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The Virtual Brain: a simulator of primate brain network dynamics

Overview of attention for article published in Frontiers in Neuroinformatics, January 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 859)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

blogs
2 blogs
twitter
46 X users
patent
1 patent
facebook
1 Facebook page
wikipedia
1 Wikipedia page
googleplus
3 Google+ users
reddit
2 Redditors

Readers on

mendeley
513 Mendeley
citeulike
2 CiteULike
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Title
The Virtual Brain: a simulator of primate brain network dynamics
Published in
Frontiers in Neuroinformatics, January 2013
DOI 10.3389/fninf.2013.00010
Pubmed ID
Authors

Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide, Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa

Abstract

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 46 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 513 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 1%
Switzerland 3 <1%
Germany 3 <1%
United Kingdom 3 <1%
Spain 2 <1%
Finland 2 <1%
Canada 2 <1%
Brazil 1 <1%
France 1 <1%
Other 4 <1%
Unknown 486 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 114 22%
Researcher 111 22%
Student > Master 50 10%
Student > Bachelor 32 6%
Professor 21 4%
Other 85 17%
Unknown 100 19%
Readers by discipline Count As %
Neuroscience 125 24%
Engineering 61 12%
Agricultural and Biological Sciences 48 9%
Computer Science 46 9%
Medicine and Dentistry 28 5%
Other 71 14%
Unknown 134 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 54. 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 12 January 2022.
All research outputs
#843,983
of 26,565,554 outputs
Outputs from Frontiers in Neuroinformatics
#17
of 859 outputs
Outputs of similar age
#6,157
of 294,695 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#2
of 36 outputs
Altmetric has tracked 26,565,554 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 859 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done particularly well, scoring higher than 98% 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 294,695 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.