Minibox: Custom solo or semi-group housing chambers for long term housing of rats with miniscopes

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Abstract

In this detailed procedure, we include open-source methodologies using ‘solidworks’ designs for creating solo or semi-group housing units for rats wearing miniscopes for long periods of time. Builds are optimized to preserve rat health and prevent hardware destruction. We include all prices and suggestions for purchasing strategies to reduce overall build-costs.

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Chambers are optimized for long-term housing to protect rats wearing delicate headstages (e.g., miniscopes).
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Designed to be low-cost, efficient supplement to operant chambers and provides numerous benefits to long-term miniscope imaging. The housing chambers can be augmented by installing cameras, commutators, or different types of floor grids depending on experimental conditions.
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The chambers can also be secured to one another to create “rat-duplexes”, allowing experimenters to control the degree of social isolation.

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Most cited references 32

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DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Pranav Mamidanna, Venkatesh N. Murthy, Mackenzie Weygandt Mathis … (2018)

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.

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Using DeepLabCut for 3D markerless pose estimation across species and behaviors

Tanmay Nath, Alexander Mathis, An Chen … (2019)

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The Loneliness Pandemic: Loneliness and Other Concomitants of Depression, Anxiety and their Comorbidity During the COVID-19 Outbreak

Yuval Palgi, Amit Shrira, Lia Ring … (2020)

Highlights • Loneliness was the main risk factor for depression, anxiety and their comorbidity. • Probable clinical level of depression and anxiety was not predicted by risk for COVID-19 complications. • adults above 60, displayed greater resilience to psychiatric disorders associated with the COVID-19 crisis.

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Author and article information

Contributors

Nicholas J. Beacher

Da-Ting Lin

Journal

Journal ID (nlm-ta): MethodsX

Journal ID (iso-abbrev): MethodsX

Title: MethodsX

Publisher: Elsevier

ISSN (Electronic): 2215-0161

Publication date PMC-release: 16 August 2024

Publication date Collection: December 2024

Publication date (Electronic): 16 August 2024

Volume: 13

Electronic Location Identifier: 102921

Affiliations

[a ]Intramural Research Program, National Institute on Drug Abuse, 333 Cassell Drive, Baltimore, MD 21224, USA

[b ]University of California Davis Health, School of Medicine, Department of Neurology, Alzheimer's Disease Research Center-East Bay, 100 North Wiget Lane, Suite 150, Walnut Creek, CA 94598, USA

[c ]The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, MD 21205, USA

Author notes

[* ]Corresponding author. nicholas.beacher@ 123456nih.gov

Article

Publisher Item ID: S2215-0161(24)00372-8 Publisher ID: 102921

DOI: 10.1016/j.mex.2024.102921

PMC ID: 11382000

PubMed ID: 39253000

SO-VID: f885d092-e9c5-488e-af18-9fb285fb552f

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 22 July 2024

Date accepted : 15 August 2024

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