.mat'];
+ end
+ save(data2save_ONOFF, 'aOFF')
+ end
%export to LCModel Stimulus OFF
addSinglet0ppm = true;
+ if exist('modulus','var') && (modulus == 1)
+ aOFF = a;
+ aOFF.Data{1} = complex(real(data(:,:,:,:,:,:,:,:,:,:,:,2).*2),zeros(size(data(:,:,:,:,:,:,:,:,:,:,:,2))));
+% aOFF.Data{1} = real(data(:,:,:,:,:,:,:,:,:,:,:,1).*2);
+ end
if nBlocks == 5
aOFF.ExportLcmRaw('',[filename '_' num2str(visualType) 'OFF_withBOLDCorr128'], addSinglet0ppm);
elseif nBlocks == 10
- aOFF.ExportLcmRaw('',[filename '_' num2str(visualType) 'OFF_withBOLDCorr64'], addSinglet0ppm);
+ if ~isempty(strfind(BOLDType,'64#1'))
+ aOFF.ExportLcmRaw('',[filename '_' num2str(visualType) 'OFF_withBOLDCorr64#1'], addSinglet0ppm);
+ else
+ aOFF.ExportLcmRaw('',[filename '_' num2str(visualType) 'OFF_withBOLDCorr64'], addSinglet0ppm);
+ end
end
end
diff --git a/fMRS_processing/splinefit.m b/fMRS_processing/splinefit.m
new file mode 100644
index 0000000000000000000000000000000000000000..c448a516e773f105a4c7f6f278e5ae84ee2e5820
--- /dev/null
+++ b/fMRS_processing/splinefit.m
@@ -0,0 +1,526 @@
+function pp = splinefit(varargin)
+%SPLINEFIT Fit a spline to noisy data.
+% PP = SPLINEFIT(X,Y,BREAKS) fits a piecewise cubic spline with breaks
+% (knots) BREAKS to the noisy data (X,Y). X is a vector and Y is a vector
+% or an ND array. If Y is an ND array, then X(j) and Y(:,...,:,j) are
+% matched. Use PPVAL to evaluate PP.
+%
+% PP = SPLINEFIT(X,Y,P) where P is a positive integer interpolates the
+% breaks linearly from the sorted locations of X. P is the number of
+% spline pieces and P+1 is the number of breaks.
+%
+% OPTIONAL INPUT
+% Argument places 4 to 8 are reserved for optional input.
+% These optional arguments can be given in any order:
+%
+% PP = SPLINEFIT(...,'p') applies periodic boundary conditions to
+% the spline. The period length is MAX(BREAKS)-MIN(BREAKS).
+%
+% PP = SPLINEFIT(...,'r') uses robust fitting to reduce the influence
+% from outlying data points. Three iterations of weighted least squares
+% are performed. Weights are computed from previous residuals.
+%
+% PP = SPLINEFIT(...,BETA), where 0 < BETA < 1, sets the robust fitting
+% parameter BETA and activates robust fitting ('r' can be omitted).
+% Default is BETA = 1/2. BETA close to 0 gives all data equal weighting.
+% Increase BETA to reduce the influence from outlying data. BETA close
+% to 1 may cause instability or rank deficiency.
+%
+% PP = SPLINEFIT(...,N) sets the spline order to N. Default is a cubic
+% spline with order N = 4. A spline with P pieces has P+N-1 degrees of
+% freedom. With periodic boundary conditions the degrees of freedom are
+% reduced to P.
+%
+% PP = SPLINEFIT(...,CON) applies linear constraints to the spline.
+% CON is a structure with fields 'xc', 'yc' and 'cc':
+% 'xc', x-locations (vector)
+% 'yc', y-values (vector or ND array)
+% 'cc', coefficients (matrix).
+%
+% Constraints are linear combinations of derivatives of order 0 to N-2
+% according to
+%
+% cc(1,j)*y(x) + cc(2,j)*y'(x) + ... = yc(:,...,:,j), x = xc(j).
+%
+% The maximum number of rows for 'cc' is N-1. If omitted or empty 'cc'
+% defaults to a single row of ones. Default for 'yc' is a zero array.
+%
+% EXAMPLES
+%
+% % Noisy data
+% x = linspace(0,2*pi,100);
+% y = sin(x) + 0.1*randn(size(x));
+% % Breaks
+% breaks = [0:5,2*pi];
+%
+% % Fit a spline of order 5
+% pp = splinefit(x,y,breaks,5);
+%
+% % Fit a spline of order 3 with periodic boundary conditions
+% pp = splinefit(x,y,breaks,3,'p');
+%
+% % Constraints: y(0) = 0, y'(0) = 1 and y(3) + y"(3) = 0
+% xc = [0 0 3];
+% yc = [0 1 0];
+% cc = [1 0 1; 0 1 0; 0 0 1];
+% con = struct('xc',xc,'yc',yc,'cc',cc);
+%
+% % Fit a cubic spline with 8 pieces and constraints
+% pp = splinefit(x,y,8,con);
+%
+% % Fit a spline of order 6 with constraints and periodicity
+% pp = splinefit(x,y,breaks,con,6,'p');
+%
+% See also SPLINE, PPVAL, PPDIFF, PPINT
+% Author: Jonas Lundgren <splinefit@gmail.com> 2010
+% 2009-05-06 Original SPLINEFIT.
+% 2010-06-23 New version of SPLINEFIT based on B-splines.
+% 2010-09-01 Robust fitting scheme added.
+% 2010-09-01 Support for data containing NaNs.
+% 2011-07-01 Robust fitting parameter added.
+% Check number of arguments
+narginchk(3,8);
+% Check arguments
+[x,y,dim,breaks,n,periodic,beta,constr] = arguments(varargin{:});
+% Evaluate B-splines
+base = splinebase(breaks,n);
+pieces = base.pieces;
+A = ppval(base,x);
+% Bin data
+[~,ibin] = histc(x,[-inf,breaks(2:end-1),inf]);
+% Sparse system matrix
+mx = numel(x);
+ii = [ibin; ones(n-1,mx)];
+ii = cumsum(ii,1);
+jj = repmat(1:mx,n,1);
+if periodic
+ ii = mod(ii-1,pieces) + 1;
+ A = sparse(ii,jj,A,pieces,mx);
+else
+ A = sparse(ii,jj,A,pieces+n-1,mx);
+end
+% Don't use the sparse solver for small problems
+if pieces < 20*n/log(1.7*n)
+ A = full(A);
+end
+% Solve
+if isempty(constr)
+ % Solve Min norm(u*A-y)
+ u = lsqsolve(A,y,beta);
+else
+ % Evaluate constraints
+ B = evalcon(base,constr,periodic);
+ % Solve constraints
+ [Z,u0] = solvecon(B,constr);
+ % Solve Min norm(u*A-y), subject to u*B = yc
+ y = y - u0*A;
+ A = Z*A;
+ v = lsqsolve(A,y,beta);
+ u = u0 + v*Z;
+end
+% Periodic expansion of solution
+if periodic
+ jj = mod(0:pieces+n-2,pieces) + 1;
+ u = u(:,jj);
+end
+% Compute polynomial coefficients
+ii = [repmat(1:pieces,1,n); ones(n-1,n*pieces)];
+ii = cumsum(ii,1);
+jj = repmat(1:n*pieces,n,1);
+C = sparse(ii,jj,base.coefs,pieces+n-1,n*pieces);
+coefs = u*C;
+coefs = reshape(coefs,[],n);
+% Make piecewise polynomial
+pp = mkpp(breaks,coefs,dim);
+%--------------------------------------------------------------------------
+function [x,y,dim,breaks,n,periodic,beta,constr] = arguments(varargin)
+%ARGUMENTS Lengthy input checking
+% x Noisy data x-locations (1 x mx)
+% y Noisy data y-values (prod(dim) x mx)
+% dim Leading dimensions of y
+% breaks Breaks (1 x (pieces+1))
+% n Spline order
+% periodic True if periodic boundary conditions
+% beta Robust fitting parameter, no robust fitting if beta = 0
+% constr Constraint structure
+% constr.xc x-locations (1 x nx)
+% constr.yc y-values (prod(dim) x nx)
+% constr.cc Coefficients (?? x nx)
+% Reshape x-data
+x = varargin{1};
+mx = numel(x);
+x = reshape(x,1,mx);
+% Remove trailing singleton dimensions from y
+y = varargin{2};
+dim = size(y);
+while numel(dim) > 1 && dim(end) == 1
+ dim(end) = [];
+end
+my = dim(end);
+% Leading dimensions of y
+if numel(dim) > 1
+ dim(end) = [];
+else
+ dim = 1;
+end
+% Reshape y-data
+pdim = prod(dim);
+y = reshape(y,pdim,my);
+% Check data size
+if mx ~= my
+ mess = 'Last dimension of array y must equal length of vector x.';
+ error('arguments:datasize',mess)
+end
+% Treat NaNs in x-data
+inan = find(isnan(x));
+if ~isempty(inan)
+ x(inan) = [];
+ y(:,inan) = [];
+ mess = 'All data points with NaN as x-location will be ignored.';
+ warning('arguments:nanx',mess)
+end
+% Treat NaNs in y-data
+inan = find(any(isnan(y),1));
+if ~isempty(inan)
+ x(inan) = [];
+ y(:,inan) = [];
+ mess = 'All data points with NaN in their y-value will be ignored.';
+ warning('arguments:nany',mess)
+end
+% Check number of data points
+mx = numel(x);
+if mx == 0
+ error('arguments:nodata','There must be at least one data point.')
