1
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Mimura K, Matsumoto J, Mochihashi D, Nakamura T, Nishijo H, Higuchi M, Hirabayashi T, Minamimoto T. Unsupervised decomposition of natural monkey behavior into a sequence of motion motifs. Commun Biol 2024; 7:1080. [PMID: 39227400 DOI: 10.1038/s42003-024-06786-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 08/27/2024] [Indexed: 09/05/2024] Open
Abstract
Nonhuman primates (NHPs) exhibit complex and diverse behavior that typifies advanced cognitive function and social communication, but quantitative and systematical measure of this natural nonverbal processing has been a technical challenge. Specifically, a method is required to automatically segment time series of behavior into elemental motion motifs, much like finding meaningful words in character strings. Here, we propose a solution called SyntacticMotionParser (SMP), a general-purpose unsupervised behavior parsing algorithm using a nonparametric Bayesian model. Using three-dimensional posture-tracking data from NHPs, SMP automatically outputs an optimized sequence of latent motion motifs classified into the most likely number of states. When applied to behavioral datasets from common marmosets and rhesus monkeys, SMP outperformed conventional posture-clustering models and detected a set of behavioral ethograms from publicly available data. SMP also quantified and visualized the behavioral effects of chemogenetic neural manipulations. SMP thus has the potential to dramatically improve our understanding of natural NHP behavior in a variety of contexts.
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Affiliation(s)
- Koki Mimura
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan.
- Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo, 190-0014, Japan.
| | - Jumpei Matsumoto
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, 930-8555, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, 930-8555, Japan
| | - Daichi Mochihashi
- Department of Statistical Inference and Mathematics, The Institute of Statistical Mathematics, Tokyo, 190-9562, Japan
| | - Tomoaki Nakamura
- Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Tokyo, 182-8585, Japan
| | - Hisao Nishijo
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, 930-8555, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, 930-8555, Japan
| | - Makoto Higuchi
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Toshiyuki Hirabayashi
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Takafumi Minamimoto
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan.
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2
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Mountoufaris G, Nair A, Yang B, Kim DW, Vinograd A, Kim S, Linderman SW, Anderson DJ. A line attractor encoding a persistent internal state requires neuropeptide signaling. Cell 2024:S0092-8674(24)00906-1. [PMID: 39191257 DOI: 10.1016/j.cell.2024.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 06/23/2024] [Accepted: 08/07/2024] [Indexed: 08/29/2024]
Abstract
Internal states drive survival behaviors, but their neural implementation is poorly understood. Recently, we identified a line attractor in the ventromedial hypothalamus (VMH) that represents a state of aggressiveness. Line attractors can be implemented by recurrent connectivity or neuromodulatory signaling, but evidence for the latter is scant. Here, we demonstrate that neuropeptidergic signaling is necessary for line attractor dynamics in this system by using cell-type-specific CRISPR-Cas9-based gene editing combined with single-cell calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1+ neurons that control aggression diminished attack, reduced persistent neural activity, and eliminated line attractor dynamics while only slightly reducing overall neural activity and sex- or behavior-specific tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor in mammals. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.
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Affiliation(s)
- George Mountoufaris
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA
| | - Aditya Nair
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Program in Computation and Neural Systems, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA
| | - Bin Yang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA
| | - Dong-Wook Kim
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA
| | - Amit Vinograd
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA
| | - Samuel Kim
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA
| | - Scott W Linderman
- Department of Statistics, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA
| | - David J Anderson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA; Howard Hughes Medical Institute, Pasadena, CA 91001, USA.
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3
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Cheung KYM, Nair A, Li LY, Shapiro MG, Anderson DJ. Population coding of predator imminence in the hypothalamus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.12.607651. [PMID: 39211163 PMCID: PMC11360964 DOI: 10.1101/2024.08.12.607651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Hypothalamic VMHdm SF1 neurons are activated by predator cues and are necessary and sufficient for instinctive defensive responses. However, such data do not distinguish which features of a predator encounter are encoded by VMHdm SF1 neural activity. To address this issue, we imaged VMHdm SF1 neurons at single-cell resolution in freely behaving mice exposed to a natural predator in varying contexts. Our results reveal that VMHdm SF1 neurons do not represent different defensive behaviors, but rather encode predator identity and multiple predator-evoked internal states, including threat-evoked fear/anxiety; neophobia or arousal; predator imminence; and safety. Notably, threat and safety are encoded bi-directionally by anti-correlated subpopulations. Finally, individual differences in predator defensiveness are correlated with differences in VMHdm SF1 response dynamics. Thus, different threat-related internal state variables are encoded by distinct neuronal subpopulations within a genetically defined, anatomically restricted hypothalamic cell class. Highlights Distinct subsets of VMHdm SF1 neurons encode multiple predator-evoked internal states. Anti-correlated subsets encode safety vs. threat in a bi-directional mannerA population code for predator imminence is identified using a novel assay VMHdm SF1 dynamics correlate with individual variation in predator defensiveness.
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4
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Blau A, Schaffer ES, Mishra N, Miska NJ, Paninski L, Whiteway MR. A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms. ARXIV 2024:arXiv:2407.16727v1. [PMID: 39108294 PMCID: PMC11302674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms - which include tree-based models, deep neural networks, and graphical models - differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species - fly, mouse, and human - we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets.
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Affiliation(s)
- Ari Blau
- Department of Statistics Columbia University
| | | | | | | | - Liam Paninski
- Department of Statistics Zuckerman Institute Columbia University
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5
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Goss K, Bueno-Junior LS, Stangis K, Ardoin T, Carmon H, Zhou J, Satapathy R, Baker I, Jones-Tinsley CE, Lim MM, Watson BO, Sueur C, Ferrario CR, Murphy GG, Ye B, Hu Y. Quantifying social roles in multi-animal videos using subject-aware deep-learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.07.602350. [PMID: 39026890 PMCID: PMC11257443 DOI: 10.1101/2024.07.07.602350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Analyzing social behaviors is critical for many fields, including neuroscience, psychology, and ecology. While computational tools have been developed to analyze videos containing animals engaging in limited social interactions under specific experimental conditions, automated identification of the social roles of freely moving individuals in a multi-animal group remains unresolved. Here we describe a deep-learning-based system - named LabGym2 - for identifying and quantifying social roles in multi-animal groups. This system uses a subject-aware approach: it evaluates the behavioral state of every individual in a group of two or more animals while factoring in its social and environmental surroundings. We demonstrate the performance of subject-aware deep-learning in different species and assays, from partner preference in freely-moving insects to primate social interactions in the field. Our subject-aware deep learning approach provides a controllable, interpretable, and efficient framework to enable new experimental paradigms and systematic evaluation of interactive behavior in individuals identified within a group.
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Affiliation(s)
- Kelly Goss
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
- These authors contributed equally
| | - Lezio S. Bueno-Junior
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- These authors contributed equally
| | - Katherine Stangis
- Michigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Théo Ardoin
- Master Biodiversité Ecologie et Evolution, Université Paris-Saclay, Orsay, France
- Magistère de Biologie, Université Paris-Saclay, Orsay, France
| | - Hanna Carmon
- Department of Pharmacology and Psychology Department (Biopsychology), University of Michigan, Ann Arbor, MI 48109, USA
| | - Jie Zhou
- Department of Computer Science, Northern Illinois University, DeKalb, IL 60115, USA
| | - Rohan Satapathy
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Isabelle Baker
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Carolyn E. Jones-Tinsley
- Veterans Affairs VISN20 Northwest MIRECC, VA Portland Health Care System, Portland, OR 97239, USA
- Oregon Alzheimer’s Disease Research Center, Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Miranda M. Lim
- Veterans Affairs VISN20 Northwest MIRECC, VA Portland Health Care System, Portland, OR 97239, USA
- Oregon Alzheimer’s Disease Research Center, Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Brendon O. Watson
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Cédric Sueur
- Université de Strasbourg, IPHC UMR7178, CNRS, Strasbourg, France
- ANTHROPO-LAB, ETHICS EA 7446, Université Catholique de Lille, Lille, France
| | - Carrie R. Ferrario
- Department of Pharmacology and Psychology Department (Biopsychology), University of Michigan, Ann Arbor, MI 48109, USA
| | - Geoffrey G. Murphy
- Michigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Bing Ye
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yujia Hu
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
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6
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Simoes de Souza FM, Williamson R, McCullough C, Teel A, Futia G, Ma M, True A, Crimaldi JP, Gibson E, Restrepo D. Miniscope Recording Calcium Signals at Hippocampus of Mice Navigating an Odor Plume. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598681. [PMID: 38915584 PMCID: PMC11195275 DOI: 10.1101/2024.06.12.598681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Mice navigate an odor plume with a complex spatiotemporal structure in the dark to find the source of odorants. This article describes a protocol to monitor behavior and record Ca 2+ transients in dorsal CA1 stratum pyramidale neurons in hippocampus (dCA1) in mice navigating an odor plume in a 50 cm x 50 cm x 25 cm odor arena. An epifluorescence miniscope focused through a GRIN lens imaged Ca 2+ transients in dCA1 neurons expressing the calcium sensor GCaMP6f in Thy1-GCaMP6f mice. The paper describes the behavioral protocol to train the mice to perform this odor plume navigation task in an automated odor arena. The methods include a step-by-step procedure for the surgery for GRIN lens implantation and baseplate placement for imaging GCaMP6f in CA1. The article provides information on real-time tracking of the mouse position to automate the start of the trials and delivery of a sugar water reward. In addition, the protocol includes information on using of an interface board to synchronize metadata describing the automation of the odor navigation task and frame times for the miniscope and a digital camera tracking mouse position. Moreover, the methods delineate the pipeline used to process GCaMP6f fluorescence movies by motion correction using NorMCorre followed by identification of regions of interest with EXTRACT. Finally, the paper describes an artificial neural network approach to decode spatial paths from CA1 neural ensemble activity to predict mouse navigation of the odor plume. SUMMARY This protocol describes how to investigate the brain-behavior relationship in hippocampal CA1 in mice navigating an odor plume. This article provides a step-by-step protocol, including the surgery to access imaging of the hippocampus, behavioral training, miniscope GCaMP6f recording and processing of the brain and behavioral data to decode the mouse position from ROI neural activity.
