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Scripts_Sharing/Motion/MatlabScripts/GlobalUtilities/PLS corrado/myPLS_outputPlots.m
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myPLS_outputPlots.m
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Vi=spm_vol(scanNameList{1});
bootMin = 2.3;
startup;
if CONST_PLS_IMPLEMENTATION == 1
%% adapt signs
for iLV=1:NUM_LVs,
[~,maxID] = max(abs(U(:,iLV)));
if U(maxID,iLV)<0
U(:,iLV)=-U(:,iLV);
V(:,iLV)=-V(:,iLV);
end
end
%% plot Miplab implementation results
Vres = V*S./Vb_std;
Ures = U*S./Ub_std;
% if ~exist('Lx','var')
Lx = X*V;
% end
% if ~exist('Ly','var')
for iG = 1:length(unique(subj_grouping_analysis))
Ly(subj_grouping_analysis==iG,:) = Y(subj_grouping_analysis==iG,:)*U(iG:iG+NUM_BEHAV_SCORES-1,:);
end
% end
% % adapt sign for contrast PLS
% if strfind(CONST_CONDITION,'contrast')
% if mean(Lx(subj_grouping==1))>mean(Lx(subj_grouping==2)) % if Lx control (1) greater than Lx 22q11 (2)
% U = -U;
% V = -V;
% Ures = -Ures;
% Vres = -Vres;
% Lx = -Lx;
% Ly = -Ly;
% end
% end
%% Plots Lx
CONST_PLOT={'o','o','o'};
for iG = 1:length(CONST_GROUPS)
% CONST_LEGEND{iG} = groupNames{iG};
CONST_LEGEND{iG} = CONST_GROUPS{iG}(1:3);
end
%%
for iter_lv=1:NUM_LVs,
if sprob(iter_lv)<0.2
clear LxPlot;
nG=length(CONST_GROUPS);
LxPlot = nan(length(Lx),nG);
for iG=1:nG,
tmp = Lx(find(subj_grouping==iG),iter_lv);
LxPlot(1:length(tmp),iG)=tmp;
end;
LxPlot(sum(isnan(LxPlot),2)==nG,:)=[]; % delete nan rows
fig = figure('Position',[508,524,327,266]);
hold on;
for iG=1:length(CONST_GROUPS),
Lx1 = Lx(find(subj_grouping==iG),iter_lv);
plot(~Lx1+iG,Lx1,CONST_PLOT{iG});
end;
boxplot(LxPlot);
% legend(CONST_LEGEND,'Location','southeast');
set(gca,'XTick',1:length(CONST_GROUPS),'XTickLabel',CONST_LEGEND,...
'XLim',[0,length(CONST_GROUPS)+1],'YLim',[1.1*min(Lx(:)), 1.1*max(Lx(:))]);
ylabel(['Brain score ' num2str(iter_lv)]);
title('Brain scores, Lx = X*V');
fig.PaperPositionMode = 'auto';
print(sprintf('%s/myPLS_%s_Lx%d_NORM%d%d',...
CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV),'-depsc2','-painters');
saveas(gcf,sprintf('%s/myPLS_%s_Lx%d_NORM%d%d',...
CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
end
end
%%
for iter_lv=1:NUM_LVs,
if sprob(iter_lv)<0.2
clear LxPlot;
nG=length(CONST_GROUPS);
LxPlot = nan(length(Lx0),nG);
for iG=1:nG,
tmp = Lx0(find(subj_grouping==iG),iter_lv);
LxPlot(1:length(tmp),iG)=tmp;
end;
LxPlot(sum(isnan(LxPlot),2)==nG,:)=[]; % delete nan rows
fig = figure('Position',[508,524,327,266]);
hold on;
for iG=1:length(CONST_GROUPS),
Lx1 = Lx0(find(subj_grouping==iG),iter_lv);
plot(~Lx1+iG,Lx1,CONST_PLOT{iG});
end;
boxplot(LxPlot);
% legend(CONST_LEGEND,'Location','southeast');
set(gca,'XTick',1:length(CONST_GROUPS),'XTickLabel',CONST_LEGEND,...
