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/m2 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.

An extraocular electrical stimulation approach to slow down the progression of retinal degeneration in an animal model.

Retinal diseases such as retinitis pigmentosa (RP) and age-related macular degeneration (AMD) are characterized by unrelenting neuronal death. However, electrical stimulation has been shown to induce neuroprotective changes in the retina capable of slowing down the progression of retinal blindness. In this work, a multi-scale computational model and modeling platform were used to design electrical stimulation strategies to better target the bipolar cells (BCs), that along with photoreceptors are affected at the early stage of retinal degenerative diseases. Our computational findings revealed that biphasic stimulus pulses of long pulse duration could decrease the activation threshold of BCs, and the differential stimulus threshold between ganglion cells (RGCs) and BCs, offering the potential of targeting the BCs during the early phase of degeneration. In vivo experiments were performed to evaluate the electrode placement and parameters found to target bipolar cells and evaluate the safety and efficacy of the treatment. Results indicate that the proposed transcorneal Electrical Stimulation (TES) strategy can attenuate retinal degeneration in a Royal College of Surgeon (RCS) rodent model, offering the potential to translate this work to clinical practice.

Impact of Retinal Degeneration on Response of ON and OFF Cone Bipolar Cells to Electrical Stimulation.

In retinal degenerative diseases, such as retinitis pigmentosa (RP) and age-related macular degeneration (AMD), the photoreceptors become stressed and start to degenerate in the early stages of the disease. Retinal prosthetic devices have been developed to restore vision in patients by applying electrical stimulation to the surviving retinal cells. However, these devices provide limited visual perception as the therapeutic interventions are generally considered in the later stages of the disease when only inner retinal layer cells are left. A potential treatment option for retinal degenerative diseases in the early stages can be stimulating bipolar cells, which receive presynaptic signals from photoreceptors. In this work, we constructed computational models of healthy and degenerated (both ON and OFF-type) cone bipolar cells (CBCs) with realistic morphologies extracted from connectomes of the healthy and early-stage degenerated rabbit retina. We examined these cells' membrane potential and axon terminal calcium current differences when subjected to electrical stimulation. In addition, we investigated how differently healthy and degenerated cells behave with respect to various stimulation parameters, including pulse duration and cells' distance from the stimulating electrode. The results suggested that regardless of the position of the OFF CBCs in the retina model, there is not a significant difference between the membrane potential of healthy and degenerate cells when electrically stimulated. However, the healthy ON CBC axon terminal membrane potential rising time-constant is shorter (0.29 ± 0.03 ms) than the degenerated cells (0.8 ± 0.07 ms). Moreover, the ionic calcium channels at the axon terminals of the cells have a higher concentration and higher current in degenerated cells (32.24 ± 6.12 pA) than the healthy cells (13.64 ± 2.88 pA) independently of the cell's position.

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.

Electrode Spacing and Current Distribution in Electrical Stimulation of Peripheral Nerve: A Computational Modeling Study using Realistic Nerve Models.

Electrical stimulation of peripheral nerves has long been used and proven effective in restoring function caused by disease or injury. Accurate placement of electrodes is often critical to properly excite the nerve and yield the desired outcome. Computational modeling is becoming an important tool that can guide the rapid development and optimization of such implantable neural stimulation devices. Here, we developed a heterogeneous very high-resolution computational model of a realistic peripheral nerve stimulated by a current source through cuff electrodes. We then calculated the current distribution inside the nerve and investigated the effect of electrodes spacing on current penetration. In the present study, we first describe model implementation and calibration; we then detail the methodology we use to calculate current distribution and apply it to characterize the effect of electrodes distance on current penetration. Our computational results indicate that when the source and return cuff electrodes are placed close to each other, the penetration depth in the nerve is shallower than the cases in which the electrode distance is larger. This study outlines the utility of the proposed computational methods and anatomically correct high-resolution models in guiding and optimizing experimental nerve stimulation protocols.

A Computational Model Simulates Light-Evoked Responses in the Retinal Cone Pathway.

