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bs_calc_FC.m
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bs_calc_FC.m
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function bs_calc_FC(varargin)
global h
if h.monte_carlo_flag == 1
h.waitfor_txt.String = sprintf('Time-Frequency Analyses of Peak Data\n\n Calculating ...'); drawnow;
else
h.waitfor_panel.Visible='on';
h.waitfor_txt.String = sprintf('Time-Frequency Analyses of Peak Data\n\n Calculating ...'); drawnow;
end
hm1 = msgbox(sprintf('Running Time-Frequency Analyses of Peak Data\n\n Calculating ...'));
if length(unique(h.current_3D_peak_idx(1:3)))<3 % means that there is replication of the found peak in the peaks found
vx_pos = h.inv_soln(h.current_inv_soln).leadfield.voxel_pos(h.current_3D_peak_idx(1:3),:);
hm = msgbox(sprintf('Peaks nearest to the True sources are the same\n Nearest to Source 1 = %.f %.f %.f mm (index=%.f)\n Nearest to Source 2 = %.f %.f %.f mm (index=%.f)\n Nearest to Source 3 = %.f %.f %.f mm (index=%.f)\n\nTime-Frequency results will be incorrect for the repeated peaks\n',vx_pos(1,:), h.current_3D_peak_idx(1), vx_pos(2,:), h.current_3D_peak_idx(2),vx_pos(3,:), h.current_3D_peak_idx(3)));
end
TB = str2num(h.edit_wavelet_TB.String); % wavelet parameter --> The larger the time-bandwidth parameter, the more spread out the wavelet is in time and narrower the wavelet is in frequency.
% setting peaks to true source locations
% h.current_3D_peak_idx = h.sim_data.cfg.source.vx_idx;
cfg=h.sim_data.cfg;
%% Plotting --> Confirming
if length(h.current_3D_peak_idx)>3
hm2=msgbox(sprintf('\nSelected closest peak locations to True Sources.\n\nConducting PLV/PLI analyses.\n'));
h.current_3D_peak_idx = h.current_3D_peak_idx(1:length(h.sim_data.cfg.source.vx_idx));
end
if ~isempty(h.current_3D_peak_idx) && length(h.current_3D_peak_idx)<=3
switch h.inv_soln(h.current_inv_soln).Type
case {'SPA' 'LCMV (FT)' 'SAM (FT)' 'sLORETA (FT)' 'dics (FT)' 'pcc (FT)' 'SAM (FT)'} % scalar inverse solutions
h.current_peak_swf_trials = nan(size(h.sim_data.sens_final,1), length(h.current_3D_peak_idx),size(h.sim_data.sens_final,3));
for t=1:size(h.sim_data.sens_final,3)
h.current_peak_swf_trials(:,:,t) = [h.inv_soln(h.current_inv_soln).soln.wts(:,h.current_3D_peak_idx)'*squeeze(h.sim_data.sens_final(:,:,t))']';
h.current_peak_swf_trials(:,:,t) = bsxfun(@minus, h.current_peak_swf_trials(:,:,t), nanmean(h.current_peak_swf_trials(h.sim_data.cfg.study.base_samps,:,t)));
end
case {'SIA' 'MIA''sMCMV' 'bRAPBeam' 'TrapMUSIC'} % multi-source beamformers
h.current_peak_swf_trials = nan(size(h.sim_data.sens_final,1), length(h.current_3D_peak_idx),size(h.sim_data.sens_final,3));
for t=1:size(h.sim_data.sens_final,3)
h.current_peak_swf_trials(:,:,t) = [h.inv_soln(h.current_inv_soln).soln.wts(:,h.current_3D_peak_idx)'*squeeze(h.sim_data.sens_final(:,:,t))']';
% % % using nulled wts calculated from Ninv Noise Covarianace to get better supression of noise because it doesn't include possible active source as in Rinv
