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Liu J, Ye J, Ji C, Ren W, He Y, Xu F, Wang F. Mapping the Behavioral Signatures of Shank3b Mice in Both Sexes. Neurosci Bull 2024:10.1007/s12264-024-01237-8. [PMID: 38900384 DOI: 10.1007/s12264-024-01237-8] [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: 09/10/2023] [Accepted: 11/20/2023] [Indexed: 06/21/2024] Open
Abstract
Autism spectrum disorders (ASD) are characterized by social and repetitive abnormalities. Although the ASD mouse model with Shank3b mutations is widely used in ASD research, the behavioral phenotype of this model has not been fully elucidated. Here, a 3D-motion capture system and linear discriminant analysis were used to comprehensively record and analyze the behavioral patterns of male and female Shank3b mutant mice. It was found that both sexes replicated the core and accompanied symptoms of ASD, with significant sex differences. Further, Shank3b heterozygous knockout mice exhibited distinct autistic behaviors, that were significantly different from those those observed in the wild type and homozygous knockout groups. Our findings provide evidence for the inclusion of both sexes and experimental approaches to efficiently characterize heterozygous transgenic models, which are more clinically relevant in autistic studies.
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Affiliation(s)
- Jingjing Liu
- NMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal Products, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Shenzhen Key Laboratory of Viral Vectors for Biomedicine, Translational Research Center for the Nervous System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Key Laboratory of Quality Control Technology for Virus-Based Therapeutics, Guangdong Provincial Medical Products Administration, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jialin Ye
- Shenzhen Key Lab of Translational Research for Brain Diseases, Translational Research Center for the Nervous System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Chunyuan Ji
- Shenzhen Key Lab of Translational Research for Brain Diseases, Translational Research Center for the Nervous System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Wenting Ren
- Shenzhen Key Lab of Translational Research for Brain Diseases, Translational Research Center for the Nervous System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Youwei He
- NMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal Products, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Shenzhen Key Laboratory of Viral Vectors for Biomedicine, Translational Research Center for the Nervous System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Key Laboratory of Quality Control Technology for Virus-Based Therapeutics, Guangdong Provincial Medical Products Administration, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Fuqiang Xu
- NMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal Products, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Shenzhen Key Laboratory of Viral Vectors for Biomedicine, Translational Research Center for the Nervous System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Key Laboratory of Quality Control Technology for Virus-Based Therapeutics, Guangdong Provincial Medical Products Administration, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Feng Wang
- NMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal Products, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Shenzhen Key Lab of Translational Research for Brain Diseases, Translational Research Center for the Nervous System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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2
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Wu B, Zhao C, Zheng X, Peng Z, Liu M. Observation of Agonistic Behavior in Pacific White Shrimp ( Litopenaeus vannamei) and Transcriptome Analysis. Animals (Basel) 2024; 14:1691. [PMID: 38891739 PMCID: PMC11171402 DOI: 10.3390/ani14111691] [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: 05/08/2024] [Revised: 06/02/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024] Open
Abstract
Agonistic behavior has been identified as a limiting factor in the development of intensive L. vannamei aquaculture. However, the characteristics and molecular mechanisms underlying agonistic behavior in L. vannamei remain unclear. In this study, we quantified agonistic behavior through a behavioral observation system and generated a comprehensive database of eyestalk and brain ganglion tissues obtained from both aggressive and nonaggressive L. vannamei employing transcriptome analysis. The results showed that there were nine behavior patterns in L. vannamei which were correlated, and the fighting followed a specific process. Transcriptome analysis revealed 5083 differentially expressed genes (DEGs) in eyestalk and 1239 DEGs in brain ganglion between aggressive and nonaggressive L. vannamei. Moreover, these DEGs were primarily enriched in the pathways related to the energy metabolism process and signal transduction. Specifically, the phototransduction (dme04745) signaling pathway emerges as a potential key pathway for the adjustment of the L. vannamei agonistic behavior. The G protein-coupled receptor kinase 1-like (LOC113809193) was screened out as a significant candidate gene within the phototransduction pathway. Therefore, these findings contribute to an enhanced comprehension of crustacean agonistic behavior and provide a theoretical basis for the selection and breeding of L. vannamei varieties suitable for high-density aquaculture environments.
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Affiliation(s)
- Bo Wu
- Ninghai Institute of Mariculture Breeding and Seed Industry, Zhejiang Wanli University, Ningbo 315000, China; (B.W.); (C.Z.); (X.Z.)
| | - Chenxi Zhao
- Ninghai Institute of Mariculture Breeding and Seed Industry, Zhejiang Wanli University, Ningbo 315000, China; (B.W.); (C.Z.); (X.Z.)
| | - Xiafei Zheng
- Ninghai Institute of Mariculture Breeding and Seed Industry, Zhejiang Wanli University, Ningbo 315000, China; (B.W.); (C.Z.); (X.Z.)
| | - Zhilan Peng
- Zhejiang Engineering Research Center for Aquacultural Seeds Industry and Green Cultivation Technologies, College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo 315000, China;
| | - Minhai Liu
- Ninghai Institute of Mariculture Breeding and Seed Industry, Zhejiang Wanli University, Ningbo 315000, China; (B.W.); (C.Z.); (X.Z.)
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3
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Oesch LT, Ryan MB, Churchland AK. From innate to instructed: A new look at perceptual decision-making. Curr Opin Neurobiol 2024; 86:102871. [PMID: 38569230 PMCID: PMC11162954 DOI: 10.1016/j.conb.2024.102871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024]
Abstract
Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as "naturalistic") or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals' pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.
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Affiliation(s)
- Lukas T Oesch
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
| | - Michael B Ryan
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States. https://twitter.com/NeuroMikeRyan
| | - Anne K Churchland
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States.
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4
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Sabnis G, Hession L, Mahoney JM, Mobley A, Santos M, Kumar V. Visual detection of seizures in mice using supervised machine learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596520. [PMID: 38868170 PMCID: PMC11167691 DOI: 10.1101/2024.05.29.596520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
Seizures are caused by abnormally synchronous brain activity that can result in changes in muscle tone, such as twitching, stiffness, limpness, or rhythmic jerking. These behavioral manifestations are clear on visual inspection and the most widely used seizure scoring systems in preclinical models, such as the Racine scale in rodents, use these behavioral patterns in semiquantitative seizure intensity scores. However, visual inspection is time-consuming, low-throughput, and partially subjective, and there is a need for rigorously quantitative approaches that are scalable. In this study, we used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from noninvasive video data. Using the PTZ-induced seizure model in mice, we trained video-only classifiers to predict ictal events, combined these events to predict an univariate seizure intensity for a recording session, as well as time-varying seizure intensity scores. Our results show, for the first time, that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, noninvasive, and standardized seizure scoring for downstream applications such as neurogenetics and therapeutic discovery.
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Affiliation(s)
| | | | | | | | | | - Vivek Kumar
- The Jackson Laboratory, Bar Harbor, ME USA
- School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME USA
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5
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Sridhar G, Vergassola M, Marques JC, Orger MB, Costa AC, Wyart C. Uncovering multiscale structure in the variability of larval zebrafish navigation. ARXIV 2024:arXiv:2405.17143v1. [PMID: 38855549 PMCID: PMC11160889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Animals chain movements into long-lived motor strategies, exhibiting variability across scales that reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build Markov models of movement sequences that bridges across time scales and enables a quantitative comparison of behavioral phenotypes among individuals. Applied to larval zebrafish responding to diverse sensory cues, we uncover a hierarchy of long-lived motor strategies, dominated by changes in orientation distinguishing cruising versus wandering strategies. Environmental cues induce preferences along these modes at the population level: while fish cruise in the light, they wander in response to aversive stimuli, or in search for appetitive prey. As our method encodes the behavioral dynamics of each individual fish in the transitions among coarse-grained motor strategies, we use it to uncover a hierarchical structure in the phenotypic variability that reflects exploration-exploitation trade-offs. Across a wide range of sensory cues, a major source of variation among fish is driven by prior and/or immediate exposure to prey that induces exploitation phenotypes. A large degree of variability that is not explained by environmental cues unravels motivational states that override the sensory context to induce contrasting exploration-exploitation phenotypes. Altogether, by extracting the timescales of motor strategies deployed during navigation, our approach exposes structure among individuals and reveals internal states tuned by prior experience.
