Evidence map›Paper›PMID 18441034›Full record

ArticleBiophysical journal2008

Diffusive coupling and network periodicity: a computational study.

Eun-Hyoung Park, Zhouyan Feng, Dominique M Durand

Abstract read
In one paragraph

Article in Biophysical journal, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Potassium diffusive coupling in neural networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2010
    Review
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

3 authors.

Eun-Hyoung ParkNeural Engineering Center, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA.
Zhouyan Feng
Dominique M Durand

Funding

Analysis &Control of NonSynaptic Epileptiform ActivityR01NS040785 · NINDS · CASE WESTERN RESERVE UNIVERSITY · PI DURAND, DOMINIQUE M · 2001 to 2004
$913k
NINDS NIH HHS NS40785-03NINDS NIH HHS R01 NS040785
6 · The paper itself

Abstract

Diffusive coupling (nearest-neighbor coupling) is the most common type of coupling present in many systems. Previous experimental and theoretical studies have shown that potassium lateral diffusion coupling (i.e., diffusive coupling) can be responsible for synchronization of neuronal activity. Recent in vivo experiments carried out with anesthetized rat hippocampus suggested that the extracellular potassium could play an important role in the generation of a novel type of epileptiform nonsynaptic activity. Yet, the role of potassium in the generation of seizures remains controversial. We tested the hypothesis that potassium lateral diffusion coupling is responsible for the coupling mechanisms for network periodicity in a nonsynaptic model of epilepsy in vivo using a CA1 pyramidal neuron network model The simulation results show that 1), potassium lateral diffusion coupling is crucial for establishing epileptiform activity similar to that generated experimentally; and 2), there exists a scaling relation between the critical coupling strength and the number of cells in the network. The results not only agree with the theoretical prediction, but strongly suggest that potassium lateral diffusion coupling, a physiological realization of the concept of diffusive coupling, can play an important role in entraining periodicity in a nonsynaptic neural network.

Indexed as

Biological ClocksModels, NeurologicalSynaptic TransmissionBrainComputer SimulationDiffusionEpilepsyHumansModels, ChemicalNerve NetPeriodicityPotassiumPotassium

Identifiers

PMID18441034
PMCPMC2479614

What OpenQuestion holds

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Registered trials

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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.