+end
+% Sort data
+if any(diff(x) < 0)
+ [x,isort] = sort(x);
+ y = y(:,isort);
+end
+% Breaks
+if isscalar(varargin{3})
+ % Number of pieces
+ p = varargin{3};
+ if ~isreal(p) || ~isfinite(p) || p < 1 || fix(p) < p
+ mess = 'Argument #3 must be a vector or a positive integer.';
+ error('arguments:breaks1',mess)
+ end
+ if x(1) < x(end)
+ % Interpolate breaks linearly from x-data
+ dx = diff(x);
+ ibreaks = linspace(1,mx,p+1);
+ [~,ibin] = histc(ibreaks,[0,2:mx-1,mx+1]);
+ breaks = x(ibin) + dx(ibin).*(ibreaks-ibin);
+ else
+ breaks = x(1) + linspace(0,1,p+1);
+ end
+else
+ % Vector of breaks
+ breaks = reshape(varargin{3},1,[]);
+ if isempty(breaks) || min(breaks) == max(breaks)
+ mess = 'At least two unique breaks are required.';
+ error('arguments:breaks2',mess);
+ end
+end
+% Unique breaks
+if any(diff(breaks) <= 0)
+ breaks = unique(breaks);
+end
+% Optional input defaults
+n = 4; % Cubic splines
+periodic = false; % No periodic boundaries
+robust = false; % No robust fitting scheme
+beta = 0.5; % Robust fitting parameter
+constr = []; % No constraints
+% Loop over optional arguments
+for k = 4:nargin
+ a = varargin{k};
+ if ischar(a) && isscalar(a) && lower(a) == 'p'
+ % Periodic conditions
+ periodic = true;
+ elseif ischar(a) && isscalar(a) && lower(a) == 'r'
+ % Robust fitting scheme
+ robust = true;
+ elseif isreal(a) && isscalar(a) && isfinite(a) && a > 0 && a < 1
+ % Robust fitting parameter
+ beta = a;
+ robust = true;
+ elseif isreal(a) && isscalar(a) && isfinite(a) && a > 0 && fix(a) == a
+ % Spline order
+ n = a;
+ elseif isstruct(a) && isscalar(a)
+ % Constraint structure
+ constr = a;
+ else
+ error('arguments:nonsense','Failed to interpret argument #%d.',k)
+ end
+end
+% No robust fitting
+if ~robust
+ beta = 0;
+end
+% Check exterior data
+h = diff(breaks);
+xlim1 = breaks(1) - 0.01*h(1);
+xlim2 = breaks(end) + 0.01*h(end);
+if x(1) < xlim1 || x(end) > xlim2
+ if periodic
+ % Move data inside domain
+ P = breaks(end) - breaks(1);
+ x = mod(x-breaks(1),P) + breaks(1);
+ % Sort
+ [x,isort] = sort(x);
+ y = y(:,isort);
+ else
+ mess = 'Some data points are outside the spline domain.';
+ warning('arguments:exteriordata',mess)
+ end
+end
+% Return
+if isempty(constr)
+ return
+end
+% Unpack constraints
+xc = [];
+yc = [];
+cc = [];
+names = fieldnames(constr);
+for k = 1:numel(names)
+ switch names{k}
+ case {'xc'}
+ xc = constr.xc;
+ case {'yc'}
+ yc = constr.yc;
+ case {'cc'}
+ cc = constr.cc;
+ otherwise
+ mess = 'Unknown field ''%s'' in constraint structure.';
+ warning('arguments:unknownfield',mess,names{k})
+ end
+end
+% Check xc
+if isempty(xc)
+ mess = 'Constraints contains no x-locations.';
+ error('arguments:emptyxc',mess)
+else
+ nx = numel(xc);
+ xc = reshape(xc,1,nx);
+end
+% Check yc
+if isempty(yc)
+ % Zero array
+ yc = zeros(pdim,nx);
+elseif numel(yc) == 1
+ % Constant array
+ yc = zeros(pdim,nx) + yc;
+elseif numel(yc) ~= pdim*nx
+ % Malformed array
+ error('arguments:ycsize','Cannot reshape yc to size %dx%d.',pdim,nx)
+else
+ % Reshape array
+ yc = reshape(yc,pdim,nx);
+end
+% Check cc
+if isempty(cc)
+ cc = ones(size(xc));
+elseif numel(size(cc)) ~= 2
+ error('arguments:ccsize1','Constraint coefficients cc must be 2D.')
