A semi-automated method for quantifying optokinetic reflex tracking acuity

The study of murine behavioral responses to visual stimuli is a key component of understanding mammalian visual circuitry. One notable response is the optokinetic reflex (OKR), a highly conserved innate behavior necessary for image stabilization on the retina. The OKR provides a robust readout of image tracking ability and has been extensively studied to understand the logic of visual system circuitry and function in mice from different genetic backgrounds. The OKR consists of two phases: a slow tracking phase as the eye follows a stimulus to the edge of the visual plane, and a compensatory fast phase saccade that maintains the image within the visual field. Assessment of the OKR has previously relied on counting individual compensatory eye saccades to estimate tracking speed. To obtain a more direct quantification of tracking ability, we have developed a novel, semi-automated analysis program that allows for rapid and reproducible quantification of unidirectional tracking gains, in addition to being adaptable to any video-oculography equipment. Our analysis program allows for the selection of slow tracking phases, modeling of the vertical and horizontal eye vectors, quantification of eye movement relative to the stimulus, and organization of resultant data into a usable spreadsheet for statistical and graphical comparisons. This quantitative and streamlined analysis pipeline provides a faster and more direct measurement of OKR responses, thereby facilitating further study of visual behavior responses.

We describe here a semi-automated quantitative analysis method that directly measures eye tracking resulting from murine visual system responses to two-dimensional image motion. A Python-based user interface and analysis algorithm allows for higher throughput and more quantitative measurements of eye tracking parameters than previous methods.