% h.current_peak_swf_trials(:,:,t) = [h.inv_soln(h.current_inv_soln).soln.nulled_wts(:,h.current_3D_peak_idx)'*squeeze(h.sim_data.sens_final(:,:,t))']';
h.current_peak_swf_trials(:,:,t) = bsxfun(@minus, h.current_peak_swf_trials(:,:,t), nanmean(h.current_peak_swf_trials(h.sim_data.cfg.study.base_samps,:,t)));
end
case {'MNE (FT)' 'eLORETA (FT)' 'LCMV (BST)' 'MNE (BST)' 'sLORETA (BST)'} % vector inverse solutions
% picking orientation with maximal response in active interval to generate a source waveform
h.current_peak_swf = h.inv_soln(h.current_inv_soln).soln.avg.pow(h.inv_soln(h.current_inv_soln).leadfield.inside==1,:)';
% re-order wts in case different dimensions --> wts dims should be [chans x voxels x ori]
dims1 = size(h.inv_soln(h.current_inv_soln).soln.wts);
dims2 = [length(h.inv_soln(h.current_inv_soln).leadfield.label), size(h.inv_soln(h.current_inv_soln).leadfield.voxel_pos,1) 3];
if ~isequal(dims1,dims2) % reordering wts
dims=[]; for a=1:3; dims(a) = find(dims1(a)==dims2); end
wts = permute( h.inv_soln(h.current_inv_soln).soln.wts,dims);
else
wts = h.inv_soln(h.current_inv_soln).soln.wts;
end
% source waveforms for 3-vector dipoles per voxel
swf=[];
for ox=1:size(wts,3)
% try
swf(:,ox,:) = (squeeze(wts(:,h.current_3D_peak_idx,ox))'*squeeze(nanmean(h.sim_data.sens_final,3))')';
% catch
% wts = reshape(wts,[size(wts,1)/size(h.sim_data.sens_final,2) size(h.sim_data.sens_final,2) size(wts,2)]);
% h.inv_soln(h.current_inv_soln).soln.wts = permute(wts,[1 3 2]);
% swf(:,ox,:) = [squeeze(h.inv_soln(h.current_inv_soln).soln.wts(h.current_3D_peak_idx,ox,:))*squeeze(nanmean(h.sim_data.sens_final,3))']';
% end
swf(:,ox,:) = bsxfun(@minus, swf(:,ox,:), nanmean(swf(h.sim_data.cfg.study.base_samps,ox,:)));
end
[~,max_ori] = max(squeeze(rms(swf(h.sim_data.cfg.study.bl_bmf.act_samps,:,:),1)));
h.current_peak_swf_trials = nan(size(h.sim_data.sens_final,1), length(h.current_3D_peak_idx),size(h.sim_data.sens_final,3));
for v=1:length(h.current_3D_peak_idx)
for t=1:size(h.sim_data.sens_final,3)
h.current_peak_swf_trials(:,v,t) = [squeeze(h.inv_soln(h.current_inv_soln).soln.wts(:,h.current_3D_peak_idx(v),max_ori(v)))'*squeeze(h.sim_data.sens_final(:,:,t))']';
end
end
h.current_peak_swf_trials = bsxfun(@minus, h.current_peak_swf_trials, nanmean(h.current_peak_swf_trials(h.sim_data.cfg.study.base_samps,:,:)));
end
%% %%%%%%%%%%%%%%%%%%% Time-Frequency Analyses %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% TFR & PLV/PLI parameters
[num_chans,num_freqs,num_minmaxfr]=size(cfg.source.sig_freqs);
lat=cfg.study.lat_sim;
min_max_freq=cfg.study.plot_freq_int;
%% calculate wavelets (total & induced) under signal final
clear wt wt_ind wt_evk;
sig_ind = bsxfun(@minus,h.current_peak_swf_trials,nanmean(h.current_peak_swf_trials,3)); % induced by subtracting mean across trials (i.e., evoked response)
fprintf('Calculating wavelets ...\n')
for v=1:num_chans
%% Wavelets - Total Power
% wt_param=[3 30]; %[3 60];
for t=1:size(h.current_peak_swf_trials,3)
% [wt(:,:,v,t),F,coi_wt]=cwt(squeeze(h.current_peak_swf_trials(:,v,t)),'morse',cfg.study.srate,'WaveletParameters',wt_param); % total power