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Affiliation(s)
- Gautam Sridhar
- Sorbonne University, Paris Brain Institute (ICM), Inserm U1127, CNRS UMR 7225, Paris, France
| | - Massimo Vergassola
- Laboratoire de Physique de l’Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - João C. Marques
- Champalimaud Research, Champalimaud Centre for the Unknown, Avenida Brasília, Doca de Pedrouços, Lisboa 1400-038, Portugal
| | - Michael B. Orger
- Champalimaud Research, Champalimaud Centre for the Unknown, Avenida Brasília, Doca de Pedrouços, Lisboa 1400-038, Portugal
| | - Antonio Carlos Costa
- Sorbonne University, Paris Brain Institute (ICM), Inserm U1127, CNRS UMR 7225, Paris, France
- Laboratoire de Physique de l’Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Claire Wyart
- Sorbonne University, Paris Brain Institute (ICM), Inserm U1127, CNRS UMR 7225, Paris, France
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6
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Concetti C, Viskaitis P, Grujic N, Duss SN, Privitera M, Bohacek J, Peleg-Raibstein D, Burdakov D. Exploratory Rearing Is Governed by Hypothalamic Melanin-Concentrating Hormone Neurons According to Locus Ceruleus. J Neurosci 2024; 44:e0015242024. [PMID: 38575343 PMCID: PMC11112542 DOI: 10.1523/jneurosci.0015-24.2024] [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: 01/03/2024] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 04/06/2024] Open
Abstract
Information seeking, such as standing on tiptoes to look around in humans, is observed across animals and helps survival. Its rodent analog-unsupported rearing on hind legs-was a classic model in deciphering neural signals of cognition and is of intense renewed interest in preclinical modeling of neuropsychiatric states. Neural signals and circuits controlling this dedicated decision to seek information remain largely unknown. While studying subsecond timing of spontaneous behavioral acts and activity of melanin-concentrating hormone (MCH) neurons (MNs) in behaving male and female mice, we observed large MN activity spikes that aligned to unsupported rears. Complementary causal, loss and gain of function, analyses revealed specific control of rear frequency and duration by MNs and MCHR1 receptors. Activity in a key stress center of the brain-the locus ceruleus noradrenaline cells-rapidly inhibited MNs and required functional MCH receptors for its endogenous modulation of rearing. By defining a neural module that both tracks and controls rearing, these findings may facilitate further insights into biology of information seeking.
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Affiliation(s)
- Cristina Concetti
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Paulius Viskaitis
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Nikola Grujic
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Sian N Duss
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Mattia Privitera
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Johannes Bohacek
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Daria Peleg-Raibstein
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Denis Burdakov
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
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7
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Lv S, Wang J, Chen X, Liao X. STPoseNet: A real-time spatiotemporal network model for robust mouse pose estimation. iScience 2024; 27:109772. [PMID: 38711440 PMCID: PMC11070338 DOI: 10.1016/j.isci.2024.109772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Animal behavior analysis plays a crucial role in contemporary neuroscience research. However, the performance of the frame-by-frame approach may degrade in scenarios with occlusions or motion blur. In this study, we propose a spatiotemporal network model based on YOLOv8 to enhance the accuracy of key-point detection in mouse behavioral experimental videos. This model integrates a time-domain tracking strategy comprising two components: the first part utilizes key-point detection results from the previous frame to detect potential target locations in the subsequent frame; the second part employs Kalman filtering to analyze key-point changes prior to detection, allowing for the estimation of missing key-points. In the comparison of pose estimation results between our approach, YOLOv8, DeepLabCut and SLEAP on videos of three mouse behavioral experiments, our approach demonstrated significantly superior performance. This suggests that our method offers a new and effective means of accurately tracking and estimating pose in mice through spatiotemporal processing.
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Affiliation(s)
- Songyan Lv
- Guangxi Key Laboratory of Special Biomedicine & Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China
| | - Jincheng Wang
- Guangxi Key Laboratory of Special Biomedicine & Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China
| | - Xiaowei Chen
- Guangxi Key Laboratory of Special Biomedicine & Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China
| | - Xiang Liao
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing 400030, China
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8
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Sridhar G, Vergassola M, Marques JC, Orger MB, Costa AC, Wyart C. Uncovering multiscale structure in the variability of larval zebrafish navigation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.594521. [PMID: 38798455 PMCID: PMC11118365 DOI: 10.1101/2024.05.16.594521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Animals chain movements into long-lived motor strategies, resulting in variability that ultimately reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build models that bridges across time scales that enable a quantitative comparison of behavioral phenotypes among individuals. Applied to larval zebrafish exposed to diverse sensory cues, we uncover a hierarchy of long-lived motor strategies, dominated by changes in orientation distinguishing cruising and wandering strategies. Environmental cues induce preferences along these modes at the population level: while fish cruise in the light, they wander in response to aversive (dark) stimuli or in search for prey. Our method enables us to encode the behavioral dynamics of each individual fish in the transitions among coarse-grained motor strategies. By doing so, we uncover a hierarchical structure to the phenotypic variability that corresponds to exploration-exploitation trade-offs. Within a wide range of sensory cues, a major source of variation among fish is driven by prior and immediate exposure to prey that induces exploitation phenotypes. However, a large degree of variability is unexplained by environmental cues, pointing to hidden states that override the sensory context to induce contrasting exploration-exploitation phenotypes. Altogether, our approach extracts the timescales of motor strategies deployed during navigation, exposing undiscovered structure among individuals and pointing to internal states tuned by prior experience.
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9
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Yin C, Melin MD, Rojas-Bowe G, Sun XR, Couto J, Gluf S, Kostiuk A, Musall S, Churchland AK. Spontaneous movements and their impact on neural activity fluctuate with latent engagement states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.26.546404. [PMID: 37425720 PMCID: PMC10327038 DOI: 10.1101/2023.06.26.546404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Existing work demonstrates that animals alternate between engaged and disengaged states during perceptual decision-making. To understand the neural signature of these states, we performed cortex-wide measurements of neural activity in mice making auditory decisions. The trial-averaged magnitude of neural activity was similar in the two states. However, the trial-to-trial variance in neural activity was higher during disengagement. To understand this increased variance, we trained separate linear encoding models on neural data from each state. The models demonstrated that although task variables and task-aligned movements impacted neural activity similarly during the two states, movements that are independent of task events explained more variance during disengagement. Behavioral analyses uncovered that during disengagement, movements become uncoupled to task events. Taken together, these results argue that the neural signature of disengagement, though obscured in trial-averaged neural activity, is evident in trial-to-trial variability driven by changing patterns of spontaneous movements.