+elseif size(cc,2) ~= nx
+ mess = 'Last dimension of cc must equal length of xc.';
+ error('arguments:ccsize2',mess)
+end
+% Check high order derivatives
+if size(cc,1) >= n
+ if any(any(cc(n:end,:)))
+ mess = 'Constraints involve derivatives of order %d or larger.';
+ error('arguments:difforder',mess,n-1)
+ end
+ cc = cc(1:n-1,:);
+end
+% Check exterior constraints
+if min(xc) < xlim1 || max(xc) > xlim2
+ if periodic
+ % Move constraints inside domain
+ P = breaks(end) - breaks(1);
+ xc = mod(xc-breaks(1),P) + breaks(1);
+ else
+ mess = 'Some constraints are outside the spline domain.';
+ warning('arguments:exteriorconstr',mess)
+ end
+end
+% Pack constraints
+constr = struct('xc',xc,'yc',yc,'cc',cc);
+%--------------------------------------------------------------------------
+function pp = splinebase(breaks,n)
+%SPLINEBASE Generate B-spline base PP of order N for breaks BREAKS
+breaks = breaks(:); % Breaks
+breaks0 = breaks'; % Initial breaks
+h = diff(breaks); % Spacing
+pieces = numel(h); % Number of pieces
+deg = n - 1; % Polynomial degree
+% Extend breaks periodically
+if deg > 0
+ if deg <= pieces
+ hcopy = h;
+ else
+ hcopy = repmat(h,ceil(deg/pieces),1);
+ end
+ % to the left
+ hl = hcopy(end:-1:end-deg+1);
+ bl = breaks(1) - cumsum(hl);
+ % and to the right
+ hr = hcopy(1:deg);
+ br = breaks(end) + cumsum(hr);
+ % Add breaks
+ breaks = [bl(deg:-1:1); breaks; br];
+ h = diff(breaks);
+ pieces = numel(h);
+end
+% Initiate polynomial coefficients
+coefs = zeros(n*pieces,n);
+coefs(1:n:end,1) = 1;
+% Expand h
+ii = [1:pieces; ones(deg,pieces)];
+ii = cumsum(ii,1);
+ii = min(ii,pieces);
+H = h(ii(:));
+% Recursive generation of B-splines
+for k = 2:n
+ % Antiderivatives of splines
+ for j = 1:k-1
+ coefs(:,j) = coefs(:,j).*H/(k-j);
+ end
+ Q = sum(coefs,2);
+ Q = reshape(Q,n,pieces);
+ Q = cumsum(Q,1);
+ c0 = [zeros(1,pieces); Q(1:deg,:)];
+ coefs(:,k) = c0(:);
+ % Normalize antiderivatives by max value
+ fmax = repmat(Q(n,:),n,1);
+ fmax = fmax(:);
+ for j = 1:k
+ coefs(:,j) = coefs(:,j)./fmax;
+ end
+ % Diff of adjacent antiderivatives
+ coefs(1:end-deg,1:k) = coefs(1:end-deg,1:k) - coefs(n:end,1:k);
+ coefs(1:n:end,k) = 0;
+end
+% Scale coefficients
+scale = ones(size(H));
+for k = 1:n-1
+ scale = scale./H;
+ coefs(:,n-k) = scale.*coefs(:,n-k);
+end
+% Reduce number of pieces
+pieces = pieces - 2*deg;
+% Sort coefficients by interval number
+ii = [n*(1:pieces); deg*ones(deg,pieces)];
+ii = cumsum(ii,1);
+coefs = coefs(ii(:),:);
+% Make piecewise polynomial
+pp = mkpp(breaks0,coefs,n);
+%--------------------------------------------------------------------------
+function B = evalcon(base,constr,periodic)
+%EVALCON Evaluate linear constraints
+% Unpack structures
+breaks = base.breaks;
+pieces = base.pieces;
+n = base.order;
+xc = constr.xc;
+cc = constr.cc;
+% Bin data
+[~,ibin] = histc(xc,[-inf,breaks(2:end-1),inf]);
+% Evaluate constraints
+nx = numel(xc);
+B0 = zeros(n,nx);
+for k = 1:size(cc,1)
+ if any(cc(k,:))
+ B0 = B0 + repmat(cc(k,:),n,1).*ppval(base,xc);
+ end
+ % Differentiate base
+ coefs = base.coefs(:,1:n-k);
+ for j = 1:n-k-1
+ coefs(:,j) = (n-k-j+1)*coefs(:,j);
+ end
+ base.coefs = coefs;
+ base.order = n-k;
+end
+% Sparse output
+ii = [ibin; ones(n-1,nx)];
+ii = cumsum(ii,1);
+jj = repmat(1:nx,n,1);
+if periodic
+ ii = mod(ii-1,pieces) + 1;
+ B = sparse(ii,jj,B0,pieces,nx);
+else
+ B = sparse(ii,jj,B0,pieces+n-1,nx);
+end
+%--------------------------------------------------------------------------
+function [Z,u0] = solvecon(B,constr)
+%SOLVECON Find a particular solution u0 and null space Z (Z*B = 0)
+% for constraint equation u*B = yc.
+yc = constr.yc;
+tol = 1000*eps;
+% Remove blank rows
+ii = any(B,2);
+B2 = full(B(ii,:));
+% Null space of B2
+if isempty(B2)
+ Z2 = [];
+else
+ % QR decomposition with column permutation
+ [Q,R,~] = qr(B2);
+ R = abs(R);
+ jj = all(R < R(1)*tol, 2);
+ Z2 = Q(:,jj)';
+end
+% Sizes
+[m,ncon] = size(B);
+m2 = size(B2,1);
+nz = size(Z2,1);
+% Sparse null space of B
+Z = sparse(nz+1:nz+m-m2,find(~ii),1,nz+m-m2,m);
+Z(1:nz,ii) = Z2;
+% Warning rank deficient
+if nz + ncon > m2
+ mess = 'Rank deficient constraints, rank = %d.';
+ warning('solvecon:deficient',mess,m2-nz);
+end
+% Particular solution
+u0 = zeros(size(yc,1),m);
+if any(yc(:))
+ % Non-homogeneous case
+ u0(:,ii) = yc/B2;
+ % Check solution
+ if norm(u0*B - yc,'fro') > norm(yc,'fro')*tol
+ mess = 'Inconsistent constraints. No solution within tolerance.';
+ error('solvecon:inconsistent',mess)
+ end
+end
+%--------------------------------------------------------------------------
+function u = lsqsolve(A,y,beta)
+%LSQSOLVE Solve Min norm(u*A-y)
+% Avoid sparse-complex limitations
+if issparse(A) && ~isreal(y)
+ A = full(A);
+end
+% Solution
+u = y/A;
+% Robust fitting
+if beta > 0
+ [m,n] = size(y);
+ alpha = 0.5*beta/(1-beta)/m;
+ for k = 1:3
+ % Residual
+ r = u*A - y;
+ rr = r.*conj(r);
+ rrmean = sum(rr,2)/n;
+ rrmean(~rrmean) = 1;
+ rrhat = (alpha./rrmean)'*rr;
+ % Weights
+ w = exp(-rrhat);
+ spw = spdiags(w',0,n,n);
+ % Solve weighted problem
+ u = (y*spw)/(A*spw);
+ end
+end
\ No newline at end of file
diff --git a/fMRS_processing/subtractBaseline_GradEcho.m b/fMRS_processing/subtractBaseline_GradEcho.m
new file mode 100644
index 0000000000000000000000000000000000000000..4dae3a69916292ac942b8d62b88e5dfe695c2194
--- /dev/null
+++ b/fMRS_processing/subtractBaseline_GradEcho.m
@@ -0,0 +1,164 @@
+function subtractBaseline_GradEcho(currentDataPath,subjectsLCModelOutputPath,currentLCModelFit,currentLCModelFitWithoutMetabolite, Met);
+
+% currentLCModelMatFit = strrep(strcat(currentLCModelFit, '.mat'),'#','_');
+%
+% indexStart = strfind(currentLCModelFitWithoutMetabolite,'_woBaseline');
+% if ~isempty(indexStart)
+% currentLCModelOutputFit = currentLCModelFitWithoutMetabolite(1:indexStart-1);
+% currentLCModelCoordFit = strcat(currentLCModelOutputFit, '.coord');
+% else
+% currentLCModelCoordFit = strcat(currentLCModelFitWithoutMetabolite, '.coord');
+% end
+
+% if ~isempty(strfind(currentLCModelFitWithoutMetabolite, '_v2'))
+ currentRawOutputFile = 'D:\PAPER\1H_fMRS\Data\Output\2020-02-28_1706_fMRS\adiff.coord';
+% else
+% currentRawOutputFile = strcat(currentLCModelFit, '_woBaseline');
+% end
+
+% if Met == 'Lac'
+% Met = 1.314;
+% elseif Met == 'NAA'
+ Met = 3.911;
+% else
+% error('Met needs to be Lac or NAA');
+% end
+% metabolitesToSubtract = {'Glu'};
+doNullingOfDownField = false;
+% reconstructedSpectra = strcat(currentDataPath,currentLCModelMatFit);
+% reconstructedSpectraWithoutMetabolite = strcat(currentDataPath, currentRawOutputFile);
+reconstructedSpectra = 'D:\PAPER\1H_fMRS\Data\2020-02-28_1706_fMRS\adiff.mat';
+reconstructedSpectraWithoutMetabolite = strcat('D:\PAPER\1H_fMRS\Data\2020-02-28_1706_fMRS\', currentRawOutputFile);
+
+phase90 = -90 * pi/180;
+zeroFillFactor = 2;
+
+%plot configs
+FontSize = 14;
+LineWidth = 1.5;
+
+% load(reconstructedSpectra, 'a');
+a = load(reconstructedSpectra);
+fields = fieldnames(a);
+a = a.(fields{1});
+subjectsLCModelOutputPath = 'D:\PAPER\1H_fMRS\Data\Output\2020-02-28_1706_fMRS\';
+currentLCModelCoordFit = 'adiff.coord';
+[nOfPoints, ppmVector, phasedData, fitData, baselineData, residualData, ...
+ metaboliteNames, metaboliteData, tableConcentrations, ppmStart, ppmEnd, ...
+ scanFrequency, frequencyShift, ppmGap] = ...
