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Kaneko T, Matsumoto J, Lu W, Zhao X, Ueno-Nigh LR, Oishi T, Kimura K, Otsuka Y, Zheng A, Ikenaka K, Baba K, Mochizuki H, Nishijo H, Inoue KI, Takada M. Deciphering social traits and pathophysiological conditions from natural behaviors in common marmosets. Curr Biol 2024:S0960-9822(24)00676-6. [PMID: 38889723 DOI: 10.1016/j.cub.2024.05.033] [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: 04/16/2024] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024]
Abstract
Nonhuman primates (NHPs) are indispensable animal models by virtue of the continuity of behavioral repertoires across primates, including humans. However, behavioral assessment at the laboratory level has so far been limited. Employing the application of three-dimensional (3D) pose estimation and the optimal integration of subsequent analytic methodologies, we demonstrate that our artificial intelligence (AI)-based approach has successfully deciphered the ethological, cognitive, and pathological traits of common marmosets from their natural behaviors. By applying multiple deep neural networks trained with large-scale datasets, we established an evaluation system that could reconstruct and estimate the 3D poses of the marmosets, a small NHP that is suitable for analyzing complex natural behaviors in laboratory setups. We further developed downstream analytic methodologies to quantify a variety of behavioral parameters beyond motion kinematics. We revealed the distinct parental roles of male and female marmosets through automated detections of food-sharing behaviors using a spatial-temporal filter on 3D poses. Employing a recurrent neural network to analyze 3D pose time series data during social interactions, we additionally discovered that marmosets adjusted their behaviors based on others' internal state, which is not directly observable but can be inferred from the sequence of others' actions. Moreover, a fully unsupervised approach enabled us to detect progressively appearing symptomatic behaviors over a year in a Parkinson's disease model. The high-throughput and versatile nature of an AI-driven approach to analyze natural behaviors will open a new avenue for neuroscience research dealing with big-data analyses of social and pathophysiological behaviors in NHPs.
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Affiliation(s)
- Takaaki Kaneko
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan.
| | - Jumpei Matsumoto
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan; Research Center for Idling Brain Science, University of Toyama, Toyama 930-0194, Japan
| | - Wanyi Lu
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Xincheng Zhao
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Louie Richard Ueno-Nigh
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Takao Oishi
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Kei Kimura
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Yukiko Otsuka
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Andi Zheng
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Kensuke Ikenaka
- Department of Neurology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Kousuke Baba
- Department of Neurology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Hideki Mochizuki
- Department of Neurology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Hisao Nishijo
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan; Research Center for Idling Brain Science, University of Toyama, Toyama 930-0194, Japan; Faculty of Human Sciences, University of East Asia, Shimonoseki, Yamaguchi 751-8503, Japan
| | - Ken-Ichi Inoue
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Masahiko Takada
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan; Department of Neurology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan.
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2
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Crombie D, Spacek MA, Leibold C, Busse L. Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales. PLoS Biol 2024; 22:e3002614. [PMID: 38743775 PMCID: PMC11093384 DOI: 10.1371/journal.pbio.3002614] [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: 07/14/2023] [Accepted: 04/05/2024] [Indexed: 05/16/2024] Open
Abstract
The processing of sensory information, even at early stages, is influenced by the internal state of the animal. Internal states, such as arousal, are often characterized by relating neural activity to a single "level" of arousal, defined by a behavioral indicator such as pupil size. In this study, we expand the understanding of arousal-related modulations in sensory systems by uncovering multiple timescales of pupil dynamics and their relationship to neural activity. Specifically, we observed a robust coupling between spiking activity in the mouse dorsolateral geniculate nucleus (dLGN) of the thalamus and pupil dynamics across timescales spanning a few seconds to several minutes. Throughout all these timescales, 2 distinct spiking modes-individual tonic spikes and tightly clustered bursts of spikes-preferred opposite phases of pupil dynamics. This multi-scale coupling reveals modulations distinct from those captured by pupil size per se, locomotion, and eye movements. Furthermore, coupling persisted even during viewing of a naturalistic movie, where it contributed to differences in the encoding of visual information. We conclude that dLGN spiking activity is under the simultaneous influence of multiple arousal-related processes associated with pupil dynamics occurring over a broad range of timescales.
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Affiliation(s)
- Davide Crombie
- Division of Neuroscience, Faculty of Biology, LMU Munich, Munich, Germany
- Graduate School of Systemic Neurosciences, LMU Munich, Munich, Germany
| | - Martin A. Spacek
- Division of Neuroscience, Faculty of Biology, LMU Munich, Munich, Germany
| | - Christian Leibold
- Division of Neuroscience, Faculty of Biology, LMU Munich, Munich, Germany
- Fakultät für Biologie & Bernstein Center Freiburg, Albert-Ludwigs-Universität Freiburg, Freiburg im Breisgau, Germany
| | - Laura Busse
- Division of Neuroscience, Faculty of Biology, LMU Munich, Munich, Germany
- Bernstein Center for Computational Neuroscience, Munich, Germany
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3
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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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4
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Hasnain MA, Birnbaum JE, Nunez JLU, Hartman EK, Chandrasekaran C, Economo MN. Separating cognitive and motor processes in the behaving mouse. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.23.554474. [PMID: 37662199 PMCID: PMC10473744 DOI: 10.1101/2023.08.23.554474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
The cognitive processes supporting complex animal behavior are closely associated with ubiquitous movements responsible for our posture, facial expressions, ability to actively sample our sensory environments, and other critical processes. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes, making it challenging to dissociate the neural dynamics that support cognitive processes from those supporting related movements. In such cases, a critical issue is whether cognitive processes are separable from related movements, or if they are driven by common neural mechanisms. Here, we demonstrate how the separability of cognitive and motor processes can be assessed, and, when separable, how the neural dynamics associated with each component can be isolated. We establish a novel two-context behavioral task in mice that involves multiple cognitive processes and show that commonly observed dynamics taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories. Further, properly accounting for movement revealed that largely separate populations of cells encode cognitive and motor variables, in contrast to the 'mixed selectivity' often reported. Accurately isolating the dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function and evaluating the function of the cell types of which neural circuits are composed.
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Affiliation(s)
- Munib A. Hasnain
- Department of Biomedical Engineering, Boston University, Boston, MA
- Center for Neurophotonics, Boston University, Boston, MA
| | - Jaclyn E. Birnbaum
- Graduate Program for Neuroscience, Boston University, Boston, MA
- Center for Neurophotonics, Boston University, Boston, MA
| | | | - Emma K. Hartman
- Department of Biomedical Engineering, Boston University, Boston, MA
| | - Chandramouli Chandrasekaran
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
- Department of Neurobiology & Anatomy, Boston University, Boston, MA
- Center for Systems Neuroscience, Boston University, Boston, MA
| | - Michael N. Economo
- Department of Biomedical Engineering, Boston University, Boston, MA
- Center for Neurophotonics, Boston University, Boston, MA
- Center for Systems Neuroscience, Boston University, Boston, MA
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5
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Bialek W, Shaevitz JW. Long Timescales, Individual Differences, and Scale Invariance in Animal Behavior. PHYSICAL REVIEW LETTERS 2024; 132:048401. [PMID: 38335334 DOI: 10.1103/physrevlett.132.048401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 11/27/2023] [Indexed: 02/12/2024]
Abstract
The explosion of data on animal behavior in more natural contexts highlights the fact that these behaviors exhibit correlations across many timescales. However, there are major challenges in analyzing these data: records of behavior in single animals have fewer independent samples than one might expect. In pooling data from multiple animals, individual differences can mimic long-ranged temporal correlations; conversely, long-ranged correlations can lead to an overestimate of individual differences. We suggest an analysis scheme that addresses these problems directly, apply this approach to data on the spontaneous behavior of walking flies, and find evidence for scale-invariant correlations over nearly three decades in time, from seconds to one hour. Three different measures of correlation are consistent with a single underlying scaling field of dimension Δ=0.180±0.005.
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Affiliation(s)
- William Bialek
- Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
- Center for Studies in Physics and Biology, Rockefeller University, New York, New York 10065, USA
| | - Joshua W Shaevitz
- Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
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6
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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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7
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Weinreb C, Pearl J, Lin S, Osman MAM, Zhang L, Annapragada S, Conlin E, Hoffman R, Makowska S, Gillis WF, Jay M, Ye S, Mathis A, Mathis MW, Pereira T, Linderman SW, Datta SR. Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532307. [PMID: 36993589 PMCID: PMC10055085 DOI: 10.1101/2023.03.16.532307] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.
