Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus

Toward Safety Protocols for Peripheral Nerve Stimulation (PNS): A Computational and Experimental Approach

As the clinical applicability of peripheral nerve stimulation (PNS) expands, the need for PNS-specific safety criteria becomes pressing. This study addresses this need, utilizing a novel machine learning and computational bio-electromagnetics modeling platform to establish a safety criterion that captures the effects of fields and currents induced on axons. Our approach is comprised of three steps: experimentation, model creation, and predictive simulation. We collected high-resolution images of control and stimulated rat sciatic nerve samples at varying stimulation intensities and performed high-resolution image segmentation. These segmented images were used to train machine learning tools for the automatic classification of morphological properties of control and stimulated PNS nerves. Concurrently, we utilized our quasi-static Admittance Method-NEURON (AM-NEURON) computational platform to create realistic nerve models and calculate induced currents and charges, both critical elements of nerve safety criteria. These steps culminate in a cellular-level correlation between morphological changes and electrical stimulation parameters. This correlation informs the determination of thresholds of electrical parameters that are found to be associated with damage, such as maximum cell charge density. The proposed methodology and resulting criteria combine experimental findings with computational modeling to generate a safety threshold curve that captures the relationship between stimulation current and the potential for axonal damage. Although focused on a specific exposure condition, the approach presented here marks a step towards developing context-specific safety criteria in PNS neurostimulation, encouraging similar analyses across varied neurostimulation scenarios. Bioelectromagnetics.

Characterization of Induced Current Density During Transcorneal Electrical Stimulation to Promote Neuroprotection in the Degenerating Retina

OBJECTIVE

Transcorneal electrical stimulation (TES) is a promising approach to delay retinal degeneration by inducing extracellular electric field-driven neuroprotective effects within photoreceptors. Although achieving precise electric field control is feasible in vitro, characterizing these fields becomes intricate and largely unexplored in vivo due to uneven distribution in the heterogeneous body. In this paper, we investigate and characterize electric fields within the retina during TES to assess the potential for therapeutic approaches Methods: We developed a computational model of a rat's head, enabling us to generate predictive simulations of the voltage and current density induced in the retina. Subsequently, an in vivo experimental setup involving Royal College of Surgeon (RCS) rats was implemented to measure the voltage across the retina using identical electrode configurations as employed in the simulations.

RESULTS

A stimulation amplitude of 0.2-0.3 mA may be necessary during TES in rats to induce a current density of at least 20 A/[Formula: see text] in the retina, which is the lower limit for triggering neuroprotective effects according to culture studies on neural cells. Measurement taken from cadaveric pigs' eyes revealed that a stimulation amplitude of 1 mA is necessary for achieving the same current density.

CONCLUSION

The computational modeling approach presented in this study was validated with experimental data and can be leveraged for predictive simulations to optimize the electrode design and stimulation parameters of TES.

SIGNIFICANCE

Once validated, the flexibility and low research cost of computational models are valuable in optimization studies where testing on live subjects is not feasible.

Adaptive octree meshes for simulation of extracellular electrophysiology

Objective.The interaction between neural tissues and artificial electrodes is crucial for understanding and advancing neuroscientific research and therapeutic applications. However, accurately modeling this space around the neurons rapidly increases the computational complexity of neural simulations.Approach.This study demonstrates a dynamically adaptive simulation method that greatly accelerates computation by adjusting spatial resolution of the simulation as needed. Use of an octree structure for the mesh, in combination with the admittance method for discretizing conductivity, provides both accurate approximation and ease of modification on-the-fly.Main results.In tests of both local field potential estimation and multi-electrode stimulation, dynamically adapted meshes achieve accuracy comparable to high-resolution static meshes in an order of magnitude less time.Significance.The proposed simulation pipeline improves model scalability, allowing greater detail with fewer computational resources. The implementation is available as an open-source Python module, providing flexibility and ease of reuse for the broader research community.

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

Electrical stimulation of the peripheral nervous system is a promising therapeutic option for several conditions; however, its effects on tissue and the safety of the stimulation remain poorly understood. In order to devise stimulation protocols that enhance therapeutic efficacy without the risk of causing tissue damage, we constructed computational models of peripheral nerve and stimulation cuffs based on extremely high-resolution cross-sectional images of the nerves using the most recent advances in computing power and machine learning techniques. We developed nerve models using nonstimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to explore how nerve damage affects the induced current density distribution. Using our in-house computational, quasi-static, platform, and the Admittance Method (AM), we estimated the induced current distribution within the nerves and compared it for healthy and damaged nerves. We also estimated the extent of localized cell damage in both healthy and damaged nerve samples. When the nerve is damaged, as demonstrated principally by the decreased nerve fiber packing, the current penetrates deeper into the over-stimulated nerve than in the healthy sample. As safety limits for electrical stimulation of peripheral nerves still refer to the Shannon criterion to distinguish between safe and unsafe stimulation, the capability this work demonstrated is an important step toward the development of safety criteria that are specific to peripheral nerve and make use of the latest advances in computational bioelectromagnetics and machine learning, such as Python-based AM and CNN-based nerve image segmentation.

