Stimulation-based control of dynamic brain networks

TL;DR

This paper uses a computational brain network model to understand how targeted stimulation affects brain states, revealing the roles of different regions and network architecture in modulating global brain activity.

Contribution

It introduces a data-driven nonlinear brain dynamics model validated by network control theory, linking regional controllability to stimulation impact and mapping effects to cognitive systems.

Findings

Default mode system causes large global changes despite structural constraints

Regional controllability predicts stimulation impact

Network architecture influences stimulation outcomes

Abstract

The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement.