Neurophysiologically-based computational platform for the characterization and optimization of retinal prosthetic stimulation

Wayne State University, Detroit MI

Investigators

Abstract

CBET-0756098 Cottaris Millions of people who have lost their sight due to retinal neurodegenerative diseases, such as retinitis pigmentosa and age-related manular degeneration, might benefit from retinal prostheses designed to artificially stimulate remaining retinal circuits. However, retinal prostheses have induced only amorphous light percepts in clinical trials with blind individuals, indicating that perception of even rudimentary shapes is still elusive. The most likely reason for this failure is that present prosthetic stimulation methods are incapable of inducing naturalistic spiking patterns in specific sub-populations of retinal ganglion cells (RGCs). Recently however, it was shown that the duration and polarity of stimulating pulses injected via single electrodes have potentially beneficial effects on RGC activity. These findings could be catalytic for the design of next generation retinal prosthesis stimulation algorithms, if there were evidence that similarly beneficial effects occur during multi-site prosthetic retinal stimulation, leading to an increase in the spatial information transmitted to the brain. The PI's long-term goal is to discover multi-site retinal stimulation paradigms that are capable of maximizing the amount of spatial information transmitted to the brain, and thus would have the greatest potential for generating spatially-structured visual percepts in blind humans. To achieve this goal, he examines the resolution with which the primary visual cortex (V1) decodes spatial information transmitted by different paradigms of multi-site prosthetic retinal stimulation. Using this approach, he was able to measure the resolution with which single pulse prosthetic stimuli are represented in V1. In the proposed work, the developed platform will be employed to decode V1 LFP responses to spatially-patterned prosthetic stimuli and to determine the resolution with which such stimuli are represented in V1. An optimization technique will update various parameters of prosthetic stimulation iteratively until the measured resolution is maximized. Additionally, the spatial information capacity of a novel stimulation paradigm will be examined, and finally, the relationship between visual and prosthetic stimulation - induced LFP responses will be characterized in an effort to further optimize the efficacy of prosthetic stimulation of the retina. The proposed research allows a rational study and optimization of retinal prosthetic stimulation paradigms and has the potential to revolutionize the design of future retinal implants which strive to induce spatially patterned visual percepts. The developed platform can serve as a standardized model for testing retinal prostheses developed by different groups around the world. The findings of the proposed experiments will have implications for millions of blind people worldwide. Finally, the developed algorithms for information extraction from the brain and their application to retinal prosthetic stimulation can form the basis for a graduate course in advanced neural prosthetics with an emphasis on computational modeling.