Literature DB >> 23957722

Characterizing visual performance in mice: an objective and automated system based on the optokinetic reflex.

Boris Benkner1, Marion Mutter, Gerrit Ecke, Thomas A Münch.   

Abstract

Testing optokinetic head or eye movements is an established method to determine visual performance of laboratory animals, including chickens, guinea pigs, mice, or fish. It is based on the optokinetic reflex which causes the animals to track a drifting stripe pattern with eye and head movements. We have developed an improved version of the optomotor test with better control over the stimulus parameters, as well as a high degree of automation. The stripe pattern is presented on computer monitors surrounding the animal. By tracking the head position of freely moving animals in real time, the visual angle under which the stripes of the pattern appeared was kept constant even for changing head positions. Furthermore, an algorithm was developed for automated evaluation of the tracking performance of the animal. Comparing the automatically determined behavioral score with manual assessment of the animals' tracking behavior confirmed the reliability of our methodology. As an example, we reproduced the known contrast sensitivity function of wild type mice. Furthermore, the progressive decline in visual performance of a mouse model of retinal degeneration, rd10, was demonstrated. 2013 APA, all rights reserved

Entities:  

Mesh:

Year:  2013        PMID: 23957722     DOI: 10.1037/a0033944

Source DB:  PubMed          Journal:  Behav Neurosci        ISSN: 0735-7044            Impact factor:   1.912


  19 in total

1.  A system to measure the Optokinetic and Optomotor response in mice.

Authors:  Friedrich Kretschmer; Szilard Sajgo; Viola Kretschmer; Tudor C Badea
Journal:  J Neurosci Methods       Date:  2015-08-14       Impact factor: 2.390

2.  Comparison of optomotor and optokinetic reflexes in mice.

Authors:  Friedrich Kretschmer; Momina Tariq; Walid Chatila; Beverly Wu; Tudor Constantin Badea
Journal:  J Neurophysiol       Date:  2017-04-19       Impact factor: 2.714

3.  High-Throughput Automatic Training System for Spatial Working Memory in Free-Moving Mice.

Authors:  Shimin Zou; Chengyu Tony Li
Journal:  Neurosci Bull       Date:  2019-04-11       Impact factor: 5.203

Review 4.  Psychophysical testing in rodent models of glaucomatous optic neuropathy.

Authors:  Stephanie L Grillo; Peter Koulen
Journal:  Exp Eye Res       Date:  2015-07-02       Impact factor: 3.467

5.  Rod nuclear architecture determines contrast transmission of the retina and behavioral sensitivity in mice.

Authors:  Kaushikaram Subramanian; Martin Weigert; Oliver Borsch; Heike Petzold; Alfonso Garcia-Ulloa; Eugene W Myers; Marius Ader; Irina Solovei; Moritz Kreysing
Journal:  Elife       Date:  2019-12-11       Impact factor: 8.140

6.  Bipolar cell targeted optogenetic gene therapy restores parallel retinal signaling and high-level vision in the degenerated retina.

Authors:  Jakub Kralik; Michiel van Wyk; Nino Stocker; Sonja Kleinlogel
Journal:  Commun Biol       Date:  2022-10-20

7.  The Rpe65 rd12 allele exerts a semidominant negative effect on vision in mice.

Authors:  Charles B Wright; Micah A Chrenek; Wei Feng; Shannon E Getz; Todd Duncan; Machelle T Pardue; Yue Feng; T Michael Redmond; Jeffrey H Boatright; John M Nickerson
Journal:  Invest Ophthalmol Vis Sci       Date:  2014-04-17       Impact factor: 4.799

8.  Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats.

Authors:  Kaavya Gudapati; Anayesha Singh; Danielle Clarkson-Townsend; Stephen Q Phillips; Amber Douglass; Andrew J Feola; Rachael S Allen
Journal:  J Vis Exp       Date:  2020-10-23       Impact factor: 1.355

9.  Virtual reality systems for rodents.

Authors:  Kay Thurley; Aslı Ayaz
Journal:  Curr Zool       Date:  2016-06-30       Impact factor: 2.624

10.  High-Throughput Automatic Training System for Odor-Based Learned Behaviors in Head-Fixed Mice.

Authors:  Zhe Han; Xiaoxing Zhang; Jia Zhu; Yulei Chen; Chengyu T Li
Journal:  Front Neural Circuits       Date:  2018-02-13       Impact factor: 3.492

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