Control of Dynamics in Brain Networks

TL;DR

This paper reviews recent advances in controlling brain network dynamics, highlighting theoretical models, control strategies, and potential applications in medicine and cognitive enhancement.

Contribution

It provides a comprehensive overview of control methods for large-scale neural systems, integrating mathematical approaches with practical neuroscience applications.

Findings

Development of control strategies for neural networks

Insights into mechanisms underlying brain control processes

Implications for medical interventions like deep-brain stimulation

Abstract

The ability to effectively control brain dynamics holds great promise for the enhancement of cognitive function in humans, and the betterment of their quality of life. Yet, successfully controlling dynamics in neural systems is challenging, in part due to the immense complexity of the brain and the large set of interactions that can drive any single change. While we have gained some understanding of the control of single neurons, the control of large-scale neural systems -- networks of multiply interacting components -- remains poorly understood. Efforts to address this gap include the construction of tools for the control of brain networks, mostly adapted from control and dynamical systems theory. Informed by current opportunities for practical intervention, these theoretical contributions provide models that draw from a wide array of mathematical approaches. We present intriguing recent developments for effective strategies of control in dynamic brain networks, and we also describe potential mechanisms that underlie such processes. We review efforts in the control of general neurophysiological processes with implications for brain development and cognitive function, as well as the control of altered neurophysiological processes in medical contexts such as anesthesia administration, seizure suppression, and deep-brain stimulation for Parkinson's disease. We conclude with a forward-looking discussion regarding how emerging results from network control -- especially approaches that deal with nonlinear dynamics or more realistic trajectories for control transitions -- could be used to directly address pressing questions in neuroscience.