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7
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Biderman D, Whiteway MR, Hurwitz C, Greenspan N, Lee RS, Vishnubhotla A, Warren R, Pedraja F, Noone D, Schartner MM, Huntenburg JM, Khanal A, Meijer GT, Noel JP, Pan-Vazquez A, Socha KZ, Urai AE, Cunningham JP, Sawtell NB, Paninski L. Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools. Nat Methods 2024; 21:1316-1328. [PMID: 38918605 DOI: 10.1038/s41592-024-02319-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/17/2024] [Indexed: 06/27/2024]
Abstract
Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce 'Lightning Pose', an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We released a cloud application that allows users to label data, train networks and process new videos directly from the browser.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Anup Khanal
- University of California, Los Angeles, Los Angeles, CA, USA
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8
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Goodwin NL, Choong JJ, Hwang S, Pitts K, Bloom L, Islam A, Zhang YY, Szelenyi ER, Tong X, Newman EL, Miczek K, Wright HR, McLaughlin RJ, Norville ZC, Eshel N, Heshmati M, Nilsson SRO, Golden SA. Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience. Nat Neurosci 2024; 27:1411-1424. [PMID: 38778146 PMCID: PMC11268425 DOI: 10.1038/s41593-024-01649-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 04/12/2024] [Indexed: 05/25/2024]
Abstract
The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.
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Affiliation(s)
- Nastacia L Goodwin
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Graduate Program in Neuroscience, University of Washington, Seattle, WA, USA
- Center of Excellence in Neurobiology of Addiction, Pain and Emotion (NAPE), University of Washington, Seattle, WA, USA
| | - Jia J Choong
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Sophia Hwang
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Kayla Pitts
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Liana Bloom
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Aasiya Islam
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Yizhe Y Zhang
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Graduate Program in Neuroscience, University of Washington, Seattle, WA, USA
- Center of Excellence in Neurobiology of Addiction, Pain and Emotion (NAPE), University of Washington, Seattle, WA, USA
| | - Eric R Szelenyi
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Center of Excellence in Neurobiology of Addiction, Pain and Emotion (NAPE), University of Washington, Seattle, WA, USA
| | - Xiaoyu Tong
- New York University Neuroscience Institute, New York, NY, USA
| | - Emily L Newman
- Department of Psychiatry, Harvard Medical School McLean Hospital, Belmont, MA, USA
| | - Klaus Miczek
- Department of Psychology, Tufts University, Medford, MA, USA
| | - Hayden R Wright
- Department of Integrative Physiology and Neuroscience, Washington State University, Pullman, WA, USA
- Graduate Program in Neuroscience, Washington State University, Pullman, WA, USA
| | - Ryan J McLaughlin
- Department of Integrative Physiology and Neuroscience, Washington State University, Pullman, WA, USA
- Graduate Program in Neuroscience, Washington State University, Pullman, WA, USA
| | | | - Neir Eshel
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Mitra Heshmati
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Graduate Program in Neuroscience, University of Washington, Seattle, WA, USA
- Center of Excellence in Neurobiology of Addiction, Pain and Emotion (NAPE), University of Washington, Seattle, WA, USA
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
| | - Simon R O Nilsson
- Department of Biological Structure, University of Washington, Seattle, WA, USA.
| | - Sam A Golden
- Department of Biological Structure, University of Washington, Seattle, WA, USA.
- Graduate Program in Neuroscience, University of Washington, Seattle, WA, USA.
- Center of Excellence in Neurobiology of Addiction, Pain and Emotion (NAPE), University of Washington, Seattle, WA, USA.
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9
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Chen Z, Jia G, Zhou Q, Zhang Y, Quan Z, Chen X, Fukuda T, Huang Q, Shi Q. ARBUR, a machine learning-based analysis system for relating behaviors and ultrasonic vocalizations of rats. iScience 2024; 27:109998. [PMID: 38947508 PMCID: PMC11214285 DOI: 10.1016/j.isci.2024.109998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/01/2024] [Accepted: 05/14/2024] [Indexed: 07/02/2024] Open
Abstract
Deciphering how different behaviors and ultrasonic vocalizations (USVs) of rats interact can yield insights into the neural basis of social interaction. However, the behavior-vocalization interplay of rats remains elusive because of the challenges of relating the two communication media in complex social contexts. Here, we propose a machine learning-based analysis system (ARBUR) that can cluster without bias both non-step (continuous) and step USVs, hierarchically detect eight types of behavior of two freely behaving rats with high accuracy, and locate the vocal rat in 3-D space. ARBUR reveals that rats communicate via distinct USVs during different behaviors. Moreover, we show that ARBUR can indicate findings that are long neglected by former manual analysis, especially regarding the non-continuous USVs during easy-to-confuse social behaviors. This work could help mechanistically understand the behavior-vocalization interplay of rats and highlights the potential of machine learning algorithms in automatic animal behavioral and acoustic analysis.
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Affiliation(s)
- Zhe Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
- Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China
| | - Guanglu Jia
- Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China
- Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Qijie Zhou
- Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China
- Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Yulai Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
- Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China
| | - Zhenzhen Quan
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Xuechao Chen
- Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China
- Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Toshio Fukuda
- Institute of Innovation for Future Society, Nagoya University, Nagoya, Japan
| | - Qiang Huang
- Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China
- Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Qing Shi
- Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China
- Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
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10
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Fang C, Wu Z, Zheng H, Yang J, Ma C, Zhang T. MCP: Multi-Chicken Pose Estimation Based on Transfer Learning. Animals (Basel) 2024; 14:1774. [PMID: 38929393 PMCID: PMC11200378 DOI: 10.3390/ani14121774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/07/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Poultry managers can better understand the state of poultry through poultry behavior analysis. As one of the key steps in behavior analysis, the accurate estimation of poultry posture is the focus of this research. This study mainly analyzes a top-down pose estimation method of multiple chickens. Therefore, we propose the "multi-chicken pose" (MCP), a pose estimation system for multiple chickens through deep learning. Firstly, we find the position of each chicken from the image via the chicken detector; then, an estimate of the pose of each chicken is made using a pose estimation network, which is based on transfer learning. On this basis, the pixel error (PE), root mean square error (RMSE), and image quantity distribution of key points are analyzed according to the improved chicken keypoint similarity (CKS). The experimental results show that the algorithm scores in different evaluation metrics are a mean average precision (mAP) of 0.652, a mean average recall (mAR) of 0.742, a percentage of correct keypoints (PCKs) of 0.789, and an RMSE of 17.30 pixels. To the best of our knowledge, this is the first time that transfer learning has been used for the pose estimation of multiple chickens as objects. The method can provide a new path for future poultry behavior analysis.
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Affiliation(s)
- Cheng Fang
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.)
| | - Zhenlong Wu
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.)
| | - Haikun Zheng
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.)
| | - Jikang Yang
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.)
| | - Chuang Ma
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.)
| | - Tiemin Zhang
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.)
- National Engineering Research Center for Breeding Swine Industry, Guangzhou 510642, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
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11
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Vinograd A, Nair A, Linderman SW, Anderson DJ. Intrinsic Dynamics and Neural Implementation of a Hypothalamic Line Attractor Encoding an Internal Behavioral State. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.595051. [PMID: 38826298 PMCID: PMC11142118 DOI: 10.1101/2024.05.21.595051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Line attractors are emergent population dynamics hypothesized to encode continuous variables such as head direction and internal states. In mammals, direct evidence of neural implementation of a line attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles. Estrogen receptor type 1 (Esr1)-expressing neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) show line attractor dynamics in male mice during fighting. We hypothesized that these dynamics may encode continuous variation in the intensity of an internal aggressive state. Here, we report that these neurons also show line attractor dynamics in head-fixed mice observing aggression. We exploit this finding to identify and perturb line attractor-contributing neurons using 2-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations demonstrate that integration and persistent activity are intrinsic properties of these neurons which drive the system along the line attractor, while transient off-manifold perturbations reveal rapid relaxation back into the attractor. Furthermore, stimulation and imaging reveal selective functional connectivity among attractor-contributing neurons. Intriguingly, individual differences among mice in line attractor stability were correlated with the degree of functional connectivity among contributing neurons. Mechanistic modelling indicates that dense subnetwork connectivity and slow neurotransmission are required to explain our empirical findings. Our work bridges circuit and manifold paradigms, shedding light on the intrinsic and operational dynamics of a behaviorally relevant mammalian line attractor.
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Affiliation(s)
- Amit Vinograd
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, USA
- Tianqiao and Chrissy Chen Institute for Neuroscience Caltech; Pasadena, USA
- Howard Hughes Medical Institute; Chevy Chase, USA
| | - Aditya Nair
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, USA
- Tianqiao and Chrissy Chen Institute for Neuroscience Caltech; Pasadena, USA
- Howard Hughes Medical Institute; Chevy Chase, USA
| | - Scott W. Linderman
- Department of Statistics, Stanford University, Stanford, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, USA
| | - David J. Anderson
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, USA
- Tianqiao and Chrissy Chen Institute for Neuroscience Caltech; Pasadena, USA
- Howard Hughes Medical Institute; Chevy Chase, USA
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12
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Grammer J, Valles R, Bowles A, Zelikowsky M. SAUSI: a novel assay for measuring social anxiety and motivation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.13.594023. [PMID: 38798428 PMCID: PMC11118329 DOI: 10.1101/2024.05.13.594023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Social anxiety is one of the most prevalent mental health disorders, though the underlying neurobiology is poorly understood. Progress in understanding the etiology of social anxiety has been hindered by the lack of comprehensive tools to assess social anxiety in model systems. Here, we created a new behavioral task - Selective Access to Unrestricted Social Interaction (SAUSI), which combines elements of social motivation, hesitancy, decision-making, and free interaction to enable the wholistic assessment of social anxiety-like behaviors in mice. Using this novel assay, we found that social isolation-induced social anxiety-like behaviors in female mice are largely driven by increases in social fear, social hesitancy, and altered ultrasonic vocalizations. Deep learning analyses were able to computationally identify a unique behavioral footprint underlying the state produced by social isolation, demonstrating the compatibility of modern computational approaches with SAUSI. Finally, we compared the results of SAUSI to traditionally social assays including the 3-chamber sociability assay and the resident intruder task. This revealed that behavioral changes induced by isolation were highly context dependent, and that while fragments of social anxiety measured in SAUSI were replicable across other tasks, a wholistic assessment was not obtainable from these alternative assays. Our findings debut a novel task for the behavioral toolbox - one which overcomes limitations of previous assays, allowing for both social choice as well as free interaction, and offers a new approach for assessing social anxiety in rodents.