'XLim',[0,length(CONST_GROUPS)+1],'YLim',[1.1*min(Lx0(:)), 1.1*max(Lx0(:))]);
ylabel(['Brain score ' num2str(iter_lv)]);
title('Brain scores, Lx0 = X*V');
fig.PaperPositionMode = 'auto';
print(sprintf('%s/myPLS_%s_Lx0%d_NORM%d%d',...
CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV),'-depsc2','-painters');
saveas(gcf,sprintf('%s/myPLS_%s_Lx0%d_NORM%d%d',...
CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
end
end
% Write out LV
for iter_lv=1:NUM_LVs,
if sprob(iter_lv)<0.2
Vi.fname=sprintf('%s/myPLS_%s_BS%d_NORM%d%d_brain.nii',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV);
Vi.descrip='myPLS - Brain saliency';
tmp=mask;
idx=find(mask);
tmp(idx)=Vres(:,iter_lv);
spm_write_vol(Vi,tmp);
end
end;
% Brain maps
for iter_lv=1:NUM_LVs,
if sprob(iter_lv)<0.2
load(fullfile(codeBasePath,'source','general','defaultSlicePlot'));
obj = slicePlot;
set(gcf,'name',sprintf('LV %d - Lx = X*V',iter_lv));
set(gcf,'Position',[484 77 560 751]);
obj.figure=gcf;
obj.img(1).vol=spm_vol(structFile);
obj.img(2).vol=spm_vol(sprintf('%s/myPLS_%s_BS%d_NORM%d%d_brain.nii',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
obj.img(3).vol=spm_vol(sprintf('%s/myPLS_%s_BS%d_NORM%d%d_brain.nii',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
[mx mn] = slover('volmaxmin', obj.img(2).vol);
obj.img(2).range=[min(bootMin,mx-0.1) min(5,mx)];
obj.img(3).range=[max(-bootMin,mn+0.1) max(mn,-5)];
obj.img(2).cmap='hot';
obj.img(3).cmap='winter';
obj.img(1).cmap=obj.img(1).cmap*2; % making struct brighter
% if strfind(opts.meas,'seedCorr_PCC')
% obj.img(4)=obj.img(3);
% obj.img(4).vol=spm_vol('/Users/daniela/Data/wwPCCsphere.nii');
% obj.img(4).range=[0.9 1];
% obj.img(4).cmap=[0 1 0; 0 1 0];
% end
% if strfind(opts.meas,'seedCorr_ACC')
% obj.img(4)=obj.img(3);
% obj.img(4).vol=spm_vol('/Users/daniela/Data/wwACCsphere.nii');
% obj.img(4).range=[0.9 1];
% obj.img(4).cmap=[0 1 0; 0 1 0];
% end
obj.slices=-30:5:55;
obj.xslices=5;
paint(obj);
% save figure
saveas(gcf,sprintf('%s/myPLS_%s_BS%d_NORM%d%d_brain',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV),'epsc');
end
end
%% Behavior saliencies
for iter_lv=1:NUM_LVs
if sprob(iter_lv)<0.2
% fig=figure('Position',[726,298,518,249]);
% fig=figure('Position',[726 298 306 438]);
fig=figure('Position',[440 547 312 244]);
% bar(U(:,iter_lv));
bar(Ub_mean(:,iter_lv)/S(iter_lv,iter_lv));
hold on;
h=ploterr([1:NUM_LVs], ...
U(:,iter_lv),[],...
Ub_std(:,iter_lv)/S(iter_lv,iter_lv),'r.', 'abshhxy', 0.1);
set(h(1), 'marker', 'none'); % remove marker
set(h(2),'LineWidth',2);
% ylabel(sprintf('Behavioral saliencies (LV %s)',str{iter_lv}),'FontSize',12);%res.out(iter_lv).