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Affiliation(s)
- Caleb Weinreb
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jonah Pearl
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Sherry Lin
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Libby Zhang
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | | | - Eli Conlin
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Red Hoffman
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Sofia Makowska
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Maya Jay
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Shaokai Ye
- Brain Mind and Neuro-X Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alexander Mathis
- Brain Mind and Neuro-X Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mackenzie Weygandt Mathis
- Brain Mind and Neuro-X Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Talmo Pereira
- Salk Institute for Biological Studies, La Jolla, USA
| | - Scott W. Linderman
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
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8
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Barnard IL, Onofrychuk TJ, Toderash AD, Patel VN, Glass AE, Adrian JC, Laprairie RB, Howland JG. High-THC Cannabis Smoke Impairs Incidental Memory Capacity in Spontaneous Tests of Novelty Preference for Objects and Odors in Male Rats. eNeuro 2023; 10:ENEURO.0115-23.2023. [PMID: 37973381 PMCID: PMC10714893 DOI: 10.1523/eneuro.0115-23.2023] [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: 04/08/2023] [Revised: 09/19/2023] [Accepted: 10/16/2023] [Indexed: 11/19/2023] Open
Abstract
Working memory is an executive function that orchestrates the use of limited amounts of information, referred to as working memory capacity, in cognitive functions. Cannabis exposure impairs working memory in humans; however, it is unclear whether Cannabis facilitates or impairs rodent working memory and working memory capacity. The conflicting literature in rodent models may be at least partly because of the use of drug exposure paradigms that do not closely mirror patterns of human Cannabis use. Here, we used an incidental memory capacity paradigm where a novelty preference is assessed after a short delay in spontaneous recognition-based tests. Either object or odor-based stimuli were used in test variations with sets of identical [identical stimuli test (IST)] and different [different stimuli test (DST)] stimuli (three or six) for low-memory and high-memory loads, respectively. Additionally, we developed a human-machine hybrid behavioral quantification approach which supplements stopwatch-based scoring with supervised machine learning-based classification. After validating the spontaneous IST and DST in male rats, 6-item test versions with the hybrid quantification method were used to evaluate the impact of acute exposure to high-Δ9-tetrahydrocannabinol (THC) or high-CBD Cannabis smoke on novelty preference. Under control conditions, male rats showed novelty preference in all test variations. We found that high-THC, but not high-CBD, Cannabis smoke exposure impaired novelty preference for objects under a high-memory load. Odor-based recognition deficits were seen under both low-memory and high-memory loads only following high-THC smoke exposure. Ultimately, these data show that Cannabis smoke exposure impacts incidental memory capacity of male rats in a memory load-dependent, and stimuli-specific manner.
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Affiliation(s)
- Ilne L Barnard
- Department of Anatomy, Physiology, and Pharmacology, University of Saskatchewan, Saskatoon, Saskatchewan, S7N5E5, Canada
| | - Timothy J Onofrychuk
- Department of Anatomy, Physiology, and Pharmacology, University of Saskatchewan, Saskatoon, Saskatchewan, S7N5E5, Canada
| | - Aaron D Toderash
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, S7N5C9, Canada
| | - Vyom N Patel
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, S7N5C9, Canada
| | - Aiden E Glass
- Department of Anatomy, Physiology, and Pharmacology, University of Saskatchewan, Saskatoon, Saskatchewan, S7N5E5, Canada
| | - Jesse C Adrian
- Department of Anatomy, Physiology, and Pharmacology, University of Saskatchewan, Saskatoon, Saskatchewan, S7N5E5, Canada
| | - Robert B Laprairie
- College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Saskatchewan, S7N5E5, Canada
- Department of Pharmacology, College of Medicine, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada
| | - John G Howland
- Department of Anatomy, Physiology, and Pharmacology, University of Saskatchewan, Saskatoon, Saskatchewan, S7N5E5, Canada
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9
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Knight A, Gschwind T, Galer P, Worrell GA, Litt B, Soltesz I, Beniczky S. Artificial intelligence in epilepsy phenotyping. Epilepsia 2023:10.1111/epi.17833. [PMID: 37983589 PMCID: PMC11102939 DOI: 10.1111/epi.17833] [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: 11/12/2023] [Accepted: 11/17/2023] [Indexed: 11/22/2023]
Abstract
Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI-based automated and semi-automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high-frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.
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Affiliation(s)
| | - Tilo Gschwind
- Department of Neurosurgery, Stanford University, Stanford, USA
| | - Peter Galer
- Center for Neuroengineering and Therapeutics; Department of Bioengineering; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, USA
| | | | - Brian Litt
- Center for Neuroengineering and Therapeutics; Department of Bioengineering; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, USA
| | - Sándor Beniczky
- Danish Epilepsy Centre Filadelfia, Dianalund, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
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10
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Keles MF, Sapci A, Brody C, Palmer I, Le C, Tastan O, Keles S, Wu MN. Deep Phenotyping of Sleep in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564733. [PMID: 37961473 PMCID: PMC10635029 DOI: 10.1101/2023.10.30.564733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Sleep is an evolutionarily conserved behavior, whose function is unknown. Here, we present a method for deep phenotyping of sleep in Drosophila, consisting of a high-resolution video imaging system, coupled with closed-loop laser perturbation to measure arousal threshold. To quantify sleep-associated microbehaviors, we trained a deep-learning network to annotate body parts in freely moving flies and developed a semi-supervised computational pipeline to classify behaviors. Quiescent flies exhibit a rich repertoire of microbehaviors, including proboscis pumping (PP) and haltere switches, which vary dynamically across the night. Using this system, we characterized the effects of optogenetically activating two putative sleep circuits. These data reveal that activating dFB neurons produces micromovements, inconsistent with sleep, while activating R5 neurons triggers PP followed by behavioral quiescence. Our findings suggest that sleep in Drosophila is polyphasic with different stages and set the stage for a rigorous analysis of sleep and other behaviors in this species.
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Affiliation(s)
- Mehmet F. Keles
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Ali Sapci
- Department of Computer Science, Sabanci University, Tuzla, Istanbul, 34956, Turkey
| | - Casey Brody
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Isabelle Palmer
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Christin Le
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Oznur Tastan
- Department of Computer Science, Sabanci University, Tuzla, Istanbul, 34956, Turkey
| | - Sunduz Keles
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Mark N. Wu
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21287, USA
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11
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Hu H, Zhang R, Fong T, Rhodin H, Murphy TH. Standardized 3D test object for multi-camera calibration during animal pose capture. NEUROPHOTONICS 2023; 10:046602. [PMID: 37942210 PMCID: PMC10629347 DOI: 10.1117/1.nph.10.4.046602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 10/05/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023]
Abstract
Accurate capture of animal behavior and posture requires the use of multiple cameras to reconstruct three-dimensional (3D) representations. Typically, a paper ChArUco (or checker) board works well for correcting distortion and calibrating for 3D reconstruction in stereo vision. However, measuring the error in two-dimensional (2D) is also prone to bias related to the placement of the 2D board in 3D. We proposed a procedure as a visual way of validating camera placement, and it also can provide some guidance about the positioning of cameras and potential advantages of using multiple cameras. We propose the use of a 3D printable test object for validating multi-camera surround-view calibration in small animal video capture arenas. The proposed 3D printed object has no bias to a particular dimension and is designed to minimize occlusions. The use of the calibrated test object provided an estimate of 3D reconstruction accuracy. The approach reveals that for complex specimens such as mice, some view angles will be more important for accurate capture of keypoints. Our method ensures accurate 3D camera calibration for surround image capture of laboratory mice and other specimens.
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Affiliation(s)
- Hao Hu
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, British Columbia, Canada
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
| | - Roark Zhang
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, British Columbia, Canada
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
| | - Tony Fong
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, British Columbia, Canada
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
| | - Helge Rhodin
- University of British Columbia, Department of Computer Science, Vancouver, British Columbia, Canada
| | - Timothy H. Murphy
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, British Columbia, Canada
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
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12
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Voloh B, Maisson DJN, Cervera RL, Conover I, Zambre M, Hayden B, Zimmermann J. Hierarchical action encoding in prefrontal cortex of freely moving macaques. Cell Rep 2023; 42:113091. [PMID: 37656619 PMCID: PMC10591875 DOI: 10.1016/j.celrep.2023.113091] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 06/23/2023] [Accepted: 08/18/2023] [Indexed: 09/03/2023] Open
Abstract
Our natural behavioral repertoires include coordinated actions of characteristic types. To better understand how neural activity relates to the expression of actions and action switches, we studied macaques performing a freely moving foraging task in an open environment. We developed a novel analysis pipeline that can identify meaningful units of behavior, corresponding to recognizable actions such as sitting, walking, jumping, and climbing. On the basis of transition probabilities between these actions, we found that behavior is organized in a modular and hierarchical fashion. We found that, after regressing out many potential confounders, actions are associated with specific patterns of firing in each of six prefrontal brain regions and that, overall, encoding of action category is progressively stronger in more dorsal and more caudal prefrontal regions. Together, these results establish a link between selection of units of primate behavior on one hand and neuronal activity in prefrontal regions on the other.
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Affiliation(s)
- Benjamin Voloh
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - David J-N Maisson
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Indirah Conover
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mrunal Zambre
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Benjamin Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, USA.
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13
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Butler DJ, Keim AP, Ray S, Azim E. Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models. Nat Commun 2023; 14:5866. [PMID: 37752123 PMCID: PMC10522643 DOI: 10.1038/s41467-023-41565-3] [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: 05/30/2022] [Accepted: 09/08/2023] [Indexed: 09/28/2023] Open
Abstract
Deep learning-based markerless tracking has revolutionized studies of animal behavior. Yet the generalizability of trained models tends to be limited, as new training data typically needs to be generated manually for each setup or visual environment. With each model trained from scratch, researchers track distinct landmarks and analyze the resulting kinematic data in idiosyncratic ways. Moreover, due to inherent limitations in manual annotation, only a sparse set of landmarks are typically labeled. To address these issues, we developed an approach, which we term GlowTrack, for generating orders of magnitude more training data, enabling models that generalize across experimental contexts. We describe: a) a high-throughput approach for producing hidden labels using fluorescent markers; b) a multi-camera, multi-light setup for simulating diverse visual conditions; and c) a technique for labeling many landmarks in parallel, enabling dense tracking. These advances lay a foundation for standardized behavioral pipelines and more complete scrutiny of movement.
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Affiliation(s)
- Daniel J Butler
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Alexander P Keim
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Shantanu Ray
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA.
