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

Herramienta basada en Python para la evaluación cuantitativa del reflejo optocinético en animales

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.

Autor destacado: Un método de análisis simplificado y accesible para cuantificar las respuestas de seguimiento del reflejo optocinético

PyOKR: Un método semiautomatizado para cuantificar la capacidad de seguimiento de los reflejos optocinéticos

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.

Cuantificación basada en Python del reflejo optocinético para el análisis del movimiento ocular de los animales

Para comenzar, descargue e instale el software necesario para el reflejo optocinético basado en Python o el análisis PyOKR. En un equipo Windows, ejecute desde PyOKR import OKR_win como o, seguido de o.run. Después de abrir la interfaz de usuario, haga clic en archivo y luego en abrir para abrir un navegador para seleccionar el archivo de onda deseado.Para elegir una carpeta de salida, haga clic en exportar carpeta. En el archivo de salida, introduzca el nombre del archivo de análisis final. Seleccione Establecer tema en archivo para configurar el programa para un animal individual.Para establecer los parámetros del estímulo, vaya a seleccionar la dirección del estímulo y defina una direccionalidad entre los cuatro puntos cardinales. En seleccionar tipo de estímulo, elija unidireccional, oscilatorio u oblicuo. Luego, usando las funciones de cabeza y cola, establezca la cantidad de tiempo sin estímulo al principio y al final.Establezca la longitud de la época, la longitud del estímulo posterior y el número de épocas. Para estímulos unidireccionales y oblicuos, establezca la velocidad horizontal y la velocidad vertical en grados por segundo y especifique la velocidad de fotogramas de captura. Para estímulos sinusoidales, ajuste la frecuencia y la amplitud.Usando generar vector de estímulo a partir de parámetros, haga el modelo apropiado a partir de la información de estímulo de entrada y haga clic en seleccionar época para escanear a través del archivo de onda total. Haga clic en datos sin filtrar o en filtrar datos para el ajuste preliminar y seleccionar automáticamente las sacadas de fase rápida en función de los cambios de velocidad máxima. En los datos sin filtrar, confirme que las sacadas se seleccionan con precisión con un punto azul.Guarde los puntos con el botón central del ratón y cierre el gráfico. Si desea el filtrado automático, establezca el umbral de puntuación Z del filtro y haga clic en filtrar datos para filtrar automáticamente los movimientos sacádicos. Después de una selección sacádica adecuada, presione el ajuste del punto para seleccionar la región que desea eliminar.Modifique los puntos superior e inferior y guarde con los botones del ratón. Usando el orden polinómico establecido, defina el modelo polinómico que se ajusta a las fases lentas individuales. Seleccione el análisis final para generar los modelos de fase lenta y calcular las distancias, las velocidades y las ganancias de seguimiento promediadas a lo largo de la época.Seleccione ver gráfico 2D o ver gráfico 3D para ver el gráfico bidimensional o tridimensional de las regiones seleccionadas. Seleccione agregar época para guardar para generar valores colectivos y seleccione ver conjunto de datos actual para ver todos los valores agregados y promedios para un animal determinado. Después de repetir todo el proceso para todos los archivos de un animal determinado, genere un conjunto de datos final que contenga todos los datos de ondas.Finalmente, exporte el conjunto de datos a través de exportar datos y continúe con los siguientes datos de animales. El análisis PyOKR en ratones knockout condicionales Tbx5 indicó que estos animales conservan ganancias normales de seguimiento horizontal en comparación con los ratones de tipo salvaje. Sin embargo, estos ratones mostraron una pérdida significativa del seguimiento vertical, con ganancias casi nulas en respuesta a los estímulos ascendentes y descendentes.Además, el análisis de las respuestas sinusoidales confirmó que los ratones knockout condicionales Tbx5 exhibieron mayores ganancias de seguimiento horizontal, mientras que mostraron una disminución significativa del seguimiento vertical.

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

Research

JoVE Journal

Neuroscience

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

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

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 ...