Evidence map›Paper›PMID 33556620›Full record

ArticleBrain stimulation

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

Open access · goldAbstract read
In one paragraph

Article in Brain stimulation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.6field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 44 citations in OpenAlex.

  1. Trial
  2. A guide to neuromodulation in drug-resistant epilepsy.Epileptic disorders : international epilepsy journal with videotape · 2026
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  11. Closed-loop neurostimulation for the treatment of psychiatric disorders.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Sharon ChiangDepartment of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States. Electronic address: Sharon.Chiang@ucsf.edu.
Ankit N KhambhatiDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States.
Emily T WangDepartment of Statistics, Rice University, Houston, TX, United States.
Marina VannucciDepartment of Statistics, Rice University, Houston, TX, United States.
Edward F ChangDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States.
Vikram R RaoDepartment of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
University of California, San Francisco · USRice University · US

Funding

TRAINING PROGRAM IN COMPUTATIONAL BIOLOGY AND MEDICINET15LM007093 · NLM · RICE UNIVERSITY · PI Lydia E. Kavraki · 1992 to 2026
$20.8M
NLM NIH HHS T15 LM007093
6 · The paper itself

Abstract

backgroundAn 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.

methodsWe 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.

resultsHigher 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.

conclusionThe 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.

Indexed as

Deep Brain StimulationDrug Resistant EpilepsyAdultElectrocorticographyFemaleHumansImplantable NeurostimulatorsRetrospective StudiesSeizuresElectrocorticographyEpilepsyNeuromodulationRNSSeizure risk

Identifiers

PMID33556620
PMCPMC8083819
OpenAlexW3089429375

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.