ylabel('Design salience','FontSize',12)
set(gca,'XTickLabel',CONST_BEHAV_NAMES,'YLim',[min(get(gca,'YLim')) 1.2],'FontSize',12);
fix_xticklabels(gca,0.5,{'FontSize',11});
% set(gca,'XTickLabelRotation',70);
fig.PaperPositionMode = 'auto';
print(sprintf('%s/myPLS_%s_BS%d_NORM%d%d_behavior',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV),'-depsc2','-painters');
saveas(gcf,sprintf('%s/myPLS_%s_BS%d_NORM%d%d_behavior',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
end
end;
hold off;
%% age correlation plots
clear legText
for iLV=1:NUM_LVs
if sprob(iLV)<0.2
CONST_PLOT={'b.','r.','k.'};
fig = figure('Position',[508,524,327,266]);
for iG = 1:length(CONST_GROUPS)
ageGr=subject.age(subj_grouping==iG);
LxGr=Lx(subj_grouping==iG,iLV);
pl(iG) = plot(ageGr,LxGr,CONST_PLOT{iG},'Markersize',25);
hold on;
set(gca,'XLim',[min(ageGr) max(ageGr)]);
lineFit = polyfit(ageGr,LxGr,1);lineFit = refline(lineFit);
lineFit.Color = CONST_PLOT{iG}(1);set(lineFit,'LineWidth',2);
legText{iG} = [CONST_LEGEND{iG} ': rho = ' num2str(corrLxAge(iG,iLV),'%.2f') ', p = ' num2str(corrLxAge_p(iG,iLV),'%.3f')];
end
set(gca,'XLim',[min(subject.age)-1 max(subject.age)+1]);
if length(CONST_GROUPS)==1
leg=legend(pl,legText{1},'Location','northoutside');
elseif length(CONST_GROUPS)==2
leg=legend(pl,legText{1},legText{2},'Location','northoutside');
elseif length(CONST_GROUPS)==3
leg=legend(pl,legText{1},legText{2},legText{3},'Location','northoutside');
end
set(leg,'Box','off','FontSize',12,'FontWeight','bold');
ylabel(['Brain scores' num2str(iLV)],'FontSize',12);
xlabel('Age','FontSize',12);
fig.PaperPositionMode = 'auto';
print(fullfile(CONST_OUTPUT_PATH,['myPLS_corrage_BS' num2str(iLV)]),'-depsc2','-painters');
savefig(fullfile(CONST_OUTPUT_PATH,['myPLS_corrage_BS' num2str(iLV)]));
end
end
%% motion correlation plots
clear legText
for iLV=1:NUM_LVs
if sprob(iLV)<0.2
CONST_PLOT={'b.','r.','k.'};
fig = figure('Position',[508,524,327,266]);
for iG = 1:length(CONST_GROUPS)
motGr=cell2mat({subject.motionAfterScrubbing(subj_grouping==iG).MeanFD_Power}');
LxGr=Lx(subj_grouping==iG,iLV);
pl(iG) = plot(motGr,LxGr,CONST_PLOT{iG},'Markersize',25);
hold on;
set(gca,'XLim',[min(motGr) max(motGr)]);
lineFit = polyfit(motGr,LxGr,1);lineFit = refline(lineFit);
lineFit.Color = CONST_PLOT{iG}(1);set(lineFit,'LineWidth',2);
[corrLxFD,corrLxFD_p]=corr(motGr,LxGr,'Type','Spearman');
legText{iG} = [CONST_LEGEND{iG} ': rho = ' num2str(corrLxFD,'%.2f') ', p = ' num2str(corrLxFD_p,'%.3f')];
end
set(gca,'XLim',[min(cell2mat({subject.motionAfterScrubbing.MeanFD_Power}'))-0.05 max(cell2mat({subject.motionAfterScrubbing.MeanFD_Power}'))+0.05]);
if length(CONST_GROUPS)==1
leg=legend(pl,legText{1},'Location','northoutside');
elseif length(CONST_GROUPS)==2
leg=legend(pl,legText{1},legText{2},'Location','northoutside');
elseif length(CONST_GROUPS)==3
leg=legend(pl,legText{1},legText{2},legText{3},'Location','northoutside');
end
set(leg,'Box','off','FontSize',12,'FontWeight','bold');
ylabel(['Brain scores' num2str(iLV)],'FontSize',12);
xlabel('mean FD, after scrubbing','FontSize',12);
fig.PaperPositionMode = 'auto';
print(fullfile(CONST_OUTPUT_PATH,['myPLS_corrFD_BS' num2str(iLV)]),'-depsc2','-painters');
savefig(fullfile(CONST_OUTPUT_PATH,['myPLS_corrFD_BS' num2str(iLV)]));
end
end
%% save results
if ~exist('noSave','var') || ~noSave
fig=figure('Position',[261,578,275,213]);
plot(diag(S),'ko-');
xlabel('LVs');
ylabel('singular value');
hold on;
errorbar(1:NUM_LVs,mean(permsamp,2),std(permsamp,[],2),'r-');
legend({'observed','surrogates'});
for iter=1:NUM_LVs,
str{iter}=sprintf('%d (p=%5.4f)',iter,sprob(iter));
fprintf('LV%s\n',str{iter});
end;
axis([0.8 NUM_LVs+.2 0 max(diag(S))*1.2]);
set(gca,'XTick',1:NUM_LVs,'XTickLabel',str);
hold off;
fig.PaperPositionMode = 'auto';
print(sprintf('%s/myPLS_%s_LVs_NORM%d%d',CONST_OUTPUT_PATH,...