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

Although electrical stimulation is an established treatment option for multiple central nervous and peripheral nervous system diseases, its effects on the tissue and subsequent safety of the stimulation are not well understood. Therefore, it is crucial to design stimulation protocols that maximize therapeutic efficacy while avoiding any potential tissue damage. Further, the stimulation levels need to be adjusted regularly to ensure that they are safe even with the changes to the nerve due to long-term stimulation. Using the latest advances in computing capabilities and machine learning approaches, we developed computational models of peripheral nerve stimulation based on very high-resolution cross-sectional images of the nerves. We generated nerve models constructed from non-stimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to examine how the current density distribution is affected by nerve damage. Using our in-house numerical solver, the Admittance Method (AM), we computed the induced current distribution inside the nerves and compared the current penetration for healthy and damaged nerves. Our computational results indicate that when the nerve is damaged, primarily evidenced by the decreased nerve fiber packing, the current penetrates deeper inside the nerve than in the healthy case. As safety limits for electrical stimulation of biological tissue are still debated, we ultimately aim to utilize our computational models to determine refined safety criteria and help design safer and more efficacious electrical stimulation protocols.

Selective Activation of Retinal Ganglion Cell Subtypes Through Targeted Electrical Stimulation Parameters

To restore vision to the low vision, epiretinal implants have been developed to electrically stimulate the healthy retinal ganglion cells (RGCs) in the degenerate retina. Given the diversity of retinal ganglion cells as well as the difference in their visual function, selective activation of RGCs subtypes can significantly improve the quality of the restored vision. Our recent results demonstrated that with the proper modulation of the current amplitude, small D1-bistratified cells with the contribution to blue/yellow color opponent pathway can be selectively activated at high frequency (200 Hz). The computational results correlated with the clinical findings revealing the blue sensation of 5/7 subjects with epiretinal implants at high frequency. Here we further explored the impacts of alterations in pulse duration and interphase gap on the response of RGCs at high frequency. We used the developed RGCs, A2-monostratified and D1-bistratified, and examined their response to a range of pulse durations (0.1−1.2 ms) and interphase gaps (0−1 ms). We found that the use of short pulse durations with no interphase gap at high frequency increases the differential response of RGCs, offering better opportunities for selective activation of D1 cells. The presence of the interphase gap has shown to reduce the overall differential response of RGCs. We also explored how the low density of calcium channels enhances the responsiveness of RGCs at high frequency.

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

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: (a) the latency of BCs increases as stimulation pulse duration lengthens; conversely, this latency decreases as the current amplitude increases. (b) 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 to 57.5µA). (c) The hyperpolarization-activated cyclic nucleotide-gated channel was a prominent contributor to the reduced threshold of BCs in response to long anodic-first stimulus pulses. (d) 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.

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

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.

Targeted Stimulation of Retinal Ganglion Cells in Epiretinal Prostheses: A Multiscale Computational Study

Retinal prostheses aim at restoring partial sight to patients that are blind due to retinal degenerative diseases by electrically stimulating the surviving healthy retinal neurons. Ideally, the electrical stimulation of the retina is intended to induce localized, focused, percepts only; however, some epiretinal implant subjects have reported seeing elongated phosphenes in a single electrode stimulation due to the axonal activation of retinal ganglion cells (RGCs). This issue can be addressed by properly devising stimulation waveforms so that the possibility of inducing axonal activation of RGCs is minimized. While strategies to devise electrical stimulation waveforms to achieve a focal RGCs response have been reported in literature, the underlying mechanisms are not well understood. This article intends to address this gap; we developed morphologically and biophysically realistic computational models of two classified RGCs: D1-bistratified and A2-monostratified. Computational results suggest that the sodium channel band (SOCB) is less sensitive to modulations in stimulation parameters than the distal axon (DA), and DA stimulus threshold is less sensitive to physiological differences among RGCs. Therefore, over a range of RGCs distal axon diameters, short-pulse symmetric biphasic waveforms can enhance the stimulation threshold difference between the SOCB and the DA. Appropriately designed waveforms can avoid axonal activation of RGCs, implying a consequential reduction of undesired strikes in the visual field.