Automatic Identification of Axon Bundle Activation for Epiretinal Prosthesis

Feasibility Assessment of an Optically Powered Digital Retinal Prosthesis Architecture for Retinal Ganglion Cell Stimulation

Clinical trials previously demonstrated the notable capacity to elicit visual percepts in blind patients affected with retinal diseases by electrically stimulating the remaining neurons on the retina. However, these implants restored very limited visual acuity and required transcutaneous cables traversing the eyeball, leading to reduced reliability and complex surgery with high postoperative infection risks. To overcome the limitations imposed by cables, a retinal implant architecture in which near-infrared illumination carries both power and data through the pupil to a digital stimulation controller is presented. A high efficiency multi-junction photovoltaic cell transduces the optical power to a CMOS stimulator capable of delivering flexible interleaved sequential stimulation through a diamond microelectrode array. To demonstrate the capacity to elicit a neural response with this approach while complying with the optical irradiance limit at the pupil, fluorescence imaging with a calcium indicator is used on a degenerate rat retina. The power delivered by the laser at the permissible irradiance of 4 mW/mm2 at 850 nm is shown to be sufficient to both power the stimulator ASIC and elicit a response in retinal ganglion cells (RGCs), with the ability to generate of up to 35 000 pulses per second at the average stimulation threshold. This confirms the feasibility of generating a response in RGCs with an infrared-powered digital architecture capable of delivering complex sequential stimulation patterns at high repetition rates, albeit with some limitations.

Precise control of neural activity using dynamically optimized electrical stimulation

Neural implants have the potential to restore lost sensory function by electrically evoking the complex naturalistic activity patterns of neural populations. However, it can be difficult to predict and control evoked neural responses to simultaneous multi-electrode stimulation due to nonlinearity of the responses. We present a solution to this problem and demonstrate its utility in the context of a bidirectional retinal implant for restoring vision. A dynamically optimized stimulation approach encodes incoming visual stimuli into a rapid, greedily chosen, temporally dithered and spatially multiplexed sequence of simple stimulation patterns. Stimuli are selected to optimize the reconstruction of the visual stimulus from the evoked responses. Temporal dithering exploits the slow time scales of downstream neural processing, and spatial multiplexing exploits the independence of responses generated by distant electrodes. The approach was evaluated using an experimental laboratory prototype of a retinal implant: large-scale, high-resolution multi-electrode stimulation and recording of macaque and rat retinal ganglion cells ex vivo. The dynamically optimized stimulation approach substantially enhanced performance compared to existing approaches based on static mapping between visual stimulus intensity and current amplitude. The modular framework enabled parallel extensions to naturalistic viewing conditions, incorporation of perceptual similarity measures, and efficient implementation for an implantable device. A direct closed-loop test of the approach supported its potential use in vision restoration.

Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach

Objective. Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs dynamical control systems to detect and decipher electrically evoked neural activity despite the presence of electrical artifacts.Approach. Our proposed method leverages the unique spatiotemporal patterns of neural activity and electrical artifacts to distinguish and identify individual neural spikes. We designed distinctive dynamical models for both the stimulation artifact and each neuron observed during spontaneous neural activity. We can estimate which neurons were active by analyzing the recorded voltage responses across multiple electrodes post-stimulation. This technique also allows us to exclude signals from electrodes heavily affected by stimulation artifacts, such as the stimulating electrode itself, yet still accurately differentiate between evoked spikes and electrical artifacts.Main results. We applied our method to high-density multi-electrode recordings from the primate retina in anex vivosetup, using a grid of 512 electrodes. Through repeated electrical stimulations at varying amplitudes, we were able to construct activation curves for each neuron. The curves obtained with our method closely resembled those derived from manual spike sorting. Additionally, the stimulation thresholds we estimated strongly agreed with those determined through manual analysis, demonstrating high reliability (R2=0.951for human 1 andR2=0.944for human 2).Significance. Our method can effectively separate evoked neural spikes from stimulation artifacts by exploiting the distinct spatiotemporal propagation patterns captured by a dense, large-scale multi-electrode array. This technique holds promise for future applications in real-time closed-loop stimulation systems and for managing multi-channel stimulation strategies.