CONST_CONDITION,CONST_NORM_IMAGING,CONST_NORM_BEHAV),'-depsc2','-painters');
saveas(gcf,sprintf('%s/myPLS_%s_LVs_NORM%d%d',CONST_OUTPUT_PATH,CONST_CONDITION,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
end
% save results
if ~exist('noSave','var') || ~noSave
res.opts = opts;
res.subject = subject;
res.in = struct('NUM_PERMS',NUM_PERMS,...
'NUM_BOOTSTRAP',NUM_BOOTSTRAP,'subj_grouping',subj_grouping,...
'CONST_BEHAV_NAMES',{CONST_BEHAV_NAMES},'CONST_NORM_BEHAV',CONST_NORM_BEHAV,'CONST_NORM_IMAGING',...
CONST_NORM_IMAGING,'CONST_OUTPUT_PATH',CONST_OUTPUT_PATH,'CONST_GROUPS',{CONST_GROUPS},...
'scanNameList',{scanNameList},'X',X,'Y',Y);
res.out = struct('U',U,'S',S,'V',V,'sprob',sprob,'str',{str},...
'Ub_mean',Ub_mean,'Vb_mean',Vb_mean,'Ub_mean2',Ub_mean2,'Vb_mean2',Vb_mean2,...
'Ub_std',Ub_std,'Vb_std',Vb_std,'Lx',Lx,'Ly',Ly,'permsamp',permsamp);
resFile = fullfile(CONST_OUTPUT_PATH,['myPLS_results_NORM' num2str(CONST_NORM_IMAGING) num2str(CONST_NORM_BEHAV) '.mat']);
save(resFile,'res','NUM_PERMS','NUM_BOOTSTRAP','subj_grouping','CONST_BEHAV_NAMES',...
'CONST_NORM_BEHAV','CONST_NORM_IMAGING','CONST_OUTPUT_PATH','CONST_GROUPS',...
'scanNameList','X','Y','X0','Y0','U','S','V','sprob','str','Ub_mean','Vb_mean',...
'Ub_mean2','Vb_mean2','Ub_std','Vb_std','Lx','Ly','permsamp',...
'AALfile','TemplateGMfile','TemplateWMfile','corrLxAge','corrLxAge_p',...
