Virtual prototyping for retinal prosthesis patients

项目摘要/摘要 视网膜营养不良，如视网膜色素变性和黄斑变性导致进行性丧失 光感受器，导致全球1000多万人严重视力障碍。视觉 神经假体(仿生眼)的目标是通过电刺激大脑中剩余的细胞来恢复功能性视觉。 视网膜，类似于人工耳蜗术。各种各样的神经假体要么正在开发中(例如 光遗传学、大脑皮层)或植入患者体内(例如视网膜下或视网膜前电信号)。一个限制因素 影响所有设备类型的是由交互引起的感知扭曲和随后的信息丢失 植入技术和潜在的神经生理学之间的关系。了解这些问题的原因 扭曲并找到缓解它们的方法对于当前和未来视力的成功至关重要 修复技术。在这份提案中，人类视觉心理物理学、计算建模、数据驱动 方法，与虚拟现实(VR)相结合，开发和实验验证优化 视网膜前假体的刺激方案。这种方法类似于飞机的虚拟样机 和其他复杂系统：使用植入电子系统和视觉系统的高质量模型 以生成一个“虚拟病人”。视网膜电生理和视觉行为数据将用于开发 并验证患者在受到电刺激时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对电学的预期感知结果的模拟 通过多种电刺激模式进行刺激。这些将作为培训集用于 机器学习算法，将模型的输入输出函数倒置，找出电刺激 最好地复制任何期望的感知体验的协议。该模型还可以用来模拟 虚拟现实环境中视觉受试者对真实患者的期望感知体验 病人的。这些虚拟患者将被用来发现预处理方法(例如，边缘增强， 重定目标、清理)，以改善VR中的行为表现。尽管目前的视网膜假体有 已在全球250多名患者中植入，改进刺激方案的实验仍在进行中 具有挑战性，而且成本高昂。在VR中实现虚拟患者提供了一种负担得起的实用替代方案 测试新刺激方案的高通量实验。产生良好虚拟现实的刺激方案 性能将在真实的假肢患者身上进行实验验证，并与Second Sight合作 医疗产品公司和Pixium Vision是该领域的两家领先的设备制造商。这部作品具有 显著提高视觉神经假体作为个体治疗选择的有效性的潜力 患有致盲的视网膜疾病。

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.