Retinal Resistivity Mapped by Electrical Resistance Tomography

Retinal resistivity is a key parameter for modeling both the electric field in tissue and the resulting neural response - a crucial step in designing retinal prostheses, especially since both depend on resistivity nonlinearly. Previous studies using penetrating microelectrodes reported vastly different values, varying by as much as two orders of magnitude, likely due to parasitic capacitance in microelectrodes, compression of tissue, and its rupture by the inserted probe, allowing the conductive medium to seep in and alter the measurement. To measure retinal resistivity without compression or rupture, we applied electrical resistance tomography using a planar multielectrode array with a 30 µm pitch. Voltage waveforms generated in response to current injected from one electrode were measured on other electrodes in the array, at distances ranging from 30 to 600 µm. Data from wild-type and degenerated rat retinas were analyzed using two methods: a commercial inversion software, Res2DInv, and a custom machine learning-based decoder, with healthy retina approximated as a 4-layer structure and degenerated retina - as a 3-layer one. In healthy retina, average resistivity was 2.7 kΩ·cm for the combined ganglion cell-nerve fiber layer, 1.6 kΩ·cm for the inner plexiform layer, 2.3 kΩ·cm for the outer and inner nuclear layers, and 1.0 kΩ·cm for the photoreceptor inner and outer segments; a single-layer approximation yielded 1.7 kΩ·cm. In degenerated retina, average resistivity was 3.4 kΩ·cm for the ganglion cell-nerve fiber layer, 1.1 kΩ·cm for the inner plexiform layer, and 4 kΩ·cm for the inner nuclear layer, with a single-layer approximation of 2 kΩ·cm.

Designing retinal prostheses to restore vision after loss of photoreceptors requires accurate modeling of electric field in tissue, but published resistivity values vary by two orders of magnitude due to inconsistencies with penetrating microelectrode-based measurements. We adapted electrical resistance tomography, a non-destructive technique from geophysics based on inverse problem solving, and developed a machine learning-based decoder to map resistivity in rat retinas using a planar microelectrode array. Our analysis yields quantitative, layer-specific resistivities for healthy and degenerated retina. Beyond retinal prostheses, this approach offers a general, deformation-free strategy for measuring electrical properties of biological tissues.