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14
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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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15
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Legan AW, Vogt CC, Sheehan MJ. Postural analysis reveals persistent changes in paper wasp foundress behavioral state after conspecific challenge. Ecol Evol 2023; 13:e10436. [PMID: 37664514 PMCID: PMC10469045 DOI: 10.1002/ece3.10436] [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: 05/09/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 09/05/2023] Open
Abstract
Vigilant animals detect and respond to threats in the environment, often changing posture and movement patterns. Vigilance is modulated not only by predators but also by conspecific threats. In social animals, precisely how conspecific threats alter vigilance behavior over time is relevant to long-standing hypotheses about social plasticity. We report persistent effects of a simulated conspecific challenge on behavior of wild northern paper wasp foundresses, Polistes fuscatus. During the founding phase of the colony cycle, conspecific wasps can usurp nests from the resident foundress, representing a severe threat. We used automated tracking to monitor the movement and posture of P. fuscatus foundresses in response to simulated intrusions. Wasps displayed increased movement, greater bilateral wing extension, and reduced antennal separation after the threat was removed. These changes were not observed after presentation with a wooden dowel. By rapidly adjusting individual behavior after fending off an intruder, paper wasp foundresses might invest in surveillance of potential threats, even when such threats are no longer immediately present. The prolonged vigilance-like behavioral state observed here is relevant to plasticity of social recognition processes in paper wasps.
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Affiliation(s)
- Andrew W. Legan
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and BehaviorCornell UniversityIthacaNew YorkUSA
- Department of EntomologyUniversity of ArizonaTucsonArizonaUSA
| | - Caleb C. Vogt
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and BehaviorCornell UniversityIthacaNew YorkUSA
| | - Michael J. Sheehan
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and BehaviorCornell UniversityIthacaNew YorkUSA
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16
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Tian R, Li Y, Zhao H, Lyu W, Zhao J, Wang X, Lu H, Xu H, Ren W, Tan QQ, Shi Q, Wang GD, Zhang YP, Lai L, Mi J, Jiang YH, Zhang YQ. Modeling SHANK3-associated autism spectrum disorder in Beagle dogs via CRISPR/Cas9 gene editing. Mol Psychiatry 2023; 28:3739-3750. [PMID: 37848710 DOI: 10.1038/s41380-023-02276-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 08/30/2023] [Accepted: 09/14/2023] [Indexed: 10/19/2023]
Abstract
Despite intensive studies in modeling neuropsychiatric disorders especially autism spectrum disorder (ASD) in animals, many challenges remain. Genetic mutant mice have contributed substantially to the current understanding of the molecular and neural circuit mechanisms underlying ASD. However, the translational value of ASD mouse models in preclinical studies is limited to certain aspects of the disease due to the apparent differences in brain and behavior between rodents and humans. Non-human primates have been used to model ASD in recent years. However, a low reproduction rate due to a long reproductive cycle and a single birth per pregnancy, and an extremely high cost prohibit a wide use of them in preclinical studies. Canine model is an appealing alternative because of its complex and effective dog-human social interactions. In contrast to non-human primates, dog has comparable drug metabolism as humans and a high reproduction rate. In this study, we aimed to model ASD in experimental dogs by manipulating the Shank3 gene as SHANK3 mutations are one of most replicated genetic defects identified from ASD patients. Using CRISPR/Cas9 gene editing, we successfully generated and characterized multiple lines of Beagle Shank3 (bShank3) mutants that have been propagated for a few generations. We developed and validated a battery of behavioral assays that can be used in controlled experimental setting for mutant dogs. bShank3 mutants exhibited distinct and robust social behavior deficits including social withdrawal and reduced social interactions with humans, and heightened anxiety in different experimental settings (n = 27 for wild-type controls and n = 44 for mutants). We demonstrate the feasibility of producing a large number of mutant animals in a reasonable time frame. The robust and unique behavioral findings support the validity and value of a canine model to investigate the pathophysiology and develop treatments for ASD and potentially other psychiatric disorders.
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Affiliation(s)
- Rui Tian
- State Key Laboratory of Molecular Developmental Biology, and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yuan Li
- Beijing Sinogene Biotechnology Co. Ltd, Beijing, China
| | - Hui Zhao
- State Key Laboratory of Molecular Developmental Biology, and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- Key Laboratory of Regenerative Biology, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Wen Lyu
- State Key Laboratory of Molecular Developmental Biology, and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jianping Zhao
- Beijing Sinogene Biotechnology Co. Ltd, Beijing, China
| | - Xiaomin Wang
- Key Laboratory of Regenerative Biology, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Heng Lu
- Department of Life Science and Medicine, University of Science and Technology of China, Hefei, 230022, China
- State Key Laboratory of Genetic Resources and Evolution, and Center for Excellence in Animal Evolution and Genetics, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China
| | - Huijuan Xu
- State Key Laboratory of Molecular Developmental Biology, and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Wei Ren
- State Key Laboratory of Molecular Developmental Biology, and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qing-Quan Tan
- State Key Laboratory of Molecular Developmental Biology, and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qi Shi
- State Key Laboratory of Molecular Developmental Biology, and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Guo-Dong Wang
- State Key Laboratory of Genetic Resources and Evolution, and Center for Excellence in Animal Evolution and Genetics, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, and Center for Excellence in Animal Evolution and Genetics, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China
| | - Liangxue Lai
- Key Laboratory of Regenerative Biology, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Jidong Mi
- Beijing Sinogene Biotechnology Co. Ltd, Beijing, China
| | - Yong-Hui Jiang
- Department of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, 06510, USA.
| | - Yong Q Zhang
- State Key Laboratory of Molecular Developmental Biology, and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
- School of Life Sciences, Hubei University, Wuhan, China.
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17
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Jankowski MM, Polterovich A, Kazakov A, Niediek J, Nelken I. An automated, low-latency environment for studying the neural basis of behavior in freely moving rats. BMC Biol 2023; 21:172. [PMID: 37568111 PMCID: PMC10416379 DOI: 10.1186/s12915-023-01660-9] [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: 07/21/2022] [Accepted: 07/10/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Behavior consists of the interaction between an organism and its environment, and is controlled by the brain. Brain activity varies at sub-second time scales, but behavioral measures are usually coarse (often consisting of only binary trial outcomes). RESULTS To overcome this mismatch, we developed the Rat Interactive Foraging Facility (RIFF): a programmable interactive arena for freely moving rats with multiple feeding areas, multiple sound sources, high-resolution behavioral tracking, and simultaneous electrophysiological recordings. The paper provides detailed information about the construction of the RIFF and the software used to control it. To illustrate the flexibility of the RIFF, we describe two complex tasks implemented in the RIFF, a foraging task and a sound localization task. Rats quickly learned to obtain rewards in both tasks. Neurons in the auditory cortex as well as neurons in the auditory field in the posterior insula had sound-driven activity during behavior. Remarkably, neurons in both structures also showed sensitivity to non-auditory parameters such as location in the arena and head-to-body angle. CONCLUSIONS The RIFF provides insights into the cognitive capabilities and learning mechanisms of rats and opens the way to a better understanding of how brains control behavior. The ability to do so depends crucially on the combination of wireless electrophysiology and detailed behavioral documentation available in the RIFF.
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Affiliation(s)
- Maciej M Jankowski
- The Edmond and Lily Safra Center for Brain Sciences and the Department of Neurobiology, Silberman Institute of Life Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
- BioTechMed Center, Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland
| | - Ana Polterovich
- The Edmond and Lily Safra Center for Brain Sciences and the Department of Neurobiology, Silberman Institute of Life Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alex Kazakov
- The Edmond and Lily Safra Center for Brain Sciences and the Department of Neurobiology, Silberman Institute of Life Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Johannes Niediek
- The Edmond and Lily Safra Center for Brain Sciences and the Department of Neurobiology, Silberman Institute of Life Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Israel Nelken
- The Edmond and Lily Safra Center for Brain Sciences and the Department of Neurobiology, Silberman Institute of Life Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel.
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18
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Deserno M, Bozek K. WormSwin: Instance segmentation of C. elegans using vision transformer. Sci Rep 2023; 13:11021. [PMID: 37419938 PMCID: PMC10328995 DOI: 10.1038/s41598-023-38213-7] [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: 05/15/2023] [Accepted: 07/05/2023] [Indexed: 07/09/2023] Open
Abstract
The possibility to extract motion of a single organism from video recordings at a large-scale provides means for the quantitative study of its behavior, both individual and collective. This task is particularly difficult for organisms that interact with one another, overlap, and occlude parts of their bodies in the recording. Here we propose WormSwin-an approach to extract single animal postures of Caenorhabditis elegans (C. elegans) from recordings of many organisms in a single microscope well. Based on transformer neural network architecture our method segments individual worms across a range of videos and images generated in different labs. Our solutions offers accuracy of 0.990 average precision ([Formula: see text]) and comparable results on the benchmark image dataset BBBC010. Finally, it allows to segment challenging overlapping postures of mating worms with an accuracy sufficient to track the organisms with a simple tracking heuristic. An accurate and efficient method for C. elegans segmentation opens up new opportunities for studying of its behaviors previously inaccessible due to the difficulty in the worm extraction from the video frames.
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Affiliation(s)
- Maurice Deserno
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, North Rhine-Westphalia, Germany.
- Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, North Rhine-Westphalia, Germany.
- Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, North Rhine-Westphalia, Germany.
| | - Katarzyna Bozek
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, North Rhine-Westphalia, Germany
- Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, North Rhine-Westphalia, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, North Rhine-Westphalia, Germany
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19
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Mimica B, Tombaz T, Battistin C, Fuglstad JG, Dunn BA, Whitlock JR. Behavioral decomposition reveals rich encoding structure employed across neocortex in rats. Nat Commun 2023; 14:3947. [PMID: 37402724 DOI: 10.1038/s41467-023-39520-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/16/2023] [Indexed: 07/06/2023] Open
Abstract
The cortical population code is pervaded by activity patterns evoked by movement, but it remains largely unknown how such signals relate to natural behavior or how they might support processing in sensory cortices where they have been observed. To address this we compared high-density neural recordings across four cortical regions (visual, auditory, somatosensory, motor) in relation to sensory modulation, posture, movement, and ethograms of freely foraging male rats. Momentary actions, such as rearing or turning, were represented ubiquitously and could be decoded from all sampled structures. However, more elementary and continuous features, such as pose and movement, followed region-specific organization, with neurons in visual and auditory cortices preferentially encoding mutually distinct head-orienting features in world-referenced coordinates, and somatosensory and motor cortices principally encoding the trunk and head in egocentric coordinates. The tuning properties of synaptically coupled cells also exhibited connection patterns suggestive of area-specific uses of pose and movement signals, particularly in visual and auditory regions. Together, our results indicate that ongoing behavior is encoded at multiple levels throughout the dorsal cortex, and that low-level features are differentially utilized by different regions to serve locally relevant computations.
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Affiliation(s)
- Bartul Mimica
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, 100190, NJ, USA.
| | - Tuçe Tombaz
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Olav Kyrres Gate 9, 7030, Trondheim, Norway
| | - Claudia Battistin
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Olav Kyrres Gate 9, 7030, Trondheim, Norway
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Jingyi Guo Fuglstad
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Olav Kyrres Gate 9, 7030, Trondheim, Norway
| | - Benjamin A Dunn
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Olav Kyrres Gate 9, 7030, Trondheim, Norway
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Jonathan R Whitlock
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Olav Kyrres Gate 9, 7030, Trondheim, Norway.
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20
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Huang Z, Chung M, Tao K, Watarai A, Wang MY, Ito H, Okuyama T. Ventromedial prefrontal neurons represent self-states shaped by vicarious fear in male mice. Nat Commun 2023; 14:3458. [PMID: 37400435 DOI: 10.1038/s41467-023-39081-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 05/26/2023] [Indexed: 07/05/2023] Open
Abstract
Perception of fear induced by others in danger elicits complex vicarious fear responses and behavioral outputs. In rodents, observing a conspecific receive aversive stimuli leads to escape and freezing behavior. It remains unclear how these behavioral self-states in response to others in fear are neurophysiologically represented. Here, we assess such representations in the ventromedial prefrontal cortex (vmPFC), an essential site for empathy, in an observational fear (OF) paradigm in male mice. We classify the observer mouse's stereotypic behaviors during OF using a machine-learning approach. Optogenetic inhibition of the vmPFC specifically disrupts OF-induced escape behavior. In vivo Ca2+ imaging reveals that vmPFC neural populations represent intermingled information of other- and self-states. Distinct subpopulations are activated and suppressed by others' fear responses, simultaneously representing self-freezing states. This mixed selectivity requires inputs from the anterior cingulate cortex and the basolateral amygdala to regulate OF-induced escape behavior.
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Affiliation(s)
- Ziyan Huang
- Laboratory of Behavioral Neuroscience, Institute for Quantitative Biosciences (IQB), The University of Tokyo, Tokyo, Japan
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Myung Chung
- Laboratory of Behavioral Neuroscience, Institute for Quantitative Biosciences (IQB), The University of Tokyo, Tokyo, Japan
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kentaro Tao
- Laboratory of Behavioral Neuroscience, Institute for Quantitative Biosciences (IQB), The University of Tokyo, Tokyo, Japan
| | - Akiyuki Watarai
- Laboratory of Behavioral Neuroscience, Institute for Quantitative Biosciences (IQB), The University of Tokyo, Tokyo, Japan
| | - Mu-Yun Wang
- Laboratory of Behavioral Neuroscience, Institute for Quantitative Biosciences (IQB), The University of Tokyo, Tokyo, Japan
| | - Hiroh Ito
- Laboratory of Behavioral Neuroscience, Institute for Quantitative Biosciences (IQB), The University of Tokyo, Tokyo, Japan
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Teruhiro Okuyama
- Laboratory of Behavioral Neuroscience, Institute for Quantitative Biosciences (IQB), The University of Tokyo, Tokyo, Japan.
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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21
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Voloh B, Eisenreich BR, Maisson DJN, Ebitz RB, Park HS, Hayden BY, Zimmermann J. Hierarchical organization of rhesus macaque behavior. OXFORD OPEN NEUROSCIENCE 2023; 2:kvad006. [PMID: 37577290 PMCID: PMC10421634 DOI: 10.1093/oons/kvad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/11/2023] [Accepted: 06/12/2023] [Indexed: 08/15/2023]
Abstract
Primatologists, psychologists and neuroscientists have long hypothesized that primate behavior is highly structured. However, delineating that structure has been impossible due to the difficulties of precision behavioral tracking. Here we analyzed a dataset consisting of continuous measures of the 3D position of two male rhesus macaques (Macaca mulatta) performing three different tasks in a large unrestrained environment over several hours. Using an unsupervised embedding approach on the tracked joints, we identified commonly repeated pose patterns, which we call postures. We found that macaques' behavior is characterized by 49 distinct postures, lasting an average of 0.6 seconds. We found evidence that behavior is hierarchically organized, in that transitions between poses tend to occur within larger modules, which correspond to identifiable actions; these actions are further organized hierarchically. Our behavioral decomposition allows us to identify universal (cross-individual and cross-task) and unique (specific to each individual and task) principles of behavior. These results demonstrate the hierarchical nature of primate behavior, provide a method for the automated ethogramming of primate behavior, and provide important constraints on neural models of pose generation.
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Affiliation(s)
- Benjamin Voloh
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - Benjamin R Eisenreich
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - David J-N Maisson
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - R Becket Ebitz
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - Hyun Soo Park
- Department of Computer Science and Engineering, University of Minnesota, 40 Church St, Minneapolis, MN 55455, USA
| | - Benjamin Y Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - Jan Zimmermann
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
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22
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Su F, Wang Y, Wei M, Wang C, Wang S, Yang L, Li J, Yuan P, Luo DG, Zhang C. Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning. Neurosci Bull 2023; 39:893-910. [PMID: 36571715 PMCID: PMC10264345 DOI: 10.1007/s12264-022-00988-6] [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: 05/14/2022] [Accepted: 08/29/2022] [Indexed: 12/27/2022] Open
Abstract
Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions. Traditional tracking methods (e.g., marking each animal with dye or surgically implanting microchips) can be invasive and may have an impact on the social behavior being measured. To overcome these shortcomings, video-based methods for tracking unmarked animals, such as fruit flies and zebrafish, have been developed. However, tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction patterns. In this study, we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network (R-CNN) deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion technology. The system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing behavior. The proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.
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Affiliation(s)
- Feng Su
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing, 100069, China
- Chinese Institute for Brain Research, Beijing, 102206, China
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Nanjing, 210000, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Yangzhen Wang
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Mengping Wei
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing, 100069, China
| | - Chong Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Shaoli Wang
- The Key Laboratory of Developmental Genes and Human Disease, Institute of Life Sciences, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Lei Yang
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing, 100069, China
| | - Jianmin Li
- Institute for Artificial Intelligence, the State Key Laboratory of Intelligence Technology and Systems, Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Peijiang Yuan
- School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.
| | - Dong-Gen Luo
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.
| | - Chen Zhang
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing, 100069, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Nanjing, 210000, China.
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23
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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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24
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Zador A, Escola S, Richards B, Ölveczky B, Bengio Y, Boahen K, Botvinick M, Chklovskii D, Churchland A, Clopath C, DiCarlo J, Ganguli S, Hawkins J, Körding K, Koulakov A, LeCun Y, Lillicrap T, Marblestone A, Olshausen B, Pouget A, Savin C, Sejnowski T, Simoncelli E, Solla S, Sussillo D, Tolias AS, Tsao D. Catalyzing next-generation Artificial Intelligence through NeuroAI. Nat Commun 2023; 14:1597. [PMID: 36949048 PMCID: PMC10033876 DOI: 10.1038/s41467-023-37180-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 03/03/2023] [Indexed: 03/24/2023] Open
Abstract
Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.
