Large-scale RF mapping without visual input for neuroprostheses in macaque and human visual cortex

Abstract

High-channel-count neuroprostheses could one day restore functional vision in blind individuals by delivering electrical pulses to electrodes in the visual cortex that elicit perceptions known as phosphenes. However, if a high number of electrodes are used, it becomes challenging and time-consuming to map the visual field locations of all phosphenes. Furthermore, many blind users are not able to maintain stable fixation, impeding the localization of phosphenes, or may perceive spontaneous visual phenomena that interfere with detection of electrically induced phosphenes. Here, we introduce NEural Unsupervised electrode mapping (NEUmap), a rapid, largely automated method for phosphene mapping that extracts spatial patterns in spontaneous activity across the visual cortex. As correlations between neuronal activity on nearby electrodes are stronger than those between distant electrodes, we first use dimensionality-reduction algorithms to generate maps of relative positions of electrodes. We then convert these maps from relative to absolute visual field coordinates while the subject maps out a small number of phosphenes manually. NEUmap generated maps across ~300-700 electrodes in each of two sighted monkeys and across 73-91 electrodes in each of three blind human volunteers. We report that the method allows rapid mapping of many electrodes using less than a second of resting-state data, with minimal effort from the subject, in the absence of vision.