% [wt_ind(:,:,v,t),F,coi_wt]=cwt(squeeze(sig_ind(:,v,t)),'morse',cfg.study.srate,'WaveletParameters',wt_param); %induced power
[wt(:,:,v,t),F,coi_wt]=cwt(squeeze(h.current_peak_swf_trials(:,v,t)),'morse',cfg.study.srate,'TimeBandwidth',TB); % total power
[wt_ind(:,:,v,t),F,coi_wt]=cwt(squeeze(sig_ind(:,v,t)),'morse',cfg.study.srate,'TimeBandwidth',TB); %induced power
end
% [wt_evk(:,:,v),F,coi_wt]=cwt(squeeze(nanmean(h.current_peak_swf_trials(:,v,:),3)),'morse',cfg.study.srate,'WaveletParameters',wt_param); % evoked power
[wt_evk(:,:,v),F,coi_wt]=cwt(squeeze(nanmean(h.current_peak_swf_trials(:,v,:),3)),'morse',cfg.study.srate,'TimeBandwidth',TB); % evoked power
end
F2=flipud(F); wt2=flipud(wt); wt2_ind=flipud(wt_ind); wt2_evk=flipud(wt_evk);
ss=find(cfg.study.lat_sim<=cfg.study.base_int(1)); bs(1)=ss(end);
ss=find(cfg.study.lat_sim<=cfg.study.base_int(2)); bs(2)=ss(end);
base_samps=bs(1):bs(2);
wt3=abs(wt2); % converting to real
wt3_ind=abs(wt2_ind); % converting to real
wt3_evk=abs(wt2_evk); % converting to real
% wt_based=20*log10(bsxfun(@rdivide,wt3,nanmean(wt3(:,base_samps,:),2))); % dB
% % dividing by baseline then multiply 100 to get percent then baseline
% wt_based=bsxfun(@rdivide,wt3,nanmean(wt3(:,base_samps,:,:),2))*100; % percentage
% wt_ind_based=bsxfun(@rdivide,wt3_ind,nanmean(wt3_ind(:,base_samps,:,:),2))*100; % percentage
% wt_evk_based=bsxfun(@rdivide,wt3_evk,nanmean(nanmean(wt3(:,base_samps,:),2),3))*100; % percentage
% % baselining
% wt_based=bsxfun(@minus,wt_based,nanmean(wt_based(:,base_samps,:,:),2)); % percentage
% wt_ind_based=bsxfun(@minus,wt_ind_based,nanmean(wt_ind_based(:,base_samps,:,:),2)); % percentage
% wt_evk_based=bsxfun(@minus,wt_evk_based,nanmean(wt_evk_based(:,base_samps,:),2)); % percentage
%
%
% Decibel
% all
db_wt = 10*bsxfun(@minus,log10(wt3),log10(nanmean(wt3(:,base_samps,:,:),2)));
wt_based = bsxfun(@minus,db_wt,nanmean(db_wt(:,base_samps,:,:),2)); % decibel baselined
% induced
db_wt = 10*bsxfun(@minus,log10(wt3_ind),log10(nanmean(wt3_ind(:,base_samps,:,:),2)));
wt_ind_based = bsxfun(@minus,db_wt,nanmean(db_wt(:,base_samps,:,:),2)); % decibel baselined
% evoked
db_wt = 10*bsxfun(@minus,log10(wt3_evk),log10(nanmean(wt3_evk(:,base_samps,:,:),2)));
wt_evk_based = bsxfun(@minus,db_wt,nanmean(db_wt(:,base_samps,:,:),2)); % decibel baselined
avg_wt=nanmean(wt_based,4);
avg_wt_ind=nanmean(wt_ind_based,4);
avg_wt_evk=nanmean(wt_evk_based,4);
%% PLV/PLI calculations based on wavelets
fprintf('Calculating PLV & PLI ...\n')
sf=find(F2<=min_max_freq(1)); if isempty(sf); sf=1;end
ef=find(F2<=min_max_freq(2)); if isempty(ef); ef=length(F2);end
f_samps=sf(end):ef(end);
phase_data=angle(wt2(f_samps,:,:,:));
F_plv=F2(f_samps);
coi_wt2=coi_wt; coi_wt2(coi_wt>max(F2(f_samps)))=nan; coi_wt2(coi_wt<min(F2(f_samps)))=nan;
clear plv_data pli_data;
chan_contrasts=nchoosek(1:size(h.current_peak_swf_trials,2),2); surg_flag=0; num_resamps=1;
clear plv_data pli_data dpli_data;
for f=1:size(phase_data,1)
