python-based quantification of optokinetic reflex for animal eye movement analysis

Python-Based Tool for Quantitative Optokinetic Reflex Assessment in Animals

Image stabilization relies on precise oculomotor responses to compensate for global optic flow that occurs during self-motion. This stabilization is driven primarily by two motor responses: the optokinetic reflex (OKR) and the vestibulo-ocular reflex (VOR)1,2,3. Slow global motion across the retina induces the OKR, which elicits reflexive eye rotation in the corresponding direction to stabilize the image1,2. This movement, known as the slow phase, is interrupted by compensatory saccades, known as the fast phase, in which the eye rapidly resets in the opposite direction to allow for a new slow phase. Here, we define these fast-phase saccades as eye-tracking movements (ETMs). Whereas the VOR relies on the vestibular system to elicit eye movements to compensate for head movements3, the OKR is initiated in the retina by the firing of ON and subsequent signaling to the Accessory Optic System (AOS) in the midbrain4,5. Due to its direct reliance on retinal circuits, the OKR has been frequently used to determine visual tracking ability in both research and clinical settings6,7.
The OKR has been studied extensively as a tool for assessing basic visual ability2,6,8, DSGC development9,10,11,12, oculomotor responses13, and physiological differences among genetic backgrounds7. The OKR is evaluated in head-fixed animals presented with a moving stimulus14. Oculomotor responses are typically captured using a variety of video tools, and eye-tracking motions are captured as OKR waveforms in the horizontal and vertical directions9. To quantify tracking ability, two primary metrics have been described: tracking gain (the velocity of the eye relative to the velocity of the stimulus) and ETM frequency (the number of fast phase saccades over a given time frame). Calculation of gain has been used historically to directly measure angular velocity of the eye to estimate tracking ability; however, these calculations are labor intensive and can be arbitrarily derived based on video-oculography collection methods and subsequent quantification. For more rapid OKR assessment, counting of ETM frequency has been used as an alternate method for measuring tracking acuity7. Although this provides a fairly accurate estimation of tracking ability, this method relies on an indirect metric to quantify the slow phase response and introduces a number of biases. These include an observer bias in saccade determination, a reliance on temporally consistent saccadic responses across a set epoch, and an inability to assess the magnitude of the slow phase response.
In order to address these concerns with current OKR assessment approaches and to enable a high throughput in-depth quantification of OKR parameters, we have developed a new analysis method to quantify OKR waveforms. Our approach uses an accessible Python-based software platform named &#34;PyOKR.&#34; Using this software, modeling and quantification of OKR slow phase responses can be studied in greater depth and with increased parameterization. The software provides accessible and reproducible quantitative assessments of responses to a myriad of visual stimuli and also two-dimensional visual tracking in response to horizontal and vertical motion.

Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses

PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability

We're interested in understanding the mechanisms that regulate visual motion processing behaviors, such as the optokinetic reflex. We developed PyOKR as an accessible and uniform platform to reproducibly quantify a wide range of visual responses under various conditions to further our understanding of these behaviors. There's no unified method within the field to quantify optokinetic reflex responses, which can cause disparities when comparing data between laboratories.We hope that PyOKR can help standardize how these data are analyzed to provide an accessible, unbiased, and robust tool to study these visual responses. Existing methods usually differ between laboratories, and are often custom designed for their specific needs. PyOKR offers a unified method that is user-friendly, accessible, and adaptable to different experimental designs.With a combination of automated analysis and user input, it can generate unbiased, accurate results to answer the user's desired questions. The use of our new PyOKR analysis method will facilitate the study of visual response behaviors in many contexts, such as genetic or pharmacological manipulation. Because of its accessibility and adaptability, it'll allow researchers to efficiently quantify responses to answer new questions about how visual circuits form and function.With the help of PyOKR, we hope to identify new mechanisms that regulate the development of the direction selective circuits that drive optokinetic reflexes. Using this approach with neuro circuit perturbation will continue to help us study the development and function of these critical visual systems.

Python-Based Quantification of Optokinetic Reflex for Animal Eye Movement Analysis

python-based-quantification-optokinetic-reflex-for-animal-eye

Research

JoVE Journal

Neuroscience

63 Views.  The Johns Hopkins University School of Medicine. Python-Based Quantification of Optokinetic Reflex for Animal Eye Movement Analysis

Video: Python-Based Quantification of Optokinetic Reflex for Animal Eye Movement Analysis

The study of behavioral responses to visual stimuli is a key component of understanding visual system function. 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 visual system circuitry and function in animals 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 resets the position of the eye in the orbit. Previous methods of tracking gain quantification, although reliable, are labor intensive and can be subjective or arbitrarily derived. To obtain more rapid and reproducible quantification of eye tracking ability, we have developed a novel&#160;semi-automated analysis program, PyOKR, that allows for quantification of two-dimensional eye tracking motion in response to any directional stimulus, in addition to being adaptable to any type of video-oculography equipment. This method provides automated filtering, selection of slow tracking phases, modeling of vertical and horizontal eye vectors, quantification of eye movement gains relative to stimulus speed, and organization of resultant data into a usable spreadsheet for statistical and graphical comparisons. This quantitative and streamlined analysis pipeline, readily accessible via PyPI import, provides a fast and direct measurement of OKR responses, thereby facilitating the study of visual behavioral responses.

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

The study of behavioral responses to visual stimuli is a key component of understanding visual system function. One notable response is the optokinetic ...