Literature DB >> 24637442

Automated, quantitative cognitive/behavioral screening of mice: for genetics, pharmacology, animal cognition and undergraduate instruction.

C R Gallistel1, Fuat Balci2, David Freestone3, Aaron Kheifets4, Adam King5.   

Abstract

We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the "cloud" and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in cabinet; two such cabinets (16 environments) may be controlled by one computer.

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Year:  2014        PMID: 24637442      PMCID: PMC4140415          DOI: 10.3791/51047

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  24 in total

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Authors:  Trudy F C Mackay; Eric A Stone; Julien F Ayroles
Journal:  Nat Rev Genet       Date:  2009-08       Impact factor: 53.242

Review 2.  Neurogenetics of food anticipation.

Authors:  Etienne Challet; Jorge Mendoza; Hugues Dardente; Paul Pévet
Journal:  Eur J Neurosci       Date:  2009-10-26       Impact factor: 3.386

3.  Hippocampal lesions cause learning deficits in inbred mice in the Morris water maze and conditioned-fear task.

Authors:  S F Logue; R Paylor; J M Wehner
Journal:  Behav Neurosci       Date:  1997-02       Impact factor: 1.912

4.  Differences between inbred strains of mice in Morris water maze performance.

Authors:  M Upchurch; J M Wehner
Journal:  Behav Genet       Date:  1988-01       Impact factor: 2.805

5.  The desert ant odometer: a stride integrator that accounts for stride length and walking speed.

Authors:  Matthias Wittlinger; Rüdiger Wehner; Harald Wolf
Journal:  J Exp Biol       Date:  2007-01       Impact factor: 3.312

6.  Kinetics of matching.

Authors:  T A Mark; C R Gallistel
Journal:  J Exp Psychol Anim Behav Process       Date:  1994-01

7.  A portrait of the substrate for self-stimulation.

Authors:  C R Gallistel; P Shizgal; J S Yeomans
Journal:  Psychol Rev       Date:  1981-05       Impact factor: 8.934

8.  Time and Associative Learning.

Authors:  Peter D Balsam; Michael R Drew; C R Gallistel
Journal:  Comp Cogn Behav Rev       Date:  2010

9.  How vision and movement combine in the hippocampal place code.

Authors:  Guifen Chen; John A King; Neil Burgess; John O'Keefe
Journal:  Proc Natl Acad Sci U S A       Date:  2012-12-19       Impact factor: 11.205

10.  Food anticipatory activity behavior of mice across a wide range of circadian and non-circadian intervals.

Authors:  Matthew D Luby; Cynthia T Hsu; Scott A Shuster; Christian M Gallardo; Ralph E Mistlberger; Oliver D King; Andrew D Steele
Journal:  PLoS One       Date:  2012-05-25       Impact factor: 3.240

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  6 in total

1.  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

2.  Theoretical implications of quantitative properties of interval timing and probability estimation in mouse and rat.

Authors:  Aaron Kheifets; David Freestone; C R Gallistel
Journal:  J Exp Anal Behav       Date:  2017-06-27       Impact factor: 2.468

3.  An approach to monitoring home-cage behavior in mice that facilitates data sharing.

Authors:  Edoardo Balzani; Matteo Falappa; Fuat Balci; Valter Tucci
Journal:  Nat Protoc       Date:  2018-05-17       Impact factor: 13.491

4.  Cognitive assessment of mice strains heterozygous for cell-adhesion genes reveals strain-specific alterations in timing.

Authors:  C R Gallistel; Valter Tucci; Patrick M Nolan; Melitta Schachner; Igor Jakovcevski; Aaron Kheifets; Luendro Barboza
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2014-01-20       Impact factor: 6.237

Review 5.  Serotonin, neural markers, and memory.

Authors:  Alfredo Meneses
Journal:  Front Pharmacol       Date:  2015-07-21       Impact factor: 5.810

6.  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

  6 in total

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