Characterization of scotopic and mesopic rod signaling pathways in dogs using the On–Off electroretinogram

Abstract

Background The On–Off, or long flash, full field electroretinogram (ERG) separates retinal responses to flash onset and offset. Depending on degree of dark-adaptation and stimulus strength the On and Off ERG can be shaped by rod and cone photoreceptors and postreceptoral cells, including ON and OFF bipolar cells. Interspecies differences have been shown, with predominantly positive Off-response in humans and other primates and a negative Off-response in rodents and dogs. However, the rod signaling pathways that contribute to these differential responses have not been characterized. In this study, we designed a long flash protocol in the dog that varied in background luminance and stimulus strength allowing for some rod components to be present to better characterize how rod pathways vary from scotopic to mesopic conditions. Results With low background light the rod a-wave remains while the b-wave is significantly reduced resulting in a predominantly negative waveform in mesopic conditions. Through modeling and subtraction of the rod-driven response, we show that rod bipolar cells saturate with dimmer backgrounds than rod photoreceptors, resulting in rod hyperpolarization contributing to a large underlying negativity with mesopic backgrounds. Conclusions Reduction in rod bipolar cell responses in mesopic conditions prior to suppression of rod photoreceptor responses may reflect the changes in signaling pathway of rod-driven responses needed to extend the range of lighting conditions over which the retina functions.

Review of various feature extraction approaches for ERG signal analysis: Advantages and drawbacks

“…Training deep learning models can be computationally intensive and often demands specialized hardware. Moreover, deploying these models in real-time or resource-constrained environments may pose challenges due to the computational demands[47,48]. Despite these limitations, ANN-based feature extraction approaches hold promise in ERG signal analysis.…”