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A Mechanistic Neural Field Theory of How Anesthesia Suppresses Consciousness: Synaptic Drive Dynamics, Bifurcations, Attractors, and Partial State Equipartitioning

Overview of attention for article published in The Journal of Mathematical Neuroscience, October 2015
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1 tweeter

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1 Dimensions

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21 Mendeley
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Title
A Mechanistic Neural Field Theory of How Anesthesia Suppresses Consciousness: Synaptic Drive Dynamics, Bifurcations, Attractors, and Partial State Equipartitioning
Published in
The Journal of Mathematical Neuroscience, October 2015
DOI 10.1186/s13408-015-0032-7
Pubmed ID
Authors

Saing Paul Hou, Wassim M. Haddad, Nader Meskin, James M. Bailey

Abstract

With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we use dynamical system theory to develop a mechanistic mean field model for neural activity to study the abrupt transition from consciousness to unconsciousness as the concentration of the anesthetic agent increases. The proposed synaptic drive firing-rate model predicts the conscious-unconscious transition as the applied anesthetic concentration increases, where excitatory neural activity is characterized by a Poincaré-Andronov-Hopf bifurcation with the awake state transitioning to a stable limit cycle and then subsequently to an asymptotically stable unconscious equilibrium state. Furthermore, we address the more general question of synchronization and partial state equipartitioning of neural activity without mean field assumptions. This is done by focusing on a postulated subset of inhibitory neurons that are not themselves connected to other inhibitory neurons. Finally, several numerical experiments are presented to illustrate the different aspects of the proposed theory.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 5%
China 1 5%
Unknown 19 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 29%
Student > Bachelor 3 14%
Student > Doctoral Student 2 10%
Student > Master 2 10%
Professor 1 5%
Other 5 24%
Unknown 2 10%
Readers by discipline Count As %
Engineering 6 29%
Computer Science 3 14%
Neuroscience 2 10%
Agricultural and Biological Sciences 2 10%
Medicine and Dentistry 2 10%
Other 4 19%
Unknown 2 10%

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 07 October 2015.
All research outputs
#2,948,556
of 6,265,888 outputs
Outputs from The Journal of Mathematical Neuroscience
#20
of 41 outputs
Outputs of similar age
#103,556
of 194,104 outputs
Outputs of similar age from The Journal of Mathematical Neuroscience
#1
of 1 outputs
Altmetric has tracked 6,265,888 research outputs across all sources so far. This one is in the 29th percentile – i.e., 29% of other outputs scored the same or lower than it.
So far Altmetric has tracked 41 research outputs from this source. They receive a mean Attention Score of 2.5. This one scored the same or higher as 21 of them.
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 194,104 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them