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Affiliation(s)
- Jordan Grammer
- Department of Neurobiology, University of Utah, United States
| | - Rene Valles
- Department of Neurobiology, University of Utah, United States
| | - Alexis Bowles
- Department of Neurobiology, University of Utah, United States
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13
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Biderman D, Whiteway MR, Hurwitz C, Greenspan N, Lee RS, Vishnubhotla A, Warren R, Pedraja F, Noone D, Schartner M, Huntenburg JM, Khanal A, Meijer GT, Noel JP, Pan-Vazquez A, Socha KZ, Urai AE, Cunningham JP, Sawtell NB, Paninski L. Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.28.538703. [PMID: 37162966 PMCID: PMC10168383 DOI: 10.1101/2023.04.28.538703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce "Lightning Pose," an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry, and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post-hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We release a cloud application that allows users to label data, train networks, and predict new videos directly from the browser.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Anup Khanal
- University of California Los Angeles, Los Angeles, USA
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14
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Tillmann JF, Hsu AI, Schwarz MK, Yttri EA. A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior. Nat Methods 2024; 21:703-711. [PMID: 38383746 DOI: 10.1038/s41592-024-02200-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/29/2024] [Indexed: 02/23/2024]
Abstract
To identify and extract naturalistic behavior, two methods have become popular: supervised and unsupervised. Each approach carries its own strengths and weaknesses (for example, user bias, training cost, complexity and action discovery), which the user must consider in their decision. Here, an active-learning platform, A-SOiD, blends these strengths, and in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups with a fraction of the usual training data, while attaining expansive classification through directed unsupervised classification. In socially interacting mice, A-SOiD outperformed standard methods despite requiring 85% less training data. Additionally, it isolated ethologically distinct mouse interactions via unsupervised classification. We observed similar performance and efficiency using nonhuman primate and human three-dimensional pose data. In both cases, the transparency in A-SOiD's cluster definitions revealed the defining features of the supervised classification through a game-theoretic approach. To facilitate use, A-SOiD comes as an intuitive, open-source interface for efficient segmentation of user-defined behaviors and discovered sub-actions.
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Affiliation(s)
- Jens F Tillmann
- Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany
| | - Alexander I Hsu
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Martin K Schwarz
- Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany.
| | - Eric A Yttri
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
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15
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Mykins M, Bridges B, Jo A, Krishnan K. Multidimensional Analysis of a Social Behavior Identifies Regression and Phenotypic Heterogeneity in a Female Mouse Model for Rett Syndrome. J Neurosci 2024; 44:e1078232023. [PMID: 38199865 PMCID: PMC10957218 DOI: 10.1523/jneurosci.1078-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/01/2023] [Accepted: 11/17/2023] [Indexed: 01/12/2024] Open
Abstract
Regression is a key feature of neurodevelopmental disorders such as autism spectrum disorder, Fragile X syndrome, and Rett syndrome (RTT). RTT is caused by mutations in the X-linked gene methyl-CpG-binding protein 2 (MECP2). It is characterized by an early period of typical development with subsequent regression of previously acquired motor and speech skills in girls. The syndromic phenotypes are individualistic and dynamic over time. Thus far, it has been difficult to capture these dynamics and syndromic heterogeneity in the preclinical Mecp2-heterozygous female mouse model (Het). The emergence of computational neuroethology tools allows for robust analysis of complex and dynamic behaviors to model endophenotypes in preclinical models. Toward this first step, we utilized DeepLabCut, a marker-less pose estimation software to quantify trajectory kinematics and multidimensional analysis to characterize behavioral heterogeneity in Het in the previously benchmarked, ethologically relevant social cognition task of pup retrieval. We report the identification of two distinct phenotypes of adult Het: Het that display a delay in efficiency in early days and then improve over days like wild-type mice and Het that regress and perform worse in later days. Furthermore, regression is dependent on age and behavioral context and can be detected in the initial days of retrieval. Together, the novel identification of two populations of Het suggests differential effects on neural circuitry, opens new avenues to investigate the underlying molecular and cellular mechanisms of heterogeneity, and designs better studies for stratifying therapeutics.
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Affiliation(s)
- Michael Mykins
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee
| | - Benjamin Bridges
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee
| | - Angela Jo
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee
| | - Keerthi Krishnan
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee
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16
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Yurimoto T, Kumita W, Sato K, Kikuchi R, Oka G, Shibuki Y, Hashimoto R, Kamioka M, Hayasegawa Y, Yamazaki E, Kurotaki Y, Goda N, Kitakami J, Fujita T, Inoue T, Sasaki E. Development of a 3D tracking system for multiple marmosets under free-moving conditions. Commun Biol 2024; 7:216. [PMID: 38383741 PMCID: PMC10881507 DOI: 10.1038/s42003-024-05864-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 01/26/2024] [Indexed: 02/23/2024] Open
Abstract
Assessment of social interactions and behavioral changes in nonhuman primates is useful for understanding brain function changes during life events and pathogenesis of neurological diseases. The common marmoset (Callithrix jacchus), which lives in a nuclear family like humans, is a useful model, but longitudinal automated behavioral observation of multiple animals has not been achieved. Here, we developed a Full Monitoring and Animal Identification (FulMAI) system for longitudinal detection of three-dimensional (3D) trajectories of each individual in multiple marmosets under free-moving conditions by combining video tracking, Light Detection and Ranging, and deep learning. Using this system, identification of each animal was more than 97% accurate. Location preferences and inter-individual distance could be calculated, and deep learning could detect grooming behavior. The FulMAI system allows us to analyze the natural behavior of individuals in a family over their lifetime and understand how behavior changes due to life events together with other data.
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Affiliation(s)
- Terumi Yurimoto
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Wakako Kumita
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Kenya Sato
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Rika Kikuchi
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Gohei Oka
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Yusuke Shibuki
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Rino Hashimoto
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Michiko Kamioka
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Yumi Hayasegawa
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Eiko Yamazaki
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Yoko Kurotaki
- Center of Basic Technology in Marmoset, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Norio Goda
- Public Digital Transformation Department, Hitachi, Ltd., Shinagawa, 140-8512, Japan
| | - Junichi Kitakami
- Vision AI Solution Design Department Hitachi Solutions Technology, Ltd, Tachikawa, 190-0014, Japan
| | - Tatsuya Fujita
- Engineering Department Eastern Japan division, Totec Amenity Limited, Shinjuku, 163-0417, Japan
| | - Takashi Inoue
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan
| | - Erika Sasaki
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Medicine and Life Science, Kawasaki, 210-0821, Japan.
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17
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Popik P, Cyrano E, Piotrowska D, Holuj M, Golebiowska J, Malikowska-Racia N, Potasiewicz A, Nikiforuk A. Effects of ketamine on rat social behavior as analyzed by DeepLabCut and SimBA deep learning algorithms. Front Pharmacol 2024; 14:1329424. [PMID: 38269275 PMCID: PMC10806163 DOI: 10.3389/fphar.2023.1329424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 12/13/2023] [Indexed: 01/26/2024] Open
Abstract
Traditional methods of rat social behavior assessment are extremely time-consuming and susceptible to the subjective biases. In contrast, novel digital techniques allow for rapid and objective measurements. This study sought to assess the feasibility of implementing a digital workflow to compare the effects of (R,S)-ketamine and a veterinary ketamine preparation Vetoquinol (both at 20 mg/kg) on the social behaviors of rat pairs. Historical and novel videos were used to train the DeepLabCut neural network. The numerical data generated by DeepLabCut from 14 video samples, representing various body parts in time and space were subjected to the Simple Behavioral Analysis (SimBA) toolkit, to build classifiers for 12 distinct social and non-social behaviors. To validate the workflow, previously annotated by the trained observer historical videos were analyzed with SimBA classifiers, and regression analysis of the total time of social interactions yielded R 2 = 0.75, slope 1.04; p < 0.001 (N = 101). Remarkable similarities between human and computer annotations allowed for using the digital workflow to analyze 24 novel videos of rats treated with vehicle and ketamine preparations. Digital workflow revealed similarities in the reduction of social behavior by both compounds, and no substantial differences between them. However, the digital workflow also demonstrated ketamine-induced increases in self-grooming, increased transitions from social contacts to self-grooming, and no effects on adjacent lying time. This study confirms and extends the utility of deep learning in analyzing rat social behavior and highlights its efficiency and objectivity. It provides a faster and objective alternative to human workflow.
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18
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Syeda A, Zhong L, Tung R, Long W, Pachitariu M, Stringer C. Facemap: a framework for modeling neural activity based on orofacial tracking. Nat Neurosci 2024; 27:187-195. [PMID: 37985801 PMCID: PMC10774130 DOI: 10.1038/s41593-023-01490-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 10/10/2023] [Indexed: 11/22/2023]
Abstract
Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracker and a deep neural network encoder for predicting neural activity. Our algorithm for tracking mouse orofacial behaviors was more accurate than existing pose estimation tools, while the processing speed was several times faster, making it a powerful tool for real-time experimental interventions. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used the keypoints as inputs to a deep neural network which predicts the activity of ~50,000 simultaneously-recorded neurons and, in visual cortex, we doubled the amount of explained variance compared to previous methods. Using this model, we found that the neuronal activity clusters that were well predicted from behavior were more spatially spread out across cortex. We also found that the deep behavioral features from the model had stereotypical, sequential dynamics that were not reversible in time. In summary, Facemap provides a stepping stone toward understanding the function of the brain-wide neural signals and their relation to behavior.
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Affiliation(s)
- Atika Syeda
- HHMI Janelia Research Campus, Ashburn, VA, USA.
| | - Lin Zhong
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Renee Tung
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Will Long
- HHMI Janelia Research Campus, Ashburn, VA, USA
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19
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Li B, Ma C, Huang YA, Ding X, Silverman D, Chen C, Darmohray D, Lu L, Liu S, Montaldo G, Urban A, Dan Y. Circuit mechanism for suppression of frontal cortical ignition during NREM sleep. Cell 2023; 186:5739-5750.e17. [PMID: 38070510 DOI: 10.1016/j.cell.2023.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 09/06/2023] [Accepted: 11/09/2023] [Indexed: 12/24/2023]
Abstract
Conscious perception is greatly diminished during sleep, but the underlying circuit mechanism is poorly understood. We show that cortical ignition-a brain process shown to be associated with conscious awareness in humans and non-human primates-is strongly suppressed during non-rapid-eye-movement (NREM) sleep in mice due to reduced cholinergic modulation and rapid inhibition of cortical responses. Brain-wide functional ultrasound imaging and cell-type-specific calcium imaging combined with optogenetics showed that activity propagation from visual to frontal cortex is markedly reduced during NREM sleep due to strong inhibition of frontal pyramidal neurons. Chemogenetic activation and inactivation of basal forebrain cholinergic neurons powerfully increased and decreased visual-to-frontal activity propagation, respectively. Furthermore, although multiple subtypes of dendrite-targeting GABAergic interneurons in the frontal cortex are more active during wakefulness, soma-targeting parvalbumin-expressing interneurons are more active during sleep. Chemogenetic manipulation of parvalbumin interneurons showed that sleep/wake-dependent cortical ignition is strongly modulated by perisomatic inhibition of pyramidal neurons.
