2023_Blaschke_Stroke_tDCS_rsfMRI/scripts/addendum/plot_mNet.m

clear all
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Prelude: basic setup
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
targetFolder    = 'D:\Labor\Projects\Stroke_tDCS_rsfmri\B_rsfmri_stroke_tDCS_mice\data';
time            = {'baseline','Day 3','Day 14','Day 21'};
group           = {'Control','PT sham', 'PT tDCS'};
plotX           = 1; % check if plot results
saveX           =0; % check if save results
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

cd 'D:\Labor\Analyses\Toolbox\fMRI';
addpath('FSLNets')           
addpath('L1precision')
addpath('pwling')            
addpath('../Tools/BCT')
at={' @ '};
sp={' '};
cd (targetFolder)
folder_idx  = pwd; % get folder ID to store data
files_idx   = dir([folder_idx,'/*mat']); % list data2load
baseFileName = files_idx.name;
fullFileName = fullfile(folder_idx, baseFileName);
dirInfos = dir(fullFileName);
% clear ('folder_idx','files_idx','baseFileName','fullFileName');
c=date;
c=strrep(c,'-','_');
nt=length(time);

clx=[  0.7    0.7    0.7;  1.0000    0.4118    0.1608;0.0745    0.6235    1.0000];

cd (targetFolder)
load 'data.mat';
load 'labels.mat';
load 'subjects.mat';
load 'mouse_modules_M.mat';
% load 'mNetZ.mat';
% mNet=matZ;

if saveX==1
    if ~exist(strcat(c,'_results'))
        mkdir(strcat(c,'_results'));
    end
    cd(strcat(c,'_results'))
    savefolder=pwd;
end

%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) get stored Matrices
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% A: whole network [mNet]
% extract saved mNet from files
for ri = 1:size(data,2)
   for li = 1:size(data(ri).subj,2)
        for ti = 1:size(data(ri).subj(li).tp,2)
            
                mnx=data(ri).subj(li).tp(ti).mNet;
                if ~isempty(mnx)
                mNet(:,:,(ri-1)*4+ti,li)=mnx;
                end
            
        end
%         rnx=data(ri).subj(li).mNet;
%         ROI(:,:,li,ri)=rnx;
   end
end

mNet(mNet==0)=nan;
clear ('mnx','rnx','li','ri');
mNet_mean = squeeze(mean(mNet,4,'omitnan'));

% delta Mat of group-means to baseline

for i=1:length(group)
    ix=i-1;
    for kx=1:length(time)
        delta_mNet(:,:,ix*nt+kx)=(mNet_mean(:,:,ix*nt+kx)-mNet_mean(:,:,ix*nt+1));
        DD_mNet(:,:,ix*nt+kx)=(mNet_mean(:,:,ix*nt+kx)-mNet_mean(:,:,ix*nt+1))-(mNet_mean(:,:,4+kx)-mNet_mean(:,:,4+1));
        
        %         -(ROI_group(:,:,kx)-ROI_group(:,:,1));
    end
end


%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 2) sensomotor sub-network
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ROI14=mNet(labels.nr14,labels.nr14,:,:);
clear 'mnx'
inp_delta=ROI14;
inp_delta(inp_delta==0)=nan;
ROI_group=squeeze(mean(inp_delta,3,'omitnan'));

for i=1:length(group)
    ix=i-1;
for kx=1:length(time)
    deltaROI(:,:,ix*nt+kx)=(ROI_group(:,:,ix*nt+kx)-ROI_group(:,:,ix*nt+1));
    %         -(ROI_group(:,:,kx)-ROI_group(:,:,1));
end
end

%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 3) plot raw group mean of networks
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[X,Y,On] = grid_communities(M);

if plotX==1
   for i=1:length(group)
       ix=i-1;
       for k=3%1:length(time)

%        mixed=triu(ROI_group_delta(:,:,ix*4+k))+tril(ROI_group(:,:,ix*4+k));     
        name=(strcat(group(i),' -', time(k)));
        ID=(strcat(time(k)));
%         plotmat_values_rsfMRI(ROI_group(:,:,ix*length(time)+k),targetFolder,dirInfos,[-1 1],0,labels.ROI_short);
          
            plotmat_values_rsfMRI(delta_mNet(On,On,ix*nt+k),targetFolder,dirInfos,[-.75 .75],0,[]);
            hold on
            axis square;
            ylabel([])
            plot(X,Y,'k','linewidth',2)

%         plot(get(gca,'xlim'),[mean(get(gca,'ylim')), mean(get(gca,'ylim'))],'k-','linewidth',2);
%         plot([mean(get(gca,'xlim')), mean(get(gca,'xlim'))], get(gca,'ylim'),'k-','linewidth',2);
        
        title(strcat(name))%,'; mean node strength = ', num2str(norm_ROI_delta(ix*4+k)))); 
        ax=gca;
        ax.FontSize=20;
        ax.FontName='Times';
        nmx=char(strcat(name,'_ROI_delta.png'));
        nm_fig=char(strcat(name,'_ROI_delta.fig'));
        
        if saveX==1
          cd (savefolder)
          saveas(gcf,char(nmx));
          saveas(gcf,char(nm_fig));
        end
%         pause(2)
%         close
       end
   end
end