↓ Skip to main content

Computational Models for Calcium-Mediated Astrocyte Functions

Overview of attention for article published in Frontiers in Computational Neuroscience, April 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
1 blog
twitter
12 X users
facebook
1 Facebook page

Citations

dimensions_citation
73 Dimensions

Readers on

mendeley
106 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Computational Models for Calcium-Mediated Astrocyte Functions
Published in
Frontiers in Computational Neuroscience, April 2018
DOI 10.3389/fncom.2018.00014
Pubmed ID
Authors

Tiina Manninen, Riikka Havela, Marja-Leena Linne

Abstract

The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most often in calcium dynamics, synchronization, information transfer, and plasticity in vitro, but also in vascular events, hyperexcitability, and homeostasis. Our goal here is to present the state-of-the-art in computational modeling of astrocytes in order to facilitate better understanding of the functions and dynamics of astrocytes in the brain. Due to the large number of models, we concentrated on a hundred models that include biophysical descriptions for calcium signaling and dynamics in astrocytes. We categorized the models into four groups: single astrocyte models, astrocyte network models, neuron-astrocyte synapse models, and neuron-astrocyte network models to ease their use in future modeling projects. We characterized the models based on which earlier models were used for building the models and which type of biological entities were described in the astrocyte models. Features of the models were compared and contrasted so that similarities and differences were more readily apparent. We discovered that most of the models were basically generated from a small set of previously published models with small variations. However, neither citations to all the previous models with similar core structure nor explanations of what was built on top of the previous models were provided, which made it possible, in some cases, to have the same models published several times without an explicit intention to make new predictions about the roles of astrocytes in brain functions. Furthermore, only a few of the models are available online which makes it difficult to reproduce the simulation results and further develop the models. Thus, we would like to emphasize that only via reproducible research are we able to build better computational models for astrocytes, which truly advance science. Our study is the first to characterize in detail the biophysical and biochemical mechanisms that have been modeled for astrocytes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 106 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 25%
Researcher 16 15%
Student > Bachelor 13 12%
Student > Master 10 9%
Student > Doctoral Student 9 8%
Other 12 11%
Unknown 20 19%
Readers by discipline Count As %
Neuroscience 41 39%
Engineering 13 12%
Medicine and Dentistry 6 6%
Computer Science 5 5%
Agricultural and Biological Sciences 4 4%
Other 12 11%
Unknown 25 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 13 December 2018.
All research outputs
#2,770,919
of 26,529,613 outputs
Outputs from Frontiers in Computational Neuroscience
#101
of 1,499 outputs
Outputs of similar age
#54,403
of 347,068 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#3
of 24 outputs
Altmetric has tracked 26,529,613 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,499 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has done particularly well, scoring higher than 93% of its peers.
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 347,068 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.