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Affiliation(s)
- Chaoqun Yin
- UCLA Neuroscience Interdepartmental Program
- Department of Neurobiology, University of California, Los Angeles
| | - Maxwell D Melin
- UCLA Neuroscience Interdepartmental Program
- UCLA-Caltech Medical Scientist Training Program
- Department of Neurobiology, University of California, Los Angeles
| | - Gabriel Rojas-Bowe
- UCLA Neuroscience Interdepartmental Program
- Department of Neurobiology, University of California, Los Angeles
| | | | - João Couto
- Department of Neurobiology, University of California, Los Angeles
| | | | - Alex Kostiuk
- UCLA Neuroscience Interdepartmental Program
- UCLA-Caltech Medical Scientist Training Program
- Department of Neurobiology, University of California, Los Angeles
| | - Simon Musall
- Institute of Biological Information Processing (IBI-3), Forschungszentrum Jülich
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Grammer J, Valles R, Bowles A, Zelikowsky M. SAUSI: a novel assay for measuring social anxiety and motivation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.13.594023. [PMID: 38798428 PMCID: PMC11118329 DOI: 10.1101/2024.05.13.594023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Social anxiety is one of the most prevalent mental health disorders, though the underlying neurobiology is poorly understood. Progress in understanding the etiology of social anxiety has been hindered by the lack of comprehensive tools to assess social anxiety in model systems. Here, we created a new behavioral task - Selective Access to Unrestricted Social Interaction (SAUSI), which combines elements of social motivation, hesitancy, decision-making, and free interaction to enable the wholistic assessment of social anxiety-like behaviors in mice. Using this novel assay, we found that social isolation-induced social anxiety-like behaviors in female mice are largely driven by increases in social fear, social hesitancy, and altered ultrasonic vocalizations. Deep learning analyses were able to computationally identify a unique behavioral footprint underlying the state produced by social isolation, demonstrating the compatibility of modern computational approaches with SAUSI. Finally, we compared the results of SAUSI to traditionally social assays including the 3-chamber sociability assay and the resident intruder task. This revealed that behavioral changes induced by isolation were highly context dependent, and that while fragments of social anxiety measured in SAUSI were replicable across other tasks, a wholistic assessment was not obtainable from these alternative assays. Our findings debut a novel task for the behavioral toolbox - one which overcomes limitations of previous assays, allowing for both social choice as well as free interaction, and offers a new approach for assessing social anxiety in rodents.
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Affiliation(s)
- Jordan Grammer
- Department of Neurobiology, University of Utah, United States
| | - Rene Valles
- Department of Neurobiology, University of Utah, United States
| | - Alexis Bowles
- Department of Neurobiology, University of Utah, United States
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11
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Nasello C, Poppi LA, Wu J, Kowalski TF, Thackray JK, Wang R, Persaud A, Mahboob M, Lin S, Spaseska R, Johnson CK, Gordon D, Tissir F, Heiman GA, Tischfield JA, Bocarsly M, Tischfield MA. Human mutations in high-confidence Tourette disorder genes affect sensorimotor behavior, reward learning, and striatal dopamine in mice. Proc Natl Acad Sci U S A 2024; 121:e2307156121. [PMID: 38683996 PMCID: PMC11087812 DOI: 10.1073/pnas.2307156121] [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: 05/05/2023] [Accepted: 02/13/2024] [Indexed: 05/02/2024] Open
Abstract
Tourette disorder (TD) is poorly understood, despite affecting 1/160 children. A lack of animal models possessing construct, face, and predictive validity hinders progress in the field. We used CRISPR/Cas9 genome editing to generate mice with mutations orthologous to human de novo variants in two high-confidence Tourette genes, CELSR3 and WWC1. Mice with human mutations in Celsr3 and Wwc1 exhibit cognitive and/or sensorimotor behavioral phenotypes consistent with TD. Sensorimotor gating deficits, as measured by acoustic prepulse inhibition, occur in both male and female Celsr3 TD models. Wwc1 mice show reduced prepulse inhibition only in females. Repetitive motor behaviors, common to Celsr3 mice and more pronounced in females, include vertical rearing and grooming. Sensorimotor gating deficits and rearing are attenuated by aripiprazole, a partial agonist at dopamine type II receptors. Unsupervised machine learning reveals numerous changes to spontaneous motor behavior and less predictable patterns of movement. Continuous fixed-ratio reinforcement shows that Celsr3 TD mice have enhanced motor responding and reward learning. Electrically evoked striatal dopamine release, tested in one model, is greater. Brain development is otherwise grossly normal without signs of striatal interneuron loss. Altogether, mice expressing human mutations in high-confidence TD genes exhibit face and predictive validity. Reduced prepulse inhibition and repetitive motor behaviors are core behavioral phenotypes and are responsive to aripiprazole. Enhanced reward learning and motor responding occur alongside greater evoked dopamine release. Phenotypes can also vary by sex and show stronger affection in females, an unexpected finding considering males are more frequently affected in TD.
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Affiliation(s)
- Cara Nasello
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ08854
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ08854
| | - Lauren A. Poppi
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ08854
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ08854
- Child Health Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ08901
| | - Junbing Wu
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ08854
- Child Health Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ08901
| | - Tess F. Kowalski
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ08854
- Child Health Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ08901
| | - Joshua K. Thackray
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ08854
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ08854
| | - Riley Wang
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ08854
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ08854
| | - Angelina Persaud
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ08854
- Child Health Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ08901
| | - Mariam Mahboob
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers New Jersey Medical School and Rutgers Biomedical and Health Sciences, Newark, NJ07103
| | - Sherry Lin
- Department of Neurobiology, Harvard Medical School, Boston, MA02115
| | - Rodna Spaseska
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ08854
| | - C. K. Johnson
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ08854
| | - Derek Gordon
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ08854
| | - Fadel Tissir
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha34110, Qatar
- Laboratory of Developmental Neurobiology, Institute of Neuroscience, Université Catholique de Louvain, Brussels1200, Belgium
| | - Gary A. Heiman
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ08854
| | - Jay A. Tischfield
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ08854
| | - Miriam Bocarsly
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers New Jersey Medical School and Rutgers Biomedical and Health Sciences, Newark, NJ07103
| | - Max A. Tischfield
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ08854
- Child Health Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ08901
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12
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Ahmedt-Aristizabal D, Armin MA, Hayder Z, Garcia-Cairasco N, Petersson L, Fookes C, Denman S, McGonigal A. Deep learning approaches for seizure video analysis: A review. Epilepsy Behav 2024; 154:109735. [PMID: 38522192 DOI: 10.1016/j.yebeh.2024.109735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/06/2024] [Accepted: 03/03/2024] [Indexed: 03/26/2024]
Abstract
Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis. Each module can be customized and improved by adapting more accurate and robust deep learning approaches as these evolve. Finally, we discuss challenges and research directions for future studies.
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Affiliation(s)
- David Ahmedt-Aristizabal
- Imaging and Computer Vision Group, CSIRO Data61, Australia; SAIVT Laboratory, Queensland University of Technology, Australia.
| | | | - Zeeshan Hayder
- Imaging and Computer Vision Group, CSIRO Data61, Australia.
| | - Norberto Garcia-Cairasco
- Physiology Department and Neuroscience and Behavioral Sciences Department, Ribeirão Preto Medical School, University of São Paulo, Brazil.
| | - Lars Petersson
- Imaging and Computer Vision Group, CSIRO Data61, Australia.
| | - Clinton Fookes
- SAIVT Laboratory, Queensland University of Technology, Australia.
| | - Simon Denman
- SAIVT Laboratory, Queensland University of Technology, Australia.
| | - Aileen McGonigal
- Neurosciences Centre, Mater Hospital, Australia; Queensland Brain Institute, The University of Queensland, Australia.
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13
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Chen C, Altafi M, Corbu MA, Trenk A, van den Munkhof H, Weineck K, Bender F, Carus-Cadavieco M, Bakhareva A, Korotkova T, Ponomarenko A. The dynamic state of a prefrontal-hypothalamic-midbrain circuit commands behavioral transitions. Nat Neurosci 2024; 27:952-963. [PMID: 38499854 PMCID: PMC11089001 DOI: 10.1038/s41593-024-01598-3] [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: 08/26/2022] [Accepted: 02/12/2024] [Indexed: 03/20/2024]
Abstract
Innate behaviors meet multiple needs adaptively and in a serial order, suggesting the existence of a hitherto elusive brain dynamics that brings together representations of upcoming behaviors during their selection. Here we show that during behavioral transitions, possible upcoming behaviors are encoded by specific signatures of neuronal populations in the lateral hypothalamus (LH) that are active near beta oscillation peaks. Optogenetic recruitment of intrahypothalamic inhibition at this phase eliminates behavioral transitions. We show that transitions are elicited by beta-rhythmic inputs from the prefrontal cortex that spontaneously synchronize with LH 'transition cells' encoding multiple behaviors. Downstream of the LH, dopamine neurons increase firing during beta oscillations and also encode behavioral transitions. Thus, a hypothalamic transition state signals alternative future behaviors, encodes the one most likely to be selected and enables rapid coordination with cognitive and reward-processing circuitries, commanding adaptive social contact and eating behaviors.