Partial vision restoration on degenerated retina can be achieved by electrically stimulating the surviving retinal ganglion cells via implanted electrodes to elicit a signal corresponding to the natural response of the cells. Realistic computational models of electrical stimulation of the retina can prove useful to test different stimulation strategies and improve the performance of retinal implants. Simulation of healthy retinal networks and their dynamical response to natural light stimulation may also help us understand how retinal processing takes place via a series of electrical signals flowing through different stages of retinal processing, ultimately giving rise to visual percepts. Such models may provide further insights on retinal network processing and thus guide the design of retinal prostheses and their stimulation protocols to generate more natural percepts. This work aims to characterize the photocurrent generated by healthy cone photoreceptors in response to a light flash stimulation and the resulting membrane potential for the photoreceptors and its postsynaptic cone bipolar cells. A simple network of ten cone photoreceptors synapsing with a cone bipolar cell is simulated using the NEURON environment and validated against patch-clamp recordings of cone photoreceptors and ON-type bipolar cells (ON-BC). The results presented will be valuable in modeling light-evoked or electrically stimulated retinal networks that comprise cone pathways. The computational models and methods developed in this work will serve as an integral building block in the development of large and realistic retinal networks.Clinical Relevance- Accurate computational model of a retinal neural network can help in predicting cell responses to electrical stimulation in vision restoration therapies using prostheses. It can be leveraged to optimize the stimulation parameters to match the natural light response of the network as closely as possible.

Modeling ON Cone Bipolar Cells for Electrical Stimulation.

Retinal prosthetic systems have been developed to help blind patients suffering from retinal degenerative diseases gain some useful form of vision. Various experimental and computational studies have been performed to test electrical stimulation strategies that can improve the performance of these devices. Detailed computational models of retinal neurons, such as retinal ganglion cells (RGCs) and bipolar cells (BCs), allow us to explore the mechanisms underlying the response of cells to electrical stimulation. While electrophysiological studies have shown the presence of voltage-gated ionic channels in different regions of BCs, many of the existing cone BCs models are assumed to be passive or only contain calcium channels at the synaptic terminals. We have utilized our Admittance Method (AM)-NEURON computational platform to implement a more realistic model of ON-BCs. Our model closely replicates the recent patch-clamp experiments directly measuring the response of ON-BCs to epiretinal electrical stimulation and thereby predicts the regional distributions of the ionic channels. Our computational results further indicate that outward potassium current strongly contributes to the depolarizing voltage transient of ON-BCs in response to electrical stimulation.

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.

Model-based comparison of current flow in rod bipolar cells of healthy and early-stage degenerated retina.

Retinal degenerative diseases, such as retinitis pigmentosa, are generally thought to initiate with the loss of photoreceptors, though recent work suggests that plasticity and remodeling occurs prior to photoreceptor cell loss. This degeneration subsequently leads to death of other retinal neurons, creating functional alterations and extensive remodeling of retinal networks. Retinal prosthetic devices stimulate the surviving retinal cells by applying external current using implanted electrodes. Although these devices restore partial vision, the quality of restored vision is limited. Further knowledge about the precise changes in degenerated retina as the disease progresses is essential to understand how current flows in retinas undergoing degenerative disease and to improve the performance of retinal prostheses. We developed computational models that describe current flow from rod photoreceptors to rod bipolar cells (RodBCs) in the healthy and early-stage degenerated retina. Morphologically accurate models of retinal cells with their synapses are constructed based on retinal connectome datasets, created using serial section transmission electron microscopy (TEM) images of 70 nm-thick slices of either healthy (RC1) or early-stage degenerated (RPC1) rabbit retina. The passive membrane and active ion currents of each cell are implemented using conductance-based models in the Neuron simulation environment. In response to photocurrent input at rod photoreceptors, the simulated membrane potential at RodBCs in early degenerate tissue is approximately 10-20 mV lower than that of RodBCs of that observed in wild type retina. Results presented here suggest that although RodBCs in RPC1 show early, altered morphology compared to RC1, the lower membrane potential is primarily a consequence of reduced rod photoreceptor input to RodBCs in the degenerated retina. Frequency response and step input analyses suggest that individual cell responses of RodBCs in either healthy or early-degenerated retina, prior to substantial photoreceptor cell loss, do not differ significantly.

Simulation-Based Optimization of Figure-of-Eight Coil Designs and Orientations for Magnetic Stimulation of Peripheral Nerve.

