Promoting and Optimizing the Use of 3D-Printed Objects in Spontaneous Recognition Memory Tasks in Rodents: A Method for Improving Rigor and Reproducibility

Spontaneous recognition memory tasks are widely used to assess cognitive function in rodents and have become commonplace in the characterization of rodent models of neurodegenerative, neuropsychiatric and neurodevelopmental disorders. Leveraging an animal’s innate preference for novelty, these tasks...

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Bibliographic Details
Published in:eNeuro Vol. 8; no. 5; p. ENEURO.0319-21.2021
Main Authors: Inayat, Mehreen, Cruz-Sanchez, Arely, Thorpe, Hayley H. A., Frie, Jude A., Richards, Blake A, Khokhar, Jibran Y., Arruda-Carvalho, Maithe
Format: Journal Article
Language:English
Published: Society for Neuroscience 01-09-2021
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Summary:Spontaneous recognition memory tasks are widely used to assess cognitive function in rodents and have become commonplace in the characterization of rodent models of neurodegenerative, neuropsychiatric and neurodevelopmental disorders. Leveraging an animal’s innate preference for novelty, these tasks use object exploration to capture the what, where and when components of recognition memory. Choosing and optimizing objects is a key feature when designing recognition memory tasks. Although the range of objects used in these tasks varies extensively across studies, object features can bias exploration, influence task difficulty and alter brain circuit recruitment. Here, we discuss the advantages of using 3D-printed objects in rodent spontaneous recognition memory tasks. We provide strategies for optimizing their design and usage, and offer a repository of tested, open-source designs for use with commonly used rodent species. The easy accessibility, low-cost, renewability and flexibility of 3D-printed open-source designs make this approach an important step toward improving rigor and reproducibility in rodent spontaneous recognition memory tasks.
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Author contributions: M.I., A.C.-S., H.H.A.T., J.A.F., B.A.R., J.Y.K., and M.A.-C. designed research; M.I., A.C.-S., H.H.A.T., and J.A.F. performed research; M.I., A.C.-S., H.H.A.T., and J.A.F. analyzed data; B.A.R., J.Y.K., and M.A.-C. wrote the paper.
M.I. and A.C.-S. contributed equally to this work.
This work was supported by a Natural Science and Engineering Council of Canada (NSERC) USRA grant (M.I.); the CGS-D Grant CGSD3-534884–2019 (to A.C.-S.); a Vanier award (J.A.F.); Natural Science and Engineering Council of Canada Grants RGPIN-2017-06344 (to M.A.-C.), RGPIN-2019-05121 (to J.Y.K.), and RGPIN-2020-05105 (to B.A.R.); CIFAR (Learning in Machines and Brains Fellowship; B.A.R.), Brain and Behavior Research Foundation (National Alliance for Research on Schizophrenia and Depression Young Investigator 26016); the Canadian Institutes of Health Research (CIHR) Grant PJT 399790); the SickKids Foundation/CIHR–Institute of Human Development, Child and Youth Health Grant NI19-1132R); and the Human Frontier Science Program Organization Grant CDA00009/2018 (to M.A.-C.).
The authors declare no competing financial interests.
ISSN:2373-2822
2373-2822
DOI:10.1523/ENEURO.0319-21.2021