Literature DB >> 19106295

A robust automated system elucidates mouse home cage behavioral structure.

Evan H Goulding1, A Katrin Schenk, Punita Juneja, Adrienne W MacKay, Jennifer M Wade, Laurence H Tecott.   

Abstract

Patterns of behavior exhibited by mice in their home cages reflect the function and interaction of numerous behavioral and physiological systems. Detailed assessment of these patterns thus has the potential to provide a powerful tool for understanding basic aspects of behavioral regulation and their perturbation by disease processes. However, the capacity to identify and examine these patterns in terms of their discrete levels of organization across diverse behaviors has been difficult to achieve and automate. Here, we describe an automated approach for the quantitative characterization of fundamental behavioral elements and their patterns in the freely behaving mouse. We demonstrate the utility of this approach by identifying unique features of home cage behavioral structure and changes in distinct levels of behavioral organization in mice with single gene mutations altering energy balance. The robust, automated, reproducible quantification of mouse home cage behavioral structure detailed here should have wide applicability for the study of mammalian physiology, behavior, and disease.

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Year:  2008        PMID: 19106295      PMCID: PMC2634928          DOI: 10.1073/pnas.0809053106

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  36 in total

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Journal:  Neurosci Biobehav Rev       Date:  2001-07       Impact factor: 8.989

4.  The dominance analysis approach for comparing predictors in multiple regression.

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5.  Quantitative analysis of rat activity in the home cage by infrared monitoring. Application to the acute toxicity testing of acetanilide and phenylmercuric acetate.

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6.  Differential contributions of dopamine D1, D2, and D3 receptors to MDMA-induced effects on locomotor behavior patterns in mice.

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Journal:  Neuropsychopharmacology       Date:  2006-07-19       Impact factor: 7.853

7.  The power of automated high-resolution behavior analysis revealed by its application to mouse models of Huntington's and prion diseases.

Authors:  Andrew D Steele; Walker S Jackson; Oliver D King; Susan Lindquist
Journal:  Proc Natl Acad Sci U S A       Date:  2007-01-29       Impact factor: 11.205

8.  Home cage activity and activity-based measures of anxiety in 129P3/J, 129X1/SvJ and C57BL/6J mice.

Authors:  Xiangdong Tang; Larry D Sanford
Journal:  Physiol Behav       Date:  2004-12-08

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Review 10.  Neurobehavioral assessment in the information age.

Authors:  Laurence H Tecott; Eric J Nestler
Journal:  Nat Neurosci       Date:  2004-05       Impact factor: 24.884

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  64 in total

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Authors:  Neil E Rowland
Journal:  Behav Brain Res       Date:  2011-11-28       Impact factor: 3.332

Review 2.  Genetic contributions to behavioural diversity at the gene-environment interface.

Authors:  Andres Bendesky; Cornelia I Bargmann
Journal:  Nat Rev Genet       Date:  2011-11-08       Impact factor: 53.242

3.  Elucidation of The Behavioral Program and Neuronal Network Encoded by Dorsal Raphe Serotonergic Neurons.

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Journal:  Neuropsychopharmacology       Date:  2015-09-18       Impact factor: 7.853

4.  Cellular telephones measure activity and lifespace in community-dwelling adults: proof of principle.

Authors:  Ana Katrin Schenk; Bradley C Witbrodt; Carrie A Hoarty; Richard H Carlson; Evan H Goulding; Jane F Potter; Stephen J Bonasera
Journal:  J Am Geriatr Soc       Date:  2011-02-02       Impact factor: 5.562

5.  Simultaneous behavioral characterizations: Embracing complexity.

Authors:  Richard Paylor
Journal:  Proc Natl Acad Sci U S A       Date:  2008-12-23       Impact factor: 11.205

6.  A Bayesian nonparametric approach for mapping dynamic quantitative traits.

Authors:  Zitong Li; Mikko J Sillanpää
Journal:  Genetics       Date:  2013-06-14       Impact factor: 4.562

7.  Biaryl amides and hydrazones as therapeutics for prion disease in transgenic mice.

Authors:  Duo Lu; Kurt Giles; Zhe Li; Satish Rao; Elena Dolghih; Joel R Gever; Michal Geva; Manuel L Elepano; Abby Oehler; Clifford Bryant; Adam R Renslo; Matthew P Jacobson; Stephen J Dearmond; B Michael Silber; Stanley B Prusiner
Journal:  J Pharmacol Exp Ther       Date:  2013-08-21       Impact factor: 4.030

8.  A flexible estimating equations approach for mapping function-valued traits.

Authors:  Hao Xiong; Evan H Goulding; Elaine J Carlson; Laurence H Tecott; Charles E McCulloch; Saunak Sen
Journal:  Genetics       Date:  2011-07-29       Impact factor: 4.562

9.  Animal models: inside the minds of mice and men.

Authors:  Monya Baker
Journal:  Nature       Date:  2011-07-06       Impact factor: 49.962

10.  High-throughput phenotyping of avoidance learning in mice discriminates different genotypes and identifies a novel gene.

Authors:  G Maroteaux; M Loos; S van der Sluis; B Koopmans; E Aarts; K van Gassen; A Geurts; D A Largaespada; B M Spruijt; O Stiedl; A B Smit; M Verhage
Journal:  Genes Brain Behav       Date:  2012-08-02       Impact factor: 3.449

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