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      • Record: found
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      Is Open Access

      A modular, cost-effective, versatile, open-source operant box solution for long-term miniscope imaging, 3D tracking, and deep learning behavioral analysis

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          Abstract

          In this procedure we have included an open-source method for a customized operant chamber optimized for long-term miniature microscope (miniscope) recordings.

          • •

            The miniscope box is designed to function with custom or typical med-associates style accessories (e.g., houselights, levers, etc.).

          • •

            The majority of parts can be directly purchased which minimizes the need for skilled and time-consuming labor.

          • •

            We include designs and estimated pricing for a single box but it is recommended to build these in larger batches to efficiently utilize bulk ordering of certain components.

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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.
          Article 0 comments Cited 1522 times – based on 0 reviews     Review now
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            Using DeepLabCut for 3D markerless pose estimation across species and behaviors

            Tanmay Nath, Alexander Mathis, An Chen … (2019)
            Article 0 comments Cited 463 times – based on 0 reviews     Review now
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              CaImAn an open source tool for scalable calcium imaging data analysis

              Andrea Giovannucci, Johannes Friedrich, Pat Gunn … (2019)
              Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.
              Article 0 comments Cited 320 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Nicholas J. Beacher
                Journal
                Journal ID (nlm-ta): MethodsX
                Journal ID (iso-abbrev): MethodsX
                Title: MethodsX
                Publisher: Elsevier
                ISSN (Electronic): 2215-0161
                Publication date PMC-release: 16 April 2024
                Publication date Collection: June 2024
                Publication date (Electronic): 16 April 2024
                Volume: 12
                Electronic Location Identifier: 102721
                Affiliations
                [a ]Intramural Research Program, National Institute on Drug Abuse, 333 Cassell Drive, Baltimore, MD 21224, United States
                [b ]Penn Memory Center, University of Pennsylvania, Philadelphia, PA, United States
                [c ]The Feinberg School of Medicine at Northwestern University, Chicago, IL, United States
                Author notes
                [* ]Corresponding author. Nicholas.beacher@ 123456nih.gov
                Article
                Publisher Item ID: S2215-0161(24)00174-2 Publisher ID: 102721
                DOI: 10.1016/j.mex.2024.102721
                PMC ID: 11041912
                PubMed ID: 38660044
                SO-VID: 64dc61d6-18a1-4af3-abad-8c6c6773e780
                Copyright © © 2024 The Authors. Published by Elsevier B.V.
                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 : 20 December 2023
                Date accepted : 15 April 2024
                Categories
                Subject: Neuroscience

                Keywords: miniscope,operant,custom,rats,imaging,the miniscope box
                Data availability:
                Keywords: miniscope, operant, custom, rats, imaging, the miniscope box

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