COMPLEX BRAIN NETWORK

The set of connections in the brain is usually investigated by means of experimental tests based on neural imaging data (fMRI, PET, DTI), and exploiting mathematical models which interpret the brain as a complex network.

Basically, we can distinguish between two kinds of neural connections, namely the structural connections and the functional connections. The structural connectivity refers to white matter projections linking cortical and subcortical regions. The functional connectivity concerns patterns of statistical dependence among neural elements.

The complex organization of the brain connectivity is not completely understood. Graph Theory reveals to be a powerful tool for investigating structural and functional connectivity, due to  its versatility (it is applied to very different problems, from social interaction to Neuroscience), empirical use (it was born from real world problems) and usefulness (it analyzes complex systems in a simpler way).

Our research group has recently developed a mathematical model based on graph thoery that includes and extends previous approaches appeared in the literature. It provides a quantitative way for computing a  connectivity weight  for any pair of neural nodes, during a task, or at the resting state. 

The model has been already tested both on synthetic and real data, and we are currently cooperating with different neurological institutes on possible applications