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      Automated Tracking of Animal Posture and Movement during Exploration and Sensory Orientation Behaviors

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          Abstract

          Background

          The nervous functions of an organism are primarily reflected in the behavior it is capable of. Measuring behavior quantitatively, at high-resolution and in an automated fashion provides valuable information about the underlying neural circuit computation. Accordingly, computer-vision applications for animal tracking are becoming a key complementary toolkit to genetic, molecular and electrophysiological characterization in systems neuroscience.

          Methodology/Principal Findings

          We present Sensory Orientation Software ( SOS) to measure behavior and infer sensory experience correlates. SOS is a simple and versatile system to track body posture and motion of single animals in two-dimensional environments. In the presence of a sensory landscape, tracking the trajectory of the animal's sensors and its postural evolution provides a quantitative framework to study sensorimotor integration. To illustrate the utility of SOS, we examine the orientation behavior of fruit fly larvae in response to odor, temperature and light gradients. We show that SOS is suitable to carry out high-resolution behavioral tracking for a wide range of organisms including flatworms, fishes and mice.

          Conclusions/Significance

          Our work contributes to the growing repertoire of behavioral analysis tools for collecting rich and fine-grained data to draw and test hypothesis about the functioning of the nervous system. By providing open-access to our code and documenting the software design, we aim to encourage the adaptation of SOS by a wide community of non-specialists to their particular model organism and questions of interest.

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          Most cited references35

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          High-throughput Ethomics in Large Groups of Drosophila

          We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting within a planar arena. Our system includes machine vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly, or as a vector that concisely captures the statistical properties of all behaviors displayed within a given period. We found that behavioral differences between individuals are consistent over time and are sufficient to accurately predict gender and genotype. In addition, we show that the relative positions of flies during social interactions vary according to gender, genotype, and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.
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            High-Throughput Behavioral Analysis in C. elegans

            We have designed a real-time computer vision system, the Multi-Worm Tracker (MWT), that can simultaneously quantify the behavior of dozens of Caenorhabditis elegans on a traditional petri plate at video rates. Three traditional behavioral paradigms are examined using this system: spontaneous movement on food, where the behavior changes over tens of minutes; chemotaxis, where turning events must be detected accurately to determine strategy; and habituation of response to tap, where the response is stochastic and changes over time. In each case, manual analysis or automated single-worm tracking would be tedious and time-consuming, but the MWT system allows rapid quantification of behavior with minimal human effort. Thus, this system will enablelarge scale forward and reverse genetic screens for complex behaviors.
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              • Article: not found

              Chemotaxis in Escherichia coli analysed by three-dimensional tracking.

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                9 August 2012
                : 7
                : 8
                : e41642
                Affiliations
                [1 ]European Molecular Biology Laboratory/Center for Genomic Regulation Systems Biology Unit, Center for Genomic Regulation & Universitat Pompeu Fabra, Barcelona, Spain
                [2 ]Department of Electrical Engineering and Computer Science, Université de Liège, Liege Sart-Tilman, Belgium
                [3 ]Joseph Henry Laboratories of Physics & Lewis-Sigler Institute for Integrative Genomics Princeton University, Princeton, New Jersey, United States of America
                Imperial College London, United Kingdom
                Author notes

                Competing Interests: ML is a PLoS ONE Editorial Board member. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

                Conceived and designed the experiments: AGM ML. Performed the experiments: AGM. Analyzed the data: AGM. Contributed reagents/materials/analysis tools: AGM NP GJS. Wrote the paper: AGM ML.

                Article
                PONE-D-12-00187
                10.1371/journal.pone.0041642
                3415430
                22912674
                9ac62db3-2e4b-44a7-8d4d-6948816673f8
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 December 2011
                : 28 June 2012
                Page count
                Pages: 9
                Funding
                AGM is supported by the Juan de la Cierva program from the Spanish Ministry of Science and Innovation. NP was funded through an ERASMUS scholarship granted by the University of Liège. GJS is supported in part by grants PHY-0650617 and IIS-0613435 from the National Science Foundation,by grant P50 GM071508 from the National Institutes of Health, and by the Swartz Foundation. ML acknowledges funding from the Spanish Ministry of Science and Innovation (BFU2008-00362 and BFU2009-07757-E/BMC), the European Molecular Biology Laboratory/Center for Genomic Regulation Systems Biology Program and a Marie Curie Reintegration Grant (PIRG02-GA-2007-224791). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Computational Biology
                Computational Neuroscience
                Sensory Systems
                Model Organisms
                Animal Models
                Drosophila Melanogaster
                Neuroscience
                Computational Neuroscience
                Sensory Systems
                Behavioral Neuroscience
                Motor Systems
                Neuroethology
                Sensory Perception
                Sensory Systems
                Computer Science
                Software Engineering
                Software Tools

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                Uncategorized

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