Although magnetic neural stimulation has many advantages over electrical neural stimulation, its main disadvantages are higher energy requirement and poor stimulation selectivity. The orientation and location of the coil with respect to the stimulation site play a critical role in determining the stimulation threshold and stimulation selectivity. Utilizing numerical simulations in this work, we optimized the design parameters, orientation, and positioning of magnetic coils with respect to the peripheral nerve for improved stimulation efficacy. Specifically, we investigated different orientations and positions of the figure-of-eight coils for neural stimulation of the rat sciatic nerve. We also examined the effect of coil design parameters (number of layers and turns) and different coil electrical configurations (opposite vs. same direction of coil currents and series vs. parallel coil connections) on the stimulation threshold. We leveraged the multi-resolution impedance method and a heterogeneous multi-fascicular anatomical model of rat sciatic nerve to explore the possibility of selective stimulation as well. Neural excitation of a nerve fiber was implemented by an equivalent cable model and Frankenhaeuser-Huxley equations using NEURON software. Results suggest that inter-fascicular selectivity could be achieved by properly orienting and positioning the coil with respect to the nerve. Further, by orienting the figure-of-eight coil at an angle of 90° and 6 mm offset, we could switch between primarily activating one fascicle (and barely activating the other) and reversing those roles by merely switching the current direction in the two coils of the figure-of-eight coil.

Stimulus waveform design for decreasing charge and increasing stimulation selectivity in retinal prostheses.

Retinal degenerative diseases, such as retinitis pigmentosa, begin with damage to the photoreceptor layer of the retina. In the absence of presynaptic input from photoreceptors, networks of electrically coupled AII amacrine and cone bipolar cells have been observed to exhibit oscillatory behaviour and result in spontaneous firing of ganglion cells. This ganglion cell activity could interfere with external stimuli provided by retinal prosthetic devices and potentially degrade their performance. In this work, the authors computationally investigate stimulus waveform designs, which can improve the performance of retinal prostheses by suppressing undesired spontaneous firing of ganglion cells and generating precise temporal spiking patterns. They utilise a multi-scale computational model for electrical stimulation of degenerated retina based on the admittance method and NEURON simulation environments. They present a class of asymmetric biphasic pulses that can generate precise ganglion cell firing patterns with up to 55% lower current requirements compared to traditional symmetric biphasic pulses. This lower current results in activation of only proximal ganglion cells, provides more focused stimulation and lowers the risk of tissue damage.

Selective stimulation of rat sciatic nerve using an array of mm-size magnetic coils: a simulation study.

This work proposes and computationally investigate the use of magnetic neural stimulation as an alternative to electrical stimulation to achieve selective activation of rat sciatic nerve. In particular, they assess the effectiveness of an array of small coils to obtain selective neural stimulation, as compared to a single coil. Specifically, an array of four mm-sized coils is used to stimulate rat sciatic nerve, targeting the regions of fascicles that are associated with different muscles of the leg. To evaluate the selectivity of activation, a three-dimensional heterogeneous multi-resolution nerve model is implemented using the impedance method for the computation of the magnetic and electric fields in the nerve. The performance metric 'selectivity index' is defined that measures the recruitment of the targeted region compared to other non-targeted regions of the nerve. The selectivity index takes values between -1 (least selective) and 1 (most selective). For each targeted region, a selectivity index of 0.75 or better is predicted for the proposed array configuration. The results suggest that an array of coils can provide superior spatial control of the electric field induced in the neural tissue compared to traditional extraneural electrode arrays, thus opening the possibility to applications where selective neurostimulation is of interest.

Reduced Heat Generation During Magnetic Stimulation of Rat Sciatic Nerve Using Current Waveform Truncation.

Current truncating circuit designs used in some controllable pulse width transcranial magnetic stimulation systems can be adapted for use with the peripheral nervous system. Such a scaled-down stimulator produces neuromuscular activation using less stimulus energy than described in previous reports of sciatic nerve stimulation. To evaluate the energy reductions possible with current truncation, we performed six in vivo experiments in rats where the magnetic stimulating coil abutted the sciatic nerve. We used electromyographic data to quantify neuromuscular response, with a criterion level of 20%-of-maximum to indicate a useful level of neuromuscular activation. The energy required to evoke this criterion response from muscles innervated by the sciatic nerve was reduced by approximately 34% from 10.7J with a stimulus waveform lasting 300 [Formula: see text] to 7.1J with a waveform lasting 50 [Formula: see text]. In water, the 300 [Formula: see text] pulse heated the coil by 0.30°C whereas the 50 [Formula: see text] pulse heated the coil by 0.15°C. Truncated-waveform magnetic stimulation systems can be used in basic research and clinical applications not requiring rapidly pulsed stimuli. An example of such a clinical application is left vagus nerve stimulation, a treatment that is reported to reduce epileptic partial-onset seizures.