[PLV]=calc_PLV_ath(squeeze(phase_data(f,:,:,:)),chan_contrasts,surg_flag,num_resamps);
PLI_win=range(cfg.study.lat_sim)/50; PLI_win_overlap=PLI_win/2;
[PLI]=calc_PLI_ath(squeeze(phase_data(f,:,:,:)),cfg.study.srate,cfg.study.lat_sim,PLI_win,PLI_win_overlap,chan_contrasts,surg_flag,num_resamps);
plv_data(f,:,:)=PLV.PLV; pli_data(f,:,:)=PLI.PLI; dpli_data(f,:,:)=PLI.dPLI;
end
pli_lat=PLI.lat;
plv_based=bsxfun(@minus,plv_data,nanmean(plv_data(:,:,base_samps),3));
ss=find(pli_lat<=0);
ss=find(pli_lat<=cfg.study.base_int(1)); if isempty(ss); bs(1)=1; else; bs(1)=ss(end); end
ss=find(pli_lat<=cfg.study.base_int(2)); bs(2)=ss(end);
base_samps_pli=bs(1):bs(2);
pli_based=bsxfun(@minus,pli_data,nanmean(pli_data(:,:,base_samps_pli),3));
dpli_based=bsxfun(@minus,dpli_data,nanmean(dpli_data(:,:,base_samps_pli),3));
if h.monte_carlo_flag == 0
%% %%%%%%%%%%%%%%%%%%% Plotting Results %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Initializing plotting parameters
% num_iter=1500; % number of iterations to find PLVs
pos=round(cfg.source.sig_start*cfg.study.srate)-(cfg.study.lat_sim(1)*cfg.study.srate); % 3sigs x Nfreqs
num_clmns=num_chans; num_rows=num_freqs;
min_max=[-100 100]; % time domain scale as percent of baseline %[-abs(max(max(max(h.current_peak_swf_trials)))) abs(max(max(max(h.current_peak_swf_trials))))]*100;
min_max2 = [-1 1]; %[-3 3]; %str2num(h.edit_plot_caxis.String); % wavelet color axis scale as percent of baseline
min_max2_evk = [-10 10]; % [-30 30]; %str2num(h.edit_plot_caxis.String); % wavelet color axis scale as percent of baseline
min_max2_ind = [-1 1]; % [-12 12]; %str2num(h.edit_plot_caxis.String); % wavelet color axis scale as percent of baseline
min_max3=[-max(max(max(abs(h.current_peak_swf_trials)))) max(max(max(abs(h.current_peak_swf_trials))))]*110; % wavelet color axis scale as percent of baseline
plv_caxis=[-.25 .25]; pli_caxis=[-.25 .25]; dpli_caxis=pli_caxis; %[-0.25 0.25];
% plv_caxis=[0 1]; pli_caxis=[0 1]; dpli_caxis=[-.5 .5]; %[-0.25 0.25];
mrk_clr=h.src_clr; % lines(length(h.current_3D_peak_idx));
plv_clr=[.7 0 .9; 1 0 1; 1 .6 0];
xtik=[-.4:.2:1.2];
f_size=10; % font size for axis & title
f_size2=8; % font size for legend
ln_style={'-' '-' '-'};
t0=find(cfg.study.lat_sim<=0); t0=t0(end);
%% figure(997): Signal final waves
figure; set(gcf,'color','w'); clf;
ax=subplot_axes(4,num_clmns,.06,.05,0,0,0);
for v=1:num_chans
%% Time-domain waves
axes(ax(v)); cla; hold on; axis on;
p1=plot(cfg.study.lat_sim,squeeze(h.current_peak_swf_trials(:,v,:))*100,'color',[1 1 1]*.6);
p2=plot(cfg.study.lat_sim,squeeze(nanmean(h.current_peak_swf_trials(:,v,:),3))*100,'color',mrk_clr(v,:),'linewidth',2);
plot([0 0],[min_max3],'k--');
axis([cfg.study.plot_time_int min_max3]); set(gca,'XTick',xtik);
title(sprintf('Source %.f',h.current_3D_peak_idx(v)),'Color',mrk_clr(v,:)); set(gca,'Fontsize',f_size); box on;
legend([p1(1),p2],{'Trials','Average'},'Location','NorthWest','FontSize',f_size2)
if v==1; ylabel('Amplitude (%)'); end
end
%% Power wavelets
for v=1:num_chans