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Affiliation(s)
- Bing Li
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Chenyan Ma
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Yun-An Huang
- Neuro-Electronics Research Flanders, VIB, Department of Neurosciences, KU Leuven, imec, Leuven, Belgium
| | - Xinlu Ding
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Daniel Silverman
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Changwan Chen
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Dana Darmohray
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Lihui Lu
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Siqi Liu
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Gabriel Montaldo
- Neuro-Electronics Research Flanders, VIB, Department of Neurosciences, KU Leuven, imec, Leuven, Belgium
| | - Alan Urban
- Neuro-Electronics Research Flanders, VIB, Department of Neurosciences, KU Leuven, imec, Leuven, Belgium
| | - Yang Dan
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA.
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20
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Stagkourakis S, Spigolon G, Marks M, Feyder M, Kim J, Perona P, Pachitariu M, Anderson DJ. Anatomically distributed neural representations of instincts in the hypothalamus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.21.568163. [PMID: 38045312 PMCID: PMC10690204 DOI: 10.1101/2023.11.21.568163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.
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Affiliation(s)
- Stefanos Stagkourakis
- Division of Biology and Biological Engineering 156-29, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, California 91125, USA
| | - Giada Spigolon
- Biological Imaging Facility, California Institute of Technology, Pasadena, California 91125, USA
| | - Markus Marks
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, USA
| | - Michael Feyder
- Division of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California 94305, USA
| | - Joseph Kim
- Division of Biology and Biological Engineering 156-29, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, California 91125, USA
| | - Pietro Perona
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, USA
| | - Marius Pachitariu
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
| | - David J. Anderson
- Division of Biology and Biological Engineering 156-29, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, California 91125, USA
- Howard Hughes Medical Institute, California Institute of Technology, 1200 East California Blvd, Pasadena, California 91125, USA
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21
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Sakata S. SaLSa: A Combinatory Approach of Semi-Automatic Labeling and Long Short-Term Memory to Classify Behavioral Syllables. eNeuro 2023; 10:ENEURO.0201-23.2023. [PMID: 37989587 PMCID: PMC10714892 DOI: 10.1523/eneuro.0201-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/19/2023] [Accepted: 11/09/2023] [Indexed: 11/23/2023] Open
Abstract
Accurately and quantitatively describing mouse behavior is an important area. Although advances in machine learning have made it possible to track their behaviors accurately, reliable classification of behavioral sequences or syllables remains a challenge. In this study, we present a novel machine learning approach, called SaLSa (a combination of semi-automatic labeling and long short-term memory-based classification), to classify behavioral syllables of mice exploring an open field. This approach consists of two major steps. First, after tracking multiple body parts, spatial and temporal features of their egocentric coordinates are extracted. A fully automated unsupervised process identifies candidates for behavioral syllables, followed by manual labeling of behavioral syllables using a graphical user interface (GUI). Second, a long short-term memory (LSTM) classifier is trained with the labeled data. We found that the classification performance was marked over 97%. It provides a performance equivalent to a state-of-the-art model while classifying some of the syllables. We applied this approach to examine how hyperactivity in a mouse model of Alzheimer's disease develops with age. When the proportion of each behavioral syllable was compared between genotypes and sexes, we found that the characteristic hyperlocomotion of female Alzheimer's disease mice emerges between four and eight months. In contrast, age-related reduction in rearing is common regardless of genotype and sex. Overall, SaLSa enables detailed characterization of mouse behavior.
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Affiliation(s)
- Shuzo Sakata
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G4 0RE, United Kingdom
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22
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Troconis EL, Seo C, Guru A, Warden MR. Serotonin neurons in mating female mice are activated by male ejaculation. Curr Biol 2023; 33:4926-4936.e4. [PMID: 37865094 PMCID: PMC10901455 DOI: 10.1016/j.cub.2023.09.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/31/2023] [Accepted: 09/28/2023] [Indexed: 10/23/2023]
Abstract
Sexual stimulation triggers changes in female physiology and behavior, including sexual satiety and preparing the uterus for pregnancy. Serotonin (5-HT) is an important regulator of reproductive physiology and sexual receptivity, but the relationship between sexual stimulation and 5-HT neural activity in females is poorly understood. Here, we investigated dorsal raphe 5-HT neural activity in female mice during sexual behavior. We found that 5-HT neural activity in mating females peaked specifically upon male ejaculation and remained elevated above baseline until disengagement. Artificial intravaginal mechanical stimulation was sufficient to elicit increased 5-HT neural activity but the delivery of ejaculatory fluids was not. Distal penis expansion ("penile cupping") at ejaculation and forceful expulsion of ejaculatory fluid each provided sufficient mechanical stimulation to elicit 5-HT neuron activation. Our study identifies a female ejaculation-specific signal in a major neuromodulatory system and shows that intravaginal mechanosensory stimulation is necessary and sufficient to drive this signal.
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Affiliation(s)
- Eileen L Troconis
- Biological and Biomedical Sciences Program, Cornell University, Ithaca, NY 14853, USA
| | - Changwoo Seo
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA; Cornell Neurotech, Cornell University, Ithaca, NY 14853, USA
| | - Akash Guru
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA; Cornell Neurotech, Cornell University, Ithaca, NY 14853, USA
| | - Melissa R Warden
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA; Cornell Neurotech, Cornell University, Ithaca, NY 14853, USA.
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23
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Mountoufaris G, Nair A, Yang B, Kim DW, Anderson DJ. Neuropeptide Signaling is Required to Implement a Line Attractor Encoding a Persistent Internal Behavioral State. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565073. [PMID: 37961374 PMCID: PMC10635056 DOI: 10.1101/2023.11.01.565073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Internal states drive survival behaviors, but their neural implementation is not well understood. Recently we identified a line attractor in the ventromedial hypothalamus (VMH) that represents an internal state of aggressiveness. Line attractors can be implemented by recurrent connectivity and/or neuromodulatory signaling, but evidence for the latter is scant. Here we show that neuropeptidergic signaling is necessary for line attractor dynamics in this system, using a novel approach that integrates cell type-specific, anatomically restricted CRISPR/Cas9-based gene editing with microendoscopic calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1 + neurons that control aggression suppressed attack, reduced persistent neural activity and eliminated line attractor dynamics, while only modestly impacting neural activity and sex- or behavior-tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.
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24
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Brickson L, Zhang L, Vollrath F, Douglas-Hamilton I, Titus AJ. Elephants and algorithms: a review of the current and future role of AI in elephant monitoring. J R Soc Interface 2023; 20:20230367. [PMID: 37963556 PMCID: PMC10645515 DOI: 10.1098/rsif.2023.0367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/23/2023] [Indexed: 11/16/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.
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Affiliation(s)
| | | | - Fritz Vollrath
- Save the Elephants, Nairobi, Kenya
- Department of Biology, University of Oxford, Oxford, UK
| | | | - Alexander J. Titus
- Colossal Biosciences, Dallas, TX, USA
- Information Sciences Institute, University of Southern California, Los Angeles, USA
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25
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Migliaro M, Ruiz-Contreras AE, Herrera-Solís A, Méndez-Díaz M, Prospéro-García OE. Endocannabinoid system and aggression across animal species. Neurosci Biobehav Rev 2023; 153:105375. [PMID: 37643683 DOI: 10.1016/j.neubiorev.2023.105375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 08/31/2023]
Abstract
This narrative review article summarizes the current state of knowledge regarding the relationship between the endocannabinoid system (ECS) and aggression across multiple vertebrate species. Experimental evidence indicates that acute administration of phytocannabinoids, synthetic cannabinoids, and the pharmacological enhancement of endocannabinoid signaling decreases aggressive behavior in several animal models. However, research on the chronic effects of cannabinoids on animal aggression has yielded inconsistent findings, indicating a need for further investigation. Cannabinoid receptors, particularly cannabinoid receptor type 1, appear to be an important part of the endogenous mechanism involved in the dampening of aggressive behavior. Overall, this review underscores the importance of the ECS in regulating aggressive behavior and provides a foundation for future research in this area.
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Affiliation(s)
- Martin Migliaro
- Grupo de Neurociencias: Laboratorio de Cannabinoides, Departamento de Fisiología, Facultad de Medicina, UNAM, Mexico.
| | - Alejandra E Ruiz-Contreras
- Grupo de Neurociencias: Laboratorio de Neurogenómica Cognitiva, Unidad de Investigación en Psicobiología y Neurociencias, Coordinación de Psicobiología y Neurociencias, Facultad de Psicología, UNAM, Mexico
| | - Andrea Herrera-Solís
- Grupo de Neurociencias: Laboratorio de Efectos Terapéuticos de los Cannabinoides, Hospital General Dr. Manuel Gea González, Secretaría de Salud, Mexico
| | - Mónica Méndez-Díaz
- Grupo de Neurociencias: Laboratorio de Cannabinoides, Departamento de Fisiología, Facultad de Medicina, UNAM, Mexico
| | - Oscar E Prospéro-García
- Grupo de Neurociencias: Laboratorio de Cannabinoides, Departamento de Fisiología, Facultad de Medicina, UNAM, Mexico
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26
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Zhou T, Cheah CCH, Chin EWM, Chen J, Farm HJ, Goh ELK, Chiam KH. ContrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals. Comput Biol Med 2023; 165:107416. [PMID: 37660568 DOI: 10.1016/j.compbiomed.2023.107416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/24/2023] [Accepted: 08/28/2023] [Indexed: 09/05/2023]
Abstract
In recent years, supervised machine learning models trained on videos of animals with pose estimation data and behavior labels have been used for automated behavior classification. Applications include, for example, automated detection of neurological diseases in animal models. However, we identify two potential problems of such supervised learning approach. First, such models require a large amount of labeled data but the labeling of behaviors frame by frame is a laborious manual process that is not easily scalable. Second, such methods rely on handcrafted features obtained from pose estimation data that are usually designed empirically. In this paper, we propose to overcome these two problems using contrastive learning for self-supervised feature engineering on pose estimation data. Our approach allows the use of unlabeled videos to learn feature representations and reduce the need for handcrafting of higher-level features from pose positions. We show that this approach to feature representation can achieve better classification performance compared to handcrafted features alone, and that the performance improvement is due to contrastive learning on unlabeled data rather than the neural network architecture. The method has the potential to reduce the bottleneck of scarce labeled videos for training and improve performance of supervised behavioral classification models for the study of interaction behaviors in animals.
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Affiliation(s)
| | - Calvin Chee Hoe Cheah
- Neuroscience and Mental Health Faculty, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Eunice Wei Mun Chin
- Neuroscience and Mental Health Faculty, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Jie Chen
- School of Biological Sciences, Nanyang Technological University, Singapore
| | - Hui Jia Farm
- Bioinformatics Institute, A*STAR, Singapore; Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Eyleen Lay Keow Goh
- Neuroscience and Mental Health Faculty, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Keng Hwee Chiam
- Bioinformatics Institute, A*STAR, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore.
