A model of ganglion axon pathways accounts for percepts elicited by retinal implants
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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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There often exists a numeric or symbolic forward model, constrained by empirical data, that can predict a neuronal or ideally perceptual response to the applied stimulus.
“In addition, there often exists a numeric or symbolic forward model, constrained by empirical data, that can predict a neuronal or ideally perceptual response to the applied stimulus (Bosking et al., 2017; Beyeler et al., 2019).”
From Towards a <i>Smart Bionic Eye</i> : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Beyeler et al., 2019
Interactions between device electronics and the underlying neurophysiology can lead to perceptual distortions that severely limit the quality of the generated visual experience.
“Interactions between the device electronics and the underlying neurophysiology can lead to perceptual distortions that severely limit the quality of the generated visual experience (Fine and Boynton, 2015; Beyeler et al., 2019; Erickson-Davis and Korzybska, 2021).”
From Towards a <i>Smart Bionic Eye</i> : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Beyeler et al., 2019
Epiretinal implants electrically activate retinal ganglion cells (RGCs) that have survived degeneration, causing them to send artificial visual signals to the brain.
“Epiretinal implants electrically activate retinal ganglion cells (RGCs) that have survived degeneration, causing them to send artificial visual signals to the brain.”
From Precise control of neural activity using dynamically optimized electrical stimulation (2024) · cites this paper as Beyeler et al., 2019
Due to inadvertent activation of underlying axon pathways, phosphenes generated with retinal prostheses may demonstrate distorted shapes that vary across subjects and even individual electrodes.
“For instance, due to inadvertent activation of underlying axon pathways, phosphenes generated with retinal prostheses may demonstrate distorted shapes that vary across subjects and even individual electrodes (Beyeler et al., 2019).”
From End-to-end optimization of prosthetic vision (2022) · cites this paper as Beyeler et al., 2019
A previous virtual human patient for electronic retinal prostheses has been used by both research groups and prosthetic companies as a research and design tool.
“This work is conceptually similar to a previous virtual human patient for electronic retinal prostheses48,96, that has been used by both research groups and prosthetic companies as a research and design tool.”
From A virtual patient simulation modeling the neural and perceptual effects of human visual cortical stimulation, from pulse trains to percepts (2024) · cites this paper as [48]
Phosphene shapes and sizes differ between electrodes and across participants.
“Since phosphene shapes and sizes differ between electrodes and across participants (D. Nanduri et al. 2008; Beyeler et al. 2019; Luo et al. 2016), which could influence the overall image produced by multi-electrode stimulation, we performed a systematic mapping experiment.”
From Sequential epiretinal stimulation improves discrimination in simple shape discrimination tasks only (2022) · cites this paper as Beyeler et al. 2019
The diversity of phosphene shapes is likely due to epiretinal stimulation activating both cell bodies beneath the electrode in the ganglion cell layer and axon bundles passing over the retinal surface in the nerve fiber layer toward the optic nerve.
“It is likely that there are a variety of phosphene shapes because stimulating through an epiretinal electrode not only activates the cell bodies that are just beneath the electrode in the ganglion cell layer, but it also activates axon bundles that pass over the surface of the retina in the nerve fiber layer while en route to the optic nerve (Beyeler et al. 2019; Esler et al. 2018; Tahayori et al.”
From Sequential epiretinal stimulation improves discrimination in simple shape discrimination tasks only (2022) · cites this paper as Beyeler et al. 2019
Phosphene appearance varied greatly across electrodes and participants.
“The appearance of phosphenes was highly variable across electrodes and participants, which is consistent with prior studies (D. Nanduri et al. 2008; Beyeler et al. 2019; Luo et al. 2016).”
From Sequential epiretinal stimulation improves discrimination in simple shape discrimination tasks only (2022) · cites this paper as Beyeler et al. 2019
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de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2022) · Journal of Vision · doi:10.1167/jov.22.2.20 · Cited by 54 · see citing works
Advances in visual prostheses: engineering and biological challenges
Borda, E., & Ghezzi, D. (2022) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ac812c · Cited by 44 · see citing works
Thorn, J. T., Migliorini, E., & Ghezzi, D. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abb5bc · Cited by 34 · see citing works
Beyeler, M., & Sanchez-Garcia, M. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/aca69d · Cited by 34 · see citing works
“In addition, there often exists a numeric or symbolic forward model, constrained by empirical data, that can predict a neuronal or ideally perceptual response to the applied stimulus (Bosking et al 2017, Beyeler et al 2019).”
