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      A virtual library for behavioral performance in standard conditions—rodent spontaneous activity in an open field during repeated testing and after treatment with drugs or brain lesions

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          Abstract

          Background

          Beyond their specific experiment, video records of behavior have future value—for example, as inputs for new experiments or for yet unknown types of analysis of behavior—similar to tissue or blood sample banks in life sciences where clinically derived or otherwise well-described experimental samples are stored to be available for some unknown potential future purpose.

          Findings

          Research using an animal model of obsessive-compulsive disorder employed a standardized paradigm where the behavior of rats in a large open field was video recorded for 55 minutes on each test. From 43 experiments, there are 19,976 such trials that amount to over 2 years of continuous recording. In addition to videos, there are 2 video-derived raw data objects: XY locomotion coordinates and plots of animal trajectory. To motivate future use, the 3 raw data objects are annotated with a general schema—one that abstracts the data records from their particular experiment while providing, at the same time, a detailed list of independent variables bearing on behavioral performance. The raw data objects are deposited as 43 datasets but constitute, functionally, a library containing 1 large dataset.

          Conclusions

          Size and annotation schema give the library high reuse potential: in applications using machine learning techniques, statistical evaluation of subtle factors, simulation of new experiments, or as educational resource. Ultimately, the library can serve both as the seed and as the test bed to create a machine-searchable virtual library of linked open datasets for behavioral performance in defined conditions.

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          Most cited references211

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

          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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            Mapping Sub-Second Structure in Mouse Behavior.

            Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that 3D mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.
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              Linked Data: Evolving the Web into a Global Data Space

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                Author and article information

                Contributors
                Journal
                Gigascience
                Gigascience
                gigascience
                GigaScience
                Oxford University Press
                2047-217X
                20 October 2022
                2022
                20 October 2022
                : 11
                : giac092
                Affiliations
                Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton , Ontario L8S 4K1, Canada
                Department of Pathology & Molecular Medicine, McMaster University , Hamilton, Ontario L8S 4K1, Canada
                Department of Systems Neurobiology, Instituto de Neurociencias (CSIC-UMH) , 03550 Sant Joan d'Alacant, Alicante, Spain
                Author notes
                Correspondence address. Henry Szechtman, Department of Psychiatry and Behavioural Neurosciences, McMaster University, 1280 Main Street West, Health Science Centre, Room 4N82, Hamilton, Ontario, Canada L8S 4K1. E-mail: szechtma@ 123456mcmaster.ca
                Author information
                https://orcid.org/0000-0003-3986-4482
                Article
                giac092
                10.1093/gigascience/giac092
                9581716
                36261217
                b1706855-6f83-4764-8d4a-835f926aa967
                © The Author(s) 2022. Published by Oxford University Press GigaScience.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 May 2022
                : 31 July 2022
                : 06 September 2022
                Page count
                Pages: 41
                Funding
                Funded by: Canadian Institutes of Health Research, DOI 10.13039/501100000024;
                Award ID: MOP-64424
                Award ID: MT-12852
                Funded by: Ontario Mental Health Foundation, DOI 10.13039/100012120;
                Funded by: Natural Sciences and Engineering Research Council of Canada, DOI 10.13039/501100000038;
                Award ID: RGPIN A0544
                Categories
                Data Note
                AcademicSubjects/SCI00960
                AcademicSubjects/SCI02254

                animal model of obsessive compulsive disorder,open field,exploration,chronic drug treatments,brain lesion treatments,behavioral sensitization,video recording,repeated testing,male rats long-evans,large dataset

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