+ extractFits(subjectsLCModelOutputPath,currentLCModelCoordFit);
+
+% align the two spectra
+fid = a.Data{1};
+spectrum = fftshift(fft(fid(:,1,1,1,1,1,1,1,1,1,1,1),size(fid,1)*zeroFillFactor));
+ppmSpectrum = a.getPpmVector(zeroFillFactor);
+
+[~, maxPpmValueSpectrum] = getMaxPeak(ppmSpectrum', real(spectrum), Met, 0.1);
+[~, maxPpmValueLCModel] = getMaxPeak(ppmVector, phasedData, Met, 0.1);
+
+frequencyShiftDiff = maxPpmValueSpectrum - maxPpmValueLCModel;
+fMRSData_withBaseline = a.ApplyFrequencyShift(frequencyShiftDiff);
+%get the aligned reconstructed spectra
+fid = fMRSData_withBaseline.Data{1};
+spectrum = fftshift(fft(fid(:,1,1,1,1,1,1,1,1,1,1,1),size(fid,1)*zeroFillFactor));
+% max of the reconstructed spectrum
+if isempty(ppmGap)
+ ppmSpectrumMask = (ppmSpectrum < ppmStart) & (ppmSpectrum > ppmEnd);
+ [~,idx] = find(ppmSpectrumMask == 1);
+ ppmSpectrumMask(idx(1)-1) = 1;
+ ppmSpectrumMask(idx(end)+1) = 1;
+ ppmMetaboliteMask = (ppmVector < ppmStart) & (ppmVector > ppmEnd);
+else
+ ppmSpectrumMask = (ppmSpectrum < ppmGap) & (ppmSpectrum > ppmEnd);
+ ppmMetaboliteMask = (ppmVector < ppmGap) & (ppmVector > ppmEnd);
+end
+maxSpectrum = max(real(spectrum(ppmSpectrumMask))); %spectrum between 0.6 and 4.1 ppm
+% max of the LCModel fit
+maxPhasedData = max(phasedData);
+% calculate scaling
+scalingFactor = maxSpectrum/maxPhasedData;
+oldSpectrum = spectrum;
+% for indexMetabolite = 1:length(metabolitesToSubtract)
+% metaboliteToSubtract = metabolitesToSubtract{indexMetabolite};
+% indexMetaboliteToSubtract = find(strcmp(metaboliteNames,metaboliteToSubtract));
+% if isempty(indexMetaboliteToSubtract)
+% error('Metabolite %s not found in fit',metaboliteToSubtract);
+% end
+
+% spectrumToSubtract = metaboliteData{1}.metaboliteSpectrum(ppmMetaboliteMask);
+ spectrumToSubtract = metaboliteData{1}.metaboliteSpectrum;
+% spectrumToSubtract = baselineData;
+ spectrumToSubtract(1) = 0;
+ %eliminate first point, which is anyway an error; Also scale it
+ spectrumToSubtractFlipped = flipud(spectrumToSubtract).*scalingFactor;
+
+ spectrumToSubtractReal = zeros(size(oldSpectrum));
+ spectrumToSubtractReal(ppmSpectrumMask) = spectrumToSubtractReal(ppmSpectrumMask) + spectrumToSubtractFlipped;
+ fidToSubtract = ifft(ifftshift(complex(spectrumToSubtractReal)));
+ fidToSubtract(length(fidToSubtract)/2:end)=0;
+ spectrumToSubtractPhased90 = fftshift(fft(fidToSubtract .* exp(1i*phase90))).*2; %multiply by 2 to account for nulling the half of the FID
+ spectrumToSubtractImag = real(spectrumToSubtractPhased90);
+ newSpectrumReal = real(oldSpectrum) - spectrumToSubtractReal;
+ newSpectrumImag = imag(oldSpectrum) - spectrumToSubtractImag;
+ gradmodSpectrum = complex(spectrumToSubtractReal,spectrumToSubtractImag);
+ gradmodSpectrumFID = ifft(ifftshift(gradmodSpectrum));
+ newSpectrum = complex(newSpectrumReal, newSpectrumImag);
+
+ if (doNullingOfDownField)
+ newSpectrum(length(newSpectrum)/2:end) = 0;
+ end
+
+ oldSpectrum = newSpectrum;
+% end
+
+figure
+% plot(ppmVector, phasedData.*scalingFactor)
+% hold on
+plot(ppmSpectrum, real(spectrum),'r')
+hold on
+plot(ppmSpectrum, real(newSpectrum),'k')
+set(gca,'xDir','reverse')
+xlim([3.5 5.9])
+title(sprintf('Difference spectrum with and w/o grad mod'))
+legend('Before subtraction','After subtration')
+
+% fig2save_Conc = fullfile(currentDataPath, strcat(strrep(currentRawOutputFile,'#','_'), '.fig'));
+% savefig(fig2save_Conc)
+
+close all
+
+% figure(101)
+% hold on
+% p1 = plot(ppmSpectrum, real(spectrum), 'r');
+% hold on
+% p2 = plot(ppmSpectrum, real(newSpectrum),'k');
+% xlim([0 4.2])
+% xlabel('[ppm]');
+% % ylim([(indexTE+1) * offsetPlot-offsetPlot*0.5 5e-5]);
+% set(gca,'xDir','reverse')
+% set(gca,'ytick',[]);
+% set(gca,'fontsize',FontSize);
+% set(p1,'LineWidth',LineWidth);
+% set(p2,'LineWidth',LineWidth);
+% title('Macromolecular baseline with and w/o the Cr peak')
+
+fMRSData_woGradMod = fMRSData_withBaseline;
+newFid = ifft(ifftshift(newSpectrum));
+fMRSData_woGradMod.Data{1} = newFid;
+% trunc_ms = 250;
+% truncPoint = floor( trunc_ms/(fMRSData_woBaseline.Parameter.Headers.DwellTimeSig_ns*1e-6));
+% fMRSData_woBaseline = fMRSData_woBaseline.Truncate(truncPoint);
+
+ExpName = fullfile(currentDataPath, strcat(currentRawOutputFile, '.fig'));
+save(strrep(reconstructedSpectraWithoutMetabolite,'#','_'),'fMRSData_woBaseline');
+% fMRSData_woBaseline.ExportLcmRaw(currentDataPath, strrep(currentRawOutputFile,'#','_'), 'Yes', 'Yes');
+end
+
+function [maxValue, maxPpmValue] = getMaxPeak(ppmVector, spectrum, peakPpm, searchAreaPpm)
+minPpm = peakPpm - searchAreaPpm;
+maxPpm = peakPpm + searchAreaPpm;
+ppmMask = (ppmVector > minPpm) & (ppmVector < maxPpm);
+tempSpectrum = spectrum .* ppmMask;
+[maxValue, maxIndex] = max(real(tempSpectrum));
+maxPpmValue = ppmVector(maxIndex);
+end
diff --git a/fMRS_processing/subtractBaseline_fMRS.m b/fMRS_processing/subtractBaseline_fMRS.m
index 4d60e2d47151a1d1ade5c15b01c834a48f1a79fe..890b7f921fda0404f4f913d85d408daf2e8cbbb2 100644
--- a/fMRS_processing/subtractBaseline_fMRS.m
+++ b/fMRS_processing/subtractBaseline_fMRS.m
@@ -25,7 +25,11 @@ else
end
% metabolitesToSubtract = {'Glu'};
doNullingOfDownField = false;
-reconstructedSpectra = strcat(currentDataPath,currentLCModelMatFit);
+if ~isempty(strfind(currentLCModelFit,'#'))
+ reconstructedSpectra = strrep(strcat(currentDataPath,strrep(currentLCModelMatFit,'_1','#1')),'_v2','');
+else
+ reconstructedSpectra = strrep(strcat(currentDataPath,currentLCModelMatFit),'_v2','');
+end
reconstructedSpectraWithoutMetabolite = strcat(currentDataPath, currentRawOutputFile);
phase90 = -90 * pi/180;
@@ -62,7 +66,7 @@ if isempty(ppmGap)
ppmSpectrumMask = (ppmSpectrum < ppmStart) & (ppmSpectrum > ppmEnd);
[~,idx] = find(ppmSpectrumMask == 1);
ppmSpectrumMask(idx(1)-1) = 1;
- ppmSpectrumMask(idx(end)+1) = 1;
+% ppmSpectrumMask(idx(end)+1) = 1;
ppmMetaboliteMask = (ppmVector < ppmStart) & (ppmVector > ppmEnd);
else
ppmSpectrumMask = (ppmSpectrum < ppmGap) & (ppmSpectrum > ppmEnd);
diff --git a/fMRS_processing/subtractGradientModulation.m b/fMRS_processing/subtractGradientModulation.m
new file mode 100644
index 0000000000000000000000000000000000000000..85b0e65810eeb1d2847698e86d6ff946d3583855
--- /dev/null
+++ b/fMRS_processing/subtractGradientModulation.m
@@ -0,0 +1,205 @@
+function subtractGradientModulation(a)
+% a needs difference spectrum in mix 1, and water spectrum in mix 2
+
+%%
+%%%%%%
+% Optimization protocol to fit modulation and subtract it from spectrum.
