2023_Blaschke_Stroke_tDCS_rsfMRI/scripts/addendum/MRI_graph.m

function [degree, strength, modularity, CPL, CC, SW] = MRI_graph(mNet, AUC)

mNetTresh = zeros(98,98,AUC(end));
CPLx = nan(1,AUC(end));
CCx = nan(1,AUC(end));
NCC = nan(1,AUC(end));
NCPL = nan(1,AUC(end));
SWx = nan(1,AUC(end));
modul = nan(1,AUC(end));
mean_degree = nan(1,AUC(end));
Stx = nan(1,AUC(end));

f = waitbar(0,strcat('0/100'));
for gi=AUC
        p=gi/100;
        waitbar(1i/100,f,num2str(gi),'/100');
        mNetD              = threshold_proportional(mNet,p);
        mNetTresh(:,:,gi)  = mNetD;
%         mNetD(mNetD==0)   = nan;
        mNetDWght   = weight_conversion(mNetD,'normalize');
        mNetDBin   = weight_conversion(mNetD,'binarize');

%         Eglob (gi)          = efficiency_wei(mNetD);         % global efficieny
        Stx(gi)                 = squeeze(mean(strengths_und(mNetD)));
        D                   = distance_wei_floyd(mNetDWght,'inv');
        Dx                   = distance_wei_floyd(mNetDBin,'inv');
        
        [~,Q]               = community_louvain(mNetD,1,[],'negative_sym');
 modul(gi)           = Q;
        dgr          = degrees_dir(mNetD);
 mean_degree(gi)     = squeeze(mean(dgr));

        lambda              = charpath(D,0,0);
        CPLx(gi)            = lambda;
        cplb                = charpath(Dx,0,0);
        clx                 = clustering_coef_wu(mNetDWght);
        CCx(gi)             = squeeze(mean(clx));
        clb                 = clustering_coef_wu(mNetDBin);

                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                    % small worldness density threshold
                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                                      
                    %   create random network to asses small worldness
                    lambda_rdm = zeros(1,100);
                    clcf_rdm = zeros(1,100);
                    for k=1:100
                        random              = makerandCIJ_und(size(mNetD,1),nnz(mNetD)/2); % compute random network with same number of nodes and edges
                        D_rdm               = distance_bin(random);
                        lambda_rdm(k)       = charpath(D_rdm,0,0);
                        clcf_rdm(k)         = mean(clustering_coef_bu(random));
                    end

                     % small-worldness 
                     norm_clcf      = abs(squeeze(mean(clb)))/abs(mean(clcf_rdm));
              NCC(gi)         = norm_clcf;
                     norm_lambda    = (cplb/(mean(lambda_rdm)));
              NCPL(gi)        = norm_lambda;
              SWx(gi) = norm_clcf/norm_lambda;
                     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
end
        
    F = findall(0,'type','figure','tag','TMWWaitbar');
    delete(F)

CC = squeeze(mean(CCx,'omitnan'));
CPL = squeeze(mean(CPLx,'omitnan'));
SW = squeeze(mean(SWx,'omitnan'));
modularity = squeeze(mean(modul,'omitnan'));
degree = squeeze(mean(mean_degree,'omitnan'));
strength = squeeze(mean(Stx,'omitnan'));
end