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Affiliation(s)
- Changwan Chen
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Institute for Systems Physiology, Faculty of Medicine, University of Cologne/University Clinic Cologne, Cologne, Germany
| | - Mahsa Altafi
- Institute of Physiology and Pathophysiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mihaela-Anca Corbu
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Institute for Systems Physiology, Faculty of Medicine, University of Cologne/University Clinic Cologne, Cologne, Germany
| | - Aleksandra Trenk
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Department of Neurophysiology and Chronobiology, Institute of Zoology and Biomedical Research, Jagiellonian University, Krakow, Poland
| | - Hanna van den Munkhof
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Institute for Systems Physiology, Faculty of Medicine, University of Cologne/University Clinic Cologne, Cologne, Germany
| | - Kristin Weineck
- Behavioural Neurodynamics Group, Leibniz Institute for Molecular Pharmacology (FMP)/NeuroCure Cluster of Excellence, Berlin, Germany
| | - Franziska Bender
- Behavioural Neurodynamics Group, Leibniz Institute for Molecular Pharmacology (FMP)/NeuroCure Cluster of Excellence, Berlin, Germany
| | - Marta Carus-Cadavieco
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Behavioural Neurodynamics Group, Leibniz Institute for Molecular Pharmacology (FMP)/NeuroCure Cluster of Excellence, Berlin, Germany
| | - Alisa Bakhareva
- Institute for Systems Physiology, Faculty of Medicine, University of Cologne/University Clinic Cologne, Cologne, Germany
| | - Tatiana Korotkova
- Max Planck Institute for Metabolism Research, Cologne, Germany.
- Institute for Systems Physiology, Faculty of Medicine, University of Cologne/University Clinic Cologne, Cologne, Germany.
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases and Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany.
| | - Alexey Ponomarenko
- Institute of Physiology and Pathophysiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
- Behavioural Neurodynamics Group, Leibniz Institute for Molecular Pharmacology (FMP)/NeuroCure Cluster of Excellence, Berlin, Germany.
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14
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Kastner DB, Williams G, Holobetz C, Romano JP, Dayan P. The choice-wide behavioral association study: data-driven identification of interpretable behavioral components. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582115. [PMID: 38464037 PMCID: PMC10925091 DOI: 10.1101/2024.02.26.582115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.
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Affiliation(s)
- David B. Kastner
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143, USA
- Lead Contact
| | - Greer Williams
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143, USA
| | - Cristofer Holobetz
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143, USA
| | - Joseph P. Romano
- Department of Statistics, Stanford University, Stanford, CA 94305, USA
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen 72076, Germany
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15
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Koparkar A, Warren TL, Charlesworth JD, Shin S, Brainard MS, Veit L. Lesions in a songbird vocal circuit increase variability in song syntax. eLife 2024; 13:RP93272. [PMID: 38635312 PMCID: PMC11026095 DOI: 10.7554/elife.93272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Complex skills like speech and dance are composed of ordered sequences of simpler elements, but the neuronal basis for the syntactic ordering of actions is poorly understood. Birdsong is a learned vocal behavior composed of syntactically ordered syllables, controlled in part by the songbird premotor nucleus HVC (proper name). Here, we test whether one of HVC's recurrent inputs, mMAN (medial magnocellular nucleus of the anterior nidopallium), contributes to sequencing in adult male Bengalese finches (Lonchura striata domestica). Bengalese finch song includes several patterns: (1) chunks, comprising stereotyped syllable sequences; (2) branch points, where a given syllable can be followed probabilistically by multiple syllables; and (3) repeat phrases, where individual syllables are repeated variable numbers of times. We found that following bilateral lesions of mMAN, acoustic structure of syllables remained largely intact, but sequencing became more variable, as evidenced by 'breaks' in previously stereotyped chunks, increased uncertainty at branch points, and increased variability in repeat numbers. Our results show that mMAN contributes to the variable sequencing of vocal elements in Bengalese finch song and demonstrate the influence of recurrent projections to HVC. Furthermore, they highlight the utility of species with complex syntax in investigating neuronal control of ordered sequences.
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Affiliation(s)
- Avani Koparkar
- Neurobiology of Vocal Communication, Institute for Neurobiology, University of TübingenTübingenGermany
| | - Timothy L Warren
- Howard Hughes Medical Institute and Center for Integrative Neuroscience, University of California San FranciscoSan FranciscoUnited States
- Departments of Horticulture and Integrative Biology, Oregon State UniversityCorvallisUnited States
| | - Jonathan D Charlesworth
- Howard Hughes Medical Institute and Center for Integrative Neuroscience, University of California San FranciscoSan FranciscoUnited States
| | - Sooyoon Shin
- Howard Hughes Medical Institute and Center for Integrative Neuroscience, University of California San FranciscoSan FranciscoUnited States
| | - Michael S Brainard
- Howard Hughes Medical Institute and Center for Integrative Neuroscience, University of California San FranciscoSan FranciscoUnited States
| | - Lena Veit
- Neurobiology of Vocal Communication, Institute for Neurobiology, University of TübingenTübingenGermany
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16
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Gradwell MA, Ozeri-Engelhard N, Eisdorfer JT, Laflamme OD, Gonzalez M, Upadhyay A, Medlock L, Shrier T, Patel KR, Aoki A, Gandhi M, Abbas-Zadeh G, Oputa O, Thackray JK, Ricci M, George A, Yusuf N, Keating J, Imtiaz Z, Alomary SA, Bohic M, Haas M, Hernandez Y, Prescott SA, Akay T, Abraira VE. Multimodal sensory control of motor performance by glycinergic interneurons of the mouse spinal cord deep dorsal horn. Neuron 2024; 112:1302-1327.e13. [PMID: 38452762 DOI: 10.1016/j.neuron.2024.01.027] [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: 06/13/2023] [Revised: 10/31/2023] [Accepted: 01/26/2024] [Indexed: 03/09/2024]
Abstract
Sensory feedback is integral for contextually appropriate motor output, yet the neural circuits responsible remain elusive. Here, we pinpoint the medial deep dorsal horn of the mouse spinal cord as a convergence point for proprioceptive and cutaneous input. Within this region, we identify a population of tonically active glycinergic inhibitory neurons expressing parvalbumin. Using anatomy and electrophysiology, we demonstrate that deep dorsal horn parvalbumin-expressing interneuron (dPV) activity is shaped by convergent proprioceptive, cutaneous, and descending input. Selectively targeting spinal dPVs, we reveal their widespread ipsilateral inhibition onto pre-motor and motor networks and demonstrate their role in gating sensory-evoked muscle activity using electromyography (EMG) recordings. dPV ablation altered limb kinematics and step-cycle timing during treadmill locomotion and reduced the transitions between sub-movements during spontaneous behavior. These findings reveal a circuit basis by which sensory convergence onto dorsal horn inhibitory neurons modulates motor output to facilitate smooth movement and context-appropriate transitions.
