Analysis of multilayer clustering algorithms for the application to brain functional connectivity

An important feature of complex networks that can help to understand their internal organization is community structure. Recognize such structures in brain networks could be crucial, as the brain functioning is thought to be based on modular organization. Moreover, brain networks are intrinsically multilayer, which essentially means they can vary in time, in frequency or other domains depending on the topology of the network. In the last decades, some multilayer clustering algorithm has been developed with the aim to identify communities in dynamic networks. However, there is still no agreement about which one is the most reliable, and a way to test and compare these algorithms under a variety of conditions is lacking. With this work, we perform a comparative analysis between different multislice clustering algorithms, evaluating their performances by means of ad-hoc implemented benchmark graphs characterized by properties that will cover a wide range of conditions. Results will seek to provide some guidelines about the choice of the more appropriate algorithm according to the different conditions. As a proof of concept, the algorithms under exam will be also applied to brain functional connectivity networks estimated from EEG signals collected during a motor imagination task, proving agreement between the simulation study conducted and the application to real data.