Recent Advances in Computational and Experimental Bioelectromagnetics for Neuroprosthetics

Stang, John; Chen, Guanbo; Kosta, Pragya; Paknahad, Javad; Machnoor, Manjunath; Iseri, Ege; Du, Jinze; Brizi, Danilo; Loizos, Kyle; Lazzi, Gianluca

doi:10.1109/iceaa.2019.8878960

Cited by 10 publications

(8 citation statements)

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“…Admittance method/NEURON computational framework. In this work, we utilized our threedimensional Admittance Method (AM)/NEURON multi-scale computational modeling platform 12,[61][62][63][64][65][66][67][68][69][70][71][72][73] to predict the electric fields generated inside retinal tissue, coupled to multi-compartmental models of neurons in order to determine the activation of realistic RGCs. The Admittance Method linked with NEURON has proven a powerful approach not only for studies of field distribution inside the tissue due to electrical stimulation, but also providing a platform to analyze realistic representations of various cell types 12,61-73 . Admittance method: constructing the retina tissue and electrodes.…”

Section: Methodsmentioning

confidence: 99%

“…However, slower rate of increase in Figure 1. A2 and D1 realistic morphologies as implemented and coded in our multiscale Admittance Method/ NEURON computational platform [61][62][63][64][65][66][67][68][69][70][71][72][73] . Left: A2-monostratified RGC ramified in the inner part of inner plexiform layer and has a larger soma and dendritic field diameters.…”

Section: Extracellular Stimulation: Frequency Response Of Rgcs the Mmentioning

confidence: 99%

“…The voltage at the center of each voxel was estimated using a linear interpolation function. Since the Admittance Method and NEURON use the same coordinates, a computational code was developed to superimpose the potential computed in the tissue volume into the NEURON model and apply it as an extracellular voltage, using the "extracellular" mechanism built into NEURON software, to each compartment in the Hodgkin-Huxley circuit in series with the membrane 12,[61][62][63][64][65][66][67][68][69][70][71] .…”

Section: Neuron: Retinal Ganglion Cell Models the Morphology Of Gangmentioning

confidence: 99%

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Color and cellular selectivity of retinal ganglion cell subtypes through frequency modulation of electrical stimulation

Paknahad

Loizos

Lan

et al. 2021

Sci Rep

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Epiretinal prostheses aim at electrically stimulating the inner most surviving retinal cells—retinal ganglion cells (RGCs)—to restore partial sight to the blind. Recent tests in patients with epiretinal implants have revealed that electrical stimulation of the retina results in the percept of color of the elicited phosphenes, which depends on the frequency of stimulation. This paper presents computational results that are predictive of this finding and further support our understanding of the mechanisms of color encoding in electrical stimulation of retina, which could prove pivotal for the design of advanced retinal prosthetics that elicit both percept and color. This provides, for the first time, a directly applicable “amplitude-frequency” stimulation strategy to “encode color” in future retinal prosthetics through a predictive computational tool to selectively target small bistratified cells, which have been shown to contribute to “blue-yellow” color opponency in the retinal circuitry. The presented results are validated with experimental data reported in the literature and correlated with findings in blind patients with a retinal prosthetic implant collected by our group.

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Section: Methodsmentioning

confidence: 99%

Section: Extracellular Stimulation: Frequency Response Of Rgcs the Mmentioning

confidence: 99%

Section: Neuron: Retinal Ganglion Cell Models the Morphology Of Gangmentioning

confidence: 99%

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Color and cellular selectivity of retinal ganglion cell subtypes through frequency modulation of electrical stimulation

Paknahad

Loizos

Lan

et al. 2021

Sci Rep

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“…Our multi-scale computational framework utilizes three-dimensional (3D) AM and NEURON simulations to model electrical stimulation of retinal tissue and simulates the responses of retinal neurons [28][29][30][31][32][33][34][35][36][37]. First, AM is used to compute the voltage distribution inside the extracellular space.…”

Section: Admittance Methods (Am)/neuron Computational Frameworkmentioning

confidence: 99%

“…In this work, we utilized our computational modeling platform, AM/NEURON [28][29][30][31][32][33][34][35][36][37], to better understand the response of retinal BCs to electrical stimulation as a function of various stimulation parameters. This modeling platform will enable us to capture factors affecting the responsiveness of BCs through alterations of the electrical stimulation waveforms.…”

Section: Introductionmentioning

confidence: 99%

Mechanisms underlying activation of retinal bipolar cells through targeted electrical stimulation: a computational study

Paknahad¹,

Kosta²,

Bouteiller³

et al. 2021

J. Neural Eng.

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Objective. Retinal implants have been developed to electrically stimulate healthy retinal neurons in the progressively degenerated retina. Several stimulation approaches have been proposed to improve the visual percept induced in patients with retinal prostheses. We introduce a computational model capable of simulating the effects of electrical stimulation on retinal neurons. Leveraging this computational platform, we delve into the underlying mechanisms influencing the sensitivity of retinal neurons’ response to various stimulus waveforms. Approach. We implemented a model of spiking bipolar cells (BCs) in the magnocellular pathway of the primate retina, diffuse BC subtypes (DB4), and utilized our multiscale Admittance Method (AM)-NEURON computational platform to characterize the response of BCs to epiretinal electrical stimulation with monophasic, symmetric, and asymmetric biphasic pulses. Main Results. Our investigations yielded four notable results: (i) The latency of BCs increases as stimulation pulse duration lengthens; conversely, this latency decreases as the current amplitude increases. (ii) Stimulation with a long anodic-first symmetric biphasic pulse (duration > 8 ms) results in a significant decrease in spiking threshold compared to stimulation with similar cathodic-first pulses (from 98.2 µA to 57.5 µA). (iii) The hyperpolarization-activated cyclic nucleotide-gated (HCN) channel was a prominent contributor to the reduced threshold of BCs in response to long anodic-first stimulus pulses. (iv) Finally, extending the study to asymmetric waveforms, our results predict a lower BCs threshold using asymmetric long anodic-first pulses compared to that of asymmetric short cathodic-first stimulation. Significance. This study predicts the effects of several stimulation parameters on spiking BCs response to electrical stimulation. Of importance, our findings shed light on mechanisms underlying the experimental observations from the literature, thus highlighting the capability of the methodology to predict and guide the development of electrical stimulation protocols to generate a desired biological response, thereby constituting an ideal testbed for the development of electroceutical devices.

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Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models

Morales

Kosta

et al. 2022

Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications

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Recent Advances in Computational and Experimental Bioelectromagnetics for Neuroprosthetics

Cited by 10 publications

References 1 publication

Color and cellular selectivity of retinal ganglion cell subtypes through frequency modulation of electrical stimulation

Color and cellular selectivity of retinal ganglion cell subtypes through frequency modulation of electrical stimulation

Mechanisms underlying activation of retinal bipolar cells through targeted electrical stimulation: a computational study

Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models

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