Learning to see via epiretinal implant stimulationin silicowith model-based deep reinforcement learning

OBJECTIVE

Diseases such as age-related macular degeneration and retinitis pigmentosa cause the degradation of the photoreceptor layer. One approach to restore vision is to electrically stimulate the surviving retinal ganglion cells with a microelectrode array such as epiretinal implants. Epiretinal implants are known to generate visible anisotropic shapes elongated along the axon fascicles of neighboring retinal ganglion cells. Recent work has demonstrated that to obtain isotropic pixel-like shapes, it is possible to map axon fascicles and avoid stimulating them by inactivating electrodes or lowering stimulation current levels. Avoiding axon fascicule stimulation aims to remove brushstroke-like shapes in favor of a more reduced set of pixel-like shapes.

APPROACH

In this study, we propose the use of isotropic and anisotropic shapes to render intelligible images on the retina of a virtual patient in a reinforcement learning environment named rlretina. The environment formalizes the task as using brushstrokes in a stroke-based rendering task.

MAIN RESULTS

We train a deep reinforcement learning agent that learns to assemble isotropic and anisotropic shapes to form an image. We investigate which error-based or perception-based metrics are adequate to reward the agent. The agent is trained in a model-based data generation fashion using the psychophysically validated axon map model to render images as perceived by different virtual patients. We show that the agent can generate more intelligible images compared to the naive method in different virtual patients.

SIGNIFICANCE

This work shares a new way to address epiretinal stimulation that constitutes a first step towards improving visual acuity in artificially-restored vision using anisotropic phosphenes.

Inference of Electrical Stimulation Sensitivity from Recorded Activity of Primate Retinal Ganglion Cells

High-fidelity electronic implants can in principle restore the function of neural circuits by precisely activating neurons via extracellular stimulation. However, direct characterization of the individual electrical sensitivity of a large population of target neurons, to precisely control their activity, can be difficult or impossible. A potential solution is to leverage biophysical principles to infer sensitivity to electrical stimulation from features of spontaneous electrical activity, which can be recorded relatively easily. Here, this approach is developed and its potential value for vision restoration is tested quantitatively using large-scale multielectrode stimulation and recording from retinal ganglion cells (RGCs) of male and female macaque monkeys ex vivo Electrodes recording larger spikes from a given cell exhibited lower stimulation thresholds across cell types, retinas, and eccentricities, with systematic and distinct trends for somas and axons. Thresholds for somatic stimulation increased with distance from the axon initial segment. The dependence of spike probability on injected current was inversely related to threshold, and was substantially steeper for axonal than somatic compartments, which could be identified by their recorded electrical signatures. Dendritic stimulation was largely ineffective for eliciting spikes. These trends were quantitatively reproduced with biophysical simulations. Results from human RGCs were broadly similar. The inference of stimulation sensitivity from recorded electrical features was tested in a data-driven simulation of visual reconstruction, revealing that the approach could significantly improve the function of future high-fidelity retinal implants.SIGNIFICANCE STATEMENT This study demonstrates that individual in situ primate retinal ganglion cells of different types respond to artificially generated, external electrical fields in a systematic manner, in accordance with theoretical predictions, that allows for prediction of electrical stimulus sensitivity from recorded spontaneous activity. It also provides evidence that such an approach could be immensely helpful in the calibration of clinical retinal implants.

High-Fidelity Reproduction of Visual Signals by Electrical Stimulation in the Central Primate Retina

