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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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Ferrari PF, Baldi J. Social neuroscience: Primate research goes wireless. Curr Biol 2024; 34:R536-R539. [PMID: 38834026 DOI: 10.1016/j.cub.2024.04.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
A new study leads the way to a more ethical and ethologically meaningful way of investigating brain functions of complex behaviors in social animals.
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Affiliation(s)
- Pier Francesco Ferrari
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, 69675 Bron Cedex, France; Université Claude Bernard Lyon 1, 69622 Lyon Cedex, France; Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy.
| | - Jacopo Baldi
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, 69675 Bron Cedex, France; Université Claude Bernard Lyon 1, 69622 Lyon Cedex, France
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Cisek P, Green AM. Toward a neuroscience of natural behavior. Curr Opin Neurobiol 2024; 86:102859. [PMID: 38583263 DOI: 10.1016/j.conb.2024.102859] [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: 11/24/2023] [Accepted: 03/04/2024] [Indexed: 04/09/2024]
Abstract
One of the most exciting new developments in systems neuroscience is the progress being made toward neurophysiological experiments that move beyond simplified laboratory settings and address the richness of natural behavior. This is enabled by technological advances such as wireless recording in freely moving animals, automated quantification of behavior, and new methods for analyzing large data sets. Beyond new empirical methods and data, however, there is also a need for new theories and concepts to interpret that data. Such theories need to address the particular challenges of natural behavior, which often differ significantly from the scenarios studied in traditional laboratory settings. Here, we discuss some strategies for developing such novel theories and concepts and some example hypotheses being proposed.
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Affiliation(s)
- Paul Cisek
- Department of Neuroscience, University of Montréal, Montréal, Québec, Canada.
| | - Andrea M Green
- Department of Neuroscience, University of Montréal, Montréal, Québec, Canada
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Monosov IE, Zimmermann J, Frank MJ, Mathis MW, Baker JT. Ethological computational psychiatry: Challenges and opportunities. Curr Opin Neurobiol 2024; 86:102881. [PMID: 38696972 PMCID: PMC11162904 DOI: 10.1016/j.conb.2024.102881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 05/04/2024]
Abstract
Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.
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Affiliation(s)
- Ilya E. Monosov
- Departments of Neuroscience, Biomedical Engineering, Electrical Engineering, and Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Michael J. Frank
- Carney Center for Computational Brain Science, Brown University, Providence, RI, USA
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Fine JM, Yoo SBM, Hayden BY. Control over a mixture of policies determines change of mind topology during continuous choice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590154. [PMID: 38712284 PMCID: PMC11071291 DOI: 10.1101/2024.04.18.590154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Behavior is naturally organized into categorically distinct states with corresponding patterns of neural activity; how does the brain control those states? We propose that states are regulated by specific neural processes that implement meta-control that can blend simpler control processes. To test this hypothesis, we recorded from neurons in the dorsal anterior cingulate cortex (dACC) and dorsal premotor cortex (PMd) while macaques performed a continuous pursuit task with two moving prey that followed evasive strategies. We used a novel control theoretic approach to infer subjects' moment-to-moment latent control variables, which in turn dictated their blend of distinct identifiable control processes. We identified low-dimensional subspaces in neuronal responses that reflected the current strategy, the value of the pursued target, and the relative value of the two targets. The top two principal components of activity tracked changes of mind in abstract and change-type-specific formats, respectively. These results indicate that control of behavioral state reflects the interaction of brain processes found in dorsal prefrontal regions that implement a mixture over low-level control policies.
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Shahidi N, Franch M, Parajuli A, Schrater P, Wright A, Pitkow X, Dragoi V. Population coding of strategic variables during foraging in freely moving macaques. Nat Neurosci 2024; 27:772-781. [PMID: 38443701 PMCID: PMC11001579 DOI: 10.1038/s41593-024-01575-w] [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: 07/24/2021] [Accepted: 01/09/2024] [Indexed: 03/07/2024]
Abstract
Until now, it has been difficult to examine the neural bases of foraging in naturalistic environments because previous approaches have relied on restrained animals performing trial-based foraging tasks. Here we allowed unrestrained monkeys to freely interact with concurrent reward options while we wirelessly recorded population activity in the dorsolateral prefrontal cortex. The animals decided when and where to forage based on whether their prediction of reward was fulfilled or violated. This prediction was not solely based on a history of reward delivery, but also on the understanding that waiting longer improves the chance of reward. The task variables were continuously represented in a subspace of the high-dimensional population activity, and this compressed representation predicted the animal's subsequent choices better than the true task variables and as well as the raw neural activity. Our results indicate that monkeys' foraging strategies are based on a cortical model of reward dynamics as animals freely explore their environment.
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Affiliation(s)
- Neda Shahidi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
- Georg-Elias-Müller-Institute for Psychology, Georg August-Universität, Göttingen, Germany
- Cognitive Neuroscience Laboratory, German Primate Center, Göttingen, Germany
| | - Melissa Franch
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
| | - Arun Parajuli
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
| | - Paul Schrater
- Department of Computer Science, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Anthony Wright
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Neuroengineering Initiative, Rice University, Houston, TX, USA.