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27
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Lang B, Kahnau P, Hohlbaum K, Mieske P, Andresen NP, Boon MN, Thöne-Reineke C, Lewejohann L, Diederich K. Challenges and advanced concepts for the assessment of learning and memory function in mice. Front Behav Neurosci 2023; 17:1230082. [PMID: 37809039 PMCID: PMC10551171 DOI: 10.3389/fnbeh.2023.1230082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 09/05/2023] [Indexed: 10/10/2023] Open
Abstract
The mechanisms underlying the formation and retrieval of memories are still an active area of research and discussion. Manifold models have been proposed and refined over the years, with most assuming a dichotomy between memory processes involving non-conscious and conscious mechanisms. Despite our incomplete understanding of the underlying mechanisms, tests of memory and learning count among the most performed behavioral experiments. Here, we will discuss available protocols for testing learning and memory using the example of the most prevalent animal species in research, the laboratory mouse. A wide range of protocols has been developed in mice to test, e.g., object recognition, spatial learning, procedural memory, sequential problem solving, operant- and fear conditioning, and social recognition. Those assays are carried out with individual subjects in apparatuses such as arenas and mazes, which allow for a high degree of standardization across laboratories and straightforward data interpretation but are not without caveats and limitations. In animal research, there is growing concern about the translatability of study results and animal welfare, leading to novel approaches beyond established protocols. Here, we present some of the more recent developments and more advanced concepts in learning and memory testing, such as multi-step sequential lockboxes, assays involving groups of animals, as well as home cage-based assays supported by automated tracking solutions; and weight their potential and limitations against those of established paradigms. Shifting the focus of learning tests from the classical experimental chamber to settings which are more natural for rodents comes with a new set of challenges for behavioral researchers, but also offers the opportunity to understand memory formation and retrieval in a more conclusive way than has been attainable with conventional test protocols. We predict and embrace an increase in studies relying on methods involving a higher degree of automatization, more naturalistic- and home cage-based experimental setting as well as more integrated learning tasks in the future. We are confident these trends are suited to alleviate the burden on animal subjects and improve study designs in memory research.
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Affiliation(s)
- Benjamin Lang
- Animal Behavior and Laboratory Animal Science, Department of Veterinary Medicine, Institute for Animal Welfare, Free University of Berlin, Berlin, Germany
- Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
| | - Pia Kahnau
- Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Katharina Hohlbaum
- Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
- Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Paul Mieske
- Animal Behavior and Laboratory Animal Science, Department of Veterinary Medicine, Institute for Animal Welfare, Free University of Berlin, Berlin, Germany
- Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
- Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Niek P. Andresen
- Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
- Computer Vision and Remote Sensing, Technical University Berlin, Berlin, Germany
| | - Marcus N. Boon
- Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
- Modeling of Cognitive Processes, Technical University of Berlin, Berlin, Germany
| | - Christa Thöne-Reineke
- Animal Behavior and Laboratory Animal Science, Department of Veterinary Medicine, Institute for Animal Welfare, Free University of Berlin, Berlin, Germany
- Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
| | - Lars Lewejohann
- Animal Behavior and Laboratory Animal Science, Department of Veterinary Medicine, Institute for Animal Welfare, Free University of Berlin, Berlin, Germany
- Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
- Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Kai Diederich
- Federal Institute for Risk Assessment (BfR), Berlin, Germany
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28
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Mösch L, Kunczik J, Breuer L, Merhof D, Gass P, Potschka H, Zechner D, Vollmar B, Tolba R, Häger C, Bleich A, Czaplik M, Pereira CB. Towards substitution of invasive telemetry: An integrated home cage concept for unobtrusive monitoring of objective physiological parameters in rodents. PLoS One 2023; 18:e0286230. [PMID: 37676867 PMCID: PMC10484458 DOI: 10.1371/journal.pone.0286230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/14/2023] [Indexed: 09/09/2023] Open
Abstract
This study presents a novel concept for a smart home cage design, tools, and software used to monitor the physiological parameters of mice and rats in animal-based experiments. The proposed system focuses on monitoring key clinical parameters, including heart rate, respiratory rate, and body temperature, and can also assess activity and circadian rhythm. As the basis of the smart home cage system, an in-depth analysis of the requirements was performed, including camera positioning, imaging system types, resolution, frame rates, external illumination, video acquisition, data storage, and synchronization. Two different camera perspectives were considered, and specific camera models, including two near-infrared and two thermal cameras, were selected to meet the requirements. The developed specifications, hardware models, and software are freely available via GitHub. During the first testing phase, the system demonstrated the potential of extracting vital parameters such as respiratory and heart rate. This technology has the potential to reduce the need for implantable sensors while providing reliable and accurate physiological data, leading to refinement and improvement in laboratory animal care.
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Affiliation(s)
- Lucas Mösch
- Department of Anaesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
| | - Janosch Kunczik
- Department of Anaesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
| | - Lukas Breuer
- Department of Anaesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
| | - Dorit Merhof
- Chair of Image Processing, Faculty of Computer and Data Science, Universität Regensburg, Regensburg, Bavaria, Germany
| | - Peter Gass
- Research Group Animal Models in Psychiatry, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Baden Württemberg, Germany
| | - Heidrun Potschka
- Institute of Pharmacology, Toxicology, and Pharmacy, Ludwig-Maximilians-University, Munich, Bavaria, Germany
| | - Dietmar Zechner
- Rudolf-Zenker-Institute of Experimental Surgery, University Medical Centre Rostock, Rostock, Mecklenburg-Western Pomerania, Germany
| | - Brigitte Vollmar
- Rudolf-Zenker-Institute of Experimental Surgery, University Medical Centre Rostock, Rostock, Mecklenburg-Western Pomerania, Germany
| | - René Tolba
- Institute of Laboratory Animal Science, Faculty of Medicine, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
| | - Christine Häger
- Institute for Laboratory Animal Science, Hannover Medical School, Hannover, Lower Saxony, Germany
| | - André Bleich
- Institute for Laboratory Animal Science, Hannover Medical School, Hannover, Lower Saxony, Germany
| | - Michael Czaplik
- Department of Anaesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
| | - Carina Barbosa Pereira
- Department of Anaesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
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29
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Kenkel W. Automated behavioral scoring: Do we even need humans? Ann N Y Acad Sci 2023; 1527:25-29. [PMID: 37497814 DOI: 10.1111/nyas.15041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The development of automated behavior scoring technology has been a tremendous boon to the study of social behavior. However, completely outsourcing behavioral analysis to a computer runs the risk of overlooking important nuances, and researchers risk distancing themselves from their very object of study. Here, I make the case that while automating analysis has been valuable, and overautomating analysis is risky, more effort should be spent automating the collection of behavioral data. Continuous automated behavioral observations conducted in situ have the promise to reduce confounding elements of social behavior research, such as handling stress, novel environments, one-time "snapshot" measures, and experimenter presence. Now that we have the capability to automatically process behavioral observations thanks to machine vision and machine learning, we would do well to leverage the same open-source ethos to increase the throughput of behavioral observation and collection. Fortunately, several such platforms have recently been developed. Repeated testing in the home environment will produce higher qualities and quantities of data, bringing us closer to realizing the ethological goals of studying animal behavior in a naturalistic context.
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Affiliation(s)
- Will Kenkel
- Department of Psychological & Brain Sciences, University of Delaware, Newark, Delaware, USA
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30
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Hu B, Seybold B, Yang S, Sud A, Liu Y, Barron K, Cha P, Cosino M, Karlsson E, Kite J, Kolumam G, Preciado J, Zavala-Solorio J, Zhang C, Zhang X, Voorbach M, Tovcimak AE, Ruby JG, Ross DA. 3D mouse pose from single-view video and a new dataset. Sci Rep 2023; 13:13554. [PMID: 37604955 PMCID: PMC10442417 DOI: 10.1038/s41598-023-40738-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 08/16/2023] [Indexed: 08/23/2023] Open
Abstract
We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos. Many human clinical conditions and their corresponding animal models result in abnormal motion, and accurately measuring 3D motion at scale offers insights into health. The 3D poses improve classification of health-related attributes over 2D representations. The inferred poses are accurate enough to estimate stride length even when the feet are mostly occluded. This method could be applied as part of a continuous monitoring system to non-invasively measure animal health, as demonstrated by its use in successfully classifying animals based on age and genotype. We introduce the Mouse Pose Analysis Dataset, the first large scale video dataset of lab mice in their home cage with ground truth keypoint and behavior labels. The dataset also contains high resolution mouse CT scans, which we use to build the shape models for 3D pose reconstruction.
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Affiliation(s)
- Bo Hu
- Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA.
| | - Bryan Seybold
- Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Shan Yang
- Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Avneesh Sud
- Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Yi Liu
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - Karla Barron
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - Paulyn Cha
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - Marcelo Cosino
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - Ellie Karlsson
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - Janessa Kite
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - Ganesh Kolumam
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - Joseph Preciado
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - José Zavala-Solorio
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - Chunlian Zhang
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - Xiaomeng Zhang
- Translational Imaging, Neuroscience Discovery, Abbvie, 1 N. Waukegan Rd., North Chicago, IL, 60064-1802, USA
| | - Martin Voorbach
- Translational Imaging, Neuroscience Discovery, Abbvie, 1 N. Waukegan Rd., North Chicago, IL, 60064-1802, USA
| | - Ann E Tovcimak
- Translational Imaging, Neuroscience Discovery, Abbvie, 1 N. Waukegan Rd., North Chicago, IL, 60064-1802, USA
| | - J Graham Ruby
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA, 94080, USA
| | - David A Ross
- Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
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31
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Yun S, Yang B, Anair JD, Martin MM, Fleps SW, Pamukcu A, Yeh NH, Contractor A, Kennedy A, Parker JG. Antipsychotic drug efficacy correlates with the modulation of D1 rather than D2 receptor-expressing striatal projection neurons. Nat Neurosci 2023; 26:1417-1428. [PMID: 37443282 PMCID: PMC10842629 DOI: 10.1038/s41593-023-01390-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 06/16/2023] [Indexed: 07/15/2023]
Abstract
Elevated dopamine transmission in psychosis is assumed to unbalance striatal output through D1- and D2-receptor-expressing spiny-projection neurons (SPNs). Antipsychotic drugs are thought to re-balance this output by blocking D2 receptors (D2Rs). In this study, we found that amphetamine-driven dopamine release unbalanced D1-SPN and D2-SPN Ca2+ activity in mice, but that antipsychotic efficacy was associated with the reversal of abnormal D1-SPN, rather than D2-SPN, dynamics, even for drugs that are D2R selective or lacking any dopamine receptor affinity. By contrast, a clinically ineffective drug normalized D2-SPN dynamics but exacerbated D1-SPN dynamics under hyperdopaminergic conditions. Consistent with antipsychotic effect, selective D1-SPN inhibition attenuated amphetamine-driven changes in locomotion, sensorimotor gating and hallucination-like perception. Notably, antipsychotic efficacy correlated with the selective inhibition of D1-SPNs only under hyperdopaminergic conditions-a dopamine-state-dependence exhibited by D1R partial agonism but not non-antipsychotic D1R antagonists. Our findings provide new insights into antipsychotic drug mechanism and reveal an important role for D1-SPN modulation.
