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Current approaches to model extracellular electrical neural microstimulation.

Joucla S, Glière A, Yvert B - Front Comput Neurosci (2014)

Bottom Line: However, this requires optimizing stimulating devices, which in turn requires a good understanding of the effects of microstimulation on cells and tissues.This approach was previously introduced in the frame of neural recording, but has never been implemented to determine the effect of extracellular stimulation on the neural response at a sub-compartment level.These modeling paradigms, depending on the situation, should help build more efficient high-density neural prostheses for CNS rehabilitation.

View Article: PubMed Central - PubMed

Affiliation: Université de Bordeaux, Institut des Neurosciences Cognitives et Intégratives d'Aquitaine, UMR5287 Bordeaux, France ; CNRS, Institut des Neurosciences Cognitives et Intégratives d'Aquitaine, UMR5287 Bordeaux, France.

ABSTRACT
Nowadays, high-density microelectrode arrays provide unprecedented possibilities to precisely activate spatially well-controlled central nervous system (CNS) areas. However, this requires optimizing stimulating devices, which in turn requires a good understanding of the effects of microstimulation on cells and tissues. In this context, modeling approaches provide flexible ways to predict the outcome of electrical stimulation in terms of CNS activation. In this paper, we present state-of-the-art modeling methods with sufficient details to allow the reader to rapidly build numerical models of neuronal extracellular microstimulation. These include (1) the computation of the electrical potential field created by the stimulation in the tissue, and (2) the response of a target neuron to this field. Two main approaches are described: First we describe the classical hybrid approach that combines the finite element modeling of the potential field with the calculation of the neuron's response in a cable equation framework (compartmentalized neuron models). Then, we present a "whole finite element" approach allowing the simultaneous calculation of the extracellular and intracellular potentials, by representing the neuronal membrane with a thin-film approximation. This approach was previously introduced in the frame of neural recording, but has never been implemented to determine the effect of extracellular stimulation on the neural response at a sub-compartment level. Here, we show on an example that the latter modeling scheme can reveal important sub-compartment behavior of the neural membrane that cannot be resolved using the hybrid approach. The goal of this paper is also to describe in detail the practical implementation of these methods to allow the reader to easily build new models using standard software packages. These modeling paradigms, depending on the situation, should help build more efficient high-density neural prostheses for CNS rehabilitation.

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Related in: MedlinePlus

Computing the mirror response of a uniform passive fiber to an extracellular potential field in the Neuron environment. (A) A 260-μm-long uniform fiber is modeled in the Neuron environment by a set of electrical compartments linked in series through the intracellular resistance Ra. Each (1-μm-long) compartment is modeled by a capacitance (cm) in parallel with a membrane conductance (gl, in series with a voltage source equal to the leakage potential El), in parallel with ion-specific active currents Ii,n. The latter are set to 0 in the current passive model. (B) Contour plot of the extracellular potential field in the y–z plane containing the compartmentalized fiber (oriented along the y axis, at z = 50 μm). Values are given in mV, for a cathodic current of −1 μA delivered by the stimulation electrode (in red) and returning through the ground surface (in blue). (C) The extracellular potential is plotted at the locations of the fiber compartments, together with the membrane response at the end of a cathodic 1-ms-long stimulation.
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Figure 3: Computing the mirror response of a uniform passive fiber to an extracellular potential field in the Neuron environment. (A) A 260-μm-long uniform fiber is modeled in the Neuron environment by a set of electrical compartments linked in series through the intracellular resistance Ra. Each (1-μm-long) compartment is modeled by a capacitance (cm) in parallel with a membrane conductance (gl, in series with a voltage source equal to the leakage potential El), in parallel with ion-specific active currents Ii,n. The latter are set to 0 in the current passive model. (B) Contour plot of the extracellular potential field in the y–z plane containing the compartmentalized fiber (oriented along the y axis, at z = 50 μm). Values are given in mV, for a cathodic current of −1 μA delivered by the stimulation electrode (in red) and returning through the ground surface (in blue). (C) The extracellular potential is plotted at the locations of the fiber compartments, together with the membrane response at the end of a cathodic 1-ms-long stimulation.

Mentions: The primary goal of this paper is to detail the general methods to model extracellular electrical neural stimulation, so that they can be readily used by others. We thus focus on the general presentation of these procedures in the Methods section and then illustrate these approaches on a particular example in the Results section, where the stimulation configuration of a neural tissue (representing either a slice or a whole structure of the Central Nervous System) laid on a 2D microelectrode array (MEA) is detailed. We show how these modeling approaches allow to design a particular electrode configuration aiming at focalizing the potential field created in the tissue (Figure 2) and to model the response of a straight fiber or a neuron taking or not into account the presence of the neural geometry in the calculation of the field (Figures 3, 4).