+% Works bad for summed spectra, doesn't work at all for individual spectra
+% since SNR is too low, modulation peak cannot be fitted
+%%%%%%
+%%
+
+a.Parameter.FreqAlignFreqDomainSettings.peaksInPpm = 4.66;
+a.Parameter.FreqAlignFreqDomainSettings.selectedMix = 2;
+a = a.FrequencyAlignFreqDomain();
+
+fid = a.Data{1}(:,1,1,1,1,1,1,1,1,1);
+ppmVector = a.getPpmVector();
+%ppm range where to minimize (DF grad mod peak)
+% ppmRange = [3.7 4.3];
+ppmRange = [5.1 5.7];
+ppmRangeIndices = (ppmVector < ppmRange(2)) & (ppmVector > ppmRange(1));
+%ppm range where to calculate baseline offset from
+ppmRangeBaseline = [9.0 13];
+ppmRangeIndicesBaseline = (ppmVector < ppmRangeBaseline(2)) & (ppmVector > ppmRangeBaseline(1));
+%ppm range for linear smoothness correction
+ppmRangeSmoothness1 = [5.4 5.45];
+ppmRangeIndicesSmoothness1 = (ppmVector < ppmRangeSmoothness1(2)) & (ppmVector > ppmRangeSmoothness1(1));
+ppmRangeSmoothness2 = [5.375 5.475];
+ppmRangeIndicesSmoothness2 = (ppmVector < ppmRangeSmoothness2(2)) & (ppmVector > ppmRangeSmoothness2(1));
+ppmRangeIndicesSmoothness2 = ppmRangeIndicesSmoothness2 & (~ppmRangeIndicesSmoothness1);
+
+time = (0:length(fid) - 1)' * (1/a.Parameter.Headers.Bandwidth_Hz);
+phase0 = 0; %phase of grad mod start value
+conc = 10; %grad mod concentration start value
+shift0 = 0; %freq shift of grad mod start value
+conc2 = 1e5; %water concentration start value
+
+%create peak to fit gradient modulation downfield
+scanFrequency = a.Parameter.Headers.ScanFrequency;
+% f = (3.99-4.66) *scanFrequency*1E-6; %Hz
+f = (5.42-4.66) *scanFrequency*1E-6; %Hz
+f_water = (4.7-4.66) *scanFrequency*1E-6; %Hz
+T2 = 0.2; %s
+singlet = exp(1i*2*pi*f*time).*exp(-time/T2);
+Fgau = 30; %parameter that sets the decay rate
+W = exp(- time.^2*(Fgau)^2);
+singletDF = singlet.*W;
+
+%create broad water peak to mimic water residual in spectrum
+T2_water = 0.03; %s
+waterPeakSpectrum = fftshift(fft(exp(1i*2*pi*f_water*time).*exp(-time/T2_water).*exp(- time.^2*(500)^2)));
+
+%scale singlet to 0.01663
+maxValSinglet2 = max(real(fftshift(fft(singletDF))));
+singletDF = singletDF ./ maxValSinglet2 .* 0.01663;
+singletSpec = fftshift(fft(singletDF));
+% figure
+% plot(ppmVector,real(gradmodSpectrum)); hold on;
+% plot(ppmVector,(real(fftshift(fft((-singlet2))))))
+% plot(ppmVector,(real(waterPeakSpectrum)))
+% xlim([5.2 5.6]); hold off
+
+x0 = [phase0, conc, shift0,conc2];
+lb = [-180, 0, -15,0];
+ub = [180, Inf, 15,Inf];
+options = optimset('Algorithm', 'trust-region-reflective', ...
+ 'FinDiffType', 'central', ...
+ 'Jacobian', 'off', ...
+ 'DerivativeCheck', 'on', ...
+ 'TolFun', 1e-40, ...
+ 'TolX', 1e-40, ...
+ 'MaxFunEvals', 20000);
+[x,resnorm,residual,exitflag,output] = lsqnonlin(@(xSolver)CostFunGradientMod(xSolver,fid, singletSpec, ppmRangeIndices, time, ...
+ ppmRangeIndicesBaseline, waterPeakSpectrum, false, ppmVector, ppmRangeIndicesSmoothness1, ppmRangeIndicesSmoothness2), x0, lb, ub,options);
+calculated_phase0 = x(1)
+calculated_conc = x(2)
+calculated_shift = x(3)
+[~, phasedGradModDF, fidShifted] = CostFunGradientMod(x, fid, singletSpec, ppmRangeIndices, time, ...
+ ppmRangeIndicesBaseline, waterPeakSpectrum, true, ppmVector, ppmRangeIndicesSmoothness1, ppmRangeIndicesSmoothness2);
+% figure;
+% spectrumShifted = fftshift(fft(fidShifted));
+% plot(ppmVector, real(spectrumShifted));
+% hold on
+% plot(ppmVector, real(phasedGradMod));
+% plot(ppmVector, real(spectrumShifted-phasedGradMod));
+
+% shift the gradient modulation back to the position in the original fid (a.Data{1})
+phasedGradModFid = ifft(ifftshift(phasedGradModDF));
+phasedGradModFid = phasedGradModFid .* exp(1i * calculated_shift *2*pi * time);
+phasedGradModDF = fftshift(fft(phasedGradModFid));
+
+figure;
+spectrum = fftshift(fft(fid));
+plot(ppmVector, real(spectrum));
+hold on
+plot(ppmVector, real(phasedGradModDF));
+plot(ppmVector, real(spectrum-phasedGradModDF));
+set(gca, 'XDir','reverse')
+
+% check frequency of MC water
+zeroFillFactor = 16;
+fidWater = a.Data{1}(:,1,1,1,1,1,1,1,2);
+spectrumWater = fftshift(fft(fidWater(:,1),size(fidWater,1)*zeroFillFactor));
+ppmSpectrum = a.getPpmVector(zeroFillFactor);
+
+[~, idxWater] = max(spectrumWater);
+ppmWater = ppmSpectrum(idxWater);
+% calculate shift gradient modulation to the diff of MC water (~- 4.66)
+phasedGradModDFfid = ifft(ifftshift(phasedGradModDF));
+spectrumPhasedGradModDF = fftshift(fft(phasedGradModDFfid(:,1),size(phasedGradModDFfid,1)*zeroFillFactor));
+[~, idxGM] = max(abs(spectrumPhasedGradModDF));
+ppmGM_DF = ppmSpectrum(idxGM);
+deltappm_DF = ppmGM_DF - ppmWater;
+
+% mirror the gradient modulation into the upfield
+% idxUF = idxWater-(idxGM-idxWater);
+% phasedGradModUF_wrongShift = flipud(-phasedGradModDF);
+% [val_wrongShift_max, idxUF_wrongShift_max] = max(real(phasedGradModUF_wrongShift));
+% [val_wrongShift_min, idxUF_wrongShift_min] = min(real(phasedGradModUF_wrongShift));
+% if abs(val_wrongShift_max) > abs(val_wrongShift_min)
+% idxUF_wrongShift = idxUF_wrongShift_max;
+% else
+% idxUF_wrongShift = idxUF_wrongShift_min;
+% end
+% if idxUF_wrongShift > idxUF
+% idxUF_Diff = idxUF_wrongShift - idxUF;
+% phasedGradModUF = phasedGradModUF_wrongShift(idxUF_Diff+1:end);
+% phasedGradModUF = [phasedGradModUF; (ones(idxUF_Diff,1).*phasedGradModUF_wrongShift(end))];
+% else
+% idxUF_Diff = idxUF - idxUF_wrongShift;
+% phasedGradModUF = (ones(idxUF_Diff,1).*phasedGradModUF_wrongShift(idxUF_Diff));
+% phasedGradModUF = [phasedGradModUF; phasedGradModUF_wrongShift(1:end-idxUF_Diff)];
+% end
+% phasedGradMod = phasedGradModUF + phasedGradModDF;
+
+ppmGM_UF = ppmWater - deltappm_DF;
+scanFrequency = a.Parameter.Headers.ScanFrequency;
+f = (ppmGM_UF-4.66) *scanFrequency*1E-6; %Hz
+f_water = (4.7-4.66) *scanFrequency*1E-6; %Hz
+T2 = 0.2; %s
+singlet = exp(1i*2*pi*f*time).*exp(-time/T2);
+Fgau = 30; %parameter that sets the decay rate
+W = exp(- time.^2*(Fgau)^2);
+singletUF = singlet.*W;
+xUF = x;
+xUF(1) = x(1)-180; %punktsymmetrisch
+xUF(3) = 0;
+maxValSingletUF = max(real(fftshift(fft(singletUF))));
+singletUF = singletUF ./ maxValSingletUF .* 0.01663;
+singletSpecUF = fftshift(fft(singletUF));
+[~, phasedGradModUF, fidUF] = CostFunGradientMod(xUF, fid, singletSpecUF, ppmRangeIndices, time, ...