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Affiliation(s)
- Mark A Gradwell
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Nofar Ozeri-Engelhard
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Neuroscience PhD program, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Jaclyn T Eisdorfer
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Olivier D Laflamme
- Dalhousie PhD program, Dalhousie University, Halifax, NS, Canada; Department of Medical Neuroscience, Atlantic Mobility Action Project, Brain Repair Center, Dalhousie University, Halifax, NS, Canada
| | - Melissa Gonzalez
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Department of Biomedical Engineering, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Aman Upadhyay
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Neuroscience PhD program, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Laura Medlock
- Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Tara Shrier
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Komal R Patel
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Adin Aoki
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Melissa Gandhi
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Gloria Abbas-Zadeh
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Olisemaka Oputa
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Joshua K Thackray
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Human Genetics Institute of New Jersey, Rutgers University, The State University of New Jersey, Piscataway, NJ, USA; Tourette International Collaborative Genetics Study (TIC Genetics)
| | - Matthew Ricci
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Arlene George
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Nusrath Yusuf
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Neuroscience PhD program, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Jessica Keating
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Zarghona Imtiaz
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Simona A Alomary
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Manon Bohic
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Michael Haas
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Yurdiana Hernandez
- W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Steven A Prescott
- Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Turgay Akay
- Department of Medical Neuroscience, Atlantic Mobility Action Project, Brain Repair Center, Dalhousie University, Halifax, NS, Canada
| | - Victoria E Abraira
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA.
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17
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Kucukdereli H, Amsalem O, Pottala T, Lim M, Potgieter L, Hasbrouck A, Lutas A, Andermann ML. Repeated stress triggers seeking of a starvation-like state in anxiety-prone female mice. Neuron 2024:S0896-6273(24)00234-4. [PMID: 38642553 DOI: 10.1016/j.neuron.2024.03.027] [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: 05/18/2023] [Revised: 01/28/2024] [Accepted: 03/27/2024] [Indexed: 04/22/2024]
Abstract
Elevated anxiety often precedes anorexia nervosa and persists after weight restoration. Patients with anorexia nervosa often describe self-starvation as pleasant, potentially because food restriction can be anxiolytic. Here, we tested whether repeated stress can cause animals to prefer a starvation-like state. We developed a virtual reality place preference paradigm in which head-fixed mice can voluntarily seek a starvation-like state induced by optogenetic stimulation of hypothalamic agouti-related peptide (AgRP) neurons. Prior to stress exposure, males but not females showed a mild aversion to AgRP stimulation. Strikingly, following multiple days of stress, a subset of females developed a strong preference for AgRP stimulation that was predicted by high baseline anxiety. Such stress-induced changes in preference were reflected in changes in facial expressions during AgRP stimulation. Our study suggests that stress may cause females predisposed to anxiety to seek a starvation state and provides a powerful experimental framework for investigating the underlying neural mechanisms.
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Affiliation(s)
- Hakan Kucukdereli
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Oren Amsalem
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Trent Pottala
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Michelle Lim
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Leilani Potgieter
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Amanda Hasbrouck
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Andrew Lutas
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Mark L Andermann
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA.
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18
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Costa AC, Sridhar G, Wyart C, Vergassola M. Fluctuating landscapes and heavy tails in animal behavior. ARXIV 2024:arXiv:2301.01111v4. [PMID: 36748006 PMCID: PMC9900967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Animal behavior is shaped by a myriad of mechanisms acting on a wide range of scales, which hampers quantitative reasoning and the identification of general principles. Here, we combine data analysis and theory to investigate the relationship between behavioral plasticity and heavy-tailed statistics often observed in animal behavior. Specifically, we first leverage high-resolution recordings of C. elegans locomotion to show that stochastic transitions among long-lived behaviors exhibit heavy-tailed first passage time distributions and correlation functions. Such heavy tails can be explained by slow adaptation of behavior over time. This particular result motivates our second step of introducing a general model where we separate fast dynamics on a quasi-stationary multi-well potential, from non-ergodic, slowly varying modes. We then show that heavy tails generically emerge in such a model, and we provide a theoretical derivation of the resulting functional form, which can become a power law with exponents that depend on the strength of the fluctuations. Finally, we provide direct support for the generality of our findings by testing them in a C. elegans mutant where adaptation is suppressed and heavy tails thus disappear, and recordings of larval zebrafish swimming behavior where heavy tails are again prevalent.
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Affiliation(s)
- Antonio Carlos Costa
- Laboratoire de Physique de l'Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Gautam Sridhar
- Sorbonne University, Paris Brain Institute (ICM), Inserm U1127, CNRS UMR 7225, Paris, France
| | - Claire Wyart
- Sorbonne University, Paris Brain Institute (ICM), Inserm U1127, CNRS UMR 7225, Paris, France
| | - Massimo Vergassola
- Laboratoire de Physique de l'Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
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19
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Biderman D, Whiteway MR, Hurwitz C, Greenspan N, Lee RS, Vishnubhotla A, Warren R, Pedraja F, Noone D, Schartner M, Huntenburg JM, Khanal A, Meijer GT, Noel JP, Pan-Vazquez A, Socha KZ, Urai AE, Cunningham JP, Sawtell NB, Paninski L. Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.28.538703. [PMID: 37162966 PMCID: PMC10168383 DOI: 10.1101/2023.04.28.538703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce "Lightning Pose," an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry, and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post-hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We release a cloud application that allows users to label data, train networks, and predict new videos directly from the browser.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Anup Khanal
- University of California Los Angeles, Los Angeles, USA
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20
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Dwivedi D, Dumontier D, Sherer M, Lin S, Mirow AM, Qiu Y, Xu Q, Liebman SA, Joseph D, Datta SR, Fishell G, Pouchelon G. Metabotropic signaling within somatostatin interneurons controls transient thalamocortical inputs during development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.21.558862. [PMID: 37790336 PMCID: PMC10542166 DOI: 10.1101/2023.09.21.558862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
During brain development, neural circuits undergo major activity-dependent restructuring. Circuit wiring mainly occurs through synaptic strengthening following the Hebbian "fire together, wire together" precept. However, select connections, essential for circuit development, are transient. They are effectively connected early in development, but strongly diminish during maturation. The mechanisms by which transient connectivity recedes are unknown. To investigate this process, we characterize transient thalamocortical inputs, which depress onto somatostatin inhibitory interneurons during development, by employing optogenetics, chemogenetics, transcriptomics and CRISPR-based strategies. We demonstrate that in contrast to typical activity-dependent mechanisms, transient thalamocortical connectivity onto somatostatin interneurons is non-canonical and involves metabotropic signaling. Specifically, metabotropic-mediated transcription, of guidance molecules in particular, supports the elimination of this connectivity. Remarkably, we found that this developmental process impacts the development of normal exploratory behaviors of adult mice.
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21
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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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22
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Mykins M, Bridges B, Jo A, Krishnan K. Multidimensional Analysis of a Social Behavior Identifies Regression and Phenotypic Heterogeneity in a Female Mouse Model for Rett Syndrome. J Neurosci 2024; 44:e1078232023. [PMID: 38199865 PMCID: PMC10957218 DOI: 10.1523/jneurosci.1078-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/01/2023] [Accepted: 11/17/2023] [Indexed: 01/12/2024] Open
Abstract
Regression is a key feature of neurodevelopmental disorders such as autism spectrum disorder, Fragile X syndrome, and Rett syndrome (RTT). RTT is caused by mutations in the X-linked gene methyl-CpG-binding protein 2 (MECP2). It is characterized by an early period of typical development with subsequent regression of previously acquired motor and speech skills in girls. The syndromic phenotypes are individualistic and dynamic over time. Thus far, it has been difficult to capture these dynamics and syndromic heterogeneity in the preclinical Mecp2-heterozygous female mouse model (Het). The emergence of computational neuroethology tools allows for robust analysis of complex and dynamic behaviors to model endophenotypes in preclinical models. Toward this first step, we utilized DeepLabCut, a marker-less pose estimation software to quantify trajectory kinematics and multidimensional analysis to characterize behavioral heterogeneity in Het in the previously benchmarked, ethologically relevant social cognition task of pup retrieval. We report the identification of two distinct phenotypes of adult Het: Het that display a delay in efficiency in early days and then improve over days like wild-type mice and Het that regress and perform worse in later days. Furthermore, regression is dependent on age and behavioral context and can be detected in the initial days of retrieval. Together, the novel identification of two populations of Het suggests differential effects on neural circuitry, opens new avenues to investigate the underlying molecular and cellular mechanisms of heterogeneity, and designs better studies for stratifying therapeutics.
