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M5_PRF_sc_vxl.m
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M5_PRF_sc_vxl.m
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clear, close all, clc
%% 1: Load physiological variables (heart rate and respiration) and global signal (GS) from MAT-File
% Set the following parameters !!
sc = 140; % choose a scan (sc) from 1-164
% -----------------------------------------
load('../Data/HCP_41_subjects_phys_GS.mat')
GS=GS_all(:,sc); HR=HR_all(:,sc); resp=zscore(resp_all(:,sc));
Ts_10 = 0.1 ; % Sampling period in seconds
time_10 = 0:Ts_10:(length(HR)-1)*Ts_10;
timeMR = time_10(ind_BOLD_10);
figure('Position',[543 425 1588 792])
ax1 = subplot(3,1,1);
plot(time_10,HR)
title('Heart rate (HR)')
ylabel('HR (bpm)')
ax2 = subplot(3,1,2);
plot(time_10,resp)
title('Respiration (HR)')
ylabel('Amplitude (a.u.)')
ax3 = subplot(3,1,3);
plot(timeMR,GS);
title('Global signal (GS)')
ylabel('Amplitude (a.u.)')
xlabel('Time (s)')
linkaxes([ax1,ax2,ax3],'x')
xlim([0,max(time_10)])
%% 2: Estimate PRF parameters
resp_s = smooth(resp,10*1.5) ;
RF=diff(resp_s); RF=[0;RF(:)]; RF = RF.^2;
ga_opts = gaoptimset('TolFun',1e-10,'StallGenLimit',20,'Generations',100,'Display','iter','UseParallel',1); % Display: iter
options = optimoptions('fmincon','Display','off','Algorithm','interior-point',...
'UseParallel',true,'MaxIterations',100,'MaxFunctionEvaluations',3000,'OptimalityTolerance',1e-8,'PlotFcn','optimplotfval'); % 'PlotFcn','optimplotfval'
PRF_par = [ 3.1 2.5 5.6 0.9 1.9 2.9 12.5 0.5 ];
ub = PRF_par+3;
lb = PRF_par-3; lb(find(lb<0))=0;
h_train = @(P) func_M5_PRF_sc_vxl(P,Ts_10,HR,RF,ind_BOLD_10,GS,1);
% PRF_par = ga(h_train,length(ub),[],[],[],[],lb,ub,[],[],ga_opts);
PRF_par = fmincon(h_train,PRF_par,[],[],[],[],lb,ub,[],options);
h = @(P) func_M5_PRF_sc_vxl(P,Ts_10,HR,RF,ind_BOLD_10,GS,0);
[obj_function,CRF_sc,RRF_sc,HR_conv,RF_conv,r_PRF_sc,yPred, HR_Fconv_MR, HR_Sconv_MR, RF_Fconv_MR, RF_Sconv_MR] = h(PRF_par);
fprintf(' ----------------------------------------------- \n')
fprintf('Correlation b/w GS and PRF output \n')
fprintf('CRF (HR): %3.2f \n',r_PRF_sc(2))
fprintf('RRF (RF): %3.2f \n',r_PRF_sc(3))
fprintf('CRF & RRF (HR & RF): %3.2f \n',r_PRF_sc(1))
%% 3: Plot output of PRF model (timeseries and PRF curves)
% Set the following parameters !!
smoothPar = 5;
fontTitle = 20;
fontLabels = 8;
fontTxt = 16;
lineW = 3;
yl1 = -5.3; yl2 = 5.5;
% -----------------------------------------
t_IR = 0:Ts_10:(length(CRF_sc)-1)*Ts_10;
screenSize = get(0,'ScreenSize'); xL = screenSize(3); yL = screenSize(4);
figure
set(gcf, 'Position', [0.2*xL 0.2*yL 0.6*xL 0.6*yL ]);
set(gcf, 'Position', [0.1*xL 0.1*yL 0.8*xL 0.8*yL ]);
ax1 = subplot(5,3,1:2);
plot(time_10,HR)
ylabel('HR (bpm)')
title(sprintf('Heart rate (HR; %2.0f±%1.0f bpm )',mean(HR),std(HR)))
ax6 = subplot(5,3,[3,6]);
plot(t_IR,CRF_sc,'LineWidth',4), grid on
title('Cardiac Response Function (CRF_{sc}) ')
xlabel('Time (s)'), ylabel('Amplitude (a.u.)')
xlim([0 60])
ax2 = subplot(5,3,4:5);
h1=plot(timeMR,smooth(GS,smoothPar),'LineWidth',lineW); hold on
h2=plot(time_10,HR_conv,'LineWidth', lineW);
legend([h1,h2],'Global signal', 'X_{HR}')
title('BOLD fluctuations due to changes in HR')
text(60, 4, sprintf('r=%3.2f ', r_PRF_sc(2)) ,'FontSize',fontTxt,'FontWeight','bold')
ylabel('Amplitude (a.u.)')
ylim([yl1, yl2])
legend('boxoff')
ax3 = subplot(5,3,7:8);
h1=plot(timeMR,smooth(GS,smoothPar),'LineWidth',lineW); hold on
h2=plot(timeMR,yPred,'LineWidth',lineW);
title('Full model')
text(60, 4, sprintf('r=%3.2f ', r_PRF_sc(1)) ,'FontSize',fontTxt,'FontWeight','bold')
ylabel('Amplitude (a.u.)')
legend([h1,h2],'Global signal','X_{FM}')
ylim([yl1, yl2])
legend('boxoff')
ax4 = subplot(5,3,10:11);
h1 = plot(timeMR,smooth(GS,smoothPar),'LineWidth',lineW); hold on
h2 = plot(time_10,RF_conv,'LineWidth',lineW);
title('BOLD fluctuations due to changes in respiration')
text(60, 4, sprintf('r=%3.2f ', r_PRF_sc(3)) ,'FontSize',fontTxt,'FontWeight','bold')
legend([h1,h2],'Global signal','X_{RF}'), legend('boxoff')
ylabel('Amplitude (a.u.)')
ylim([yl1, yl2])
ax7 = subplot(5,3,[12,15]);
plot(t_IR,RRF_sc,'LineWidth',4), grid on
title('Respiration response function (RRF_{sc}) ')
xlim([0 60])
xlabel('Time (s)'), ylabel('Amplitude (a.u.)')
ax5 = subplot(5,3,13:14);
plot(time_10,RF,'LineWidth',1), hold on
title('Respiratory flow (RF)')
ylabel('RF (a.u.)')
ylim([-0.01 0.1])
xlabel('Time (s)')
linkaxes([ax1,ax2,ax3,ax4,ax5],'x')
xlim([timeMR(1) timeMR(end)])
ax_list = [ax1,ax2,ax3,ax4,ax5,ax6,ax7];
for ax=ax_list
subplot(ax)
ax.XGrid = 'on';
ax.GridAlpha=0.7;
ax.GridLineStyle='--';
ax.FontSize = 17;
ax.FontWeight = 'bold';
end
%% 4: Create matrix of Physiological Regressors for the General linear Model
xPhys = [HR_Fconv_MR, HR_Sconv_MR, RF_Fconv_MR, RF_Sconv_MR]; xPhys = detrend(xPhys,'linear');
figure('Position', [ 316 673 1849 483])
plot(timeMR(:), xPhys)
xlabel('Time (s)')
ylabel('Amplitude (a.u.)')
subject = scans_41_subjects{sc,1};
task = scans_41_subjects{sc,2};
title(sprintf('Physiological regressors to be included in the General Linear Model for scan %s (%s) ', subject, task),'Interpreter','none')