+ ppmRangeIndicesBaseline, waterPeakSpectrum, true, ppmVector, ppmRangeIndicesSmoothness1, ppmRangeIndicesSmoothness2);
+
+% subtract gradient modulation from fid
+phasedGradMod = phasedGradModUF + phasedGradModDF;
+spectrum_woGM = spectrum-phasedGradMod;
+figure
+plot(ppmVector, phasedGradMod);
+hold on
+plot(ppmVector, spectrum);
+plot(ppmVector, spectrum_woGM);
+set(gca, 'XDir','reverse')
+legend('FitModel','Spectrum','wo GM')
+end
+function [res, phasedGradMod, fid] = CostFunGradientMod(x, fid, gradmodSpectrum, ppmRangeIndices, time, ...
+ ppmRangeIndicesBaseline, waterPeakSpectrum, do_plot, ppmVector, ppmRangeIndicesSmoothness1, ppmRangeIndicesSmoothness2)
+ phase0 = x(1);
+ conc = x(2);
+ shift = x(3);
+ conc2 = x(4);
+
+ %shift spectrum, not GM peak to fit. Have to shift spectrum back in the
+ %end
+ fid = fid .* exp(-1i * shift *2*pi * time);
+ spectrum = fftshift(fft(fid));
+ %apply phase and scale with conc the gradModSpectrum (singlet model used to fit)
+ phasedGradMod = gradmodSpectrum .* exp(-1i * pi/180 * phase0) .* conc;
+ %
+ residual = spectrum-phasedGradMod-waterPeakSpectrum.*conc2;
+
+ %calculate baseline offset
+ baselineROI = residual(ppmRangeIndicesBaseline);
+ baselineOffset = mean(baselineROI);
+ %residual
+ residual = residual-baselineOffset;
+%
+ residualROI = residual(ppmRangeIndices);
+ residualROI_1 = residual(ppmRangeIndicesSmoothness1);%peak
+ residualROI_2 = residual(ppmRangeIndicesSmoothness2);%around peak
+ nonLinearity = mean(residualROI_1) - mean(residualROI_2);%nonlinearity constraint calculation
+ nonLinearityMatrix = repmat(nonLinearity, length(residualROI),1);
+ % get the res on the desired range
+ res = [real(residualROI); imag(residualROI); real(nonLinearityMatrix); imag(nonLinearityMatrix)];
+ if do_plot
+ figure;
+ plot(ppmVector,real(spectrum-baselineOffset));
+ hold on;
+ plot(ppmVector,real(residual.*ppmRangeIndices'));
+ plot(ppmVector,real(residual));
+ plot(ppmVector,real(phasedGradMod));
+ plot(ppmVector,real(waterPeakSpectrum.*conc2))
+ set(gca, 'XDir','reverse')
+ legend('spectrum w/o offset','residual around GM DF peak','residual','phased peak to fit','water peak baseline');
+ end
+end
\ No newline at end of file
diff --git a/fMRS_processing/subtract_MMBaseline_LCModelBaseline.m b/fMRS_processing/subtract_MMBaseline_LCModelBaseline.m
new file mode 100644
index 0000000000000000000000000000000000000000..034744235071ed75de0145a00a4c2dad30b6d601
--- /dev/null
+++ b/fMRS_processing/subtract_MMBaseline_LCModelBaseline.m
@@ -0,0 +1,248 @@
+function subtract_MMBaseline_LCModelBaseline()
+pathName = 'fMRS data path'; %first version, no modulus correction for gradient sideband removal
+% pathName = 'fMRS data path modulus'; %second version, with modulus correction for gradient sideband, only UF can be processed
+% sampleCriteria = {'Output', 'LCModelConfig'};
+[localFilePathBase] = pathToDataFolder(pathName);
+StimType = {'RestON', 'RestOFF'}; %'StimON', 'StimOFF', 'RestON', 'RestOFF'
+if ~isempty(strfind(StimType{1},'Stim'))
+ visualType = 'Stim';
+elseif ~isempty(strfind(StimType{1},'Rest'))
+ visualType = 'Rest';
+else
+ error('StimType needs to be Stim or Rest');
+end
+Met = 'NAA';
+if Met == 'Lac'
+ Met = 1.314;
+elseif Met == 'NAA'
+ Met = 2.008;
+else
+ error('Met needs to be Lac or NAA');
+end
+BOLDTypes = {'_woBOLDCorr128', '_withBOLDCorr128', '_woBOLDCorr64', '_woBOLDCorr64#1', '_withBOLDCorr64', '_withBOLDCorr64#1'}; %_woBOLDCorr128, _withBOLDCorr128, _woBOLDCorr64, _woBOLDCorr64#1, _withBOLDCorr64, _withBOLDCorr64#1
+for idxBOLDType = 1:length(BOLDTypes)
+ BOLDType = BOLDTypes(idxBOLDType);
+ ordersFMRS = strcat(StimType,BOLDType);%
+ for idxOrderFMRS = 1:length(ordersFMRS)
+ orderFMRS = ordersFMRS{idxOrderFMRS};
+ MMBType = '_MMBSummed_v2'; %_MMBIndFiltered, _MMBSummed, _MMBInd, _MMBSimulated
+ % subjects = {'3333';};
+ subjects = {'2823';'5166';'9810';'2774';'1658';'5269';'1706';'1004';...
+ '4085';'3333';'4012';'4085';'6524'};
+ % prefixes = {'2020-03-10_';};
+ prefixes = {'2020-02-18_';'2020-02-21_';'2020-02-21_';'2020-02-26_';...
+ '2020-02-27_';'2020-02-27_';'2020-02-28_';'2020-03-02_';....
