A new perspective on brain stimulation interventions: Optimal stochastic tracking control of brain network dynamics

TL;DR

This paper introduces an optimal stochastic tracking control approach for brain network dynamics, aiming to synchronize unhealthy brain activity with healthy patterns, offering a new perspective for brain stimulation interventions.

Contribution

It presents the first application of stochastic tracking control to synchronize brain network dynamics, extending beyond controlling to a specific state at a fixed time.

Findings

Energy of control inversely related to network controllability

Controlling five nodes yields effective dynamics synchronization

Stochastic tracking control aligns with brain stimulation goals

Abstract

Network control theory (NCT) has recently been utilized in neuroscience to facilitate our understanding of brain stimulation effects. A particularly useful branch of NCT is optimal control, which focuses on applying theoretical and computational principles of control theory to design optimal strategies to achieve specific goals in neural processes. However, most existing research focuses on optimally controlling brain network dynamics from the original state to a target state at a specific time point. In this paper, we present the first investigation of introducing optimal stochastic tracking control strategy to synchronize the dynamics of the brain network to a target dynamics rather than to a target state at a specific time point. We utilized fMRI data from healthy groups, and cases of stroke and post-stroke aphasia. For all participants, we utilized a gradient descent optimization method to estimate the parameters for the brain network dynamic system. We then utilized optimal stochastic tracking control techniques to drive original unhealthy dynamics by controlling a certain number of nodes to synchronize with target healthy dynamics. Results show that the energy associated with optimal stochastic tracking control is negatively correlated with the intrinsic average controllability of the brain network system, while the energy of the optimal state approaching control is significantly related to the target state value. For a 100-dimensional brain network system, controlling the five nodes with the lowest tracking energy can achieve relatively acceptable dynamics control effects. Our results suggest that stochastic tracking control is more aligned with the objective of brain stimulation interventions, and is closely related to the intrinsic characteristics of the brain network system, potentially representing a new direction for future brain network optimal control research.