Literature DB >> 21307326

Unsupervised quantification of whisking and head movement in freely moving rodents.

Igor Perkon1, Andrej Kosir, Pavel M Itskov, Jurij Tasic, Mathew E Diamond.   

Abstract

The rodent whisker system has become the leading experimental paradigm for the study of active sensing. Thanks to more sophisticated behavioral paradigms, progressively better neurophysiological methods, and improved video hardware/software, there is now the prospect of defining the precise connection between the sensory apparatus and brain activity in awake, exploring animals. Achieving this ambitious goal requires quantitative, objective characterization of head and whisker kinematics. This study presents the methodology and potential uses of a new automated motion analysis routine. The program provides full quantification of head orientation and translation, as well as the angle, frequency, amplitude, and bilateral symmetry of whisking. The system operates without any need for manual tracing by the user. Quantitative comparison to whisker detection by expert humans indicates that the program's correct detection rate is at >95% even on animals with all whiskers intact. Particular attention has been paid to obtaining reliable performance under nonoptimal lighting or video conditions and at frame rates as low as 100. Variation of the zoom across time is compensated for without user intervention. The program adapts automatically to the size and shape of different species. The outcome of our testing indicates that the program can be a valuable tool in quantifying rodent sensorimotor behavior.

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Year:  2011        PMID: 21307326     DOI: 10.1152/jn.00764.2010

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  23 in total

Review 1.  Neuronal basis for object location in the vibrissa scanning sensorimotor system.

Authors:  David Kleinfeld; Martin Deschênes
Journal:  Neuron       Date:  2011-11-03       Impact factor: 17.173

2.  Microsecond-scale timing precision in rodent trigeminal primary afferents.

Authors:  Michael R Bale; Dario Campagner; Andrew Erskine; Rasmus S Petersen
Journal:  J Neurosci       Date:  2015-04-15       Impact factor: 6.167

3.  Whisker touch sensing guides locomotion in small, quadrupedal mammals.

Authors:  Robyn A Grant; Vicki Breakell; Tony J Prescott
Journal:  Proc Biol Sci       Date:  2018-06-13       Impact factor: 5.349

4.  Cerebellar potentiation and learning a whisker-based object localization task with a time response window.

Authors:  Negah Rahmati; Cullen B Owens; Laurens W J Bosman; Jochen K Spanke; Sander Lindeman; Wei Gong; Jan-Willem Potters; Vincenzo Romano; Kai Voges; Letizia Moscato; Sebastiaan K E Koekkoek; Mario Negrello; Chris I De Zeeuw
Journal:  J Neurosci       Date:  2014-01-29       Impact factor: 6.167

5.  A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents.

Authors:  Walter Vanzella; Natalia Grion; Daniele Bertolini; Andrea Perissinotto; Marco Gigante; Davide Zoccolan
Journal:  J Neurophysiol       Date:  2019-09-25       Impact factor: 2.714

6.  Active vibrissal sensing in rodents and marsupials.

Authors:  Ben Mitchinson; Robyn A Grant; Kendra Arkley; Vladan Rankov; Igor Perkon; Tony J Prescott
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2011-11-12       Impact factor: 6.237

7.  Whisking and whisker kinematics during a texture classification task.

Authors:  Yanfang Zuo; Igor Perkon; Mathew E Diamond
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2011-11-12       Impact factor: 6.237

8.  Whisker touch guides canopy exploration in a nocturnal, arboreal rodent, the Hazel dormouse (Muscardinus avellanarius).

Authors:  Kendra Arkley; Guuske P Tiktak; Vicki Breakell; Tony J Prescott; Robyn A Grant
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2017-01-20       Impact factor: 1.836

9.  Selection of head and whisker coordination strategies during goal-oriented active touch.

Authors:  Joseph B Schroeder; Jason T Ritt
Journal:  J Neurophysiol       Date:  2016-01-20       Impact factor: 2.714

10.  Real-Time Closed-Loop Feedback in Behavioral Time Scales Using DeepLabCut.

Authors:  Keisuke Sehara; Paul Zimmer-Harwood; Matthew E Larkum; Robert N S Sachdev
Journal:  eNeuro       Date:  2021-04-16
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