Current approaches to model extracellular electrical neural microstimulation.

Joucla S, Glière A, Yvert B - Front Comput Neurosci (2014)

Computing the mirror response of a uniform passive fiber to an extracellular potential field in the Neuron environment. (A) A 260-μm-long uniform fiber is modeled in the Neuron environment by a set of electrical compartments linked in series through the intracellular resistance Ra. Each (1-μm-long) compartment is modeled by a capacitance (cm) in parallel with a membrane conductance (gl, in series with a voltage source equal to the leakage potential El), in parallel with ion-specific active currents Ii,n. The latter are set to 0 in the current passive model. (B) Contour plot of the extracellular potential field in the y–z plane containing the compartmentalized fiber (oriented along the y axis, at z = 50 μm). Values are given in mV, for a cathodic current of −1 μA delivered by the stimulation electrode (in red) and returning through the ground surface (in blue). (C) The extracellular potential is plotted at the locations of the fiber compartments, together with the membrane response at the end of a cathodic 1-ms-long stimulation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928616&req=5

Figure 3: Computing the mirror response of a uniform passive fiber to an extracellular potential field in the Neuron environment. (A) A 260-μm-long uniform fiber is modeled in the Neuron environment by a set of electrical compartments linked in series through the intracellular resistance Ra. Each (1-μm-long) compartment is modeled by a capacitance (cm) in parallel with a membrane conductance (gl, in series with a voltage source equal to the leakage potential El), in parallel with ion-specific active currents Ii,n. The latter are set to 0 in the current passive model. (B) Contour plot of the extracellular potential field in the y–z plane containing the compartmentalized fiber (oriented along the y axis, at z = 50 μm). Values are given in mV, for a cathodic current of −1 μA delivered by the stimulation electrode (in red) and returning through the ground surface (in blue). (C) The extracellular potential is plotted at the locations of the fiber compartments, together with the membrane response at the end of a cathodic 1-ms-long stimulation.
Mentions: The primary goal of this paper is to detail the general methods to model extracellular electrical neural stimulation, so that they can be readily used by others. We thus focus on the general presentation of these procedures in the Methods section and then illustrate these approaches on a particular example in the Results section, where the stimulation configuration of a neural tissue (representing either a slice or a whole structure of the Central Nervous System) laid on a 2D microelectrode array (MEA) is detailed. We show how these modeling approaches allow to design a particular electrode configuration aiming at focalizing the potential field created in the tissue (Figure 2) and to model the response of a straight fiber or a neuron taking or not into account the presence of the neural geometry in the calculation of the field (Figures 3, 4).

Bottom Line: However, this requires optimizing stimulating devices, which in turn requires a good understanding of the effects of microstimulation on cells and tissues.This approach was previously introduced in the frame of neural recording, but has never been implemented to determine the effect of extracellular stimulation on the neural response at a sub-compartment level.These modeling paradigms, depending on the situation, should help build more efficient high-density neural prostheses for CNS rehabilitation.

View Article: PubMed Central - PubMed

Affiliation: Université de Bordeaux, Institut des Neurosciences Cognitives et Intégratives d'Aquitaine, UMR5287 Bordeaux, France ; CNRS, Institut des Neurosciences Cognitives et Intégratives d'Aquitaine, UMR5287 Bordeaux, France.

ABSTRACT
Nowadays, high-density microelectrode arrays provide unprecedented possibilities to precisely activate spatially well-controlled central nervous system (CNS) areas. However, this requires optimizing stimulating devices, which in turn requires a good understanding of the effects of microstimulation on cells and tissues. In this context, modeling approaches provide flexible ways to predict the outcome of electrical stimulation in terms of CNS activation. In this paper, we present state-of-the-art modeling methods with sufficient details to allow the reader to rapidly build numerical models of neuronal extracellular microstimulation. These include (1) the computation of the electrical potential field created by the stimulation in the tissue, and (2) the response of a target neuron to this field. Two main approaches are described: First we describe the classical hybrid approach that combines the finite element modeling of the potential field with the calculation of the neuron's response in a cable equation framework (compartmentalized neuron models). Then, we present a "whole finite element" approach allowing the simultaneous calculation of the extracellular and intracellular potentials, by representing the neuronal membrane with a thin-film approximation. This approach was previously introduced in the frame of neural recording, but has never been implemented to determine the effect of extracellular stimulation on the neural response at a sub-compartment level. Here, we show on an example that the latter modeling scheme can reveal important sub-compartment behavior of the neural membrane that cannot be resolved using the hybrid approach. The goal of this paper is also to describe in detail the practical implementation of these methods to allow the reader to easily build new models using standard software packages. These modeling paradigms, depending on the situation, should help build more efficient high-density neural prostheses for CNS rehabilitation.

No MeSH data available.


Related in: MedlinePlus