+ '2020-03-02_';'2020-03-10_';'2020-03-10_';'2020-03-13_';'2020-03-13_';};
+ excludeSubjects = {'5166';'9810';'5269'};
+ numberOfSubjects = length(subjects);
+ suffixFolder = '_fMRS';
+ if strcmp(MMBType,'_MMBSummed')
+ excludeMet = {'NAA';'GABA';'Gln';'Glyc';'GSH';'mI';'NAAG';'PCh';'GPC';'PE';'Scyllo';'Tau';'Glc';'NAA_NAAG';'Glu_Gln';'Glyc';'mI_Glyc';'PCh'}; %for v1
+ elseif strcmp(MMBType,'_MMBSummed_v2')
+ excludeMet = {'NAA';'Asc';'GABA';'Glc';'Gln';'GSH';'mI';'NAAG';'tCh';'PE';'Scyllo';'Tau';'NAA_NAAG';'Glu_Gln'}; %for v2
+ end
+
+ extensionTable = '.table';
+ extensionCoord = '.coord';
+ defaultfMRS = 'StimOFF';
+ defaultSubject = 'XXXX';
+ defaultSubjectsPath = 'YYYY';
+ % defaultMMB = '_MMBSummed';
+ defaultMMB = '_MMBSummed_v2';
+ defaultLCModelUser = 'jdorst';
+
+ % controlFilesBase = 'fitsettings_fMRS_XXXX_';
+ controlFilesBase = 'fitsettings_fMRS_v2_XXXX_';
+ controlFilesBaseSuffix = '.control';
+ outputFileNameBase = strcat(defaultSubject, '_');
+
+ %% file paths setup
+ controlFilesPathRemote = '/Desktop/1H_fMRS/LCModelConfig/';
+ controlFilesPathLocal = [localFilePathBase, 'LCModelConfig/'];
+ LCModelOutputFilesPathRemote = '/Desktop/1H_fMRS/Output/';
+ if ~isempty(strfind(localFilePathBase,'Modulus'))
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed_Modulus/'];
+ else
+ LCModelOutputPath = [localFilePathBase, 'Output_v2_MMBSummed/'];
+ end
+ defaultControlFile = strcat(controlFilesBase, defaultfMRS, controlFilesBaseSuffix);
+
+ subjectsPath = strcat(prefixes, subjects, suffixFolder);
+
+ subjectsControlFilesPathRemote = strcat(controlFilesPathRemote, subjectsPath, '/');
+ subjectsControlFilesPathLocal = strcat(controlFilesPathLocal, subjectsPath, '/');
+ preprocessedFilesPathLocal = strcat(localFilePathBase, subjectsPath, '/');
+ subjectsLCModelOutputFilesRemote = strcat(LCModelOutputFilesPathRemote, subjectsPath, '/');
+ subjectsLCModelOutputPaths = strcat(LCModelOutputPath, subjectsPath, '/');
+ LCModelOutputFiles = strcat(outputFileNameBase, orderFMRS, MMBType);
+ LCModelOutputFileswoBaseline = strcat(outputFileNameBase, orderFMRS, MMBType, '_woBaseline');
+
+ %% process fMRS data and save .RAW file
+ dataPath = strcat(localFilePathBase,subjectsPath, '/');
+
+ for indexCurrentSubject = 1:numberOfSubjects
+ subjectsLCModelOutputPath = subjectsLCModelOutputPaths{indexCurrentSubject};
+ currentOutputFileswoBaseline = strrep(LCModelOutputFileswoBaseline, defaultSubject, subjects{indexCurrentSubject});
+
+ currentDataPath = strcat(localFilePathBase,subjectsPath{indexCurrentSubject}, '/');
+ currentLCModelFit = strrep(strcat(outputFileNameBase, orderFMRS, '_v2'), defaultSubject, subjects{indexCurrentSubject});
+ currentLCModelMatFit = strrep(strcat(currentLCModelFit, '.mat'),'#','_');
+
+
+ indexStart = strfind(currentOutputFileswoBaseline,'_woBaseline');
+ if ~isempty(indexStart)
+ currentLCModelOutputFit = currentOutputFileswoBaseline(1:indexStart-1);
+ currentLCModelCoordFit = strcat(currentLCModelOutputFit, '.coord');
+ else
+ currentLCModelCoordFit = strcat(currentOutputFileswoBaseline, '.coord');
+ end
+
+ if ~isempty(strfind(currentOutputFileswoBaseline, '_v2'))
+ currentRawOutputFile = strcat(strrep(currentLCModelFit,'_Av','_v2_Av'), '_woBaseline_MMB');
+ else
+ currentRawOutputFile = strcat(currentLCModelFit, '_woBaseline_MMB');
+ end
+
+
+ % metabolitesToSubtract = {'Glu'};
+ doNullingOfDownField = false;
+ if ~isempty(strfind(currentLCModelFit,'#'))
+ reconstructedSpectra = strrep(strcat(currentDataPath,strrep(currentLCModelMatFit,'_1','#1')),'_v2','');
+ else
+ reconstructedSpectra = strrep(strcat(currentDataPath,currentLCModelMatFit),'_v2','');
+ end
+ reconstructedSpectraWithoutMetabolite = strcat(strcat(currentDataPath,'MMBSubtracted/'), currentRawOutputFile);
+
+ phase90 = -90 * pi/180;
+ zeroFillFactor = 2;
+
+ %plot configs
+ FontSize = 14;
+ LineWidth = 1.5;
+
+ % load(reconstructedSpectra, 'a');
+ a = load(reconstructedSpectra);
+ fields = fieldnames(a);
+ a = a.(fields{1});
+ [nOfPoints, ppmVector, phasedData, fitData, baselineData, residualData, ...
+ metaboliteNames, metaboliteData, tableConcentrations, ppmStart, ppmEnd, ...
+ scanFrequency, frequencyShift, ppmGap] = ...
+ extractFits(subjectsLCModelOutputPath,currentLCModelCoordFit);
+
+ % align the two spectra
+ fid = a.Data{1};
+ spectrum = fftshift(fft(fid(:,1,1,1,1,1,1,1,1,1,1,1),size(fid,1)*zeroFillFactor));
+ ppmSpectrum = a.getPpmVector(zeroFillFactor);
+
+ [~, maxPpmValueSpectrum] = getMaxPeak(ppmSpectrum', real(spectrum), Met, 0.1);
+ [~, maxPpmValueLCModel] = getMaxPeak(ppmVector, phasedData, Met, 0.1);
+
+ frequencyShiftDiff = maxPpmValueSpectrum - maxPpmValueLCModel;
+ fMRSData_withBaseline = a.ApplyFrequencyShift(frequencyShiftDiff);
+ %get the aligned reconstructed spectra
+ fid = fMRSData_withBaseline.Data{1};
+ spectrum = fftshift(fft(fid(:,1,1,1,1,1,1,1,1,1,1,1),size(fid,1)*zeroFillFactor));
+ % max of the reconstructed spectrum
+ if isempty(ppmGap)
+ ppmSpectrumMask = (ppmSpectrum < ppmStart) & (ppmSpectrum > ppmEnd);
+ [~,idx] = find(ppmSpectrumMask == 1);
+ ppmSpectrumMask(idx(1)-1) = 1;
+ % ppmSpectrumMask(idx(end)+1) = 1;
+ ppmMetaboliteMask = (ppmVector < ppmStart) & (ppmVector > ppmEnd);
+ else
+ ppmSpectrumMask = (ppmSpectrum < ppmGap) & (ppmSpectrum > ppmEnd);
+ ppmMetaboliteMask = (ppmVector < ppmGap) & (ppmVector > ppmEnd);
+ end
+ maxSpectrum = max(real(spectrum(ppmSpectrumMask))); %spectrum between 0.6 and 4.1 ppm
+ % max of the LCModel fit
+ maxPhasedData = max(phasedData);
+ % calculate scaling
+ scalingFactor = maxSpectrum/maxPhasedData;
+ oldSpectrum = spectrum;
+ % for indexMetabolite = 1:length(metabolitesToSubtract)
+ metaboliteToSubtract = 'Leu';
+ indexMetaboliteToSubtract = find(strcmp(metaboliteNames,metaboliteToSubtract));
+ if isempty(indexMetaboliteToSubtract)
+ error('Metabolite %s not found in fit',metaboliteToSubtract);
+ end
+ ppmMetaboliteMask(1) = 1;ppmMetaboliteMask(end) = 1;
+ spectrumToSubtract = metaboliteData{indexMetaboliteToSubtract}.metaboliteSpectrum(ppmMetaboliteMask);