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Affiliation(s)
- Anthony Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
| | - Sean Escola
- Department of Psychiatry, Columbia University, New York, NY, 10027, USA
| | - Blake Richards
- Mila, Montréal, QC, H2S 3H1, Canada
- School of Computer Science, McGill University, Montreal, Canada
- Montreal Neurological Institute, McGill University, Montreal, Canada
- Department of Neurology & Neurosurgery, McGill University, Montreal, Canada
- Learning in Machines and Brains Program, CIFAR, Toronto, Canada
| | - Bence Ölveczky
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | | | - Kwabena Boahen
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | | | | | - Anne Churchland
- Department of Neurobiology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, SW7 2BW, UK
| | - James DiCarlo
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA
| | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | | | - Konrad Körding
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Alexei Koulakov
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Yann LeCun
- Meta, Menlo Park, CA, 94025, USA
- Department of Electrical and Computer Engineering, NYU, Brooklyn, NY, 11201, USA
| | | | | | - Bruno Olshausen
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720, USA
| | - Alexandre Pouget
- Department of Basic Neurosciences, University of Geneva, Genève, 1211, Switzerland
| | - Cristina Savin
- Center for Neural Science, NYU, New York, NY, 10003, USA
| | | | - Eero Simoncelli
- Departments of Neural Science, Mathematics, and Psychology, NYU, New York, NY, 10003, USA
| | - Sara Solla
- Department of Physiology, Northwestern University, Chicago, IL, 60611, USA
| | - David Sussillo
- Meta, Menlo Park, CA, 94025, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Doris Tsao
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720, USA
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25
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Ni CL, Lin YT, Lu LY, Wang JH, Liu WC, Kuo SH, Pan MK. Tracking motion kinematics and tremor with intrinsic oscillatory property of instrumental mechanics. Bioeng Transl Med 2023; 8:e10432. [PMID: 36925695 PMCID: PMC10013767 DOI: 10.1002/btm2.10432] [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: 09/02/2022] [Accepted: 10/10/2022] [Indexed: 11/11/2022] Open
Abstract
Tracking kinematic details of motor behaviors is a foundation to study the neuronal mechanism and biology of motor control. However, most of the physiological motor behaviors and movement disorders, such as gait, balance, tremor, dystonia, and myoclonus, are highly dependent on the overall momentum of the whole-body movements. Therefore, tracking the targeted movement and overall momentum simultaneously is critical for motor control research, but it remains an unmet need. Here, we introduce the intrinsic oscillatory property (IOP), a fundamental mechanical principle of physics, as a method for motion tracking in a force plate. The overall kinetic energy of animal motions can be transformed into the oscillatory amplitudes at the designed IOP frequency of the force plate, while the target movement has its own frequency features and can be tracked simultaneously. Using action tremor as an example, we reported that force plate-based IOP approach has superior performance and reliability in detecting both tremor severity and tremor frequency, showing a lower level of coefficient of variation (CV) compared with video- and accelerometer-based motion tracking methods and their combination. Under the locomotor suppression effect of medications, therapeutic effects on tremor severity can still be quantified by dynamically adjusting the overall locomotor activity detected by IOP. We further validated IOP method in optogenetic-induced movements and natural movements, confirming that IOP can represent the intensity of general rhythmic and nonrhythmic movements, thus it can be generalized as a common approach to study kinematics.
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Affiliation(s)
- Chun-Lun Ni
- Department of Neurology Columbia University New York New York USA.,The Initiative for Columbia Ataxia and Tremor New York New York USA.,Department of Biochemistry and Molecular Biology Indiana University School of Medicine Indianapolis Indiana USA
| | - Yi-Ting Lin
- Molecular Imaging Center, National Taiwan University Taipei City Taiwan.,Department of Psychology National Taiwan University Taipei City Taiwan
| | - Liang-Yin Lu
- Molecular Imaging Center, National Taiwan University Taipei City Taiwan.,Institute of Biomedical Sciences, Academia Sinica Taipei City Taiwan
| | - Jia-Huei Wang
- Molecular Imaging Center, National Taiwan University Taipei City Taiwan.,Institute of Biomedical Sciences, Academia Sinica Taipei City Taiwan.,Department and Graduate Institute of Pharmacology National Taiwan University College of Medicine Taipei City Taiwan
| | - Wen-Chuan Liu
- Molecular Imaging Center, National Taiwan University Taipei City Taiwan.,Institute of Biomedical Sciences, Academia Sinica Taipei City Taiwan.,Department and Graduate Institute of Pharmacology National Taiwan University College of Medicine Taipei City Taiwan
| | - Sheng-Han Kuo
- Department of Neurology Columbia University New York New York USA.,The Initiative for Columbia Ataxia and Tremor New York New York USA
| | - Ming-Kai Pan
- Molecular Imaging Center, National Taiwan University Taipei City Taiwan.,Institute of Biomedical Sciences, Academia Sinica Taipei City Taiwan.,Department and Graduate Institute of Pharmacology National Taiwan University College of Medicine Taipei City Taiwan.,Department of Medical Research National Taiwan University Hospital Taipei City Taiwan.,Cerebellar Research Center National Taiwan University Hospital, Yun-Lin Branch Yun-Lin Taiwan
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26
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Horrocks EAB, Mareschal I, Saleem AB. Walking humans and running mice: perception and neural encoding of optic flow during self-motion. Philos Trans R Soc Lond B Biol Sci 2023; 378:20210450. [PMID: 36511417 PMCID: PMC9745880 DOI: 10.1098/rstb.2021.0450] [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] [Indexed: 12/15/2022] Open
Abstract
Locomotion produces full-field optic flow that often dominates the visual motion inputs to an observer. The perception of optic flow is in turn important for animals to guide their heading and interact with moving objects. Understanding how locomotion influences optic flow processing and perception is therefore essential to understand how animals successfully interact with their environment. Here, we review research investigating how perception and neural encoding of optic flow are altered during self-motion, focusing on locomotion. Self-motion has been found to influence estimation and sensitivity for optic flow speed and direction. Nonvisual self-motion signals also increase compensation for self-driven optic flow when parsing the visual motion of moving objects. The integration of visual and nonvisual self-motion signals largely follows principles of Bayesian inference and can improve the precision and accuracy of self-motion perception. The calibration of visual and nonvisual self-motion signals is dynamic, reflecting the changing visuomotor contingencies across different environmental contexts. Throughout this review, we consider experimental research using humans, non-human primates and mice. We highlight experimental challenges and opportunities afforded by each of these species and draw parallels between experimental findings. These findings reveal a profound influence of locomotion on optic flow processing and perception across species. This article is part of a discussion meeting issue 'New approaches to 3D vision'.
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Affiliation(s)
- Edward A. B. Horrocks
- Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London WC1H 0AP, UK
| | - Isabelle Mareschal
- School of Biological and Behavioural Sciences, Queen Mary, University of London, London E1 4NS, UK
| | - Aman B. Saleem
- Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London WC1H 0AP, UK
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27
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Ebrahimi AS, Orlowska-Feuer P, Huang Q, Zippo AG, Martial FP, Petersen RS, Storchi R. Three-dimensional unsupervised probabilistic pose reconstruction (3D-UPPER) for freely moving animals. Sci Rep 2023; 13:155. [PMID: 36599877 PMCID: PMC9813182 DOI: 10.1038/s41598-022-25087-4] [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: 07/22/2022] [Accepted: 11/24/2022] [Indexed: 01/05/2023] Open
Abstract
A key step in understanding animal behaviour relies in the ability to quantify poses and movements. Methods to track body landmarks in 2D have made great progress over the last few years but accurate 3D reconstruction of freely moving animals still represents a challenge. To address this challenge here we develop the 3D-UPPER algorithm, which is fully automated, requires no a priori knowledge of the properties of the body and can also be applied to 2D data. We find that 3D-UPPER reduces by [Formula: see text] fold the error in 3D reconstruction of mouse body during freely moving behaviour compared with the traditional triangulation of 2D data. To achieve that, 3D-UPPER performs an unsupervised estimation of a Statistical Shape Model (SSM) and uses this model to constrain the viable 3D coordinates. We show, by using simulated data, that our SSM estimator is robust even in datasets containing up to 50% of poses with outliers and/or missing data. In simulated and real data SSM estimation converges rapidly, capturing behaviourally relevant changes in body shape associated with exploratory behaviours (e.g. with rearing and changes in body orientation). Altogether 3D-UPPER represents a simple tool to minimise errors in 3D reconstruction while capturing meaningful behavioural parameters.
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Affiliation(s)
- Aghileh S. Ebrahimi
- grid.5379.80000000121662407Division of Neuroscience, School of Biological Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Patrycja Orlowska-Feuer
- grid.5379.80000000121662407Division of Neuroscience, School of Biological Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Qian Huang
- grid.5379.80000000121662407Division of Neuroscience, School of Biological Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Antonio G. Zippo
- grid.454291.f0000 0004 1781 1192Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Milan, Italy
| | - Franck P. Martial
- grid.5379.80000000121662407Division of Neuroscience, School of Biological Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Rasmus S. Petersen
- grid.5379.80000000121662407Division of Neuroscience, School of Biological Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Riccardo Storchi
- grid.5379.80000000121662407Division of Neuroscience, School of Biological Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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28
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Cregg JM, Mirdamadi JL, Fortunato C, Okorokova EV, Kuper C, Nayeem R, Byun AJ, Avraham C, Buonocore A, Winner TS, Mildren RL. Highlights from the 31st Annual Meeting of the Society for the Neural Control of Movement. J Neurophysiol 2023; 129:220-234. [PMID: 36541602 PMCID: PMC9844973 DOI: 10.1152/jn.00500.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Jared M Cregg
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jasmine L Mirdamadi
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Cátia Fortunato
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | | | - Clara Kuper
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rashida Nayeem
- Department of Electrical Engineering, Northeastern University, Boston, Massachusetts
| | - Andrew J Byun
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
| | - Chen Avraham
- Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beersheva, Israel
| | - Antimo Buonocore
- Werner Reichardt Centre for Integrative Neuroscience, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Department of Educational, Psychological and Communication Sciences, Suor Orsola Benincasa University, Naples, Italy
| | - Taniel S Winner
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
| | - Robyn L Mildren
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland
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29
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Jensen KT, Kadmon Harpaz N, Dhawale AK, Wolff SBE, Ölveczky BP. Long-term stability of single neuron activity in the motor system. Nat Neurosci 2022; 25:1664-1674. [PMID: 36357811 PMCID: PMC11152193 DOI: 10.1038/s41593-022-01194-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 10/03/2022] [Indexed: 11/12/2022]
Abstract
How an established behavior is retained and consistently produced by a nervous system in constant flux remains a mystery. One possible solution to ensure long-term stability in motor output is to fix the activity patterns of single neurons in the relevant circuits. Alternatively, activity in single cells could drift over time provided that the population dynamics are constrained to produce the same behavior. To arbitrate between these possibilities, we recorded single-unit activity in motor cortex and striatum continuously for several weeks as rats performed stereotyped motor behaviors-both learned and innate. We found long-term stability in single neuron activity patterns across both brain regions. A small amount of drift in neural activity, observed over weeks of recording, could be explained by concomitant changes in task-irrelevant aspects of the behavior. These results suggest that long-term stable behaviors are generated by single neuron activity patterns that are themselves highly stable.
