Ly Keith, Italiano Michael L, Shivdasani Mohit N, Tsai David, Zhang Jia-Yi, Jiang Chunhui, Lovell Nigel H, Dokos Socrates, Guo Tianruo
Graduate School of Biomedical Engineering, UNSW, Sydney, NSW, 2052, Australia; Department of Ophthalmology, Stanford University, Stanford, CA, USA; Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, USA.
Graduate School of Biomedical Engineering, UNSW, Sydney, NSW, 2052, Australia; Tyree Foundation Institute of Health Engineering (IHealthE), UNSW, Sydney, NSW, 2052, Australia.
Brain Stimul. 2025 Jan-Feb;18(1):144-163. doi: 10.1016/j.brs.2025.01.013. Epub 2025 Jan 17.
Current brain-based visual prostheses pose significant challenges impeding adoption such as the necessarily complex surgeries and occurrence of more substantial side effects due to the sensitivity of the brain. This has led to much effort toward vision restoration being focused on the more approachable part of the brain - the retina. Here we introduce a novel, parameterised simulation platform that enables study of human retinal degeneration and optimization of stimulation strategies. The platform bears immense potential for patient-specific tailoring and serves to enhance artificial vision solutions for individuals with visual impairments.
Our virtual retina is developed using the software package, NEURON. This virtual retina platform supports large-scale simulations of over 10,000 neurons whilst upholding strong biological plausibility with multiple important visual pathways and detailed network properties. The comprehensive three-dimensional model includes photoreceptors, horizontal cells, bipolar cells, amacrine cells, and midget and parasol retinal ganglion cells, with comprehensive network connectivity across various eccentricities (1 mm-5 mm from the fovea) in the human retina. The model is constructed using electrophysiology, immunohistology, and optical coherence tomography imaging data from healthy and degenerate human retinas. We validated our model by replicating numerous experimental observations from human and primate retina, with a particular focus on retinal degeneration.
We simulated interactions between diseased retinas and state-of-the-art retinal implants, shedding light on the limitations of commercial retinal prostheses. Our results suggested that appropriate stimulation settings with intraretinal prototype devices could leverage network-mediated activation to achieve activation mosaics more alike that of the retina's response to natural light, promoting the prospect of more naturalistic vision. Our study additionally highlights the importance of controlling inhibitory circuits in the retinal network to induce functionally relevant retinal activity.
This study demonstrates the potential of this software package and highlights its utility as a valuable tool for engineers, scientists, and clinicians in the design and optimization of retinal stimulation devices for both research and educational applications.
当前基于大脑的视觉假体带来了诸多重大挑战,阻碍了其应用,比如手术必然复杂,且由于大脑的敏感性会出现更严重的副作用。这使得恢复视力的大量努力都集中在大脑中更易处理的部分——视网膜。在此,我们介绍一种新颖的参数化模拟平台,该平台能够研究人类视网膜退化并优化刺激策略。该平台在针对患者的定制方面具有巨大潜力,有助于为视力受损者增强人工视觉解决方案。
我们的虚拟视网膜是使用软件包NEURON开发的。这个虚拟视网膜平台支持对超过10,000个神经元进行大规模模拟,同时通过多个重要视觉通路和详细的网络特性保持高度的生物学合理性。全面的三维模型包括光感受器、水平细胞、双极细胞、无长突细胞以及侏儒和伞状视网膜神经节细胞,具有跨越人类视网膜不同偏心率(从中央凹起1毫米 - 5毫米)的全面网络连接性。该模型是利用来自健康和退化人类视网膜的电生理学、免疫组织学以及光学相干断层扫描成像数据构建的。我们通过复制来自人类和灵长类视网膜的大量实验观察结果来验证我们的模型,特别关注视网膜退化。
我们模拟了患病视网膜与最先进的视网膜植入物之间的相互作用,揭示了商业视网膜假体的局限性。我们的结果表明,视网膜内原型设备的适当刺激设置可以利用网络介导的激活来实现更类似于视网膜对自然光反应的激活镶嵌图,从而提升更自然视觉的前景。我们的研究还强调了控制视网膜网络中抑制性回路以诱导功能相关视网膜活动的重要性。
本研究展示了该软件包的潜力,并突出了其作为工程师、科学家和临床医生在设计和优化用于研究及教育应用的视网膜刺激设备方面的宝贵工具的实用性。