This talk has been held during the XXII National Conference on Statistical Physics and Complex Systems.
An article by Gu et al. (Nat. Commun. 6, 2015) proposed to characterize brain networks, quantified using anatomical diffusion imaging, in terms of their “controllability”, drawing on concepts and methods of control theory. They reported that brain activity is controllable from a single node, and that the topology of brain net- works provides an explanation for the types of control roles that different regions play in the brain. In this talk, I first briefly review the framework of control theory applied to complex networks. I then show contrasting results on brain controllability through the analysis of five different datasets and numerical simulations.
The related work has been published in [NeuroImage] (https://www.ncbi.nlm.nih.gov/pubmed/29654874)