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Affiliation(s)
- Kristopher T Jensen
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Naama Kadmon Harpaz
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Ashesh K Dhawale
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India
| | - Steffen B E Wolff
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
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30
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Asiminas A, Lyon SA, Langston RF, Wood ER. Developmental trajectory of episodic-like memory in rats. Front Behav Neurosci 2022; 16:969871. [PMID: 36523755 PMCID: PMC9745197 DOI: 10.3389/fnbeh.2022.969871] [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: 06/15/2022] [Accepted: 11/08/2022] [Indexed: 08/17/2023] Open
Abstract
Introduction Episodic memory formation requires the binding of multiple associations to a coherent episodic representation, with rich detail of times, places, and contextual information. During postnatal development, the ability to recall episodic memories emerges later than other types of memory such as object recognition. However, the precise developmental trajectory of episodic memory, from weaning to adulthood has not yet been established in rats. Spontaneous object exploration tasks do not require training, and allow repeated testing of subjects, provided novel objects are used on each trial. Therefore, these tasks are ideally suited for the study of the ontogeny of episodic memory and its constituents (e.g., object, spatial, and contextual memory). Methods In the present study, we used four spontaneous short-term object exploration tasks over two days: object (OR), object-context (OCR), object-place (OPR), and object-place-context (OPCR) recognition to characterise the ontogeny of episodic-like memory and its components in three commonly used outbred rat strains (Lister Hooded, Long Evans Hooded, and Sprague Dawley). Results In longitudinal studies starting at 3-4 weeks of age, we observed that short term memory for objects was already present at the earliest time point we tested, indicating that it is established before the end of the third week of life (consistent with several other reports). Object-context memory developed during the fifth week of life, while both object-in-place and the episodic-like object-place-context memory developed around the seventh postnatal week. To control for the effects of previous experience in the development of associative memory, we confirmed these developmental trajectories using a cross-sectional protocol. Discussion Our work provides robust evidence for different developmental trajectories of recognition memory in rats depending on the content and/or complexity of the associations and emphasises the utility of spontaneous object exploration tasks to assess the ontogeny of memory systems with high temporal resolution.
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Affiliation(s)
- Antonis Asiminas
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
- Patrick Wild Centre, University of Edinburgh, Edinburgh, United Kingdom
- Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark
| | - Stephanie A. Lyon
- Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom
| | - Rosamund F. Langston
- Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom
| | - Emma R. Wood
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
- Patrick Wild Centre, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Brain Development and Repair, Bengaluru, India
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31
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Szechtman H, Dvorkin-Gheva A, Gomez-Marin A. A virtual library for behavioral performance in standard conditions-rodent spontaneous activity in an open field during repeated testing and after treatment with drugs or brain lesions. Gigascience 2022; 11:6756450. [PMID: 36261217 PMCID: PMC9581716 DOI: 10.1093/gigascience/giac092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/31/2022] [Accepted: 09/06/2022] [Indexed: 11/04/2022] Open
Abstract
Background Beyond their specific experiment, video records of behavior have future value—for example, as inputs for new experiments or for yet unknown types of analysis of behavior—similar to tissue or blood sample banks in life sciences where clinically derived or otherwise well-described experimental samples are stored to be available for some unknown potential future purpose. Findings Research using an animal model of obsessive-compulsive disorder employed a standardized paradigm where the behavior of rats in a large open field was video recorded for 55 minutes on each test. From 43 experiments, there are 19,976 such trials that amount to over 2 years of continuous recording. In addition to videos, there are 2 video-derived raw data objects: XY locomotion coordinates and plots of animal trajectory. To motivate future use, the 3 raw data objects are annotated with a general schema—one that abstracts the data records from their particular experiment while providing, at the same time, a detailed list of independent variables bearing on behavioral performance. The raw data objects are deposited as 43 datasets but constitute, functionally, a library containing 1 large dataset. Conclusions Size and annotation schema give the library high reuse potential: in applications using machine learning techniques, statistical evaluation of subtle factors, simulation of new experiments, or as educational resource. Ultimately, the library can serve both as the seed and as the test bed to create a machine-searchable virtual library of linked open datasets for behavioral performance in defined conditions.
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Affiliation(s)
- Henry Szechtman
- Correspondence address. Henry Szechtman, Department of Psychiatry and Behavioural Neurosciences, McMaster University, 1280 Main Street West, Health Science Centre, Room 4N82, Hamilton, Ontario, Canada L8S 4K1. E-mail:
| | - Anna Dvorkin-Gheva
- Department of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario L8S 4K1, Canada
| | - Alex Gomez-Marin
- Department of Systems Neurobiology, Instituto de Neurociencias (CSIC-UMH), 03550 Sant Joan d'Alacant, Alicante, Spain
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32
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Monsees A, Voit KM, Wallace DJ, Sawinski J, Charyasz E, Scheffler K, Macke JH, Kerr JND. Estimation of skeletal kinematics in freely moving rodents. Nat Methods 2022; 19:1500-1509. [PMID: 36253644 DOI: 10.1038/s41592-022-01634-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/02/2022] [Indexed: 11/09/2022]
Abstract
Forming a complete picture of the relationship between neural activity and skeletal kinematics requires quantification of skeletal joint biomechanics during free behavior; however, without detailed knowledge of the underlying skeletal motion, inferring limb kinematics using surface-tracking approaches is difficult, especially for animals where the relationship between the surface and underlying skeleton changes during motion. Here we developed a videography-based method enabling detailed three-dimensional kinematic quantification of an anatomically defined skeleton in untethered freely behaving rats and mice. This skeleton-based model was constrained using anatomical principles and joint motion limits and provided skeletal pose estimates for a range of body sizes, even when limbs were occluded. Model-inferred limb positions and joint kinematics during gait and gap-crossing behaviors were verified by direct measurement of either limb placement or limb kinematics using inertial measurement units. Together we show that complex decision-making behaviors can be accurately reconstructed at the level of skeletal kinematics using our anatomically constrained model.
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Affiliation(s)
- Arne Monsees
- Department of Behavior and Brain Organization, Max Planck Institute for Neurobiology of Behavior, Bonn, Germany.
| | - Kay-Michael Voit
- Department of Behavior and Brain Organization, Max Planck Institute for Neurobiology of Behavior, Bonn, Germany
| | - Damian J Wallace
- Department of Behavior and Brain Organization, Max Planck Institute for Neurobiology of Behavior, Bonn, Germany
| | - Juergen Sawinski
- Department of Behavior and Brain Organization, Max Planck Institute for Neurobiology of Behavior, Bonn, Germany
| | - Edyta Charyasz
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Department for Biomedical Magnetic Resonance, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Klaus Scheffler
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Department for Biomedical Magnetic Resonance, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Jakob H Macke
- Machine Learning in Science, Eberhard Karls University of Tübingen, Tübingen, Germany.,Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Jason N D Kerr
- Department of Behavior and Brain Organization, Max Planck Institute for Neurobiology of Behavior, Bonn, Germany.
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Hayden BY, Park HS, Zimmermann J. Automated pose estimation in primates. Am J Primatol 2022; 84:e23348. [PMID: 34855257 PMCID: PMC9160209 DOI: 10.1002/ajp.23348] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/05/2021] [Accepted: 11/09/2021] [Indexed: 11/11/2022]
Abstract
Understanding the behavior of primates is important for primatology, for psychology, and for biology more broadly. It is also important for biomedicine, where primates are an important model organism, and whose behavior is often an important variable of interest. Our ability to rigorously quantify behavior has, however, long been limited. On one hand, we can rigorously quantify low-information measures like preference, looking time, and reaction time; on the other, we can use more gestalt measures like behavioral categories tracked via ethogram, but at high cost and with high variability. Recent technological advances have led to a major revolution in behavioral measurement that offers affordable and scalable rigor. Specifically, digital video cameras and automated pose tracking software can provide measures of full-body position (i.e., pose) of primates over time (i.e., behavior) with high spatial and temporal resolution. Pose-tracking technology in turn can be used to infer behavioral states, such as eating, sleeping, and mating. We call this technological approach behavioral imaging. In this review, we situate the behavioral imaging revolution in the history of the study of behavior, argue for investment in and development of analytical and research techniques that can profit from the advent of the era of big behavior, and propose that primate centers and zoos will take on a more central role in relevant fields of research than they have in the past.
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Affiliation(s)
- Benjamin Y. Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, Department of Biomedical Engineering
| | - Hyun Soo Park
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis MN 55455
| | - Jan Zimmermann
- Department of Neuroscience, Center for Magnetic Resonance Research, Department of Biomedical Engineering
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Background climate modulates the impact of land cover on urban surface temperature. Sci Rep 2022; 12:15433. [PMID: 36104404 PMCID: PMC9474840 DOI: 10.1038/s41598-022-19431-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/29/2022] [Indexed: 11/09/2022] Open
Abstract
Cities with different background climates experience different thermal environments. Many studies have investigated land cover effects on surface urban heat in individual cities. However, a quantitative understanding of how background climates modify the thermal impact of urban land covers remains elusive. Here, we characterise land cover and their impacts on land surface temperature (LST) for 54 highly populated cities using Landsat-8 imagery. Results show that urban surface characteristics and their thermal response are distinctly different across various climate regimes, with the largest difference for cities in arid climates. Cold cities show the largest seasonal variability, with the least seasonality in tropical and arid cities. In tropical, temperate, and cold climates, normalised difference built-up index (NDBI) is the strongest contributor to LST variability during warm months followed by normalised difference vegetation index (NDVI), while normalised difference bareness index (NDBaI) is the most important factor in arid climates. These findings provide a climate-sensitive basis for future land cover planning oriented at mitigating local surface warming.