axes(ax(v+3)); cla; hold on; axis on;
surf(cfg.study.lat_sim,F2,squeeze(avg_wt(:,:,v))); view(0,90); shading interp; colormap(jet); axis tight;
plot3(cfg.study.lat_sim,coi_wt,ones(size(coi_wt)),'color',[1 1 1]*.7,'linewidth',2)
plot3([0 0],[min_max_freq],[min_max2(2) min_max2(2)],'k--');
axis([cfg.study.plot_time_int cfg.study.plot_freq_int]); caxis(min_max2); set(gca,'XTick',xtik,'Fontsize',f_size);
title(sprintf('Total Power: Source %.f',h.current_3D_peak_idx(v)),'Color',mrk_clr(v,:));
if v==1; ylabel('Frequency (Hz)'); end
axes(ax(v+6)); cla; hold on; axis on;
surf(cfg.study.lat_sim,F2,squeeze(avg_wt_evk(:,:,v))); view(0,90); shading interp; colormap(jet); axis tight;
plot3(cfg.study.lat_sim,coi_wt,ones(size(coi_wt)),'color',[1 1 1]*.7,'linewidth',2)
plot3([0 0],[min_max_freq],[min_max2_evk(2) min_max2_evk(2)],'k--');
axis([cfg.study.plot_time_int cfg.study.plot_freq_int]); caxis(min_max2_evk); set(gca,'XTick',xtik,'Fontsize',f_size);
title(sprintf('Evoked Power: Source %.f',h.current_3D_peak_idx(v)),'Color',mrk_clr(v,:));
if v==1; ylabel('Frequency (Hz)'); end
axes(ax(v+9)); cla; hold on; axis on;
surf(cfg.study.lat_sim,F2,squeeze(avg_wt_ind(:,:,v))); view(0,90); shading interp; colormap(jet); axis tight;
plot3(cfg.study.lat_sim,coi_wt,ones(size(coi_wt)),'color',[1 1 1]*.7,'linewidth',2)
plot3([0 0],[min_max_freq],[min_max2_ind(2) min_max2_ind(2)],'k--');
axis([cfg.study.plot_time_int cfg.study.plot_freq_int]); caxis(min_max2_ind); set(gca,'XTick',xtik,'Fontsize',f_size);
title(sprintf('Induced Power: Source %.f',h.current_3D_peak_idx(v)),'Color',mrk_clr(v,:));
if v==1; ylabel('Frequency (Hz)'); end
xlabel('Time (sec');
end
ax1=axes('Position',[.84 ax(6).Position(2) .1 ax(12).Position(4)]); axis off; hc=colorbar('peer',ax1,'Location','EastOutside'); ax1.Position(3)=.1; ylabel(hc,'Power (dB re:baseline)'); caxis(min_max2); hc.Label.Position=[2 0 0];
ax2=axes('Position',[.84 ax(9).Position(2) .1 ax(12).Position(4)]); axis off; hc=colorbar('peer',ax2,'Location','EastOutside'); ax2.Position(3)=.1; ylabel(hc,'Power (dB re:baseline)'); caxis(min_max2_evk); hc.Label.Position=[2 0 0];
ax3=axes('Position',[.84 ax(12).Position(2) .1 ax(12).Position(4)]); axis off; hc=colorbar('peer',ax3,'Location','EastOutside'); ax3.Position(3)=.1; ylabel(hc,'Power (dB re:baseline)'); caxis(min_max2_ind); hc.Label.Position=[2 0 0];
%% figure(998): PLV & PLI plots
figure; clf; set(gcf,'color','w');
ax=subplot_axes(3,num_clmns,.06,.05,0,0,0);
for vx=1:length(chan_contrasts)
axes(ax(vx)); cla; hold on; axis on;
surf(cfg.study.lat_sim,F_plv,squeeze(plv_based(:,vx,:))); view(0,90); shading interp; colormap(jet);
% surf(cfg.study.lat_sim,F_plv,squeeze(plv_data(:,vx,:))); view(0,90); shading interp; colormap(jet);
plot3(cfg.study.lat_sim,coi_wt2,ones(size(coi_wt2))*plv_caxis(2),'color',[1 1 1]*.7,'linewidth',2);
plot3([0 0],[min_max_freq],[1 1],'k--');
title(sprintf('PLV Source %.f vs %.f',h.current_3D_peak_idx(chan_contrasts(vx,:))),'Color',plv_clr(vx,:));
axis([cfg.study.plot_time_int cfg.study.plot_freq_int]); caxis(plv_caxis); set(gca,'XTick',xtik,'Fontsize',f_size);