Electrical stimulation of retinal ganglion cells (RGCs) with electronic implants provides rudimentary artificial vision to people blinded by retinal degeneration. However, current devices stimulate indiscriminately and therefore cannot reproduce the intricate neural code of the retina. Recent work has demonstrated more precise activation of RGCs using focal electrical stimulation with multielectrode arrays in the peripheral macaque retina, but it is unclear how effective this can be in the central retina, which is required for high-resolution vision. This work probes the neural code and effectiveness of focal epiretinal stimulation in the central macaque retina, using large-scale electrical recording and stimulation ex vivo The functional organization, light response properties, and electrical properties of the major RGC types in the central retina were mostly similar to the peripheral retina, with some notable differences in density, kinetics, linearity, spiking statistics, and correlations. The major RGC types could be distinguished by their intrinsic electrical properties. Electrical stimulation targeting parasol cells revealed similar activation thresholds and reduced axon bundle activation in the central retina, but lower stimulation selectivity. Quantitative evaluation of the potential for image reconstruction from electrically evoked parasol cell signals revealed higher overall expected image quality in the central retina. An exploration of inadvertent midget cell activation suggested that it could contribute high spatial frequency noise to the visual signal carried by parasol cells. These results support the possibility of reproducing high-acuity visual signals in the central retina with an epiretinal implant.SIGNIFICANCE STATEMENT Artificial restoration of vision with retinal implants is a major treatment for blindness. However, present-day implants do not provide high-resolution visual perception, in part because they do not reproduce the natural neural code of the retina. Here, we demonstrate the level of visual signal reproduction that is possible with a future implant by examining how accurately responses to electrical stimulation of parasol retinal ganglion cells can convey visual signals. Although the precision of electrical stimulation in the central retina was diminished relative to the peripheral retina, the quality of expected visual signal reconstruction in parasol cells was greater. These findings suggest that visual signals could be restored with high fidelity in the central retina using a future retinal implant.

Retinal Prostheses: Engineering and Clinical Perspectives for Vision Restoration

A retinal prosthesis, also known as a bionic eye, is a device that can be implanted to partially restore vision in patients with retinal diseases that have resulted in the loss of photoreceptors (e.g., age-related macular degeneration and retinitis pigmentosa). Recently, there have been major breakthroughs in retinal prosthesis technology, with the creation of numerous types of implants, including epiretinal, subretinal, and suprachoroidal sensors. These devices can stimulate the remaining cells in the retina with electric signals to create a visual sensation. A literature review of the pre-clinical and clinical studies published between 2017 and 2023 is conducted. This narrative review delves into the retinal anatomy, physiology, pathology, and principles underlying electronic retinal prostheses. Engineering aspects are explored, including electrode-retina alignment, electrode size and material, charge density, resolution limits, spatial selectivity, and bidirectional closed-loop systems. This article also discusses clinical aspects, focusing on safety, adverse events, visual function, outcomes, and the importance of rehabilitation programs. Moreover, there is ongoing debate over whether implantable retinal devices still offer a promising approach for the treatment of retinal diseases, considering the recent emergence of cell-based and gene-based therapies as well as optogenetics. This review compares retinal prostheses with these alternative therapies, providing a balanced perspective on their advantages and limitations. The recent advancements in retinal prosthesis technology are also outlined, emphasizing progress in engineering and the outlook of retinal prostheses. While acknowledging the challenges and complexities of the technology, this article highlights the significant potential of retinal prostheses for vision restoration in individuals with retinal diseases and calls for continued research and development to refine and enhance their performance, ultimately improving patient outcomes and quality of life.

Focal electrical stimulation of human retinal ganglion cells for vision restoration

Objective. Vision restoration with retinal implants is limited by indiscriminate simultaneous activation of many cells and cell types, which is incompatible with reproducing the neural code of the retina. Recent work has shown that primate retinal ganglion cells (RGCs), which transmit visual information to the brain, can be directly electrically activated with single-cell, single-spike, cell-type precision - however, this possibility has never been tested in the human retina. In this study we aim to characterize, for the first time, direct in situ extracellular electrical stimulation of individual human RGCs.Approach. Extracellular electrical stimulation of individual human RGCs was conducted in three human retinas ex vivo using a custom large-scale, multi-electrode array capable of simultaneous recording and stimulation. Measured activation properties were compared directly to extensive results from macaque.Main results. Precise activation was in many cases possible without activating overlying axon bundles, at low stimulation current levels similar to those used in macaque. The major RGC types could be identified and targeted based on their distinctive electrical signatures. The measured electrical activation properties of RGCs, combined with a dynamic stimulation algorithm, was sufficient to produce an evoked visual signal that was nearly optimal given the constraints of the interface.Significance. These results suggest the possibility of high-fidelity vision restoration in humans using bi-directional epiretinal implants.