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Stoumpou V, Vargas CDM, Schade PF, Boyd JL, Giannakopoulos T, Jarvis ED. Analysis of Mouse Vocal Communication (AMVOC): a deep, unsupervised method for rapid detection, analysis and classification of ultrasonic vocalisations. BIOACOUSTICS 2022. [DOI: 10.1080/09524622.2022.2099973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Vasiliki Stoumpou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - César D. M. Vargas
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
| | - Peter F. Schade
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
- Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA
| | - J. Lomax Boyd
- Berman Institute of Bioethics, Johns Hopkins University, Baltimore, MD, USA
| | - Theodoros Giannakopoulos
- Computational Intelligence Lab, Institute of Informatics and Telecommunications, National Center of Scientific Research 'Demokritos', Athens, Greece
| | - Erich D. Jarvis
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
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36
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Athira A, Dondorp D, Rudolf J, Peytral O, Chatzigeorgiou M. Comprehensive analysis of locomotion dynamics in the protochordate Ciona intestinalis reveals how neuromodulators flexibly shape its behavioral repertoire. PLoS Biol 2022; 20:e3001744. [PMID: 35925898 PMCID: PMC9352054 DOI: 10.1371/journal.pbio.3001744] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 07/06/2022] [Indexed: 11/19/2022] Open
Abstract
Vertebrate nervous systems can generate a remarkable diversity of behaviors. However, our understanding of how behaviors may have evolved in the chordate lineage is limited by the lack of neuroethological studies leveraging our closest invertebrate relatives. Here, we combine high-throughput video acquisition with pharmacological perturbations of bioamine signaling to systematically reveal the global structure of the motor behavioral repertoire in the Ciona intestinalis larvae. Most of Ciona’s postural variance can be captured by 6 basic shapes, which we term “eigencionas.” Motif analysis of postural time series revealed numerous stereotyped behavioral maneuvers including “startle-like” and “beat-and-glide.” Employing computational modeling of swimming dynamics and spatiotemporal embedding of postural features revealed that behavioral differences are generated at the levels of motor modules and the transitions between, which may in part be modulated by bioamines. Finally, we show that flexible motor module usage gives rise to diverse behaviors in response to different light stimuli. Vertebrate nervous systems can generate a remarkable diversity of behaviors, but how did these evolve in the chordate lineage? A study of the protochordate Ciona intestinalis reveals novel insights into how a simple chordate brain uses neuromodulators to control its behavioral repertoire.
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Affiliation(s)
- Athira Athira
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
| | - Daniel Dondorp
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
| | - Jerneja Rudolf
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
| | - Olivia Peytral
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
| | - Marios Chatzigeorgiou
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
- * E-mail:
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Ren W, Wei P, Yu S, Zhang YQ. Left-right asymmetry and attractor-like dynamics of dog’s tail wagging during dog-human interactions. iScience 2022; 25:104747. [PMID: 35942104 PMCID: PMC9356099 DOI: 10.1016/j.isci.2022.104747] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Wei Ren
- State Key Laboratory for Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Pengfei Wei
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Corresponding author
| | - Yong Q. Zhang
- State Key Laboratory for Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Corresponding author
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Mazzucato L. Neural mechanisms underlying the temporal organization of naturalistic animal behavior. eLife 2022; 11:76577. [PMID: 35792884 PMCID: PMC9259028 DOI: 10.7554/elife.76577] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/07/2022] [Indexed: 12/17/2022] Open
Abstract
Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.
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Affiliation(s)
- Luca Mazzucato
- Institute of Neuroscience, Departments of Biology, Mathematics and Physics, University of Oregon
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Knaebe B, Weiss CC, Zimmermann J, Hayden BY. The Promise of Behavioral Tracking Systems for Advancing Primate Animal Welfare. Animals (Basel) 2022; 12:ani12131648. [PMID: 35804547 PMCID: PMC9265027 DOI: 10.3390/ani12131648] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/17/2022] [Accepted: 06/21/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Computerized tracking systems for primates and other animals are one of the great inventions of the 21st century. These systems have already revolutionized the study of primatology, psychology, neuroscience, and biomedicine. Less discussed is that they also promise to greatly enhance animal welfare. Their potential benefits include identifying and reducing pain, suffering, and distress in captive populations, improving laboratory animal welfare, and applying our understanding of animal behavior to increase the “natural” behaviors in captive and wild populations, especially those under threat. We are optimistic that these changes will greatly increase the welfare of primates, including those in laboratories, zoos, primate centers, and in the wild. Abstract Recent years have witnessed major advances in the ability of computerized systems to track the positions of animals as they move through large and unconstrained environments. These systems have so far been a great boon in the fields of primatology, psychology, neuroscience, and biomedicine. Here, we discuss the promise of these technologies for animal welfare. Their potential benefits include identifying and reducing pain, suffering, and distress in captive populations, improving laboratory animal welfare within the context of the three Rs of animal research (reduction, refinement, and replacement), and applying our understanding of animal behavior to increase the “natural” behaviors in captive and wild populations facing human impact challenges. We note that these benefits are often incidental to the designed purpose of these tracking systems, a reflection of the fact that animal welfare is not inimical to research progress, but instead, that the aligned interests between basic research and welfare hold great promise for improvements to animal well-being.
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Schneider A, Zimmermann C, Alyahyay M, Steenbergen F, Brox T, Diester I. 3D pose estimation enables virtual head fixation in freely moving rats. Neuron 2022; 110:2080-2093.e10. [DOI: 10.1016/j.neuron.2022.04.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 01/13/2022] [Accepted: 04/18/2022] [Indexed: 10/18/2022]
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Marshall JD, Li T, Wu JH, Dunn TW. Leaving flatland: Advances in 3D behavioral measurement. Curr Opin Neurobiol 2022; 73:102522. [PMID: 35453000 DOI: 10.1016/j.conb.2022.02.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/25/2022] [Accepted: 02/02/2022] [Indexed: 01/10/2023]
Abstract
Animals move in three dimensions (3D). Thus, 3D measurement is necessary to report the true kinematics of animal movement. Existing 3D measurement techniques draw on specialized hardware, such as motion capture or depth cameras, as well as deep multi-view and monocular computer vision. Continued advances at the intersection of deep learning and computer vision will facilitate 3D tracking across more anatomical features, with less training data, in additional species, and within more natural, occlusive environments. 3D behavioral measurement enables unique applications in phenotyping, investigating the neural basis of behavior, and designing artificial agents capable of imitating animal behavior.
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Affiliation(s)
- Jesse D Marshall
- Harvard University, Department of Organismic and Evolutionary Biology, Cambridge, MA 02138, USA.
| | - Tianqing Li
- Duke University, Pratt School of Engineering, Department of Biomedical Engineering, Durham, NC 27708, USA. https://twitter.com/tianqingxli
| | - Joshua H Wu
- Duke University, Pratt School of Engineering, Department of Biomedical Engineering, Durham, NC 27708, USA
| | - Timothy W Dunn
- Duke University, Pratt School of Engineering, Department of Biomedical Engineering, Durham, NC 27708, USA.
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Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets. Int J Mol Sci 2022; 23:ijms23073811. [PMID: 35409169 PMCID: PMC8998543 DOI: 10.3390/ijms23073811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/26/2022] [Accepted: 03/29/2022] [Indexed: 12/10/2022] Open
Abstract
Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.
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Overman KE, Choi DM, Leung K, Shaevitz JW, Berman GJ. Measuring the repertoire of age-related behavioral changes in Drosophila melanogaster. PLoS Comput Biol 2022; 18:e1009867. [PMID: 35202388 PMCID: PMC8903287 DOI: 10.1371/journal.pcbi.1009867] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 03/08/2022] [Accepted: 01/25/2022] [Indexed: 11/18/2022] Open
Abstract
Aging affects almost all aspects of an organism—its morphology, its physiology, its behavior. Isolating which biological mechanisms are regulating these changes, however, has proven difficult, potentially due to our inability to characterize the full repertoire of an animal’s behavior across the lifespan. Using data from fruit flies (D. melanogaster) we measure the full repertoire of behaviors as a function of age. We observe a sexually dimorphic pattern of changes in the behavioral repertoire during aging. Although the stereotypy of the behaviors and the complexity of the repertoire overall remains relatively unchanged, we find evidence that the observed alterations in behavior can be explained by changing the fly’s overall energy budget, suggesting potential connections between metabolism, aging, and behavior. Aging is a ubiquitous biological phenomenon that affects many aspects of an animal’s appearance, physiology, and behavior. Our understanding of how changes in physiology lead to behavioral changes, however, has been partially limited by our ability to robustly quantify how behavior alters over timescales of days and weeks. In this study, we measure a large repertoire of behaviors of fruit flies at various ages, finding how the actions the animals perform shift with age. We observe a difference between the aging dynamics of male and female flies, and we show that many of these changes can be explained with a model of energy consumption, leading us to make predictions as to the role of metabolism in changes in aging behavior.
