Virtual prototyping for retinal prosthesis patients

项目概要/摘要 视网膜营养不良，如视网膜色素变性和黄斑变性，诱导进行性丧失， 光感受器，导致全世界超过一千万人的严重视力障碍。视觉 神经假体（“仿生眼”）旨在通过电刺激大脑中剩余的细胞来恢复功能性视力。 视网膜，类似于耳蜗植入物。各种各样的神经假体正在开发中（例如， 光遗传学、皮质）或被植入患者体内（例如视网膜下或视网膜前电）。一个限制因素 影响所有设备类型的是感知失真和随后的信息丢失， 植入技术和潜在的神经生理学之间的联系了解这些原因 扭曲和找到方法来减轻他们是至关重要的成功，目前和未来的视线 修复技术。在这个提议中，人类视觉心理物理学、计算建模、数据驱动 方法，和虚拟现实（VR）将结合起来，开发和实验验证优化 用于视网膜前膜假体的刺激方案。这种方法类似于飞机的虚拟样机 和其他复杂系统：使用高质量的植入电子和视觉系统模型， 以生成“虚拟患者”。视网膜电生理和视觉行为数据将用于开发 并且验证当电刺激时患者的预期视觉体验的计算模型。 使用该模型的一种方式将是生成电刺激的预期感知结果的模拟。 在各种各样的电刺激模式中进行电刺激。这些将被用作训练集， 机器学习算法，将反转模型的输入-输出函数以找到电刺激 最好地复制任何期望的感知体验的协议。该模型还可用于模拟 通过在VR环境中使用有视力的受试者来获得真实的患者的预期感知体验-“VR虚拟 病人的。这些虚拟患者将用于发现预处理方法（例如，边缘增强， 重新定位、整理），以提高VR中的行为表现。尽管目前的视网膜假体 已经在全球250多名患者中植入， 具有挑战性且昂贵。在VR中实施“虚拟患者”为以下方面提供了一种经济实惠且实用的替代方案： 高通量实验来测试新的刺激方案。产生良好VR的刺激方案 将与Second Sight合作，在真实的假体患者中对性能进行实验验证 Medical Products Inc.和Pixium Vision，这是该领域的两家领先设备制造商。这项工作有 潜在的显着提高视觉神经假体作为个人治疗选择的有效性 患有致盲性视网膜疾病

Project Summary/Abstract Retinal dystrophies such as retinitis pigmentosa and macular degeneration induce progressive loss of photoreceptors, resulting in profound visual impairment in more than ten million people worldwide. Visual neuroprostheses (‘bionic eyes’) aim to restore functional vision by electrically stimulating remaining cells in the retina, analogous to cochlear implants. A wide variety of neuroprostheses are either in development (e.g. optogenetics, cortical) or are being implanted in patients (e.g. subretinal or epiretinal electrical). A limiting factor that affects all device types are perceptual distortions and subsequent loss of information, caused by interactions between the implant technology and the underlying neurophysiology. Understanding the causes of these distortions and finding ways to alleviate them is critically important to the success of current and future sight restoration technologies. In this proposal, human visual psychophysics, computational modeling, data-driven approaches, and virtual reality (VR) will be combined to develop and experimentally validate optimized stimulation protocols for epiretinal prostheses. This approach is analogous to virtual prototyping for airplanes and other complex systems: to use a high-quality model of both the implant electronics and the visual system in order to generate a ‘virtual patient’. Retinal electrophysiological and visual behavioral data will be used to develop and validate a computational model of the expected visual experience of patients when electrically stimulated. One way of using this model will be to generate simulations of the expected perceptual outcome of electrical stimulation across a wide variety of electrical stimulation patterns. These will be used as a training set for machine learning algorithms that will invert the input-output function of the model to find the electrical stimulation protocol that best replicates any desired perceptual experience. The model can also be used to simulate the expected perceptual experience of real patients by using sighted subjects in a VR environment – ‘VR virtual patients’. These virtual patients will be used to discover preprocessing methods (e.g., edge enhancement, retargeting, decluttering) that improve behavioral performance in VR. Although current retinal prostheses have been implanted in over 250 patients worldwide, experimentation with improved stimulation protocols remains challenging and expensive. Implementing ‘virtual patients’ in VR offers an affordable and practical alternative for high-throughput experiments to test new stimulation protocols. Stimulation protocols that result in good VR performance will be experimentally validated in real prosthesis patients in collaboration with Second Sight Medical Products Inc. and Pixium Vision, two leading device manufacturers in the field. This work has the potential to significantly improve the effectiveness of visual neuroprostheses as a treatment option for individuals suffering from blinding retinal diseases.