2023_Blaschke_Stroke_tDCS_rsfMRI/scripts/toolbox/BCT/motif3funct_bin.m

function [f,F]=motif3funct_bin(A)
%MOTIF3FUNCT_BIN       Frequency of functional class-3 motifs
%
%   [f,F] = motif3funct_bin(A);
%
%   *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 total motif frequency of occurrence in
%   the whole network is correspondingly known as the motif fingerprint of
%   the network.
%
%   Input:      A,      binary directed connection matrix
%
%   Output:     F,      node motif frequency fingerprint
%               f,      network motif frequency fingerprint
%
%   Notes: 
%       1. The function find_motif34.m outputs the motif legend.
%       2. 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: 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 M3 ID3 N3
if isempty(N3)
    load motif34lib M3 ID3 N3            	%load motif data
end

n=length(A);                                %number of vertices in A
f=zeros(13,1);                              %motif count for whole graph
F=zeros(13,n);                          	%frequency

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

for u=1:n-2                               	%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=([false(1,v1) As(u,v1+1:n)])|V2; %and all neibs of u (>v1)
        for v2=find(V2)
            a=[A(v1,u);A(v2,u);A(u,v1);A(v2,v1);A(u,v2);A(v1,v2)];
            ind=(M3*a)==N3;                 %find all contained isomorphs
            id=ID3(ind);

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

            for h=1:mu                      %for each unique motif
                f2(h)=j(h+1)-j(h);              %and frequencies
            end

            %then add to cumulative count
            f(idu)=f(idu)+f2;
            if nargout==2
                F(idu,[u v1 v2])=F(idu,[u v1 v2])+[f2 f2 f2];
            end
        end
    end
end