Multilayer motif analysis of brain networks

In the last decade, network science has shed new light both on thestructural (anatomical) and on the functional (correlations in theactivity) connectivity among the different areas of the human brain. Theanalysis of brain networks has made possible to detect the central areasof a neural system and to identify its building blocks by looking atoverabundant small subgraphs, known as motifs. However, network analysisof the brain has so far mainly focused on anatomical and functionalnetworks as separate entities. The recently developed mathematicalframework of multi-layer networks allows us to perform an analysis ofthe human brain where the structural and functional layers areconsidered together. In this work, we describe how to classify thesubgraphs of a multiplex network, and we extend the motif analysis tonetworks with an arbitrary number of layers. We then extract multi-layermotifs in brain networks of healthy subjects by considering networkswith two layers, anatomical and functional, respectively, obtained fromdiffusion and functional magnetic resonance imaging. Results indicatethat subgraphs in which the presence of a physical connection betweenbrain areas (links at the structural layer) coexists with a non-trivialpositive correlation in their activities are statistically overabundant.Finally, we investigate the existence of a reinforcement mechanismbetween the two layers by looking at how the probability to find a linkin one layer depends on the intensity of the connection in the otherone. Showing that functional connectivity is non-trivially constrainedby the underlying anatomical network, our work contributes to a betterunderstanding of the interplay between the structure and function in thehuman brain. Published by AIP Publishing.