↓ Skip to main content

Dependence of spontaneous neuronal firing and depolarisation block on astroglial membrane transport mechanisms

Overview of attention for article published in Journal of Computational Neuroscience, June 2011
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
69 Dimensions

Readers on

mendeley
75 Mendeley
Title
Dependence of spontaneous neuronal firing and depolarisation block on astroglial membrane transport mechanisms
Published in
Journal of Computational Neuroscience, June 2011
DOI 10.1007/s10827-011-0345-9
Pubmed ID
Authors

Leiv Øyehaug, Ivar Østby, Catherine M. Lloyd, Stig W. Omholt, Gaute T. Einevoll

Abstract

Exposed to a sufficiently high extracellular potassium concentration ([K( + )]₀), the neuron can fire spontaneous discharges or even become inactivated due to membrane depolarisation ('depolarisation block'). Since these phenomena likely are related to the maintenance and propagation of seizure discharges, it is of considerable importance to understand the conditions under which excess [K( + )]₀ causes them. To address the putative effect of glial buffering on neuronal activity under elevated [K( + )](o) conditions, we combined a recently developed dynamical model of glial membrane ion and water transport with a Hodgkin-Huxley type neuron model. In this interconnected glia-neuron model we investigated the effects of natural heterogeneity or pathological changes in glial membrane transporter density by considering a large set of models with different, yet empirically plausible, sets of model parameters. We observed both the high [K( + )]₀-induced duration of spontaneous neuronal firing and the prevalence of depolarisation block to increase when reducing the magnitudes of the glial transport mechanisms. Further, in some parameter regions an oscillatory bursting spiking pattern due to the dynamical coupling of neurons and glia was observed. Bifurcation analyses of the neuron model and of a simplified version of the neuron-glia model revealed further insights about the underlying mechanism behind these phenomena. The above insights emphasise the importance of combining neuron models with detailed astroglial models when addressing phenomena suspected to be influenced by the astroglia-neuron interaction. To facilitate the use of our neuron-glia model, a CellML version of it is made publicly available.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Israel 1 1%
Japan 1 1%
Unknown 71 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 32%
Researcher 10 13%
Student > Master 9 12%
Student > Doctoral Student 7 9%
Professor 5 7%
Other 7 9%
Unknown 13 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 21%
Neuroscience 11 15%
Engineering 7 9%
Mathematics 6 8%
Computer Science 6 8%
Other 15 20%
Unknown 14 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 June 2012.
All research outputs
#18,308,895
of 22,668,244 outputs
Outputs from Journal of Computational Neuroscience
#222
of 306 outputs
Outputs of similar age
#95,762
of 113,046 outputs
Outputs of similar age from Journal of Computational Neuroscience
#5
of 6 outputs
Altmetric has tracked 22,668,244 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 306 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 113,046 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.