Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation

Sharon Chiang; Ankit N Khambhati; Emily T Wang; Marina Vannucci; Edward F Chang; Vikram R Rao

doi:10.1016/j.brs.2021.01.023

Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation

Brain Stimul. 2021 Mar-Apr;14(2):366-375. doi: 10.1016/j.brs.2021.01.023. Epub 2021 Feb 6.

Authors

Sharon Chiang¹, Ankit N Khambhati², Emily T Wang³, Marina Vannucci³, Edward F Chang², Vikram R Rao⁴

Affiliations

¹ Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States. Electronic address: Sharon.Chiang@ucsf.edu.
² Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States.
³ Department of Statistics, Rice University, Houston, TX, United States.
⁴ Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.

Abstract

Background: An implanted device for brain-responsive neurostimulation (RNS® System) is approved as an effective treatment to reduce seizures in adults with medically-refractory focal epilepsy. Clinical trials of the RNS System demonstrate population-level reduction in average seizure frequency, but therapeutic response is highly variable.

Hypothesis: Recent evidence links seizures to cyclical fluctuations in underlying risk. We tested the hypothesis that effectiveness of responsive neurostimulation varies based on current state within cyclical risk fluctuations.

Methods: We analyzed retrospective data from 25 adults with medically-refractory focal epilepsy implanted with the RNS System. Chronic electrocorticography was used to record electrographic seizures, and hidden Markov models decoded seizures into fluctuations in underlying risk. State-dependent associations of RNS System stimulation parameters with changes in risk were estimated.

Results: Higher charge density was associated with improved outcomes, both for remaining in a low seizure risk state and for transitioning from a high to a low seizure risk state. The effect of stimulation frequency depended on initial seizure risk state: when starting in a low risk state, higher stimulation frequencies were associated with remaining in a low risk state, but when starting in a high risk state, lower stimulation frequencies were associated with transition to a low risk state. Findings were consistent across bipolar and monopolar stimulation configurations.

Conclusion: The impact of RNS on seizure frequency exhibits state-dependence, such that stimulation parameters which are effective in one seizure risk state may not be effective in another. These findings represent conceptual advances in understanding the therapeutic mechanism of RNS, and directly inform current practices of RNS tuning and the development of next-generation neurostimulation systems.

Keywords: Electrocorticography; Epilepsy; Neuromodulation; RNS; Seizure risk.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adult
Deep Brain Stimulation*
Drug Resistant Epilepsy* / therapy
Electrocorticography
Female
Humans
Implantable Neurostimulators
Retrospective Studies
Seizures / therapy

Grants and funding

T15 LM007093/LM/NLM NIH HHS/United States