4.5 Article

Current approaches to model extracellular electrical neural microstimulation

Journal

Publisher

FRONTIERS RESEARCH FOUNDATION
DOI: 10.3389/fncom.2014.00013

Keywords

finite element modeling; extracellular focal microstimulation; microelectrode arrays; neural prosthesis; brain implants; ground surface configuration; compartmentalized neuron models; thin-film approximation

Funding

  1. Agence Nationale de la Recherche (ANR) [ANR-10-BLAN-0316]
  2. European Community [280433]
  3. Agence Nationale de la Recherche (ANR) [ANR-10-BLAN-0316] Funding Source: Agence Nationale de la Recherche (ANR)

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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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