InfluentialSelf-citationA computational model of phosphene appearance for epiretinal prostheses
Granley, J., & Beyeler, M. (2021) · 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629663 · Cited by 27 · see citing works
Self-citationAdvances in neuroscience, not devices, will determine the effectiveness of visual prostheses
Abbasi, B., & Rizzo, J. F. (2021) · Seminars in Ophthalmology · doi:10.1080/08820538.2021.1887902 · Cited by 19 · see citing works
Fine, I., & Boynton, G. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-65337-1 · Cited by 19 · see citing works
Self-citationCorna, A., Ramesh, P., Jetter, F., Lee, M.-J., Macke, J. H., & Zeck, G. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac0679 · Cited by 18 · see citing works
Axonal stimulation affects the linear summation of single-point perception in three Argus II users
Hou, Y., Nanduri, D., Granley, J., Weiland, J. D., & Beyeler, M. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad31c4 · Cited by 14 · see citing works
Self-citationFactors affecting two-point discrimination in argus II patients
Yücel, E. I., Sadeghi, R., Kartha, A., Montezuma, S. R., Dagnelie, G., Rokem, A., Boynton, G. M., Fine, I., & Beyeler, M. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.901337 · Cited by 13 · see citing works
Self-citationHuman-in-the-loop optimization of visual prosthetic stimulation
Fauvel, T., & Chalk, M. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac7615 · Cited by 15 · see citing works
Precise control of neural activity using dynamically optimized electrical stimulation
Shah, N. P., Phillips, A., Madugula, S., Lotlikar, A., Gogliettino, A. R., Hays, M. R., Grosberg, L., Brown, J., Dusi, A., Tandon, P., et al. (2024) · eLife · doi:10.7554/elife.83424 · Cited by 15 · see citing works
Haji Ghaffari, D., Akwaboah, A. D., Mirzakhalili, E., & Weiland, J. D. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3138297 · Cited by 10 · see citing works
Self-citationGhaffari, D. H., Chang, Y.-C., Mirzakhalili, E., & Weiland, J. D. (2021) · 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) · doi:10.1109/ner49283.2021.9441437 · Cited by 10 · see citing works
Self-citationSequential epiretinal stimulation improves discrimination in simple shape discrimination tasks only
Christie, B., Sadeghi, R., Kartha, A., Caspi, A., Tenore, F. V., Klatzky, R. L., Dagnelie, G., & Billings, S. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac7326 · Cited by 10 · see citing works
A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation
Wu, Y., Karetić, I., Stegmaier, J., Walter, P., & Merhof, D. (2023) · 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340288 · Cited by 6 · see citing works
Neural Activity Shaping in Visual Prostheses with Deep Learning
Castro, D., Grayden, D. B., Meffin, H., & Spencer, M. (2023) · doi:10.1101/2023.12.20.572123 · Cited by 4 · see citing works
Deep Learning--Based Scene Simplification for Bionic Vision
Han, N., Srivastava, S., Xu, A., Klein, D., & Beyeler, M. (2021) · arXiv · doi:10.48550/arxiv.2102.00297 · Cited by 4 · see citing works
Self-citationA computational model to design wide field-of-view optic nerve neuroprostheses
Micera, S., Romeni, S., De Luca, D., Pierantoni, L., Toni, L., Marino, G., & Moccia, S. (2023) · doi:10.21203/rs.3.rs-3218482/v1 · Cited by 2 · see citing works
End-to-end optimization of prosthetic vision
de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2020) · doi:10.1101/2020.12.19.423601 · Cited by 54 · see citing works
“For instance, due to inadvertent activation of underlying axon pathways, phosphenes generated with retinal prostheses may demonstrate distorted shapes that vary across subjects and even individual electrodes (Beyeler et al., 2019).”
BackgroundExplainable Machine Learning Predictions of Perceptual Sensitivity for Retinal Prostheses
Pogoncheff, G., Hu, Z., Rokem, A., & Beyeler, M. (2023) · doi:10.1101/2023.02.09.23285633 · Cited by 9 · see citing works
“The horizontal raphe (A, white line) was approximated by fitting a parabola to the main vascular arcade and finding the tangent to the parabola inflexion point (adapted under CC-BY from [15]).”
Self-citation
- Authors
- Beyeler, M., Nanduri, D., Weiland, J. D., Rokem, A., Boynton, G. M., & Fine, I.
- Affiliations
- University of Washington (US)
- University of Southern California (US)
- Corresponding author
- Beyeler, Michael
- Venue
- Scientific Reports
- Published
- 2019-06-24
- Volume
- 9
- Issue
- 1
- Pages
- 9199-9199
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6591412
- OpenAlex
- W2953775847
- Cited by
- 143
- References
- 82
- Citations by year
- 2026: 24 · 2025: 14 · 2024: 20 · 2023: 16 · 2022: 23 · 2021: 24 · 2020: 19 · 2019: 3
- FWCI
- 8.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (K99EY029329, EY12925, EY014645)
- Washington Research Foundation
- Gordon and Betty Moore Foundation
- Alfred P. Sloan Foundation
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- retinal prosthesis, phosphenes, Argus II, retinitis pigmentosa, macular degeneration, visual percepts, electrode stimulation, computational modeling of vision
- MeSH terms
- Aged, Computer Simulation, Electric Stimulation, Electrodes, Implanted, Female, Humans, Macular Degeneration, Male, Middle Aged, Models, Neurological, Phosphenes, Retinal Ganglion Cells, Retinitis Pigmentosa, Visual Perception, Visual Prosthesis
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