ArticleJournal of computational neuroscience2010
A model of feedback control for the charge-balanced suppression of epileptic seizures.
Article in Journal of computational neuroscience, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Deterministic, stochastic, and mean-field PDE models in neuroscience.Frontiers in computational neuroscience · 2026Review
- Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation.Frontiers in systems neuroscience · 2021Article
- Model-based robust suppression of epileptic seizures without sensory measurements.Cognitive neurodynamics · 2020Article
- Suppression of epileptic seizures via Anderson localization.Journal of the Royal Society, Interface · 2017Article
- Suppressing epileptic activity in a neural mass model using a closed-loop proportional-integral controller.Scientific reports · 2016Article
- A probabilistic method for determining cortical dynamics during seizures.Journal of computational neuroscience · 2015Article
- A probabilistic framework for a physiological representation of dynamically evolving sleep state.Journal of computational neuroscience · 2014Article
- Open loop optogenetic control of simulated cortical epileptiform activity.Journal of computational neuroscience · 2014Article
- A continuous mapping of sleep states through association of EEG with a mesoscale cortical model.Journal of computational neuroscience · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Here we present several refinements to a model of feedback control for the suppression of epileptic seizures. We utilize a stochastic partial differential equation (SPDE) model of the human cortex. First, we verify the strong convergence of numerical solutions to this model, paying special attention to the sharp spatial changes that occur at electrode edges. This allows us to choose appropriate step sizes for our simulations; because the spatial step size must be small relative to the size of an electrode in order to resolve its electrical behavior, we are able to include a more detailed electrode profile in the simulation. Then, based on evidence that the mean soma potential is not the variable most closely related to the measurement of a cortical surface electrode, we develop a new model for this. The model is based on the currents flowing in the cortex and is used for a simulation of feedback control. The simulation utilizes a new control algorithm incorporating the total integral of the applied electrical potential. Not only does this succeed in suppressing the seizure-like oscillations, but it guarantees that the applied signal will be charge-balanced and therefore unlikely to cause cortical damage.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.