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Karashchuk P, Rupp KL, Dickinson ES, Walling-Bell S, Sanders E, Azim E, Brunton BW, Tuthill JC. Anipose: A toolkit for robust markerless 3D pose estimation. Cell Rep 2021; 36:109730. [PMID: 34592148 PMCID: PMC8498918 DOI: 10.1016/j.celrep.2021.109730] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/15/2021] [Accepted: 08/27/2021] [Indexed: 01/12/2023] Open
Abstract
Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method DeepLabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. To help users get started with 3D tracking, we provide tutorials and documentation at http://anipose.org/.
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Affiliation(s)
- Pierre Karashchuk
- Neuroscience Graduate Program, University of Washington, Seattle, WA, USA
| | - Katie L. Rupp
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Evyn S. Dickinson
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sarah Walling-Bell
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Elischa Sanders
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bingni W. Brunton
- Department of Biology, University of Washington, Seattle, WA, USA,Senior author,Correspondence: (B.W.B.), (J.C.T.)
| | - John C. Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA,Senior author,Lead contact,Correspondence: (B.W.B.), (J.C.T.)
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52
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Fayat R, Delgado Betancourt V, Goyallon T, Petremann M, Liaudet P, Descossy V, Reveret L, Dugué GP. Inertial Measurement of Head Tilt in Rodents: Principles and Applications to Vestibular Research. SENSORS (BASEL, SWITZERLAND) 2021; 21:6318. [PMID: 34577524 PMCID: PMC8472891 DOI: 10.3390/s21186318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/03/2021] [Accepted: 09/13/2021] [Indexed: 12/21/2022]
Abstract
Inertial sensors are increasingly used in rodent research, in particular for estimating head orientation relative to gravity, or head tilt. Despite this growing interest, the accuracy of tilt estimates computed from rodent head inertial data has never been assessed. Using readily available inertial measurement units mounted onto the head of freely moving rats, we benchmarked a set of tilt estimation methods against concurrent 3D optical motion capture. We show that, while low-pass filtered head acceleration signals only provided reliable tilt estimates in static conditions, sensor calibration combined with an appropriate choice of orientation filter and parameters could yield average tilt estimation errors below 1.5∘ during movement. We then illustrate an application of inertial head tilt measurements in a preclinical rat model of unilateral vestibular lesion and propose a set of metrics describing the severity of associated postural and motor symptoms and the time course of recovery. We conclude that headborne inertial sensors are an attractive tool for quantitative rodent behavioral analysis in general and for the study of vestibulo-postural functions in particular.
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Affiliation(s)
- Romain Fayat
- Neurophysiologie des Circuits Cérébraux, Institut de Biologie de l’ENS (IBENS), Ecole Normale Supérieure, UMR CNRS 8197, INSERM U1024, Université PSL, 75005 Paris, France;
- Laboratoire MAP5, UMR CNRS 8145, Université Paris Descartes, 75006 Paris, France
| | | | - Thibault Goyallon
- Laboratoire Jean Kuntzmann, Université Grenoble Alpes, UMR CNRS 5224, INRIA, 38330 Montbonnot-Saint-Martin, France; (T.G.); (L.R.)
| | - Mathieu Petremann
- Preclinical Development, Sensorion SA, 34080 Montpellier, France; (V.D.B.); (M.P.); (P.L.); (V.D.)
| | - Pauline Liaudet
- Preclinical Development, Sensorion SA, 34080 Montpellier, France; (V.D.B.); (M.P.); (P.L.); (V.D.)
| | - Vincent Descossy
- Preclinical Development, Sensorion SA, 34080 Montpellier, France; (V.D.B.); (M.P.); (P.L.); (V.D.)
| | - Lionel Reveret
- Laboratoire Jean Kuntzmann, Université Grenoble Alpes, UMR CNRS 5224, INRIA, 38330 Montbonnot-Saint-Martin, France; (T.G.); (L.R.)
| | - Guillaume P. Dugué
- Neurophysiologie des Circuits Cérébraux, Institut de Biologie de l’ENS (IBENS), Ecole Normale Supérieure, UMR CNRS 8197, INSERM U1024, Université PSL, 75005 Paris, France;
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53
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McCullough MH, Goodhill GJ. Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain. Curr Opin Neurobiol 2021; 70:89-100. [PMID: 34482006 DOI: 10.1016/j.conb.2021.07.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/20/2021] [Accepted: 07/21/2021] [Indexed: 01/02/2023]
Abstract
Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.
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Affiliation(s)
- Michael H McCullough
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Geoffrey J Goodhill
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia; School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, 4072, Australia.
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54
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Gosztolai A, Günel S, Lobato-Ríos V, Pietro Abrate M, Morales D, Rhodin H, Fua P, Ramdya P. LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals. Nat Methods 2021; 18:975-981. [PMID: 34354294 PMCID: PMC7611544 DOI: 10.1038/s41592-021-01226-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 06/29/2021] [Indexed: 12/22/2022]
Abstract
Markerless three-dimensional (3D) pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D poses by multi-view triangulation of deep network-based two-dimensional (2D) pose estimates. However, triangulation requires multiple synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3D's versatility by applying it to multiple experimental systems using flies, mice, rats and macaques, and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotypical and nonstereotypical behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays and tedious calibration procedures and despite occluded body parts in freely behaving animals.
