Integration vs. Segregation in Functional Brain Networks

We propose a new methodology to evaluate the balance between segregation and integration in functional brain networks by using Singular Value Decomposition techniques. By means of magnetoencephalography, we obtain the brain activity of a control group of nineteen individuals during a memory task. Next, we project the node-to-node correlations into a complex network which is analyzed from the perspective of its modular structure encoded in the contribution matrix. In this way, we are able to study the role that nodes play inside/outside its community and to identify connector and local hubs. At the mesoscale level, the analysis of the contribution matrix allows to measure the degree of overlapping between communities and quantify how far the functional networks are from the configuration that better balances the integrated and segregated activity.