ReviewExperimental neurology2013
Computational modeling of epilepsy for an experimental neurologist.
Review in Experimental neurology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Modelling the effect of allopregnanolone on the resolution of spike-wave discharges.Journal of computational neuroscience · 2025Article
- The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives.Frontiers in network physiology · 2023Review
- Computational Models in Electroencephalography.Brain topography · 2022Review
- Artificial Intelligence and Computational Approaches for Epilepsy.Journal of epilepsy research · 2020Review
- Antiepileptic drugs induce subcritical dynamics in human cortical networks.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- The role that choice of model plays in predictions for epilepsy surgery.Scientific reports · 2019Article
- Biophysical Psychiatry-How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders.Frontiers in psychiatry · 2019Review
- Bistable Firing Pattern in a Neural Network Model.Frontiers in computational neuroscience · 2019Article
- Seizure prediction - ready for a new era.Nature reviews. Neurology · 2018Review
- A minimally invasive neurostimulation method for controlling abnormal synchronisation in the neuronal activity.PLoS computational biology · 2018Article
- The effects of paranodal myelin damage on action potential depend on axonal structure.Medical & biological engineering & computing · 2018Article
- Control of Absence Seizures by the Thalamic Feed-Forward Inhibition.Frontiers in computational neuroscience · 2017Article
- Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm.BioMed research international · 2017Article
- Computational modeling of epileptiform activities in medial temporal lobe epilepsy combined with in vitro experiments.Journal of computational neuroscience · 2016Article
- Future of seizure prediction and intervention: closing the loop.Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2015Review
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
Abstract
Computational modeling can be a powerful tool for an experimentalist, providing a rigorous mathematical model of the system you are studying. This can be valuable in testing your hypotheses and developing experimental protocols prior to experimenting. This paper reviews models of seizures and epilepsy at different scales, including cellular, network, cortical region, and brain scales by looking at how they have been used in conjunction with experimental data. At each scale, models with different levels of abstraction, the extraction of physiological detail, are presented. Varying levels of detail are necessary in different situations. Physiologically realistic models are valuable surrogates for experimental systems because, unlike in an experiment, every parameter can be changed and every variable can be observed. Abstract models are useful in determining essential parameters of a system, allowing the experimentalist to extract principles that explain the relationship between mechanisms and the behavior of the system. Modeling is becoming easier with the emergence of platforms dedicated to neuronal modeling and databases of models that can be downloaded. Modeling will never be a replacement for animal and clinical experiments, but it should be a starting point in designing experiments and understanding their results.
Indexed as
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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.