Percept-Aware Surgical Planning for Visual Cortical Prostheses with Vascular Avoidance

TL;DR

A percept-aware framework for surgical planning of cortical visual prostheses that formulates electrode placement as a constrained optimization problem in anatomical space is presented and demonstrates how differentiable percept models can inform anatomically grounded, safety-aware computer-assisted planning for cortical neural interfaces and provide a foundation for optimizing next-generation visual prostheses.

Abstract

Cortical visual prostheses aim to restore sight by electrically stimulating neurons in early visual cortex (V1). With the emergence of high-density and flexible neural interfaces, electrode placement within three-dimensional cortex has become a critical surgical planning problem. Existing strategies emphasize visual field coverage and anatomical heuristics but do not directly optimize predicted perceptual outcomes under safety constraints. We present a percept-aware framework for surgical planning of cortical visual prostheses that formulates electrode placement as a constrained optimization problem in anatomical space. Electrode coordinates are treated as learnable parameters and optimized end-to-end using a differentiable forward model of prosthetic vision. The objective minimizes task-level perceptual error while incorporating vascular avoidance and gray matter feasibility constraints. Evaluated on simulated reading and natural image tasks using realistic folded cortical geometry (FreeSurfer fsaverage), percept-aware optimization consistently improves reconstruction fidelity relative to coverage-based placement strategies. Importantly, vascular safety constraints eliminate margin violations while preserving perceptual performance. The framework further enables co-optimization of multi-electrode thread configurations under fixed insertion budgets. These results demonstrate how differentiable percept models can inform anatomically grounded, safety-aware computer-assisted planning for cortical neural interfaces and provide a foundation for optimizing next-generation visual prostheses.

Figures (4)

• Figure 1: Percept-aware optimization framework. Given 3D electrode coordinates on FreeSurfer fsaverage anatomy, target percepts, and patient retinotopy, a differentiable model of cortical prosthetic vision predicts elicited percepts. Electrode locations are iteratively updated to minimize perceptual error while enforcing vascular safety constraints. Solid arrows: forward simulation pathway. Dashed arrows: gradient signals used to update electrode positions.
• Figure 2: Comparing percept-aware electrode optimization and Visual Field Tiling. Left: Simulated phosphenes for reading (MNIST) and natural image (CIFAR-10) tasks. Right: Relative improvements in perceptual fidelity across experimental configurations.
• Figure 3: Safety-aware electrode optimization with vascular constraints. Left: Cortical surface (fsaverage left occipital) with high-resolution vascular map (red, displayed in V1 only) and optimized electrode locations (black). Right: Percept SSIM scores without (solid lines) and with (dashed lines) vascular avoidance.
• Figure 4: Simulated phosphenes using percept-aware optimization with and without multi-electrode threads under a fixed number of cortical insertions ($128$). Threaded designs improve percept quality without increasing number of cortical insertions.