  Over one third of the estimated 3 million people with epilepsy in the US are
medication resistant. Responsive neurostimulation from chronically implanted
electrodes provides a promising treatment option and alternative to resective
surgery. However, determining personalized optimal stimulation parameters,
including when and where to intervene to guarantee a positive patient outcome,
is a major open challenge. Network neuroscience and control theory offer useful
tools that may guide improvements in parameter selection for control of
anomalous neural activity. Here we use a novel method to characterize dynamic
controllability across consecutive effective connectivity (EC) networks based
on regularized partial correlations between implanted electrodes during the
onset, propagation, and termination phases of thirty-four seizures. We estimate
regularized partial correlation adjacency matrices from one-second time windows
of intracranial electrocorticography recordings using the Graphical Least
Absolute Shrinkage and Selection Operator (GLASSO). Average and modal
controllability metrics calculated from each resulting EC network track the
time-varying controllability of the brain on an evolving landscape of
conditionally dependent network interactions. We show that average
controllability increases throughout a seizure and is negatively correlated
with modal controllability throughout. Furthermore, our results support the
hypothesis that the energy required to drive the brain to a seizure-free state
from an ictal state is smallest during seizure onset; yet, we find that
applying control energy at electrodes in the seizure onset zone may not always
be energetically favorable. Our work suggests that a low-complexity model of
time-evolving controllability may offer new insights for developing and
improving control strategies targeting seizure suppression.