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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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Li J, Aoi MC, Miller CT. Representing the dynamics of natural marmoset vocal behaviors in frontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585423. [PMID: 38559173 PMCID: PMC10979968 DOI: 10.1101/2024.03.17.585423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Here we tested the respective contributions of primate premotor and prefrontal cortex to support vocal behavior. We applied a model-based GLM analysis that better accounts for the inherent variance in natural, continuous behaviors to characterize the activity of neurons throughout frontal cortex as freely-moving marmosets engaged in conversational exchanges. While analyses revealed functional clusters of neural activity related to the different processes involved in the vocal behavior, these clusters did not map to subfields of prefrontal or premotor cortex, as has been observed in more conventional task-based paradigms. Our results suggest a distributed functional organization for the myriad neural mechanisms underlying natural social interactions and has implications for our concepts of the role that frontal cortex plays in governing ethological behaviors in primates.
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Manea AMG, Maisson DJN, Voloh B, Zilverstand A, Hayden B, Zimmermann J. Neural timescales reflect behavioral demands in freely moving rhesus macaques. Nat Commun 2024; 15:2151. [PMID: 38461167 PMCID: PMC10925022 DOI: 10.1038/s41467-024-46488-1] [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: 04/03/2023] [Accepted: 02/28/2024] [Indexed: 03/11/2024] Open
Abstract
Previous work demonstrated a highly reproducible cortical hierarchy of neural timescales at rest, with sensory areas displaying fast, and higher-order association areas displaying slower timescales. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this lack of variability in the hierarchical organization of neural timescales could reflect the structure of the laboratory contexts. We posit that unconstrained paradigms are ideal to test whether the dynamics of neural timescales reflect behavioral demands. Here we measured timescales of local field potential activity while male rhesus macaques foraged in an open space. We found a hierarchy of neural timescales that differs from previous work. Importantly, although the magnitude of neural timescales expanded with task engagement, the brain areas' relative position in the hierarchy was stable. Next, we demonstrated that the change in neural timescales is dynamic and contains functionally-relevant information, differentiating between similar events in terms of motor demands and associated reward. Finally, we demonstrated that brain areas are differentially affected by these behavioral demands. These results demonstrate that while the space of neural timescales is anatomically constrained, the observed hierarchical organization and magnitude is dependent on behavioral demands.
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Affiliation(s)
- Ana M G Manea
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
| | - David J-N Maisson
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Benjamin Voloh
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Benjamin Hayden
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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Abbaspoor S, Rahman K, Zinke W, Hoffman KL. Learning of object-in-context sequences in freely-moving macaques. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.11.571113. [PMID: 38168449 PMCID: PMC10760043 DOI: 10.1101/2023.12.11.571113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Flexible learning is a hallmark of primate cognition, which arises through interactions with changing environments. Studies of the neural basis for this flexibility are typically limited by laboratory settings that use minimal environmental cues and restrict interactions with the environment, including active sensing and exploration. To address this, we constructed a 3-D enclosure containing touchscreens on its walls, for studying cognition in freely moving macaques. To test flexible learning, two monkeys completed trials consisting of a regular sequence of object selections across four touchscreens. On each screen, the monkeys had to select by touching the sole correct object item ('target') among a set of four items, irrespective of their positions on the screen. Each item was the target on exactly one screen of the sequence, making correct performance conditioned on the spatiotemporal sequence rule across screens. Both monkeys successfully learned multiple 4-item sets (N=14 and 22 sets), totaling over 50 and 80 unique, conditional item-context memoranda, with no indication of capacity limits. The enclosure allowed freedom of movements leading up to and following the touchscreen interactions. To determine whether movement economy changed with learning, we reconstructed 3D position and movement dynamics using markerless tracking software and gyroscopic inertial measurements. Whereas general body positions remained consistent across repeated sequences, fine head movements varied as monkeys learned, within and across sequence sets, demonstrating learning set or "learning to learn". These results demonstrate monkeys' rapid, capacious, and flexible learning within an integrated, multisensory 3-D space. Furthermore, this approach enables the measurement of continuous behavior while ensuring precise experimental control and behavioral repetition of sequences over time. Overall, this approach harmonizes the design features that are needed for electrophysiological studies with tasks that showcase fully situated, flexible cognition.
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Affiliation(s)
- S Abbaspoor
- Department of Psychological Sciences, Vanderbilt University, Nashville, United States
- Vanderbilt Vision Research Center, Vanderbilt University, Nashville, United States
| | - K Rahman
- Department of Psychological Sciences, Vanderbilt University, Nashville, United States
- Vanderbilt Vision Research Center, Vanderbilt University, Nashville, United States
| | - W Zinke
- Department of Psychological Sciences, Vanderbilt University, Nashville, United States
- Vanderbilt Vision Research Center, Vanderbilt University, Nashville, United States
| | - K L Hoffman
- Department of Psychological Sciences, Vanderbilt University, Nashville, United States
- Vanderbilt Vision Research Center, Vanderbilt University, Nashville, United States
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, United States
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