High spatial resolution artificial vision inferred from the spiking output of retinal ganglion cells stimulated by optogenetic and electrical means

With vision impairment affecting millions of people world-wide, various strategies aiming at vision restoration are being undertaken. Thanks to decades of extensive research, electrical stimulation approaches to vision restoration began to undergo clinical trials. Quite recently, another technique employing optogenetic therapy emerged as a possible alternative. Both artificial vision restoration strategies reported poor spatial resolution so far. In this article, we compared the spatial resolution inferred ex vivo under ideal conditions using a computational model analysis of the retinal ganglion cell (RGC) spiking activity. The RGC spiking was stimulated in epiretinal configuration by either optogenetic or electrical means. RGCs activity was recorded from the ex vivo retina of transgenic late-stage photoreceptor-degenerated mice (rd10) using a high-density Complementary Metal Oxide Semiconductor (CMOS) based microelectrode array. The majority of retinal samples were stimulated by both, optogenetic and electrical stimuli using a spatial grating stimulus. A population-level analysis of the spiking activity of identified RGCs was performed and the spatial resolution achieved through electrical and optogenetic photo-stimulation was inferred using a support vector machine classifier. The best f1 score of the classifier for the electrical stimulation in epiretinal configuration was 86% for 32 micron wide gratings and increased to 100% for 128 microns. For optogenetically activated cells, we obtained high f1 scores of 82% for 10 microns grid width for a photo-stimulation frequency of 2.5 Hz and 73% for a photo-stimulation frequency of 10 Hz. A subsequent analysis, considering only the RGCs modulated in both electrical and optogenetic stimulation protocols revealed no significant difference in the prediction accuracy between the two stimulation modalities. The results presented here indicate that a high spatial resolution can be achieved for electrical or optogenetic artificial stimulation using the activated retinal ganglion cell output.

Correlated Activity in the Degenerate Retina Inhibits Focal Response to Electrical Stimulation

Retinal prostheses have shown some clinical success in patients with retinitis pigmentosa and age-related macular degeneration. However, even after the implantation of a retinal prosthesis, the patient’s visual acuity is at best less than 20/420. Reduced visual acuity may be explained by a decrease in the signal-to-noise ratio due to the spontaneous hyperactivity of retinal ganglion cells (RGCs) found in degenerate retinas. Unfortunately, abnormal retinal rewiring, commonly observed in degenerate retinas, has rarely been considered for the development of retinal prostheses. The purpose of this study was to investigate the aberrant retinal network response to electrical stimulation in terms of the spatial distribution of the electrically evoked RGC population. An 8 × 8 multielectrode array was used to measure the spiking activity of the RGC population. RGC spikes were recorded in wild-type [C57BL/6J; P56 (postnatal day 56)], rd1 (P56), rd10 (P14 and P56) mice, and macaque [wild-type and drug-induced retinal degeneration (RD) model] retinas. First, we performed a spike correlation analysis between RGCs to determine RGC connectivity. No correlation was observed between RGCs in the control group, including wild-type mice, rd10 P14 mice, and wild-type macaque retinas. In contrast, for the RD group, including rd1, rd10 P56, and RD macaque retinas, RGCs, up to approximately 400–600 μm apart, were significantly correlated. Moreover, to investigate the RGC population response to electrical stimulation, the number of electrically evoked RGC spikes was measured as a function of the distance between the stimulation and recording electrodes. With an increase in the interelectrode distance, the number of electrically evoked RGC spikes decreased exponentially in the control group. In contrast, electrically evoked RGC spikes were observed throughout the retina in the RD group, regardless of the inter-electrode distance. Taken together, in the degenerate retina, a more strongly coupled retinal network resulted in the widespread distribution of electrically evoked RGC spikes. This finding could explain the low-resolution vision in prosthesis-implanted patients.