Evidence map›Paper›PMID 34270543›Full record

ArticlePLoS computational biology2021

An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain.

Marte J Sætra, Gaute T Einevoll, Geir Halnes

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

12 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Advances in the Electrophysiological Recordings of Long-Term Potentiation.International journal of molecular sciences · 2023
    Review
  7. Article
  8. Article
  9. Lactate supply overtakes glucose when neural computational and cognitive loads scale up.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  10. Article
  11. Superconducting Bio-Inspired Au-Nanowire-Based Neurons.Nanomaterials (Basel, Switzerland) · 2022
    Article
  12. Article
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 at 2 institutions in 1 country.

Marte J SætraDepartment of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Oslo, Norway.ORCID 0000-0002-7225-1883
Gaute T EinevollCentre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway.ORCID 0000-0002-5425-5012
Geir HalnesCentre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway.ORCID 0000-0002-4721-1599
University of Oslo · NOSimula Research Laboratory · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Within the computational neuroscience community, there has been a focus on simulating the electrical activity of neurons, while other components of brain tissue, such as glia cells and the extracellular space, are often neglected. Standard models of extracellular potentials are based on a combination of multicompartmental models describing neural electrodynamics and volume conductor theory. Such models cannot be used to simulate the slow components of extracellular potentials, which depend on ion concentration dynamics, and the effect that this has on extracellular diffusion potentials and glial buffering currents. We here present the electrodiffusive neuron-extracellular-glia (edNEG) model, which we believe is the first model to combine compartmental neuron modeling with an electrodiffusive framework for intra- and extracellular ion concentration dynamics in a local piece of neuro-glial brain tissue. The edNEG model (i) keeps track of all intraneuronal, intraglial, and extracellular ion concentrations and electrical potentials, (ii) accounts for action potentials and dendritic calcium spikes in neurons, (iii) contains a neuronal and glial homeostatic machinery that gives physiologically realistic ion concentration dynamics, (iv) accounts for electrodiffusive transmembrane, intracellular, and extracellular ionic movements, and (v) accounts for glial and neuronal swelling caused by osmotic transmembrane pressure gradients. The edNEG model accounts for the concentration-dependent effects on ECS potentials that the standard models neglect. Using the edNEG model, we analyze these effects by splitting the extracellular potential into three components: one due to neural sink/source configurations, one due to glial sink/source configurations, and one due to extracellular diffusive currents. Through a series of simulations, we analyze the roles played by the various components and how they interact in generating the total slow potential. We conclude that the three components are of comparable magnitude and that the stimulus conditions determine which of the components that dominate.

Indexed as

Electric StimulationModels, NeurologicalAction PotentialsAnimalsBrainComputational BiologyExtracellular SpaceNeurogliaNeurons

Identifiers

PMID34270543
PMCPMC8318289
OpenAlexW3180996541

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.