'corrLxAge_bootdist','corrLxAge_nulldist','Ub_vect','Vb_vect');
end
elseif CONST_PLS_IMPLEMENTATION == 2
%% adapt signs
for iLV=1:NUM_LVs,
[~,maxID] = max(abs(res.v(:,iLV)));
if res.v(maxID,iLV)<0
res.v(:,iLV)=-res.v(:,iLV);
res.boot_result.compare_u(:,iLV)=-res.boot_result.compare_u(:,iLV);
res.boot_result.orig_corr(:,iLV)=-res.boot_result.orig_corr(:,iLV);
res.boot_result.llcorr(:,iLV)=-res.boot_result.llcorr(:,iLV);
res.boot_result.ulcorr(:,iLV)=-res.boot_result.ulcorr(:,iLV);
end
end
%% plot Toronto implementation results
% write out LVs
for iter_lv=1:NUM_LVs,
if res.perm_result.sprob(iter_lv)<0.1
Vi.fname=sprintf('%s/plscmd_%s_BS%d_NORM%d%d_brain.nii',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV);
Vi.descrip='myPLS - Brain saliency';
tmp=mask;
idx=find(mask);
tmp(idx)=res.boot_result.compare_u(:,iter_lv);
spm_write_vol(Vi,tmp);
end
end;
% Brain maps
for iter_lv=1:NUM_LVs,
if res.perm_result.sprob(iter_lv)<0.1
load(fullfile(filesep,'Users','daniela','Code','source','general','defaultSlicePlot'));
obj = slicePlot;
set(gcf,'name',sprintf('LV %d - Lx = X*V',iter_lv));
set(gcf,'Position',[484 77 560 751]);
obj.figure=gcf;
obj.img(1).vol=spm_vol(structFile);
obj.img(2).vol=spm_vol(sprintf('%s/plscmd_%s_BS%d_NORM%d%d_brain.nii',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
obj.img(3).vol=spm_vol(sprintf('%s/plscmd_%s_BS%d_NORM%d%d_brain.nii',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
[mx mn] = slover('volmaxmin', obj.img(2).vol);
obj.img(2).range=[min(bootMin,mx-0.1) min(5,mx)];
obj.img(3).range=[max(-bootMin,mn+0.1) max(mn,-5)];
obj.img(2).cmap='hot';
obj.img(3).cmap='winter';
obj.img(1).cmap=obj.img(1).cmap*2; % making struct brighter
obj.slices=-30:5:55;
obj.xslices=5;
paint(obj);
% save figure
saveas(gcf,sprintf('%s/plscmd_%s_BS%d_NORM%d%d_brain',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV),'epsc');
end
end
% Behavior saliencies
for iter_lv=1:NUM_LVs
if res.perm_result.sprob(iter_lv)<0.1
v_se = res.boot_result.v_se(:,iter_lv)/res.s(iter_lv);
fig=figure('Position',[726,298,300,249]);
if NUM_BEHAV_SCORES == 1
bar(repmat(res.v(:,iter_lv)',2,1));
set(gca,'Xlim',[0.5,1.5]);
else
bar(reshape(res.v(:,iter_lv),[NUM_BEHAV_SCORES NUM_GROUPS]));
end
hold on;
for iter_group=1:NUM_GROUPS,
if NUM_GROUPS <= 2
h=ploterr((1:NUM_BEHAV_SCORES)-.15+.30*(iter_group-1), ...
res.v(NUM_BEHAV_SCORES*(iter_group-1)+(1:NUM_BEHAV_SCORES),iter_lv),[],...
v_se(NUM_BEHAV_SCORES*(iter_group-1)+(1:NUM_BEHAV_SCORES)),'r.', 'abshhxy', 0.02);
elseif NUM_GROUPS == 3
h=ploterr((1:NUM_BEHAV_SCORES)-.22+.22*(iter_group-1), ...
res.v(NUM_BEHAV_SCORES*(iter_group-1)+(1:NUM_BEHAV_SCORES),iter_lv),[],...
v_se(NUM_BEHAV_SCORES*(iter_group-1)+(1:NUM_BEHAV_SCORES)),'r.', 'abshhxy', 0.02);
end
set(h(1), 'marker', 'none'); % remove marker
set(h(2),'LineWidth',2);
end
ylabel(sprintf('Design salience',iter_lv,res.perm_result.sprob(iter_lv)));%res.out(iter_lv).
set(gca,'XTickLabel',CONST_BEHAV_NAMES,'XTickLabelRotation',0);
legend(groupNames);
fig.PaperPositionMode = 'auto';
print(sprintf('%s/plscmd_%s_BehS%d_NORM%d%d',CONST_OUTPUT_PATH,...
CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV),'-depsc2','-painters');
saveas(gcf,sprintf('%s/plscmd_%s_BehS%d_NORM%d%d',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
end
end;
hold off;
% Behavior correlations
for iter_lv=1:NUM_LVs
if res.perm_result.sprob(iter_lv)<0.1
fig = figure('Position',[726,298,300,249]);
if NUM_BEHAV_SCORES == 1
bar(repmat(res.boot_result.orig_corr(:,iter_lv)',2,1));
set(gca,'Xlim',[0.5,1.5]);
else
bar(reshape(res.boot_result.orig_corr(:,iter_lv),[NUM_BEHAV_SCORES NUM_GROUPS]));
end
hold on;
for iter_group=1:NUM_GROUPS,
idx = NUM_BEHAV_SCORES*(iter_group-1)+(1:NUM_BEHAV_SCORES);
orig_corr = res.boot_result.orig_corr(idx,iter_lv);
ll = res.boot_result.llcorr(idx,iter_lv);
ul = res.boot_result.ulcorr(idx,iter_lv);
if NUM_GROUPS <= 2
h=ploterr((1:NUM_BEHAV_SCORES)-.15+.30*(iter_group-1), ...
orig_corr,[],...
{ll,ul},'r.', 'abshhxy', 0.02);
elseif NUM_GROUPS == 3
h=ploterr((1:NUM_BEHAV_SCORES)-.22+.22*(iter_group-1), ...
orig_corr,[],...
{ll,ul},'r.', 'abshhxy', 0.02);
end
set(h(1), 'marker', 'none'); % remove marker
set(h(2),'LineWidth',2);
end
ylabel(sprintf('Design correlation',iter_lv,res.perm_result.sprob(iter_lv)));%res.out(iter_lv).
set(gca,'XTickLabel',CONST_BEHAV_NAMES,'XTickLabelRotation',0);
legend(groupNames);
fig.PaperPositionMode = 'auto';
print(sprintf('%s/plscmd_%s_BS%d_NORM%d%d_behavior',...
CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV),'-depsc2','-painters');
saveas(fig,sprintf('%s/plscmd_%s_BS%d_NORM%d%d_behavior',CONST_OUTPUT_PATH,CONST_CONDITION,iter_lv,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
end
end;
hold off;
% latent variables
fig=figure('Position',[508,524,327,266]);
plot(res.s,'ko-');
xlabel('LVs');
ylabel('singular value');
hold on;
errorbar(1:NUM_LVs,mean(res.perm_result.sperm,2),std(res.perm_result.sperm,[],2),'r-');
legend({'observed','surrogates'});
for iter=1:NUM_LVs,
str{iter}=sprintf('%d (p=%5.4f)',iter,res.perm_result.sprob(iter));
fprintf('LV%s\n',str{iter});
end;
axis([0.8 NUM_LVs+.2 0 max(res.s)*1.2]);
set(gca,'XTick',1:NUM_LVs,'XTickLabel',str);
hold off;
fig.PaperPositionMode = 'auto';
print(sprintf('%s/plscmd_%s_LVs_NORM%d%d',CONST_OUTPUT_PATH,...
CONST_CONDITION,CONST_NORM_IMAGING,CONST_NORM_BEHAV),'-depsc2','-painters');
saveas(gcf,sprintf('%s/plscmd_%s_LVs_NORM%d%d',CONST_OUTPUT_PATH,...
CONST_CONDITION,CONST_NORM_IMAGING,CONST_NORM_BEHAV));
% save results
resFile = fullfile(CONST_OUTPUT_PATH,['plscmd_results_NORM' num2str(CONST_NORM_IMAGING) num2str(CONST_NORM_BEHAV) '.mat']);
save(resFile,'res','subject','CONST_NORM_IMAGING','CONST_NORM_BEHAV','CONST_OUTPUT_PATH',...
'CONST_BEHAV_NAMES','CONST_GROUPS','groupNames','scanNameList','opts','subj_grouping','NUM_GROUPS','CONST_NUM_SUBJ',...
'AALfile','TemplateGMfile','TemplateWMfile','X0','Y0','X','Y');
end
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