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Affiliation(s)
- Adam Gosztolai
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland.
| | - Semih Günel
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland. .,Computer Vision Laboratory, EPFL, Lausanne, Switzerland.
| | - Victor Lobato-Ríos
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Marco Pietro Abrate
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Daniel Morales
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Helge Rhodin
- Department of Computer Science, UBC, Vancouver, Canada
| | - Pascal Fua
- Computer Vision Laboratory, EPFL, Lausanne, Switzerland
| | - Pavan Ramdya
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland.
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55
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Nomura H. [Behavioral analysis with machine learning]. Nihon Yakurigaku Zasshi 2021; 156:250. [PMID: 34193706 DOI: 10.1254/fpj.21034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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56
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Wood AN. New roles for dopamine in motor skill acquisition: lessons from primates, rodents, and songbirds. J Neurophysiol 2021; 125:2361-2374. [PMID: 33978497 DOI: 10.1152/jn.00648.2020] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Motor learning is a core aspect of human life and appears to be ubiquitous throughout the animal kingdom. Dopamine, a neuromodulator with a multifaceted role in synaptic plasticity, may be a key signaling molecule for motor skill learning. Though typically studied in the context of reward-based associative learning, dopamine appears to be necessary for some types of motor learning. Mesencephalic dopamine structures are highly conserved among vertebrates, as are some of their primary targets within the basal ganglia, a subcortical circuit important for motor learning and motor control. With a focus on the benefits of cross-species comparisons, this review examines how "model-free" and "model-based" computational frameworks for understanding dopamine's role in associative learning may be applied to motor learning. The hypotheses that dopamine could drive motor learning either by functioning as a reward prediction error, through passive facilitating of normal basal ganglia activity, or through other mechanisms are examined in light of new studies using humans, rodents, and songbirds. Additionally, new paradigms that could enhance our understanding of dopamine's role in motor learning by bridging the gap between the theoretical literature on motor learning in humans and other species are discussed.
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Affiliation(s)
- A N Wood
- Department of Biology and Graduate Program in Neuroscience, Emory University, Atlanta, Georgia
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57
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Dunn TW, Marshall JD, Severson KS, Aldarondo DE, Hildebrand DGC, Chettih SN, Wang WL, Gellis AJ, Carlson DE, Aronov D, Freiwald WA, Wang F, Ölveczky BP. Geometric deep learning enables 3D kinematic profiling across species and environments. Nat Methods 2021; 18:564-573. [PMID: 33875887 DOI: 10.1038/s41592-021-01106-6] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 03/02/2021] [Indexed: 11/09/2022]
Abstract
Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.
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Affiliation(s)
- Timothy W Dunn
- Duke Forge and Duke AI Health, Duke University, Durham, NC, USA. .,Duke Global Neurosurgery and Neurology Division, Department of Neurosurgery, Duke University, Durham, NC, USA.
| | - Jesse D Marshall
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
| | - Kyle S Severson
- Department of Neurobiology, Duke University, Durham, NC, USA.,Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | - Selmaan N Chettih
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - William L Wang
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Amanda J Gellis
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - David E Carlson
- Duke Forge and Duke AI Health, Duke University, Durham, NC, USA.,Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA
| | - Dmitriy Aronov
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Winrich A Freiwald
- Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA
| | - Fan Wang
- Department of Neurobiology, Duke University, Durham, NC, USA.,Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
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58
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Fujita Y, Yamashita T. Mechanisms and significance of microglia-axon interactions in physiological and pathophysiological conditions. Cell Mol Life Sci 2021; 78:3907-3919. [PMID: 33507328 PMCID: PMC11072252 DOI: 10.1007/s00018-021-03758-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/28/2020] [Accepted: 01/06/2021] [Indexed: 12/15/2022]
Abstract
Microglia are the resident immune cells of the central nervous system, and are important for cellular processes. In addition to their classical roles in pathophysiological conditions, these immune cells also dynamically interact with neurons and influence their structure and function in physiological conditions. Microglia have been shown to contact neurons at various points, including the dendrites, cell bodies, synapses, and axons, and support various developmental functions, such as neuronal survival, axon elongation, and maturation of the synaptic circuit. This review summarizes the current knowledge regarding the roles of microglia in brain development, with particular emphasis on microglia-axon interactions. We will review recent findings regarding the functions and signaling pathways involved in the reciprocal interactions between microglia and neurons. Moreover, as these interactions are altered in disease and injury conditions, we also discuss the effect and alteration of microglia-axon interactions in disease progression and the potential role of microglia in developmental brain disorders.
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Affiliation(s)
- Yuki Fujita
- Department of Molecular Neuroscience, Graduate School of Medicine, Osaka University, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
- WPI Immunology Frontier Research Center, Osaka University, 3-1, Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Toshihide Yamashita
- Department of Molecular Neuroscience, Graduate School of Medicine, Osaka University, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
- WPI Immunology Frontier Research Center, Osaka University, 3-1, Yamadaoka, Suita, Osaka, 565-0871, Japan.
- Graduate School of Frontier Bioscience, Osaka University, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
- Department of Neuro-Medical Science, Graduate School of Medicine, Osaka University, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
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