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Affiliation(s)
- Seongsik Yun
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
| | - Ben Yang
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
| | - Justin D Anair
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
| | - Madison M Martin
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
| | - Stefan W Fleps
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
| | - Arin Pamukcu
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
| | - Nai-Hsing Yeh
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
| | - Anis Contractor
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
| | - Ann Kennedy
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
| | - Jones G Parker
- Department of Neuroscience, Northwestern University, Chicago, IL, USA.
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32
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Bordes J, Miranda L, Reinhardt M, Narayan S, Hartmann J, Newman EL, Brix LM, van Doeselaar L, Engelhardt C, Dillmann L, Mitra S, Ressler KJ, Pütz B, Agakov F, Müller-Myhsok B, Schmidt MV. Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress. Nat Commun 2023; 14:4319. [PMID: 37463994 PMCID: PMC10354203 DOI: 10.1038/s41467-023-40040-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 07/07/2023] [Indexed: 07/20/2023] Open
Abstract
Severe stress exposure increases the risk of stress-related disorders such as major depressive disorder (MDD). An essential characteristic of MDD is the impairment of social functioning and lack of social motivation. Chronic social defeat stress is an established animal model for MDD research, which induces a cascade of physiological and behavioral changes. Current markerless pose estimation tools allow for more complex and naturalistic behavioral tests. Here, we introduce the open-source tool DeepOF to investigate the individual and social behavioral profile in mice by providing supervised and unsupervised pipelines using DeepLabCut-annotated pose estimation data. Applying this tool to chronic social defeat in male mice, the DeepOF supervised and unsupervised pipelines detect a distinct stress-induced social behavioral pattern, which was particularly observed at the beginning of a novel social encounter and fades with time due to habituation. In addition, while the classical social avoidance task does identify the stress-induced social behavioral differences, both DeepOF behavioral pipelines provide a clearer and more detailed profile. Moreover, DeepOF aims to facilitate reproducibility and unification of behavioral classification by providing an open-source tool, which can advance the study of rodent individual and social behavior, thereby enabling biological insights and, for example, subsequent drug development for psychiatric disorders.
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Affiliation(s)
- Joeri Bordes
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804, Munich, Germany
| | - Lucas Miranda
- Research Group Statistical Genetics, Max Planck Institute of Psychiatry, 80804, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany
| | - Maya Reinhardt
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804, Munich, Germany
| | - Sowmya Narayan
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany
| | - Jakob Hartmann
- Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, MA, 02478, USA
| | - Emily L Newman
- Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, MA, 02478, USA
| | - Lea Maria Brix
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany
| | - Lotte van Doeselaar
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany
| | - Clara Engelhardt
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804, Munich, Germany
| | - Larissa Dillmann
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804, Munich, Germany
| | - Shiladitya Mitra
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804, Munich, Germany
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, MA, 02478, USA
| | - Benno Pütz
- Research Group Statistical Genetics, Max Planck Institute of Psychiatry, 80804, Munich, Germany
| | - Felix Agakov
- Pharmatics Limited, Edinburgh, EH16 4UX, Scotland, UK
| | - Bertram Müller-Myhsok
- Research Group Statistical Genetics, Max Planck Institute of Psychiatry, 80804, Munich, Germany.
| | - Mathias V Schmidt
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804, Munich, Germany.
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33
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Van Dam EA, Noldus LPJJ, Van Gerven MAJ. Disentangling rodent behaviors to improve automated behavior recognition. Front Neurosci 2023; 17:1198209. [PMID: 37496740 PMCID: PMC10366600 DOI: 10.3389/fnins.2023.1198209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/12/2023] [Indexed: 07/28/2023] Open
Abstract
Automated observation and analysis of behavior is important to facilitate progress in many fields of science. Recent developments in deep learning have enabled progress in object detection and tracking, but rodent behavior recognition struggles to exceed 75-80% accuracy for ethologically relevant behaviors. We investigate the main reasons why and distinguish three aspects of behavior dynamics that are difficult to automate. We isolate these aspects in an artificial dataset and reproduce effects with the state-of-the-art behavior recognition models. Having an endless amount of labeled training data with minimal input noise and representative dynamics will enable research to optimize behavior recognition architectures and get closer to human-like recognition performance for behaviors with challenging dynamics.
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Affiliation(s)
- Elsbeth A. Van Dam
- Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Noldus Information Technology BV, Wageningen, Netherlands
| | - Lucas P. J. J. Noldus
- Noldus Information Technology BV, Wageningen, Netherlands
- Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Marcel A. J. Van Gerven
- Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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34
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Cury KM, Axel R. Flexible neural control of transition points within the egg-laying behavioral sequence in Drosophila. Nat Neurosci 2023; 26:1054-1067. [PMID: 37217726 PMCID: PMC10244180 DOI: 10.1038/s41593-023-01332-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/13/2023] [Indexed: 05/24/2023]
Abstract
Innate behaviors are frequently comprised of ordered sequences of component actions that progress to satisfy essential drives. Progression is governed by specialized sensory cues that induce transitions between components within the appropriate context. Here we have characterized the structure of the egg-laying behavioral sequence in Drosophila and found significant variability in the transitions between component actions that affords the organism an adaptive flexibility. We identified distinct classes of interoceptive and exteroceptive sensory neurons that control the timing and direction of transitions between the terminal components of the sequence. We also identified a pair of motor neurons that enact the final transition to egg expulsion. These results provide a logic for the organization of innate behavior in which sensory information processed at critical junctures allows for flexible adjustments in component actions to satisfy drives across varied internal and external environments.
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Affiliation(s)
- Kevin M Cury
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY, USA.
| | - Richard Axel
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY, USA.
- Howard Hughes Medical Institute, Columbia University, New York, NY, USA.
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35
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Stanley OR, Swaminathan A, Wojahn E, Ahmed ZM, Cullen KE. An Open-Source Tool for Automated Human-Level Circling Behavior Detection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.30.540066. [PMID: 37398316 PMCID: PMC10312579 DOI: 10.1101/2023.05.30.540066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Quantifying behavior and relating it to underlying biological states is of paramount importance in many life science fields. Although barriers to recording postural data have been reduced by progress in deep-learning-based computer vision tools for keypoint tracking, extracting specific behaviors from this data remains challenging. Manual behavior coding, the present gold standard, is labor-intensive and subject to intra- and inter-observer variability. Automatic methods are stymied by the difficulty of explicitly defining complex behaviors, even ones which appear obvious to the human eye. Here, we demonstrate an effective technique for detecting one such behavior, a form of locomotion characterized by stereotyped spinning, termed 'circling'. Though circling has an extensive history as a behavioral marker, at present there exists no standard automated detection method. Accordingly, we developed a technique to identify instances of the behavior by applying simple postprocessing to markerless keypoint data from videos of freely-exploring (Cib2-/-;Cib3-/-) mutant mice, a strain we previously found to exhibit circling. Our technique agrees with human consensus at the same level as do individual observers, and it achieves >90% accuracy in discriminating videos of wild type mice from videos of mutants. As using this technique requires no experience writing or modifying code, it also provides a convenient, noninvasive, quantitative tool for analyzing circling mouse models. Additionally, as our approach was agnostic to the underlying behavior, these results support the feasibility of algorithmically detecting specific, research-relevant behaviors using readily-interpretable parameters tuned on the basis of human consensus.
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Affiliation(s)
- O R Stanley
- Dept. Biomedical Engineering; Johns Hopkins University
| | - A Swaminathan
- Dept. Biomedical Engineering; Johns Hopkins University
| | - E Wojahn
- Dept. Biomedical Engineering; Johns Hopkins University
| | - Z M Ahmed
- Depts. Otorhinolaryngology-Head & Neck Surgery, Biochemistry & Molecular Biology, Ophthalmology; University of Maryland School of Medicine
| | - K E Cullen
- Dept. Biomedical Engineering; Johns Hopkins University
- Depts. Neuroscience, Otolaryngology-Head & Neck Surgery, Johns Hopkins University
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36
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Bordes J, Miranda L, Müller-Myhsok B, Schmidt MV. Advancing social behavioral neuroscience by integrating ethology and comparative psychology methods through machine learning. Neurosci Biobehav Rev 2023; 151:105243. [PMID: 37225062 DOI: 10.1016/j.neubiorev.2023.105243] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/21/2023] [Accepted: 05/20/2023] [Indexed: 05/26/2023]
Abstract
Social behavior is naturally occurring in vertebrate species, which holds a strong evolutionary component and is crucial for the normal development and survival of individuals throughout life. Behavioral neuroscience has seen different influential methods for social behavioral phenotyping. The ethological research approach has extensively investigated social behavior in natural habitats, while the comparative psychology approach was developed utilizing standardized and univariate social behavioral tests. The development of advanced and precise tracking tools, together with post-tracking analysis packages, has recently enabled a novel behavioral phenotyping method, that includes the strengths of both approaches. The implementation of such methods will be beneficial for fundamental social behavioral research but will also enable an increased understanding of the influences of many different factors that can influence social behavior, such as stress exposure. Furthermore, future research will increase the number of data modalities, such as sensory, physiological, and neuronal activity data, and will thereby significantly enhance our understanding of the biological basis of social behavior and guide intervention strategies for behavioral abnormalities in psychiatric disorders.
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Affiliation(s)
- Joeri Bordes
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804 Munich, Germany
| | - Lucas Miranda
- Research Group Statistical Genetics, Max Planck Institute of Psychiatry, 80804 Munich, Germany; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804 Munich, Germany
| | - Bertram Müller-Myhsok
- Research Group Statistical Genetics, Max Planck Institute of Psychiatry, 80804 Munich, Germany
| | - Mathias V Schmidt
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, 80804 Munich, Germany
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Fong T, Hu H, Gupta P, Jury B, Murphy TH. PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment. eNeuro 2023; 10:ENEURO.0127-22.2023. [PMID: 37185293 PMCID: PMC10198609 DOI: 10.1523/eneuro.0127-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 05/17/2023] Open
Abstract
PyMouseTracks (PMT) is a scalable and customizable computer vision and radio frequency identification (RFID)-based system for multiple rodent tracking and behavior assessment that can be set up within minutes in any user-defined arena at minimal cost. PMT is composed of the online Raspberry Pi (RPi)-based video and RFID acquisition with subsequent offline analysis tools. The system is capable of tracking up to six mice in experiments ranging from minutes to days. PMT maintained a minimum of 88% detections tracked with an overall accuracy >85% when compared with manual validation of videos containing one to four mice in a modified home-cage. As expected, chronic recording in home-cage revealed diurnal activity patterns. In open-field, it was observed that novel noncagemate mouse pairs exhibit more similarity in travel trajectory patterns than cagemate pairs over a 10-min period. Therefore, shared features within travel trajectories between animals may be a measure of sociability that has not been previously reported. Moreover, PMT can interface with open-source packages such as DeepLabCut and Traja for pose estimation and travel trajectory analysis, respectively. In combination with Traja, PMT resolved motor deficits exhibited in stroke animals. Overall, we present an affordable, open-sourced, and customizable/scalable mouse behavior recording and analysis system.