if vx==1; ylabel('Freq (Hz)'); end
axes(ax(vx+3)); cla; hold on; axis on;
surf(pli_lat,F_plv,squeeze(pli_based(:,vx,:))); view(0,90); shading interp; colormap(jet);
% surf(pli_lat,F_plv,squeeze(pli_data(:,vx,:))); view(0,90); shading interp; colormap(jet);
plot3(cfg.study.lat_sim,coi_wt2,ones(size(coi_wt2))*plv_caxis(2),'color',[1 1 1]*.7,'linewidth',2);
plot3([0 0],[min_max_freq],[1 1],'k--');
title(sprintf('PLI Source %.f vs %.f',h.current_3D_peak_idx(chan_contrasts(vx,:))),'Color',plv_clr(vx,:));
axis([cfg.study.plot_time_int cfg.study.plot_freq_int]); caxis(pli_caxis); set(gca,'XTick',xtik,'Fontsize',f_size);
if vx==1; ylabel('Freq (Hz)'); end
axes(ax(vx+6)); cla; hold on; axis on;
surf(pli_lat,F_plv,squeeze(dpli_based(:,vx,:))); view(0,90); shading interp; colormap(jet);
% surf(pli_lat,F_plv,squeeze(dpli_data(:,vx,:))-0.5); view(0,90); shading interp; colormap(jet);
plot3(cfg.study.lat_sim,coi_wt2,ones(size(coi_wt2))*plv_caxis(2),'color',[1 1 1]*.7,'linewidth',2);
% surf(cfg.study.lat_sim,Fcoh,squeeze(nanmean(wcoh,3))); view(0,90); shading interp; colormap(jet); axis tight;
plot3([0 0],[min_max_freq],[1 1],'k--');
title(sprintf('dPLI Source %.f vs %.f',h.current_3D_peak_idx(chan_contrasts(vx,:))),'Color',plv_clr(vx,:));
axis([cfg.study.plot_time_int cfg.study.plot_freq_int]); caxis(dpli_caxis); set(gca,'XTick',xtik,'Fontsize',f_size);
xlabel('Time (sec)');
if vx==1; ylabel('Freq (Hz)'); end
end
ax1=axes('Position',[.84 ax(3).Position(2) .1 ax(3).Position(4)]); axis off; hc=colorbar('peer',ax1,'Location','EastOutside'); ax1.Position(3)=.1; ylabel(hc,'PLV'); caxis(plv_caxis); hc.Label.Position=[2 0 0];
ax2=axes('Position',[.84 ax(6).Position(2) .1 ax(6).Position(4)]); axis off; hc=colorbar('peer',ax2,'Location','EastOutside'); ax2.Position(3)=.1; ylabel(hc,'PLI'); caxis(pli_caxis); hc.Label.Position=[2 0 0];
ax3=axes('Position',[.84 ax(9).Position(2) .1 ax(9).Position(4)]); axis off; hc=colorbar('peer',ax3,'Location','EastOutside'); ax3.Position(3)=.1; ylabel(hc,'dPLI'); caxis(dpli_caxis); hc.Label.Position=[2 0 0];
%% Figure(999): Modulation Index
figure; clf;
[ax]=subplot_axes(3,4,.05,.05,0,0,0);
a_idx = [1 2 5 6 9 10; 3 4 7 8 11 12];
y_lim = [0 .1];
for a=1:length(h.cfg.source.phase_amp_contrasts)
% source indices of PAC contrasts
v_fc = h.cfg.source.phase_amp_contrasts(a,1); % source index for carrier source
v_fm = h.cfg.source.phase_amp_contrasts(a,2); % source index for modulator source
% carrier and modulator indices for target frequency
fc_idx = find(h.cfg.source.sig_phase_amp_freq_idx(a,:)>0); % carrier freq index
fm_idx = h.cfg.source.sig_phase_amp_freq_idx(a,fc_idx); % modulator freq index
% carrier and modulater frequency
fc = nanmean( squeeze(h.cfg.source.sig_freqs(1,fc_idx,:)));
fm = nanmean( squeeze(h.cfg.source.sig_freqs(1,fm_idx,:)));
% finding wavelet index for fc and fm
[~,fc_wt]=min(abs(F2-fc)); % freq index of wavelet that is closest to carrier freq
[~,fm_wt]=min(abs(F2-fm)); % freq index of wavelet that is closest to carrier freq
