A new perspective on brain stimulation interventions: Optimal stochastic tracking control of brain network dynamics
Journal:
arXiv
Published Date:
Jan 15, 2025
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.