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Affiliation(s)
- Katherine E. Overman
- Department of Physics, Emory University, Atlanta, Georgia, United States of America
| | - Daniel M. Choi
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Kawai Leung
- Department of Physics, Emory University, Atlanta, Georgia, United States of America
| | - Joshua W. Shaevitz
- Department of Physics and Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, United States of America
| | - Gordon J. Berman
- Department of Physics, Emory University, Atlanta, Georgia, United States of America
- Department of Biology, Emory University, Atlanta, Georgia, United States of America
- * E-mail:
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Ebbesen CL, Froemke RC. Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography. Nat Commun 2022; 13:593. [PMID: 35105858 PMCID: PMC8807631 DOI: 10.1038/s41467-022-28153-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 01/06/2022] [Indexed: 12/25/2022] Open
Abstract
Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions and their neural correlates are lacking. Currently, most studies rely on labor-intensive methods such as manual annotation. Scalable and objective tracking methods are required to understand the neural circuits underlying social behavior. Here we describe a hardware/software system and analysis pipeline that combines 3D videography, deep learning, physical modeling, and GPU-accelerated robust optimization, with automatic analysis of neuronal receptive fields recorded in interacting mice. Our system ("3DDD Social Mouse Tracker") is capable of fully automatic multi-animal tracking with minimal errors (including in complete darkness) during complex, spontaneous social encounters, together with simultaneous electrophysiological recordings. We capture posture dynamics of multiple unmarked mice with high spatiotemporal precision (~2 mm, 60 frames/s). A statistical model that relates 3D behavior and neural activity reveals multiplexed 'social receptive fields' of neurons in barrel cortex. Our approach could be broadly useful for neurobehavioral studies of multiple animals interacting in complex low-light environments.
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Affiliation(s)
- Christian L Ebbesen
- Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, NY, 10016, USA.
- Neuroscience Institute, New York University School of Medicine, New York, NY, 10016, USA.
- Department of Otolaryngology, New York University School of Medicine, New York, NY, 10016, USA.
- Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, 10016, USA.
- Center for Neural Science, New York University, New York, NY, 10003, USA.
| | - Robert C Froemke
- Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, NY, 10016, USA.
- Neuroscience Institute, New York University School of Medicine, New York, NY, 10016, USA.
- Department of Otolaryngology, New York University School of Medicine, New York, NY, 10016, USA.
- Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, 10016, USA.
- Center for Neural Science, New York University, New York, NY, 10003, USA.
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Sainburg T, Gentner TQ. Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions. Front Behav Neurosci 2021; 15:811737. [PMID: 34987365 PMCID: PMC8721140 DOI: 10.3389/fnbeh.2021.811737] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 11/29/2021] [Indexed: 11/23/2022] Open
Abstract
Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.
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Affiliation(s)
- Tim Sainburg
- Department of Psychology, University of California, San Diego, La Jolla, CA, United States
- Center for Academic Research & Training in Anthropogeny, University of California, San Diego, La Jolla, CA, United States
| | - Timothy Q. Gentner
- Department of Psychology, University of California, San Diego, La Jolla, CA, United States
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, United States
- Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, United States
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Abstract
Modeling of metastatic disease in animal models is a critical resource to study the complexity of this multi-step process in a relevant system. Available models of metastatic disease to the brain are still far from ideal but they allow to address specific aspects of the biology or mimic clinically relevant scenarios. We not only review experimental models and their potential improvements but also discuss specific answers that could be obtained from them on unsolved aspects of clinical management.
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Affiliation(s)
- Lauritz Miarka
- Brain Metastasis Group, Molecular Oncology Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Manuel Valiente
- Brain Metastasis Group, Molecular Oncology Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
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León A, Hernandez V, Lopez J, Guzman I, Quintero V, Toledo P, Avendaño-Garrido ML, Hernandez-Linares CA, Escamilla E. Beyond Single Discrete Responses: An Integrative and Multidimensional Analysis of Behavioral Dynamics Assisted by Machine Learning. Front Behav Neurosci 2021; 15:681771. [PMID: 34737691 PMCID: PMC8562345 DOI: 10.3389/fnbeh.2021.681771] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 08/26/2021] [Indexed: 11/13/2022] Open
Abstract
Understanding behavioral systems as emergent systems comprising the environment and organism subsystems, include spatial dynamics as a primary dimension in natural settings. Nevertheless, under the standard approaches, the experimental analysis of behavior is based on the single response paradigm and the temporal distribution of discrete responses. Thus, the continuous analysis of spatial behavioral dynamics is a scarcely studied field. The technological advancements in computer vision have opened new methodological perspectives for the continuous sensing of spatial behavior. With the application of such advancements, recent studies suggest that there are multiple features embedded in the spatial dynamics of behavior, such as entropy, and that they are affected by programmed stimuli (e.g., schedules of reinforcement) at least as much as features related to discrete responses. Despite the progress, the characterization of behavioral systems is still segmented, and integrated data analysis and representations between discrete responses and continuous spatial behavior are exiguous in the experimental analysis of behavior. Machine learning advancements, such as t-distributed stochastic neighbor embedding and variable ranking, provide invaluable tools to crystallize an integrated approach for analyzing and representing multidimensional behavioral data. Under this rationale, the present work (1) proposes a multidisciplinary approach for the integrative and multilevel analysis of behavioral systems, (2) provides sensitive behavioral measures based on spatial dynamics and helpful data representations to study behavioral systems, and (3) reveals behavioral aspects usually ignored under the standard approaches in the experimental analysis of behavior. To exemplify and evaluate our approach, the spatial dynamics embedded in phenomena relevant to behavioral science, namely, water-seeking behavior and motivational operations, are examined, showing aspects of behavioral systems hidden until now.
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Affiliation(s)
- Alejandro León
- Comparative Psychology Laboratory, Centro de Estudios e Investigaciones en Conocimiento y Aprendizaje Humano, Universidad Veracruzana, Xalapa, Mexico
| | - Varsovia Hernandez
- Comparative Psychology Laboratory, Centro de Estudios e Investigaciones en Conocimiento y Aprendizaje Humano, Universidad Veracruzana, Xalapa, Mexico
| | - Juan Lopez
- Facultad de Estadística e Informática, Universidad Veracruzana, Xalapa, Mexico
| | - Isiris Guzman
- Comparative Psychology Laboratory, Centro de Estudios e Investigaciones en Conocimiento y Aprendizaje Humano, Universidad Veracruzana, Xalapa, Mexico
| | - Victor Quintero
- Comparative Psychology Laboratory, Centro de Estudios e Investigaciones en Conocimiento y Aprendizaje Humano, Universidad Veracruzana, Xalapa, Mexico
| | - Porfirio Toledo
- Facultad de Matemáticas, Universidad Veracruzana, Xalapa, Mexico
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MouseVenue3D: A Markerless Three-Dimension Behavioral Tracking System for Matching Two-Photon Brain Imaging in Free-Moving Mice. Neurosci Bull 2021; 38:303-317. [PMID: 34637091 PMCID: PMC8975979 DOI: 10.1007/s12264-021-00778-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 06/23/2021] [Indexed: 10/20/2022] Open
Abstract
Understanding the connection between brain and behavior in animals requires precise monitoring of their behaviors in three-dimensional (3-D) space. However, there is no available three-dimensional behavior capture system that focuses on rodents. Here, we present MouseVenue3D, an automated and low-cost system for the efficient capture of 3-D skeleton trajectories in markerless rodents. We improved the most time-consuming step in 3-D behavior capturing by developing an automatic calibration module. Then, we validated this process in behavior recognition tasks, and showed that 3-D behavioral data achieved higher accuracy than 2-D data. Subsequently, MouseVenue3D was combined with fast high-resolution miniature two-photon microscopy for synchronous neural recording and behavioral tracking in the freely-moving mouse. Finally, we successfully decoded spontaneous neuronal activity from the 3-D behavior of mice. Our findings reveal that subtle, spontaneous behavior modules are strongly correlated with spontaneous neuronal activity patterns.
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Williams AH, Linderman SW. Statistical neuroscience in the single trial limit. Curr Opin Neurobiol 2021; 70:193-205. [PMID: 34861596 DOI: 10.1016/j.conb.2021.10.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 09/29/2021] [Accepted: 10/27/2021] [Indexed: 11/24/2022]
Abstract
Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic 'noise' and systematic changes in the animal's cognitive and behavioral state. Disentangling these sources of variability is of great scientific interest in its own right, but it is also increasingly inescapable as neuroscientists aspire to study more complex and naturalistic animal behaviors. In these settings, behavioral actions never repeat themselves exactly and may rarely do so even approximately. Thus, new statistical methods that extract reliable features of neural activity using few, if any, repeated trials are needed. Accurate statistical modeling in this severely trial-limited regime is challenging, but still possible if simplifying structure in neural data can be exploited. We review recent works that have identified different forms of simplifying structure - including shared gain modulations across neural subpopulations, temporal smoothness in neural firing rates, and correlations in responses across behavioral conditions - and exploited them to reveal novel insights into the trial-by-trial operation of neural circuits.
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Affiliation(s)
- Alex H Williams
- Department of Statistics and Wu Tsai Neurosciences Institute, Stanford University, USA
| | - Scott W Linderman
- Department of Statistics and Wu Tsai Neurosciences Institute, Stanford University, USA.
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Karashchuk P, Rupp KL, Dickinson ES, Walling-Bell S, Sanders E, Azim E, Brunton BW, Tuthill JC. Anipose: A toolkit for robust markerless 3D pose estimation. Cell Rep 2021; 36:109730. [PMID: 34592148 PMCID: PMC8498918 DOI: 10.1016/j.celrep.2021.109730] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/15/2021] [Accepted: 08/27/2021] [Indexed: 01/12/2023] Open
Abstract
Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method DeepLabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. To help users get started with 3D tracking, we provide tutorials and documentation at http://anipose.org/.
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Affiliation(s)
- Pierre Karashchuk
- Neuroscience Graduate Program, University of Washington, Seattle, WA, USA
| | - Katie L. Rupp
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Evyn S. Dickinson
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sarah Walling-Bell
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Elischa Sanders
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bingni W. Brunton
- Department of Biology, University of Washington, Seattle, WA, USA,Senior author,Correspondence: (B.W.B.), (J.C.T.)
| | - John C. Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA,Senior author,Lead contact,Correspondence: (B.W.B.), (J.C.T.)
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