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Affiliation(s)
- Michael Mykins
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee
| | - Benjamin Bridges
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee
| | - Angela Jo
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee
| | - Keerthi Krishnan
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee
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23
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Catto A, O’Connor R, Braunscheidel KM, Kenny PJ, Shen L. FABEL: Forecasting Animal Behavioral Events with Deep Learning-Based Computer Vision. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.584610. [PMID: 38559273 PMCID: PMC10980057 DOI: 10.1101/2024.03.15.584610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Behavioral neuroscience aims to provide a connection between neural phenomena and emergent organism-level behaviors. This requires perturbing the nervous system and observing behavioral outcomes, and comparing observed post-perturbation behavior with predicted counterfactual behavior and therefore accurate behavioral forecasts. In this study we present FABEL, a deep learning method for forecasting future animal behaviors and locomotion trajectories from historical locomotion alone. We train an offline pose estimation network to predict animal body-part locations in behavioral video; then sequences of pose vectors are input to deep learning time-series forecasting models. Specifically, we train an LSTM network that predicts a future food interaction event in a specified time window, and a Temporal Fusion Transformer that predicts future trajectories of animal body-parts, which are then converted into probabilistic label forecasts. Importantly, accurate prediction of food interaction provides a basis for neurobehavioral intervention in the context of compulsive eating. We show promising results on forecasting tasks between 100 milliseconds and 5 seconds timescales. Because the model takes only behavioral video as input, it can be adapted to any behavioral task and does not require specific physiological readouts. Simultaneously, these deep learning models may serve as extensible modules that can accommodate diverse signals, such as in-vivo fluorescence imaging and electrophysiology, which may improve behavior forecasts and elucidate invervention targets for desired behavioral change.
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Affiliation(s)
- Adam Catto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Richard O’Connor
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kevin M. Braunscheidel
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Paul J. Kenny
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Li Shen
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
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24
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Lindsey J, Markowitz JE, Datta SR, Litwin-Kumar A. Dynamics of striatal action selection and reinforcement learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.14.580408. [PMID: 38464083 PMCID: PMC10925202 DOI: 10.1101/2024.02.14.580408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules. Direct-pathway (dSPN) and indirect-pathway (iSPN) neurons, which promote and suppress actions, respectively, exhibit synaptic plasticity that reinforces activity associated with elevated or suppressed dopamine release. We show that iSPN plasticity prevents successful learning, as it reinforces activity patterns associated with negative outcomes. However, this pathological behavior is reversed if functionally opponent dSPNs and iSPNs, which promote and suppress the current behavior, are simultaneously activated by efferent input following action selection. This prediction is supported by striatal recordings and contrasts with prior models of SPN representations. In our model, learning and action selection signals can be multiplexed without interference, enabling learning algorithms beyond those of standard temporal difference models.
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Affiliation(s)
- Jack Lindsey
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Jeffrey E Markowitz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | | | - Ashok Litwin-Kumar
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
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25
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Jha A, Ashwood ZC, Pillow JW. Active Learning for Discrete Latent Variable Models. Neural Comput 2024; 36:437-474. [PMID: 38363661 DOI: 10.1162/neco_a_01646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/13/2023] [Indexed: 02/18/2024]
Abstract
Active learning seeks to reduce the amount of data required to fit the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked latent variable models, which play a vital role in neuroscience, psychology, and a variety of other engineering and scientific disciplines. Here we address this gap by proposing a novel framework for maximum-mutual-information input selection for discrete latent variable regression models. We first apply our method to a class of models known as mixtures of linear regressions (MLR). While it is well known that active learning confers no advantage for linear-gaussian regression models, we use Fisher information to show analytically that active learning can nevertheless achieve large gains for mixtures of such models, and we validate this improvement using both simulations and real-world data. We then consider a powerful class of temporally structured latent variable models given by a hidden Markov model (HMM) with generalized linear model (GLM) observations, which has recently been used to identify discrete states from animal decision-making data. We show that our method substantially reduces the amount of data needed to fit GLM-HMMs and outperforms a variety of approximate methods based on variational and amortized inference. Infomax learning for latent variable models thus offers a powerful approach for characterizing temporally structured latent states, with a wide variety of applications in neuroscience and beyond.
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Affiliation(s)
- Aditi Jha
- Princeton Neuroscience Institute and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, U.S.A.
| | - Zoe C Ashwood
- Princeton Neuroscience Institute and Department of Computer Science, Princeton University, Princeton, NJ 08544, U.S.A.
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, U.S.A.
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26
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Ding SS, Fox JL, Gordus A, Joshi A, Liao JC, Scholz M. Fantastic beasts and how to study them: rethinking experimental animal behavior. J Exp Biol 2024; 227:jeb247003. [PMID: 38372042 PMCID: PMC10911175 DOI: 10.1242/jeb.247003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Humans have been trying to understand animal behavior at least since recorded history. Recent rapid development of new technologies has allowed us to make significant progress in understanding the physiological and molecular mechanisms underlying behavior, a key goal of neuroethology. However, there is a tradeoff when studying animal behavior and its underlying biological mechanisms: common behavior protocols in the laboratory are designed to be replicable and controlled, but they often fail to encompass the variability and breadth of natural behavior. This Commentary proposes a framework of 10 key questions that aim to guide researchers in incorporating a rich natural context into their experimental design or in choosing a new animal study system. The 10 questions cover overarching experimental considerations that can provide a template for interspecies comparisons, enable us to develop studies in new model organisms and unlock new experiments in our quest to understand behavior.
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Affiliation(s)
- Siyu Serena Ding
- Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
| | - Jessica L. Fox
- Department of Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Andrew Gordus
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Abhilasha Joshi
- Departments of Physiology and Psychiatry, University of California, San Francisco, CA 94158, USA
| | - James C. Liao
- Department of Biology, The Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, FL 32080, USA
| | - Monika Scholz
- Max Planck Research Group Neural Information Flow, Max Planck Institute for Neurobiology of Behavior – caesar, 53175 Bonn, Germany
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27
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Wan Y, Macias LH, Garcia LR. Unraveling the hierarchical structure of posture and muscle activity changes during mating of Caenorhabditis elegans. PNAS NEXUS 2024; 3:pgae032. [PMID: 38312221 PMCID: PMC10837012 DOI: 10.1093/pnasnexus/pgae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/16/2024] [Indexed: 02/06/2024]
Abstract
One goal of neurobiology is to explain how decision-making in neuromuscular circuits produces behaviors. However, two obstacles complicate such efforts: individual behavioral variability and the challenge of simultaneously assessing multiple neuronal activities during behavior. Here, we circumvent these obstacles by analyzing whole animal behavior from a library of Caenorhabditis elegans male mating recordings. The copulating males express the GCaMP calcium sensor in the muscles, allowing simultaneous recording of posture and muscle activities. Our library contains wild type and males with selective neuronal desensitization in serotonergic neurons, which include male-specific posterior cord motor/interneurons and sensory ray neurons that modulate mating behavior. Incorporating deep learning-enabled computer vision, we developed a software to automatically quantify posture and muscle activities. By modeling, the posture and muscle activity data are classified into stereotyped modules, with the behaviors represented by serial executions and transitions among the modules. Detailed analysis of the modules reveals previously unidentified subtypes of the male's copulatory spicule prodding behavior. We find that wild-type and serotonergic neurons-suppressed males had different usage preferences for those module subtypes, highlighting the requirement of serotonergic neurons in the coordinated function of some muscles. In the structure of the behavior, bi-module repeats coincide with most of the previously described copulation steps, suggesting a recursive "repeat until success/give up" program is used for each step during mating. On the other hand, the transition orders of the bi-module repeats reveal the sub-behavioral hierarchy males employ to locate and inseminate hermaphrodites.
