Controlling target brain regions by optimal selection of input nodes

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful. Compared to healthy individuals, patients suffering from neurological diseases generally present widely altered brain activity patterns. A promising way to help these people restore a normal brain activity balance is using brain stimulation. As brain areas are interconnected in an intricate web, locally stimulating one or more brain areas can trigger effects across several distant locations, thus evoking a complex response. To achieve a specific response, one should know where (which stimulation sites) to stimulate. Several authors have proposed to solve this puzzle by using a computational model of brain activity together with control theory, a mathematical paradigm to design perturbations with desired effects on a dynamical system. Using an accurate model of brain activity fitted to experimental data from functional magnetic resonance imaging, we show that evoking arbitrary activity patterns in the whole brain requires stimulating a large number of brain areas simultaneously, which is unfeasible with current technology. One can nevertheless focus on a more affordable objective, controlling the activity of a small set of target regions. We discuss how to optimally select stimulation sites (so as to minimize the stimulation intensity) depending on the choice of the target regions, and on the structure of the brain connectivity network.