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The Mesoscopic Modeling of Burst Suppression during Anesthesia

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

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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2 X users
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1 Wikipedia page
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1 Redditor

Citations

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66 Mendeley
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Title
The Mesoscopic Modeling of Burst Suppression during Anesthesia
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00046
Pubmed ID
Authors

David T. J. Liley, Matthew Walsh

Abstract

The burst-suppression pattern is well recognized as a distinct feature of the mammalian electroencephalogram (EEG) waveform. Consisting of alternating periods of high amplitude oscillatory and isoelectric activity, it can be induced in health by deep anesthesia as well as being evoked by a range of pathophysiological processes that include coma and anoxia. While the electroencephalographic phenomenon and clinical implications of burst suppression have been studied extensively, the physiological mechanisms underlying its emergence remain unresolved and obscure. Because electroencephalographic bursting phenomenologically resembles the bursting observed in single neurons, it would be reasonable to assume that the theoretical insights developed to understand bursting at the cellular ("microscopic") level would enable insights into the dynamical genesis of bursting at the level of the whole brain ("macroscopic"). In general action potential bursting is the result of the interplay of two time scales: a fast time scale responsible for spiking, and a slow time scale that modulates such activity. We therefore hypothesize that such fast-slow systems dynamically underpin electroencephalographic bursting. Here we show that a well-known mean field dynamical model of the electroencephalogram, the Liley model, while unable to produce burst suppression unmodified, is able to give rise to a wide variety of burst-like activity by the addition of one or more slow systems modulating model parameters speculated to be major "targets" for anesthetic action. The development of a physiologically plausible theoretical framework to account for burst suppression will lead to a more complete physiological understanding of the EEG and the mechanisms that serve to modify ongoing brain activity necessary for purposeful behavior and consciousness.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Unknown 65 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 17%
Researcher 10 15%
Student > Master 10 15%
Student > Bachelor 8 12%
Student > Postgraduate 5 8%
Other 13 20%
Unknown 9 14%
Readers by discipline Count As %
Neuroscience 18 27%
Medicine and Dentistry 14 21%
Engineering 7 11%
Agricultural and Biological Sciences 6 9%
Physics and Astronomy 3 5%
Other 6 9%
Unknown 12 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 18 November 2013.
All research outputs
#6,391,729
of 22,708,120 outputs
Outputs from Frontiers in Computational Neuroscience
#334
of 1,336 outputs
Outputs of similar age
#68,800
of 280,717 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#26
of 131 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has gotten more attention than average, scoring higher than 74% 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 280,717 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 131 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.