Purpose: : Clinical assessment of macular disease typically relies on direct analysis of retinal imaging, which does not necessarily provide a complete picture of expected vision loss. A potential advancement is a framework for predicting how retinal disease affects cortical activity and ultimately perceptual performance.
Methods: : Fundus images for low-vision patients with macular disease were segmented to create masks, used to simulate disease-specific distortion at the level of the retina. A 2-AFC perceptual task was designed with the goal to discriminate face and car images in the presence of noise. 10 subjects with normal vision performed the task and their results were assessed via psychometric curves. We simulated the cortical activity given the stimuli and used linear decoding of spike trains to generate neurometric curves for the model. The sparse linear decoder was optimized to maximize discrimination and not to match subjects’ psychometric curves. We simulated the cortical activity of low-vision subjects using the mask-distorted stimuli and carried out the decoding analysis in the same manner as normal subjects.
Results: : Shown are the mean psychometric curve for normal subjects (red), individual subjects (light red), mean neurometric curve for simulated “normal” subjects (black), and a simulated “low-vision” subject (gray). The mean simulated “normal” subject has a neurometric curve that is a reasonable match to normal subjects, for the most part falling within the inter-subject variation. For the simulated “low vision” case, the neurometric curve is shifted to the right indicating degradation in perceptual performance.
Conclusions: : Our results are promising in that they predict healthy subject perceptual performance and also result in systematic shifts in performance for simulated “low-vision” cases. Future work will quantify the predictive value of the model for a population of low-vision patients.