Time-evolving controllability of effective connectivity networks during seizure progression

Abstract

Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized 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 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 regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s 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. 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 insights for developing and improving control strategies targeting seizure suppression.

Models of communication and control for brain networks: distinctions, convergence, and future outlook

“…In the context of brain networks, the matrix A is most often chosen to be the structural connectivity matrix obtained from the imaging of white-matter tracts (Gu et al, 2015;Stiso et al, 2019). More recently effective connectivity matrices have also been encoded as A (Scheid et al, 2020;Stiso et al, 2020), as have functional connectivity matrices inferred from systems identification properties of a given system. A system is controllable when a control input u(t) is guaranteed to exist to navigate the system from a given initial state to a desired final state in a specified span of time.…”

Edge-centric analysis of time-varying functional brain networks with applications in autism spectrum disorder

“…Sliding window time-varying FC (sw-tvFC) has been used widely in order to characterize time-varying changes in brain network organization in general, but also to study how fluctuations in brain network architecture accompany cognitive processes across time [13,14]. In addition, tvFC has proven useful for generating novel biomarkers [13,[15][16][17].…”

Drug-resistant focal epilepsy in children is associated with increased modal controllability of the whole brain and epileptogenic regions

“…It is foreseeable that this data could be used to inform neuromodulation targets as has recently been reported. 21,41…”

“…20 In a complementary study, Scheid et al used a time-evolving controllability as a framework to potentially guide the optimal location and minimal energy inputs for neuromodulation treatments. 21 Here, we sought to understand changes in controllability in a cohort of children with surgically treated drug-resistant epilepsy at a single centre. Given the propensity of the epileptic brain to enter difficult-to-reach states of pathological neurophysiological activity that manifest as seizures, we hypothesized that children with focal drug-resistant epilepsy would have a higher modal controllability than healthy controls and that those with multifocal epilepsies would have even higher modal controllability than those with unifocal disease.…”