+ spectrumToSubtract = spectrumToSubtract + baselineData;
+ spectrumToSubtract(1) = 0;
+ %eliminate first point, which is anyway an error; Also scale it
+ spectrumToSubtractFlipped = flipud(spectrumToSubtract).*scalingFactor;
+
+ spectrumToSubtractReal = zeros(size(oldSpectrum));
+ spectrumToSubtractReal(ppmSpectrumMask) = spectrumToSubtractReal(ppmSpectrumMask) + spectrumToSubtractFlipped;
+ fidToSubtract = ifft(ifftshift(complex(spectrumToSubtractReal)));
+ fidToSubtract(length(fidToSubtract)/2:end)=0;
+ spectrumToSubtractPhased90 = fftshift(fft(fidToSubtract .* exp(1i*phase90))).*2; %multiply by 2 to account for nulling the half of the FID
+ spectrumToSubtractImag = real(spectrumToSubtractPhased90);
+ newSpectrumReal = real(oldSpectrum) - spectrumToSubtractReal;
+ newSpectrumImag = imag(oldSpectrum) - spectrumToSubtractImag;
+ newSpectrum = complex(newSpectrumReal, newSpectrumImag);
+
+ if (doNullingOfDownField)
+ newSpectrum(length(newSpectrum)/2:end) = 0;
+ end
+
+ oldSpectrum = newSpectrum;
+ % end
+
+ figure
+ % plot(ppmVector, phasedData.*scalingFactor)
+ % hold on
+ plot(ppmSpectrum, real(spectrum),'r')
+ hold on
+ plot(ppmSpectrum, real(newSpectrum),'k')
+ set(gca,'xDir','reverse')
+ xlim([0 4.2])
+ title(sprintf('Difference spectrum with and w/o baseline + MM'))
+ legend('Before subtraction','After subtration')
+
+ fig2save_Conc = fullfile(strcat(currentDataPath,'MMBSubtracted/'), strcat(strrep(currentRawOutputFile,'#','_'), '.fig'));
+ savefig(fig2save_Conc)
+
+ close all
+
+ % figure(101)
+ % hold on
+ % p1 = plot(ppmSpectrum, real(spectrum), 'r');
+ % hold on
+ % p2 = plot(ppmSpectrum, real(newSpectrum),'k');
+ % xlim([0 4.2])
+ % xlabel('[ppm]');
+ % % ylim([(indexTE+1) * offsetPlot-offsetPlot*0.5 5e-5]);
+ % set(gca,'xDir','reverse')
+ % set(gca,'ytick',[]);
+ % set(gca,'fontsize',FontSize);
+ % set(p1,'LineWidth',LineWidth);
+ % set(p2,'LineWidth',LineWidth);
+ % title('Macromolecular baseline with and w/o the Cr peak')
+
+ fMRSData_woBaseline = fMRSData_withBaseline;
+ newFid = ifft(ifftshift(newSpectrum));
+ fMRSData_woBaseline.Data{1} = newFid;
+ % trunc_ms = 250;
+ % truncPoint = floor( trunc_ms/(fMRSData_woBaseline.Parameter.Headers.DwellTimeSig_ns*1e-6));
+ % fMRSData_woBaseline = fMRSData_woBaseline.Truncate(truncPoint);
+
+ ExpName = fullfile(strcat(currentDataPath,'MMBSubtracted/'), strcat(currentRawOutputFile, '.fig'));
+ % % eval([currentOutputFileswoBaseline '=fMRSData_woBaseline'])
+ % % save(reconstructedSpectraWithoutMetabolite,currentRawOutputFile);
+ save(strrep(reconstructedSpectraWithoutMetabolite,'#','_'),'fMRSData_woBaseline');
+ fMRSData_woBaseline.ExportLcmRaw(strcat(currentDataPath,'MMBSubtracted/'), strrep(currentRawOutputFile,'#','_'), 'Yes', 'Yes');
+
+ end
+ end
+end
+end
+
+function [maxValue, maxPpmValue] = getMaxPeak(ppmVector, spectrum, peakPpm, searchAreaPpm)
+minPpm = peakPpm - searchAreaPpm;
+maxPpm = peakPpm + searchAreaPpm;
+ppmMask = (ppmVector > minPpm) & (ppmVector < maxPpm);
+tempSpectrum = spectrum .* ppmMask;
+[maxValue, maxIndex] = max(real(tempSpectrum));
+maxPpmValue = ppmVector(maxIndex);
+end
diff --git a/postprocessing/plot_All_Fit_Metabolites.m b/postprocessing/plot_All_Fit_Metabolites.m
index e346aa8cd6a6936829e8c4e451b06f9ec05ddc37..4f0dc25186a13874a1dbf95932a01a09b39b8c18 100644
--- a/postprocessing/plot_All_Fit_Metabolites.m
+++ b/postprocessing/plot_All_Fit_Metabolites.m
@@ -9,8 +9,9 @@ if ~iscell(FileName)
end
if ~exist('downField_MM_Met','var')
- downField_MM_Met = 'Met moiety';
- downField_MM_Met = 'DF';
+% downField_MM_Met = 'Met moiety';
+ downField_MM_Met = 'Met moiety fMRS';
+% downField_MM_Met = 'DF';
% downField_MM_Met = 'MM';
end
@@ -21,11 +22,18 @@ if ~exist('saveFigures','var')
end
switch downField_MM_Met
- case 'Met moiety'
+ case 'Met moiety'
metaboliteNames = {'Asp' 'Cr' 'Cr_CH2' 'GABA' 'Glu' 'Gln' 'GSH' 'Glyc' 'Lac' 'mI' 'NAA_as' 'NAA_ac' 'NAAG' 'Scyllo' 'Tau' 'tCho_P'};
metaboliteLabels = {'Asp';'tCr(CH_3)';'tCr(CH_2)';'GABA';'Glu';'Gln';'GSH';'Glyc';'Lac';'mI';'NAA(CH_2)';'NAA(CH_3)';'NAAG';'Scyllo';'Tau';'tCho+';};
xLimits = [0.5 4.097];
offsetMetabolite = 0.1;
+ case 'Met moiety fMRS'
+% metaboliteNames = {'Asp' 'Cr' 'PCr' 'GABA' 'Gln' 'Glu' 'GPC' 'GSH' 'Glyc' 'Lac' 'mI' 'NAA' 'NAAG' 'PE' 'Scyllo' 'Tau'};
+% metaboliteLabels = {'Asp';'Cr';'PCr';'GABA';'Gln';'Glu';'GPC';'GSH';'Glyc';'Lac';'mI';'NAA';'NAAG';'PE';'Scyllo';'Tau';};
+ metaboliteNames = {'Asc' 'Asp' 'Cr' 'PCr' 'GABA' 'Gln' 'Glu' 'GSH' 'Lac' 'mI' 'NAA' 'NAAG' 'tCh' 'PE' 'Scyllo' 'Tau'};
+ metaboliteLabels = {'Asc';'Asp';'Cr';'PCr';'GABA';'Gln';'Glu';'GSH';'Lac';'mI';'NAA';'NAAG';'tCh';'PE';'Scyllo';'Tau';};
+ xLimits = [0.5 4.097];
+ offsetMetabolite = 0.1;
case 'Met'
metaboliteNames = {'NAA' 'NAAG' 'Asp' 'Glu' 'Gln' 'GABA' 'Lac' ...
'Cr+PCr' 'Cho+GPC+PCh' 'mI' 'sI' 'Gly' 'Glc' 'GSH' 'PE' 'Tau'};
@@ -219,6 +227,7 @@ for index =1:length(FileName)
ylim([-0.21 0.115]);
ylim([-0.21 0.1]);
% ylim([-0.16 0.115]);
+ ylim([-2 1.1]);
end
set(gca,'xDir','reverse')
set(gca,'ytick',[]);
@@ -233,7 +242,7 @@ for index =1:length(FileName)
set(figId, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);
set(gca, 'Position', [0.13,0.11,0.75,0.815])
set(gca, 'Position', [0.13,0.11,0.75,0.88])
- saveas(figId, [PathName,FileName{index}(1:end-6), '.tif']);
+% saveas(figId, [PathName,FileName{index}(1:end-6), '.tif']);
savefig(figId, [PathName,FileName{index}(1:end-6), '.fig'],'compact');
end
end
diff --git a/postprocessing/startLCModel.m b/postprocessing/startLCModel.m
index e4d7a28453e05de3208aca22d9b2bd0198907a05..289ff9cd9b27503561da1142bfaa0cd8fce14f61 100644
--- a/postprocessing/startLCModel.m
+++ b/postprocessing/startLCModel.m
@@ -1,7 +1,7 @@
function [LCModelCallerInstance] = startLCModel(controlFilesPathRemote, outputFilesPathRemote, outputFilesPathLocal)
%% configuration of connection
-LCModelUser = 'tborbath@mpi.localnet';
+LCModelUser = 'jdorst';
% I recommend you to change this password and the user name above, but please don't commit it
LCModelPassword = 'XXXX';