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Affiliation(s)
- Yufeng Wan
- Department of Biology, Texas A&M University, 3258 TAMU, College Station, TX 77843, USA
| | - Luca Henze Macias
- Department of Biology, Texas A&M University, 3258 TAMU, College Station, TX 77843, USA
| | - Luis Rene Garcia
- Department of Biology, Texas A&M University, 3258 TAMU, College Station, TX 77843, USA
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28
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Tang JCY, Paixao V, Carvalho F, Silva A, Klaus A, da Silva JA, Costa RM. Dynamic behaviour restructuring mediates dopamine-dependent credit assignment. Nature 2024; 626:583-592. [PMID: 38092040 PMCID: PMC10866702 DOI: 10.1038/s41586-023-06941-5] [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: 09/16/2022] [Accepted: 12/06/2023] [Indexed: 02/02/2024]
Abstract
Animals exhibit a diverse behavioural repertoire when exploring new environments and can learn which actions or action sequences produce positive outcomes. Dopamine release after encountering a reward is critical for reinforcing reward-producing actions1-3. However, it has been challenging to understand how credit is assigned to the exact action that produced the dopamine release during continuous behaviour. Here we investigated this problem in mice using a self-stimulation paradigm in which specific spontaneous movements triggered optogenetic stimulation of dopaminergic neurons. Dopamine self-stimulation rapidly and dynamically changes the structure of the entire behavioural repertoire. Initial stimulations reinforced not only the stimulation-producing target action, but also actions similar to the target action and actions that occurred a few seconds before stimulation. Repeated pairings led to a gradual refinement of the behavioural repertoire to home in on the target action. Reinforcement of action sequences revealed further temporal dependencies of refinement. Action pairs spontaneously separated by long time intervals promoted a stepwise credit assignment, with early refinement of actions most proximal to stimulation and subsequent refinement of more distal actions. Thus, a retrospective reinforcement mechanism promotes not only reinforcement, but also gradual refinement of the entire behavioural repertoire to assign credit to specific actions and action sequences that lead to dopamine release.
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Affiliation(s)
- Jonathan C Y Tang
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Seattle Children's Research Institute, Center for Integrative Brain Research, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
| | - Vitor Paixao
- Champalimaud Neuroscience Programme, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
- Kinetikos, Coimbra, Portugal
| | - Filipe Carvalho
- Champalimaud Neuroscience Programme, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
- Open Ephys Production Site, Lisbon, Portugal
| | - Artur Silva
- Champalimaud Neuroscience Programme, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Andreas Klaus
- Champalimaud Neuroscience Programme, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Joaquim Alves da Silva
- Champalimaud Neuroscience Programme, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
- Champalimaud Experimental Clinical Research Programme, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
- NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Rui M Costa
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA.
- Allen Institute, Seattle, WA, USA.
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29
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Pang R, Baker C, Murthy M, Pillow J. Inferring neural dynamics of memory during naturalistic social communication. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577404. [PMID: 38328156 PMCID: PMC10849655 DOI: 10.1101/2024.01.26.577404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Memory processes in complex behaviors like social communication require forming representations of the past that grow with time. The neural mechanisms that support such continually growing memory remain unknown. We address this gap in the context of fly courtship, a natural social behavior involving the production and perception of long, complex song sequences. To study female memory for male song history in unrestrained courtship, we present 'Natural Continuation' (NC)-a general, simulation-based model comparison procedure to evaluate candidate neural codes for complex stimuli using naturalistic behavioral data. Applying NC to fly courtship revealed strong evidence for an adaptive population mechanism for how female auditory neural dynamics could convert long song histories into a rich mnemonic format. Song temporal patterning is continually transformed by heterogeneous nonlinear adaptation dynamics, then integrated into persistent activity, enabling common neural mechanisms to retain continuously unfolding information over long periods and yielding state-of-the-art predictions of female courtship behavior. At a population level this coding model produces multi-dimensional advection-diffusion-like responses that separate songs over a continuum of timescales and can be linearly transformed into flexible output signals, illustrating its potential to create a generic, scalable mnemonic format for extended input signals poised to drive complex behavioral responses. This work thus shows how naturalistic behavior can directly inform neural population coding models, revealing here a novel process for memory formation.
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Affiliation(s)
- Rich Pang
- Princeton Neuroscience Institute, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton, NJ and New York, NY, USA
| | - Christa Baker
- Princeton Neuroscience Institute, Princeton, NJ, USA
- Present address: Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton, NJ, USA
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30
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Pierré A, Pham T, Pearl J, Datta SR, Ritt JT, Fleischmann A. A perspective on neuroscience data standardization with Neurodata Without Borders. ARXIV 2024:arXiv:2310.04317v2. [PMID: 37873012 PMCID: PMC10593085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our labs' efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science.
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Affiliation(s)
- Andrea Pierré
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, USA
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, USA
| | - Tuan Pham
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, USA
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, USA
| | - Jonah Pearl
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | | | - Jason T. Ritt
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, USA
| | - Alexander Fleischmann
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, USA
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, USA
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31
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Badrulhisham F, Pogatzki-Zahn E, Segelcke D, Spisak T, Vollert J. Machine learning and artificial intelligence in neuroscience: A primer for researchers. Brain Behav Immun 2024; 115:470-479. [PMID: 37972877 DOI: 10.1016/j.bbi.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 10/16/2023] [Accepted: 11/08/2023] [Indexed: 11/19/2023] Open
Abstract
Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.
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Affiliation(s)
| | - Esther Pogatzki-Zahn
- Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany
| | - Daniel Segelcke
- Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany
| | - Tamas Spisak
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany; Center for Translational Neuro- and Behavioral Sciences, Department of Neurology, University Medicine Essen, Essen, Germany
| | - Jan Vollert
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom; Pain Research, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
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32
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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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33
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Abstract
Foraging animals optimize feeding decisions by adjusting both common and rare behavioral patterns. Here, we characterize the relationship between an animal's arousal state and a rare decision to leave a patch of bacterial food. Using long-term tracking and behavioral state classification, we find that food leaving decisions in Caenorhabditis elegans are coupled to arousal states across multiple timescales. Leaving emerges probabilistically over minutes from the high arousal roaming state, but is suppressed during the low arousal dwelling state. Immediately before leaving, animals have a brief acceleration in speed that appears as a characteristic signature of this behavioral motif. Neuromodulatory mutants and optogenetic manipulations that increase roaming have a coupled increase in leaving rates, and similarly acute manipulations that inhibit feeding induce both roaming and leaving. By contrast, inactivating a set of chemosensory neurons that depend on the cGMP-gated transduction channel TAX-4 uncouples roaming and leaving dynamics. In addition, tax-4-expressing sensory neurons promote lawn-leaving behaviors that are elicited by feeding inhibition. Our results indicate that sensory neurons responsive to both internal and external cues play an integrative role in arousal and foraging decisions.
