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

Hou SP, Haddad WM, Meskin N, Bailey JM - J Math Neurosci (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: A*STAR, Singapore Institute of Manufacturing Technology, Singapore, 638075, Singapore. house@SIMTech.a-star.edu.sg.

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.

No MeSH data available.


Solutions to (72) and (73) with initial conditions , , ,  for  and . The synaptic drive of the excitatory neurons  to  and two of the inhibitory neurons  and  converges to zero, whereas the synaptic drive of the inhibitory neurons  and  that themselves do not receive inhibitory inputs do not converge to zero
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Fig16: Solutions to (72) and (73) with initial conditions , , , for and . The synaptic drive of the excitatory neurons to and two of the inhibitory neurons and converges to zero, whereas the synaptic drive of the inhibitory neurons and that themselves do not receive inhibitory inputs do not converge to zero

Mentions: Hence, the biological neural network given in (79) and (80) is globally exponentially partially synchronized. As can be seen in Fig. 16, the synaptic drive of the excitatory neurons to and two of the inhibitory neurons and converges to zero, whereas the synaptic drive of the inhibitory neurons and do not converge to zero. Fig. 16


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

Hou SP, Haddad WM, Meskin N, Bailey JM - J Math Neurosci (2015)

Solutions to (72) and (73) with initial conditions , , ,  for  and . The synaptic drive of the excitatory neurons  to  and two of the inhibitory neurons  and  converges to zero, whereas the synaptic drive of the inhibitory neurons  and  that themselves do not receive inhibitory inputs do not converge to zero
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4593994&req=5

Fig16: Solutions to (72) and (73) with initial conditions , , , for and . The synaptic drive of the excitatory neurons to and two of the inhibitory neurons and converges to zero, whereas the synaptic drive of the inhibitory neurons and that themselves do not receive inhibitory inputs do not converge to zero
Mentions: Hence, the biological neural network given in (79) and (80) is globally exponentially partially synchronized. As can be seen in Fig. 16, the synaptic drive of the excitatory neurons to and two of the inhibitory neurons and converges to zero, whereas the synaptic drive of the inhibitory neurons and do not converge to zero. Fig. 16

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: A*STAR, Singapore Institute of Manufacturing Technology, Singapore, 638075, Singapore. house@SIMTech.a-star.edu.sg.

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.

No MeSH data available.