Hi Khouloud,
The Brain Connectivity Toolbox (BCT) is a MATLAB toolbox for complex network analysis of brain connectivity data. While BCT does not have explicit functions named "segregation" or "integration" indices, these concepts are generally related to measures of network segregation (such as clustering coefficient, modularity) and measures of network integration (such as characteristic path length, global efficiency).
Here's how you can calculate some of these measures using BCT:
Segregation Measures:
Clustering Coefficient: The clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. BCT provides a function to calculate the clustering coefficient for each node and the average for the network.
C = clustering_coef_bu(A); % For binary undirected graphs
C_avg = mean(C);
Modularity: Modularity quantifies the degree to which the network may be subdivided into clearly delineated groups or communities.
[Ci, Q] = modularity_und(A); % Ci is the community structure, Q is the modularity index
Integration Measures:
Characteristic Path Length: The characteristic path length is the average shortest path length in the network, which is a measure of global integration.
L = charpath(D); % D is the distance matrix (you can obtain it using the distance_bin function for binary graphs)
Global Efficiency: Global efficiency is the average inverse shortest path length in the network and is another measure of integration.
E_global = efficiency_bin(A); % For binary undirected graphs
To use these functions, you first need to have a connectivity matrix (A) which is typically a binary or weighted adjacency matrix representing the brain network. The adjacency matrix should be square, with dimensions equal to the number of nodes (brain regions) in the network.
Please note that the specific functions to use depend on whether your graph is binary or weighted, and whether it is directed or undirected. The examples above are for binary undirected graphs. BCT provides equivalent functions for weighted and/or directed graphs (e.g., clustering_coef_wu for weighted undirected graphs).
Before using these functions, make sure to preprocess your connectivity matrix appropriately (e.g., thresholding) and that you have the Brain Connectivity Toolbox added to your MATLAB path.