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Affiliation(s)
- Tony Fong
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia Canada V6T 1Z3
| | - Hao Hu
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia Canada V6T 1Z3
| | - Pankaj Gupta
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia Canada V6T 1Z3
| | - Braeden Jury
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia Canada V6T 1Z3
| | - Timothy H Murphy
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia Canada V6T 1Z3
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38
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Isik S, Unal G. Open-source software for automated rodent behavioral analysis. Front Neurosci 2023; 17:1149027. [PMID: 37139530 PMCID: PMC10149747 DOI: 10.3389/fnins.2023.1149027] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/27/2023] [Indexed: 05/05/2023] Open
Abstract
Rodent behavioral analysis is a major specialization in experimental psychology and behavioral neuroscience. Rodents display a wide range of species-specific behaviors, not only in their natural habitats but also under behavioral testing in controlled laboratory conditions. Detecting and categorizing these different kinds of behavior in a consistent way is a challenging task. Observing and analyzing rodent behaviors manually limits the reproducibility and replicability of the analyses due to potentially low inter-rater reliability. The advancement and accessibility of object tracking and pose estimation technologies led to several open-source artificial intelligence (AI) tools that utilize various algorithms for rodent behavioral analysis. These software provide high consistency compared to manual methods, and offer more flexibility than commercial systems by allowing custom-purpose modifications for specific research needs. Open-source software reviewed in this paper offer automated or semi-automated methods for detecting and categorizing rodent behaviors by using hand-coded heuristics, machine learning, or neural networks. The underlying algorithms show key differences in their internal dynamics, interfaces, user-friendliness, and the variety of their outputs. This work reviews the algorithms, capability, functionality, features and software properties of open-source behavioral analysis tools, and discusses how this emergent technology facilitates behavioral quantification in rodent research.
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Affiliation(s)
| | - Gunes Unal
- Behavioral Neuroscience Laboratory, Department of Psychology, Boğaziçi University, Istanbul, Türkiye
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Hu Y, Ferrario CR, Maitland AD, Ionides RB, Ghimire A, Watson B, Iwasaki K, White H, Xi Y, Zhou J, Ye B. LabGym: Quantification of user-defined animal behaviors using learning-based holistic assessment. CELL REPORTS METHODS 2023; 3:100415. [PMID: 37056376 PMCID: PMC10088092 DOI: 10.1016/j.crmeth.2023.100415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 10/19/2022] [Accepted: 02/01/2023] [Indexed: 03/09/2023]
Abstract
Quantifying animal behavior is important for biological research. Identifying behaviors is the prerequisite of quantifying them. Current computational tools for behavioral quantification typically use high-level properties such as body poses to identify the behaviors, which constrains the information available for a holistic assessment. Here we report LabGym, an open-source computational tool for quantifying animal behaviors without this constraint. In LabGym, we introduce "pattern image" to represent the animal's motion pattern, in addition to "animation" that shows all spatiotemporal details of a behavior. These two pieces of information are assessed holistically by customizable deep neural networks for accurate behavior identifications. The quantitative measurements of each behavior are then calculated. LabGym is applicable for experiments involving multiple animals, requires little programming knowledge to use, and provides visualizations of behavioral datasets. We demonstrate its efficacy in capturing subtle behavioral changes in diverse animal species.
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Affiliation(s)
- Yujia Hu
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Carrie R. Ferrario
- Department of Pharmacology and Psychology Department (Biopsychology), University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexander D. Maitland
- Department of Pharmacology and Psychology Department (Biopsychology), University of Michigan, Ann Arbor, MI 48109, USA
| | - Rita B. Ionides
- Department of Pharmacology and Psychology Department (Biopsychology), University of Michigan, Ann Arbor, MI 48109, USA
| | - Anjesh Ghimire
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Brendon Watson
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kenichi Iwasaki
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hope White
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yitao Xi
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jie Zhou
- Department of Computer Science, Northern Illinois University, DeKalb, IL 60115, USA
| | - Bing Ye
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
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40
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Luxem K, Sun JJ, Bradley SP, Krishnan K, Yttri E, Zimmermann J, Pereira TD, Laubach M. Open-source tools for behavioral video analysis: Setup, methods, and best practices. eLife 2023; 12:79305. [PMID: 36951911 PMCID: PMC10036114 DOI: 10.7554/elife.79305] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 03/03/2023] [Indexed: 03/24/2023] Open
Abstract
Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional 'center of mass' tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.
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Affiliation(s)
- Kevin Luxem
- Cellular Neuroscience, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Jennifer J Sun
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, United States
| | - Sean P Bradley
- Rodent Behavioral Core, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Keerthi Krishnan
- Department of Biochemistry and Cellular & Molecular Biology, University of Tennessee, Knoxville, United States
| | - Eric Yttri
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, United States
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, United States
| | - Talmo D Pereira
- The Salk Institute of Biological Studies, La Jolla, United States
| | - Mark Laubach
- Department of Neuroscience, American University, Washington D.C., United States
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41
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EXPLORE: a novel deep learning-based analysis method for exploration behaviour in object recognition tests. Sci Rep 2023; 13:4249. [PMID: 36918658 PMCID: PMC10014875 DOI: 10.1038/s41598-023-31094-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 03/06/2023] [Indexed: 03/16/2023] Open
Abstract
Object recognition tests are widely used in neuroscience to assess memory function in rodents. Despite the experimental simplicity of the task, the interpretation of behavioural features that are counted as object exploration can be complicated. Thus, object exploration is often analysed by manual scoring, which is time-consuming and variable across researchers. Current software using tracking points often lacks precision in capturing complex ethological behaviour. Switching or losing tracking points can bias outcome measures. To overcome these limitations we developed "EXPLORE", a simple, ready-to use and open source pipeline. EXPLORE consists of a convolutional neural network trained in a supervised manner, that extracts features from images and classifies behaviour of rodents near a presented object. EXPLORE achieves human-level accuracy in identifying and scoring exploration behaviour and outperforms commercial software with higher precision, higher versatility and lower time investment, in particular in complex situations. By labeling the respective training data set, users decide by themselves, which types of animal interactions on objects are in- or excluded, ensuring a precise analysis of exploration behaviour. A set of graphical user interfaces (GUIs) provides a beginning-to-end analysis of object recognition tests, accelerating a fast and reproducible data analysis without the need of expertise in programming or deep learning.
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42
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Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary. Int J Comput Vis 2023. [DOI: 10.1007/s11263-023-01768-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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43
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Shemesh Y, Chen A. A paradigm shift in translational psychiatry through rodent neuroethology. Mol Psychiatry 2023; 28:993-1003. [PMID: 36635579 PMCID: PMC10005947 DOI: 10.1038/s41380-022-01913-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 01/14/2023]
Abstract
Mental disorders are a significant cause of disability worldwide. They profoundly affect individuals' well-being and impose a substantial financial burden on societies and governments. However, despite decades of extensive research, the effectiveness of current therapeutics for mental disorders is often not satisfactory or well tolerated by the patient. Moreover, most novel therapeutic candidates fail in clinical testing during the most expensive phases (II and III), which results in the withdrawal of pharma companies from investing in the field. It also brings into question the effectiveness of using animal models in preclinical studies to discover new therapeutic agents and predict their potential for treating mental illnesses in humans. Here, we focus on rodents as animal models and propose that they are essential for preclinical investigations of candidate therapeutic agents' mechanisms of action and for testing their safety and efficiency. Nevertheless, we argue that there is a need for a paradigm shift in the methodologies used to measure animal behavior in laboratory settings. Specifically, behavioral readouts obtained from short, highly controlled tests in impoverished environments and social contexts as proxies for complex human behavioral disorders might be of limited face validity. Conversely, animal models that are monitored in more naturalistic environments over long periods display complex and ethologically relevant behaviors that reflect evolutionarily conserved endophenotypes of translational value. We present how semi-natural setups in which groups of mice are individually tagged, and video recorded continuously can be attainable and affordable. Moreover, novel open-source machine-learning techniques for pose estimation enable continuous and automatic tracking of individual body parts in groups of rodents over long periods. The trajectories of each individual animal can further be subjected to supervised machine learning algorithms for automatic detection of specific behaviors (e.g., chasing, biting, or fleeing) or unsupervised automatic detection of behavioral motifs (e.g., stereotypical movements that might be harder to name or label manually). Compared to studies of animals in the wild, semi-natural environments are more compatible with neural and genetic manipulation techniques. As such, they can be used to study the neurobiological mechanisms underlying naturalistic behavior. Hence, we suggest that such a paradigm possesses the best out of classical ethology and the reductive behaviorist approach and may provide a breakthrough in discovering new efficient therapies for mental illnesses.
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Affiliation(s)
- Yair Shemesh
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Alon Chen
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel.
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, 7610001, Israel.
- Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, 80804, Munich, Germany.
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Crawley JN. Twenty years of discoveries emerging from mouse models of autism. Neurosci Biobehav Rev 2023; 146:105053. [PMID: 36682425 DOI: 10.1016/j.neubiorev.2023.105053] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 01/12/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
More than 100 single gene mutations and copy number variants convey risk for autism spectrum disorder. To understand the extent to which each mutation contributes to the trajectory of individual symptoms of autism, molecular genetics laboratories have introduced analogous mutations into the genomes of laboratory mice and other species. Over the past twenty years, behavioral neuroscientists discovered the consequences of mutations in many risk genes for autism in animal models, using assays with face validity to the diagnostic and associated behavioral symptoms of people with autism. Identified behavioral phenotypes complement electrophysiological, neuroanatomical, and biochemical outcome measures in mutant mouse models of autism. This review describes the history of phenotyping assays in genetic mouse models, to evaluate social and repetitive behaviors relevant to the primary diagnostic criteria for autism. Robust phenotypes are currently employed in translational investigations to discover effective therapeutic interventions, representing the future direction of an intensely challenging research field.
