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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 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 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.
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 wh....
All animal experiments performed at The Johns Hopkins University School of Medicine (JHUSOM) were approved by the Institutional Animal Care and Use Committee (IACUC) at the JHUSOM. All experiments performed at the University of California, San Francisco (UCSF) were performed in accordance with protocols approved by the UCSF Institutional Animal Care and Use Program.
1. Behavioral data collection
To validate the analysis method described above, we quantified OKR tracking gain on wave traces collected from wild-type mice and a conditional knockout mutant with a known tracking deficit. In addition, to test the broader applicability of our analysis method, we analyzed traces derived from a separate cohort of wild-type mice acquired using a different video-oculography collection method. The automatic filtering of saccades facilitates OKR data processing and analysis (Figure 3). Using rec.......
PyOKR provides several advantages for studying visual responses reflected in eye movements. These include accuracy, accessibility, and data collection options, in addition to the ability to incorporate parameterization and variable stimulus speeds.
Direct eye tracking gain assessment provides an accurate characterization of eye movement that is a more direct quantitative metric than traditional manual counting of fast phase saccades (ETMs). Although useful, saccade counting provides an indirec.......
This work was supported by R01 EY032095 (ALK), VSTP pre-doctoral fellowship 5T32 EY7143-27 (JK), F31 EY-033225 (SCH), R01 EY035028 (FAD and ALK) and R01 EY-029772 (FAD).
....Name | Company | Catalog Number | Comments |
C57BL/6J mice | Jackson Labs | 664 | |
Igor Pro | WaveMetrics | RRID: SCR_000325 | |
MATLAB | MathWorks | RRID: SCR_001622 | |
Optokinetic reflex recording chamber - JHUSOM | Custom-built | N/A | As described in Al-Khindi et al.(2022)9 and Kodama et al. (2016)13 |
Optokinetic reflex recording chamber - UCSF | Custom-built | N/A | As described in Harris and Dunn, 201510 |
Python | Python Software Foundation | RRID: SCR_008394 | |
Tbx5 flox/+ mice | Gift from B. Bruneau | N/A | As described in Al-Khindi et al.(2022)9 |
Tg(Pcdh9-cre)NP276Gsat/Mmucd | MMRRC | MMRRC Stock # 036084-UCD; RRID: MMRRC_036084-UCD |
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