2023_Blaschke_Stroke_tDCS_rsfMRI/scripts/toolbox/BCT/motif4funct_wei.m

function [I,Q,F]=motif4funct_wei(W)
%MOTIF4FUNCT_WEI       Intensity and coherence of functional class-4 motifs
%
%   [I,Q,F] = motif4funct_wei(W);
%
%   *Structural motifs* are patterns of local connectivity in complex
%   networks. In contrast, *functional motifs* are all possible subsets of
%   patterns of local connectivity embedded within structural motifs. Such
%   patterns are particularly diverse in directed networks. The motif
%   frequency of occurrence around an individual node is known as the motif
%   fingerprint of that node. The motif intensity and coherence are
%   weighted generalizations of the motif frequency. The motif
%   intensity is equivalent to the geometric mean of weights of links
%   comprising each motif. The motif coherence is equivalent to the ratio
%   of geometric and arithmetic means of weights of links comprising each
%   motif.
%
%   Input:      W,      weighted directed connection matrix
%                       (all weights must be between 0 and 1)
%
%   Output:     I,      node motif intensity fingerprint
%               Q,      node motif coherence fingerprint
%               F,      node motif frequency fingerprint
%
%   Notes: 
%       1. The function find_motif34.m outputs the motif legend.
%       2. Average intensity and coherence are given by I./F and Q./F
%       3. All weights must be between 0 and 1. This may be achieved using
%          the weight_conversion.m function, as follows: 
%          W_nrm = weight_conversion(W, 'normalize');
%       4. There is a source of possible confusion in motif terminology.
%          Motifs ("structural" and "functional") are most frequently
%          considered only in the context of anatomical brain networks
%          (Sporns and Kötter, 2004). On the other hand, motifs are not
%          commonly studied in undirected networks, due to the paucity of
%          local undirected connectivity patterns.
%
%   References: Onnela et al. (2005), Phys Rev E 71:065103
%               Milo et al. (2002) Science 298:824-827
%               Sporns O, Kötter R (2004) PLoS Biol 2: e369%
%
%
%   Mika Rubinov, UNSW/U Cambridge, 2007-2015

%   Modification History:
%   2007: Original
%   2015: Improved documentation

persistent M4 ID4 N4
if isempty(N4)
    load motif34lib M4 ID4 N4               	%load motif data
end

n=length(W);                                    %number of vertices in W
I=zeros(199,n);                                 %intensity
Q=zeros(199,n);                                 %coherence
F=zeros(199,n);                                 %frequency

A=1*(W~=0);                                     %adjacency matrix
As=A|A.';                                       %symmetrized adjacency

for u=1:n-3                                     %loop u 1:n-2
    V1=[false(1,u) As(u,u+1:n)];                %v1: neibs of u (>u)
    for v1=find(V1)
        V2=[false(1,u) As(v1,u+1:n)];           %v2: all neibs of v1 (>u)
        V2(V1)=0;                               %not already in V1
        V2=V2|([false(1,v1) As(u,v1+1:n)]);     %and all neibs of u (>v1)
        for v2=find(V2)
            vz=max(v1,v2);                      %vz: largest rank node
            V3=([false(1,u) As(v2,u+1:n)]);     %v3: all neibs of v2 (>u)
            V3(V2)=0;                           %not already in V1&V2
            V3=V3|([false(1,v2) As(v1,v2+1:n)]);%and all neibs of v1 (>v2)
            V3(V1)=0;                           %not already in V1
            V3=V3|([false(1,vz) As(u,vz+1:n)]); %and all neibs of u (>vz)
            for v3=find(V3)

                w=[W(v1,u) W(v2,u) W(v3,u) W(u,v1) W(v2,v1) W(v3,v1)...
                    W(u,v2) W(v1,v2) W(v3,v2) W(u,v3) W(v1,v3) W(v2,v3)];
                a=[A(v1,u);A(v2,u);A(v3,u);A(u,v1);A(v2,v1);A(v3,v1);...
                    A(u,v2);A(v1,v2);A(v3,v2);A(u,v3);A(v1,v3);A(v2,v3)];
                ind=(M4*a)==N4;                 %find all contained isomorphs
                m=sum(ind);                     %number of isomorphs

                M=M4(ind,:).*repmat(w,m,1);
                id=ID4(ind);
                l=N4(ind);
                x=sum(M,2)./l;                  %arithmetic mean
                M(M==0)=1;                      %enable geometric mean
                i=prod(M,2).^(1./l);            %intensity
                q=i./x;                         %coherence

                [idu,j]=unique(id);             %unique motif occurences
                j=[0;j];                        %#ok<AGROW>
                mu=length(idu);                 %number of unique motifs
                i2=zeros(mu,1);
                q2=i2; f2=i2;

                for h=1:mu                      %for each unique motif
                    i2(h)=sum(i(j(h)+1:j(h+1)));    %sum all intensities,
                    q2(h)=sum(q(j(h)+1:j(h+1)));    %coherences
                    f2(h)=j(h+1)-j(h);              %and frequencies
                end

                %then add to cumulative count
                I(idu,[u v1 v2 v3])=I(idu,[u v1 v2 v3])+[i2 i2 i2 i2];
                Q(idu,[u v1 v2 v3])=Q(idu,[u v1 v2 v3])+[q2 q2 q2 q2];
                F(idu,[u v1 v2 v3])=F(idu,[u v1 v2 v3])+[f2 f2 f2 f2];
            end
        end
    end
end