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Minkowicz S, Mathews MA, Mou FH, Yoon H, Freda SN, Cui ES, Kennedy A, Kozorovitskiy Y. Striatal ensemble activity in an innate naturalistic behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.23.529669. [PMID: 36865109 PMCID: PMC9980072 DOI: 10.1101/2023.02.23.529669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Self-grooming is an innate, naturalistic behavior found in a wide variety of organisms. The control of rodent grooming has been shown to be mediated by the dorsolateral striatum through lesion studies and in-vivo extracellular recordings. Yet, it is unclear how populations of neurons in the striatum encode grooming. We recorded single-unit extracellular activity from populations of neurons in freely moving mice and developed a semi-automated approach to detect self-grooming events from 117 hours of simultaneous multi-camera video recordings of mouse behavior. We first characterized the grooming transition-aligned response profiles of striatal projection neuron and fast spiking interneuron single units. We identified striatal ensembles whose units were more strongly correlated during grooming than during the entire session. These ensembles display varied grooming responses, including transient changes around grooming transitions or sustained changes in activity throughout the duration of grooming. Neural trajectories computed from the identified ensembles retain the grooming related dynamics present in trajectories computed from all units in the session. These results elaborate striatal function in rodent self-grooming and demonstrate that striatal grooming-related activity is organized within functional ensembles, improving our understanding of how the striatum guides action selection in a naturalistic behavior.
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Affiliation(s)
- Samuel Minkowicz
- Department of Neurobiology, Northwestern University, Evanston, IL, United States
| | | | - Felicia Hoilam Mou
- Department of Neurobiology, Northwestern University, Evanston, IL, United States
| | - Hyoseo Yoon
- Department of Neurobiology, Northwestern University, Evanston, IL, United States
| | - Sara Nicole Freda
- Department of Neurobiology, Northwestern University, Evanston, IL, United States
| | - Ethan S Cui
- Department of Neurobiology, Northwestern University, Evanston, IL, United States
| | - Ann Kennedy
- Department of Neuroscience, Northwestern University, Chicago, IL, United States
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46
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Malik A, Zavadil JA, Geusz ME. Using bioluminescence to image gene expression and spontaneous behavior in freely moving mice. PLoS One 2023; 18:e0279875. [PMID: 36662734 PMCID: PMC9858005 DOI: 10.1371/journal.pone.0279875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/17/2022] [Indexed: 01/21/2023] Open
Abstract
Bioluminescence imaging (BLI) of gene expression in live animals is a powerful method for monitoring development, tumor growth, infections, healing, and other progressive, long-term biological processes. BLI remains an effective approach for reducing the number of animals needed to monitor dynamic changes in gene activity because images can be captured repeatedly from the same animals. When examining these ongoing changes, it is sometimes necessary to remove rhythmic effects on the bioluminescence signal caused by the circadian clock's daily modulation of gene expression. Furthermore, BLI using freely moving animals remains limited because the standard procedures can alter normal behaviors. Another obstacle with conventional BLI of animals is that luciferin, the firefly luciferase substrate, is usually injected into mice that are then imaged while anesthetized. Unfortunately, the luciferase signal declines rapidly during imaging as luciferin is cleared from the body. Alternatively, mice are imaged after they are surgically implanted with a pump or connected to a tether to deliver luciferin, but stressors such as this surgery and anesthesia can alter physiology, behavior, and the actual gene expression being imaged. Consequently, we developed a strategy that minimizes animal exposure to stressors before and during sustained BLI of freely moving unanesthetized mice. This technique was effective when monitoring expression of the Per1 gene that serves in the circadian clock timing mechanism and was previously shown to produce circadian bioluminescence rhythms in live mice. We used hairless albino mice expressing luciferase that were allowed to drink luciferin and engage in normal behaviors during imaging with cooled electron-multiplying-CCD cameras. Computer-aided image selection was developed to measure signal intensity of individual mice each time they were in the same posture, thereby providing comparable measurements over long intervals. This imaging procedure, performed primarily during the animal's night, is compatible with entrainment of the mouse circadian timing system to the light cycle while allowing sampling at multi-day intervals to monitor long-term changes. When the circadian expression of a gene is known, this approach provides an effective alternative to imaging immobile anesthetized animals and can removing noise caused by circadian oscillations and body movements that can degrade data collected during long-term imaging studies.
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Affiliation(s)
- Astha Malik
- Division of Gastroenterology, Hepatology, & Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Jessica A. Zavadil
- Graduate Medical Education, University of Tennessee Health Science Center, Memphis, TN, United States of America
| | - Michael E. Geusz
- Department of Biological Sciences, Bowling Green State University, Bowling Green, Ohio, United States of America
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47
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Nair A, Karigo T, Yang B, Ganguli S, Schnitzer MJ, Linderman SW, Anderson DJ, Kennedy A. An approximate line attractor in the hypothalamus encodes an aggressive state. Cell 2023; 186:178-193.e15. [PMID: 36608653 PMCID: PMC9990527 DOI: 10.1016/j.cell.2022.11.027] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/05/2022] [Accepted: 11/22/2022] [Indexed: 01/07/2023]
Abstract
The hypothalamus regulates innate social behaviors, including mating and aggression. These behaviors can be evoked by optogenetic stimulation of specific neuronal subpopulations within MPOA and VMHvl, respectively. Here, we perform dynamical systems modeling of population neuronal activity in these nuclei during social behaviors. In VMHvl, unsupervised analysis identified a dominant dimension of neural activity with a large time constant (>50 s), generating an approximate line attractor in neural state space. Progression of the neural trajectory along this attractor was correlated with an escalation of agonistic behavior, suggesting that it may encode a scalable state of aggressiveness. Consistent with this, individual differences in the magnitude of the integration dimension time constant were strongly correlated with differences in aggressiveness. In contrast, approximate line attractors were not observed in MPOA during mating; instead, neurons with fast dynamics were tuned to specific actions. Thus, different hypothalamic nuclei employ distinct neural population codes to represent similar social behaviors.
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Affiliation(s)
- Aditya Nair
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA; Howard Hughes Medical Institute; Tianqiao and Chrissy Chen Institute for Neuroscience, Caltech, Pasadena, CA 91125, USA
| | - Tomomi Karigo
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA; Howard Hughes Medical Institute; Tianqiao and Chrissy Chen Institute for Neuroscience, Caltech, Pasadena, CA 91125, USA
| | - Bin Yang
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA; Howard Hughes Medical Institute; Tianqiao and Chrissy Chen Institute for Neuroscience, Caltech, Pasadena, CA 91125, USA
| | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Mark J Schnitzer
- Howard Hughes Medical Institute; Department of Applied Physics, Stanford University, Stanford, CA, USA; Department of Biology, Stanford University, Stanford, CA, USA
| | - Scott W Linderman
- Department of Statistics, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA
| | - David J Anderson
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA; Howard Hughes Medical Institute; Tianqiao and Chrissy Chen Institute for Neuroscience, Caltech, Pasadena, CA 91125, USA.
| | - Ann Kennedy
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA; Howard Hughes Medical Institute; Tianqiao and Chrissy Chen Institute for Neuroscience, Caltech, Pasadena, CA 91125, USA; Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago IL 60611, USA.
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48
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Miranda L, Bordes J, Gasperoni S, Lopez JP. Increasing resolution in stress neurobiology: from single cells to complex group behaviors. Stress 2023; 26:2186141. [PMID: 36855966 DOI: 10.1080/10253890.2023.2186141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
Stress can have severe psychological and physiological consequences. Thus, inappropriate regulation of the stress response is linked to the etiology of mood and anxiety disorders. The generation and implementation of preclinical animal models represent valuable tools to explore and characterize the mechanisms underlying the pathophysiology of stress-related psychiatric disorders and the development of novel pharmacological strategies. In this commentary, we discuss the strengths and limitations of state-of-the-art molecular and computational advances employed in stress neurobiology research, with a focus on the ever-increasing spatiotemporal resolution in cell biology and behavioral science. Finally, we share our perspective on future directions in the fields of preclinical and human stress research.
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Affiliation(s)
- Lucas Miranda
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Joeri Bordes
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Serena Gasperoni
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Juan Pablo Lopez
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
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49
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Walsh JJ, Christoffel DJ, Malenka RC. Neural circuits regulating prosocial behaviors. Neuropsychopharmacology 2023; 48:79-89. [PMID: 35701550 PMCID: PMC9700801 DOI: 10.1038/s41386-022-01348-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/09/2022] [Accepted: 05/17/2022] [Indexed: 11/09/2022]
Abstract
Positive, prosocial interactions are essential for survival, development, and well-being. These intricate and complex behaviors are mediated by an amalgamation of neural circuit mechanisms working in concert. Impairments in prosocial behaviors, which occur in a large number of neuropsychiatric disorders, result from disruption of the coordinated activity of these neural circuits. In this review, we focus our discussion on recent findings that utilize modern approaches in rodents to map, monitor, and manipulate neural circuits implicated in a variety of prosocial behaviors. We highlight how modulation by oxytocin, serotonin, and dopamine of excitatory and inhibitory synaptic transmission in specific brain regions is critical for regulation of adaptive prosocial interactions. We then describe how recent findings have helped elucidate pathophysiological mechanisms underlying the social deficits that accompany neuropsychiatric disorders. We conclude by discussing approaches for the development of more efficacious and targeted therapeutic interventions to ameliorate aberrant prosocial behaviors.
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Affiliation(s)
- Jessica J Walsh
- Department of Pharmacology, University of North Carolina, Chapel Hill, NC, 27514, USA.
- Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA.
- Neuroscience Center, University of North Carolina, Chapel Hill, NC, 27514, USA.
| | - Daniel J Christoffel
- Neuroscience Center, University of North Carolina, Chapel Hill, NC, 27514, USA
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, 27514, USA
| | - Robert C Malenka
- Nancy Pritzker Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305-5453, USA.
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50
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Overcoming the Domain Gap in Neural Action Representations. Int J Comput Vis 2022. [DOI: 10.1007/s11263-022-01713-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
AbstractRelating behavior to brain activity in animals is a fundamental goal in neuroscience, with practical applications in building robust brain-machine interfaces. However, the domain gap between individuals is a major issue that prevents the training of general models that work on unlabeled subjects. Since 3D pose data can now be reliably extracted from multi-view video sequences without manual intervention, we propose to use it to guide the encoding of neural action representations together with a set of neural and behavioral augmentations exploiting the properties of microscopy imaging. To test our method, we collect a large dataset that features flies and their neural activity. To reduce the domain gap, during training, we mix features of neural and behavioral data across flies that seem to be performing similar actions. To show our method can generalize further neural modalities and other downstream tasks, we test our method on a human neural Electrocorticography dataset, and another RGB video data of human activities from different viewpoints. We believe our work will enable more robust neural decoding algorithms to be used in future brain-machine interfaces.
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