% sepearting sig and prepost intervals
sig_int = [h.cfg.source.sig_start(v_fc,fc_idx) h.cfg.source.sig_start(v_fc,fc_idx)+h.cfg.source.sig_durs(v_fc,fc_idx)];
ss = floor(h.cfg.study.srate *(sig_int-h.cfg.study.lat_sim(1)))+1; sig_samps = ss(1):ss(2);
prepost_int1 = [h.cfg.study.lat_sim(1) h.cfg.source.sig_start(v_fc,fc_idx)];
ss1 = floor(h.cfg.study.srate *(prepost_int1-h.cfg.study.lat_sim(1)))+1;
prepost_int2 = [sig_int(2) h.cfg.study.lat_sim(end)];
ss2 = floor(h.cfg.study.srate *(prepost_int2-h.cfg.study.lat_sim(1)))+1;
prepost_samps = [ss1(1):ss1(2) ss2(1):ss2(2)] ;
% signal PAC
fc_amp = abs(squeeze(wt2(fc_wt,sig_samps,v_fc,:))); fc_amp = reshape(fc_amp,[numel(fc_amp) 1]);
fm_phase = angle(squeeze(wt2(fm_wt,sig_samps,v_fm,:))); fm_phase = reshape(fm_phase,[numel(fm_phase) 1]);
nbin=36; phase_bin=linspace(-pi,pi,nbin);
[MI,distKL,amplP,amplQ,binEdges,binCenters]=modulationIndex(fm_phase,fc_amp,nbin);
b2=bar(ax(a_idx(1,a)),(phase_bin/(2*pi))*360,squeeze(amplP)); b2.BarWidth=1; b2.FaceColor=h.src_clr(v_fc,:); b2.EdgeColor=[1 1 1]*0;
ax(a_idx(1,a)).YLim = y_lim;
title(ax(a_idx(1,a)),sprintf('Signal: Source %.f modulated by Source %.f ', h.current_3D_peak_idx(h.PAC_source_contrasts(a,:)) ));
text(ax(a_idx(1,a)),(phase_bin(1)/(2*pi))*360,y_lim(2)*.95,sprintf('Modulation Index = %.4f',MI))
% prepost PAC
fc_amp = abs(squeeze(wt2(fc_wt,prepost_samps,v_fc,:))); fc_amp = reshape(fc_amp,[numel(fc_amp) 1]);
fm_phase = angle(squeeze(wt2(fm_wt,prepost_samps,v_fm,:))); fm_phase = reshape(fm_phase,[numel(fm_phase) 1]);
nbin=36; phase_bin=linspace(-pi,pi,nbin);
[MI,distKL,amplP,amplQ,binEdges,binCenters]=modulationIndex(fm_phase,fc_amp,nbin);
b2=bar(ax(a_idx(2,a)),(phase_bin/(2*pi))*360,squeeze(amplP)); b2.BarWidth=1; b2.FaceColor=h.src_clr(v_fc,:); b2.EdgeColor=[1 1 1]*0;
ax(a_idx(2,a)).YLim = y_lim;
title(ax(a_idx(2,a)),sprintf('Prepost: Source %.f modulated by Source %.f ', h.current_3D_peak_idx(h.PAC_source_contrasts(a,:)) ));
text(ax(a_idx(2,a)),(phase_bin(1)/(2*pi))*360,y_lim(2)*.95,sprintf('Modulation Index = %.4f',MI))
end
ax(9).YLabel.String = 'Normalized Amplitude Value'; %ax(a_idx(1,5)).YLabel.String = 'Normalized Gamma-Band Amplitude Value'; ax(a_idx(1,7)).YLabel.String = 'Normalized Gamma-Band Amplitude Value';
ax(9).XLabel.String = 'Modulator Phase (degrees)';
else
% exporting for saving during Monte Carlo
h.current_avg_wt = avg_wt; h.current_avg_wt_evk = avg_wt_evk; h.current_avg_wt_ind = avg_wt_ind;
h.current_plv_based = plv_based; h.current_pli_based = pli_based; h.current_dpli_based = dpli_based;
h.current_plv_data = plv_data; h.current_pli_data = pli_data;
h.current_TFR_freqs = F2;
h.current_coi_wt2 = coi_wt2;
h.current_PLV_freqs = F_plv;
h.current_pli_lat = pli_lat;
end
else
msgbox(sprintf('\nNo Peak Data\n\nPlease change Image Threshold\n'));
end
if exist('hm'); close(hm); end
if exist('hm1'); close(hm1); end
if exist('hm2'); close(hm2); end
if h.monte_carlo_flag ~= 1
h.waitfor_panel.Visible='off'; h.waitfor_txt.String = sprintf('Default Message');
end