Shi Jianing V, Wielaard Jim, Smith R Theodore, Sajda Paul
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
J Vis. 2011 Dec 5;11(14):4. doi: 10.1167/11.14.4.
Age-related macular degeneration (AMD) is the major cause of blindness in the developed world. Though substantial work has been done to characterize the disease, it is difficult to predict how the state of an individual's retina will ultimately affect their high-level perceptual function. In this paper, we describe an approach that couples retinal imaging with computational neural modeling of early visual processing to generate quantitative predictions of an individual's visual perception. Using a patient population with mild to moderate AMD, we show that we are able to accurately predict subject-specific psychometric performance by decoding simulated neurodynamics that are a function of scotomas derived from an individual's fundus image. On the population level, we find that our approach maps the disease on the retina to a representation that is a substantially better predictor of high-level perceptual performance than traditional clinical metrics such as drusen density and coverage. In summary, our work identifies possible new metrics for evaluating the efficacy of treatments for AMD at the level of the expected changes in high-level visual perception and, in general, typifies how computational neural models can be used as a framework to characterize the perceptual consequences of early visual pathologies.
年龄相关性黄斑变性（AMD）是发达国家失明的主要原因。尽管在表征该疾病方面已经做了大量工作，但很难预测个体视网膜状态最终将如何影响其高级感知功能。在本文中，我们描述了一种将视网膜成像与早期视觉处理的计算神经模型相结合的方法，以生成对个体视觉感知的定量预测。使用轻度至中度AMD患者群体，我们表明，通过解码作为源自个体眼底图像的暗点函数的模拟神经动力学，我们能够准确预测个体受试者的心理测量性能。在群体水平上，我们发现我们的方法将视网膜上的疾病映射到一种表征上，这种表征比诸如玻璃膜疣密度和覆盖率等传统临床指标能更好地预测高级感知性能。总之，我们的工作确定了在高级视觉感知预期变化水平上评估AMD治疗效果的可能新指标，并且总体上代表了计算神经模型如何能够用作表征早期视觉病理感知后果的框架。
