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. 2019 Jun 24;9(1):9199.
doi: 10.1038/s41598-019-45416-4.

A model of ganglion axon pathways accounts for percepts elicited by retinal implants

Michael Beyeler  1   2   3 , Devyani Nanduri  4 , James D Weiland  4   5 , Ariel Rokem  6   7 , Geoffrey M Boynton  8 , Ione Fine  8
Affiliations

A model of ganglion axon pathways accounts for percepts elicited by retinal implants

Michael Beyeler et al. Sci Rep. .

Abstract

Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts ('phosphenes'). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across subjects and electrodes. We characterized these distortions by asking users of the Argus retinal prosthesis system (Second Sight Medical Products Inc.) to draw electrically elicited percepts on a touchscreen. Using ophthalmic fundus imaging and computational modeling, we show that elicited percepts can be accurately predicted by the topographic organization of optic nerve fiber bundles in each subject's retina, successfully replicating visual percepts ranging from 'blobs' to oriented 'streaks' and 'wedges' depending on the retinal location of the stimulating electrode. This provides the first evidence that activation of passing axon fibers accounts for the rich repertoire of phosphene shape commonly reported in psychophysical experiments, which can severely distort the quality of the generated visual experience. Overall our findings argue for more detailed modeling of biological detail across neural engineering applications.

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Conflict of interest statement

Authors M.B., J.D.W., A.R., G.M.B. and I.F. are collaborators with Second Sight Medical Products Inc., the company that develops, manufactures, and markets the Argus II Retinal Prosthesis System referenced within this article. Second Sight had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Figures

Figure 1
Figure 1
Retinal implants used for the drawing task. (A) Argus I electrode array (4 × 4 electrodes of 260 μm and 520 μm diameter arranged in a checkerboard pattern). (B) Argus I subject drawings on a grid screen were captured by an external camera and recorded to a video file. (C) Video files were analyzed offline by tracking the location of the fingertip frame-by-frame and by translating the drawings to a binary image. (D) Argus II electrode array (6 × 10 electrodes of 200 μm diameter). (E) Argus II subject drawings were recorded by a touch screen monitor. (F) Subject drawings were translated to a binary image. Shapes were closed by automatically connecting the first and last tracked fingertip location, after which a floodfill was applied.
Figure 2
Figure 2
Phosphene drawing variation within and across electrodes. Drawings from individual trials are shown for the most consistent (top row in each panel) and least consistent electrodes (bottom row in each panel) for Subjects 1–4. Mean images (labeled ‘average’) were obtained by averaging drawings from individual trials aligned at their center of mass. These averaged drawings were then overlaid over the corresponding electrode in a schematic of each subject’s implant (rightmost column).
Figure 3
Figure 3
Phosphene shape analysis. (A–C) Distribution of phosphene area, orientation, and elongation for each subject (Subject 1: 60 drawings, Subject 2: 110 drawings, Subject 3: 90 drawings, Subject 4: 140 drawings). (D–F) Distribution of the variability of shape descriptors for each subject, measured as the standard error of the mean (SEM) across trials for every electrode. Each box extended from the lower to upper quartile values of the data, with a line at the median. Whiskers extended from the fifth to ninety-fifth percentiles, with data points outside that range considered outliers (‘o’). Area SEM for every electrode was normalized by the mean area of all drawings for that particular electrode.
Figure 4
Figure 4
Model of nerve fiber bundle trajectories. (A–C) The topographic organization of optic nerve fiber bundles is highly stereotyped in the human retina (adapted with permission from ref.). Fundus images from 55 human eyes (A) were superimposed by translation in order to center the foveola (B), followed by rotation and zooming to align the center of the optic disc (C). Electrical stimulation (red circle) of a nerve fiber bundle could antidromically activate ganglion cell bodies peripheral to the point of stimulation (small black circles), leading to percepts that appear elongated along the direction of the underlying nerve bundle trajectory. (D–E) The location and orientation of each subject’s implant (Subject 4 shown here) was estimated by combining their postsurgical fundus photograph (D, bottom) with a baseline presurgical image in which the fovea had been identified (D, top) to produce a registered image (E; □: foveal pit, o- optic disc). The horizontal raphe (D, white line) was approximated by fitting a parabola to the main vascular arcade and finding the tangent to the parabola inflexion point. (F) The extracted landmarks were then used to place a simulated array on a simulated map of nerve fiber bundles.
Figure 5
Figure 5
Phosphene orientations are aligned with retinal nerve fiber bundles. (A–D) Simulated map of nerve fiber bundles for Subjects 1–4 (scale bar: 1 mm, equivalent to 3.6°; shaded box: area used in null models for random array placement). Phosphene orientation is indicated as oriented bars, overlaid over the corresponding electrode in the array. Insets show example percepts; black bars show their corresponding electrodes. Note that the maps are flipped upside down so that the upper image half corresponds to the upper visual field (inferior retina). Box plots indicate the distribution of mean absolute angular errors between phosphene orientation and the tangent line of the ganglion axon pathway nearest to the corresponding electrode. For all subjects, angular errors were significantly better than would be expected from random array placement.
Figure 6
Figure 6
Phosphene drawings (left columns) contrasted against cross-validated phosphene predictions of the axon map model (center column) and the scoreboard model (right column), overlaid over a schematic of each subject’s implant. Each predicted phosphene is from the test fold of a leave-one-electrode-out cross-validation.
Figure 7
Figure 7
Comparison of log mean prediction error for the two models. Prediction error was based on the sum of differences between predicted and observed phosphene area, orientation, and elongation (see Equation 13). Each data point in the scatter plots corresponds to the mean cross-validated prediction error of all drawings associated with a particular held-out electrode. Prediction error was significantly higher for the scoreboard model compared to the axon map model (Subject 1: p < 0.001, N = 12; Subject 2: p < 0.001, N = 22; Subject 3: p < 0.001, N = 18; Subject 4: p < 0.001, N = 28; 2-tailed Wilcoxon signed-rank test). Insets in each panel show the histogram of pair-wise differences in log prediction error.
Figure 8
Figure 8
Simulated phosphenes as a function of electrode-retina distance, z. An electrode located close to the horizontal meridian (dashed line) is chosen. For small values of z, phosphene shape is dominated by axonal stimulation (λ > ρ) thus appearing elongated. Increasing z leads to an increase in ρ but leaves λ unaffected, thus leading to more compact phosphenes (ρ > λ). (A) z = 0 µm, ρ = 300 µm, λ = 500 µm, elongation: 0.977. (B) z = 200 µm, ρ = 500 µm, λ = 500 µm, elongation: 0.957. (C) z = 500 µm, ρ = 800 µm, λ = 500 µm, elongation: 0.867. (D) z = 1,000 µm, ρ = 1,600 µm, λ = 500 µm, elongation: 0.643.
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