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Affiliation(s)
- Elias Scheer
- Lulu and Anthony Wang Laboratory of Neural Circuits and Behavior, The Rockefeller UniversityNew YorkUnited States
| | - Cornelia I Bargmann
- Lulu and Anthony Wang Laboratory of Neural Circuits and Behavior, The Rockefeller UniversityNew YorkUnited States
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34
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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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Rusu A, Chevalier C, de Chaumont F, Nalesso V, Brault V, Hérault Y, Ey E. Day-to-day spontaneous social behaviours is quantitatively and qualitatively affected in a 16p11.2 deletion mouse model. Front Behav Neurosci 2023; 17:1294558. [PMID: 38173978 PMCID: PMC10763239 DOI: 10.3389/fnbeh.2023.1294558] [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: 09/14/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
Background Autism spectrum disorders affect more than 1% of the population, impairing social communication and increasing stereotyped behaviours. A micro-deletion of the 16p11.2 BP4-BP5 chromosomic region has been identified in 1% of patients also displaying intellectual disabilities. In mouse models generated to understand the mechanisms of this deletion, learning and memory deficits were pervasive in most genetic backgrounds, while social communication deficits were only detected in some models. Methods To complement previous studies, we itemized the social deficits in the mouse model of 16p11.2 deletion on a hybrid C57BL/6N × C3H.Pde6b+ genetic background. We examined whether behavioural deficits were visible over long-term observation periods lasting several days and nights, to parallel everyday-life assessment of patients. We recorded the individual and social behaviours of mice carrying a heterozygous deletion of the homologous 16p11.2 chromosomic region (hereafter Del/+) and their wild-type littermates from both sexes over two or three consecutive nights during social interactions of familiar mixed-genotype quartets of males and of females, and of same-genotype unfamiliar female pairs. Results We observed that Del/+ mice of both sexes increased significantly their locomotor activity compared to wild-type littermates. In the social domain, Del/+ mice of both sexes displayed widespread deficits, even more so in males than in females in quartets of familiar individuals. In pairs, significant perturbations of the organisation of the social communication and behaviours appeared in Del/+ females. Discussion Altogether, this suggests that, over long recording periods, the phenotype of the 16p11.2 Del/+ mice was differently affected in the locomotor activity and the social domains and between the two sexes. These findings confirm the importance of testing models in long-term conditions to provide a comprehensive view of their phenotype that will refine the study of cellular and molecular mechanisms and complement pre-clinical targeted therapeutic trials.
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Affiliation(s)
- Anna Rusu
- Université de Strasbourg, CNRS, INSERM, Institut de Génétique et de Biologie Moléculaire et Cellulaire‑UMR 7104-UMR-S 1258, Illkirch, France
| | - Claire Chevalier
- Université de Strasbourg, CNRS, INSERM, Institut de Génétique et de Biologie Moléculaire et Cellulaire‑UMR 7104-UMR-S 1258, Illkirch, France
| | - Fabrice de Chaumont
- Génétique Humaine et Fonctions Cognitives, Institut Pasteur, Université de Paris Cité, Paris, France
| | - Valérie Nalesso
- Université de Strasbourg, CNRS, INSERM, Institut de Génétique et de Biologie Moléculaire et Cellulaire‑UMR 7104-UMR-S 1258, Illkirch, France
| | - Véronique Brault
- Université de Strasbourg, CNRS, INSERM, Institut de Génétique et de Biologie Moléculaire et Cellulaire‑UMR 7104-UMR-S 1258, Illkirch, France
| | - Yann Hérault
- Université de Strasbourg, CNRS, INSERM, Institut de Génétique et de Biologie Moléculaire et Cellulaire‑UMR 7104-UMR-S 1258, Illkirch, France
- Génétique Humaine et Fonctions Cognitives, Institut Pasteur, Université de Paris Cité, Paris, France
| | - Elodie Ey
- Université de Strasbourg, CNRS, INSERM, Institut de Génétique et de Biologie Moléculaire et Cellulaire‑UMR 7104-UMR-S 1258, Illkirch, France
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Dong M, Xu C. Skeleton-Based Human Motion Prediction With Privileged Supervision. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:10419-10432. [PMID: 35446772 DOI: 10.1109/tnnls.2022.3166861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Existing supervised methods have achieved impressive performance in forecasting skeleton-based human motion. However, they often rely on action class labels in both training and inference phases. In practice, it could be a burden to request action class labels in the inference phase, and even for the training phase, the collected labels could be incomplete for sequences with a mixture of multiple actions. In this article, we take action class labels as a kind of privileged supervision that only exists in the training phase. We design a new architecture that includes a motion classification as an auxiliary task with motion prediction. To deal with potential missing labels of motion sequence, we propose a new classification loss function to exploit their relationships with those observed labels and a perceptual loss to measure the difference between ground truth sequence and generated sequence in the classification task. Experimental results on the most challenging Human 3.6M dataset and the Carnegie Mellon University (CMU) dataset demonstrate the effectiveness of the proposed algorithm to exploit action class labels for improved modeling of human dynamics.
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Kershaw KA, Pestana JE, Brooke M, Saavedra Cardona L, Graham BM. Dissociable role of the basolateral complex of the amygdala in the acquisition and extinction of conditioned fear following reproductive experience in female rats. Neurobiol Learn Mem 2023; 206:107863. [PMID: 37995803 DOI: 10.1016/j.nlm.2023.107863] [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: 09/08/2023] [Revised: 10/31/2023] [Accepted: 11/20/2023] [Indexed: 11/25/2023]
Abstract
In female rats and humans, reproductive experience (i.e., pregnancy) alters the behavioral, hormonal and molecular substrates of fear extinction. Here, we assessed whether the role of a central neural substrate of fear extinction, the basolateral amygdala (BLA), also changes following reproductive experience. Nulliparous (virgin) and primiparous (one prior pregnancy) female rats received infusions of the GABAA agonist, muscimol, to temporarily inactivate the BLA prior to fear conditioning or extinction training. In follow up experiments, the BLA was also inactivated immediately after extinction training. BLA inactivation impaired the acquisition and expression of conditioned fear in both nulliparous and primiparous rats. In nulliparous rats, BLA inactivation prior to or immediately after extinction training impaired extinction retention. In contrast, in primiparous rats, BLA inactivation prior to or immediately after extinction training did not impair extinction retention, despite suppressing freezing during extinction training. These results suggest that, consistent with past findings in males, the BLA is a central component of the neural circuitry of fear acquisition and its extinction in virgin female rats. However, after pregnancy, female rats no longer depend on the BLA to extinguish fear, despite requiring the BLA to acquire conditioned fear. Given that fear extinction forms the basis of exposure therapy for anxiety disorders in humans, the present findings may have clinical implications. To improve the efficacy of exposure therapy for anxiety disorders, we may need to target different mechanisms in females dependent on their reproductive history.
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Nasello C, Poppi LA, Wu J, Kowalski TF, Thackray JK, Wang R, Persaud A, Mahboob M, Lin S, Spaseska R, Johnson CK, Gordon D, Tissir F, Heiman GA, Tischfield JA, Bocarsly M, Tischfield MA. Human mutations in high-confidence Tourette disorder genes affect sensorimotor behavior, reward learning, and striatal dopamine in mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.28.569034. [PMID: 38077033 PMCID: PMC10705456 DOI: 10.1101/2023.11.28.569034] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2024]
Abstract
Tourette disorder (TD) is poorly understood, despite affecting 1/160 children. A lack of animal models possessing construct, face, and predictive validity hinders progress in the field. We used CRISPR/Cas9 genome editing to generate mice with mutations orthologous to human de novo variants in two high-confidence Tourette genes, CELSR3 and WWC1 . Mice with human mutations in Celsr3 and Wwc1 exhibit cognitive and/or sensorimotor behavioral phenotypes consistent with TD. Sensorimotor gating deficits, as measured by acoustic prepulse inhibition, occur in both male and female Celsr3 TD models. Wwc1 mice show reduced prepulse inhibition only in females. Repetitive motor behaviors, common to Celsr3 mice and more pronounced in females, include vertical rearing and grooming. Sensorimotor gating deficits and rearing are attenuated by aripiprazole, a partial agonist at dopamine type II receptors. Unsupervised machine learning reveals numerous changes to spontaneous motor behavior and less predictable patterns of movement. Continuous fixed-ratio reinforcement shows Celsr3 TD mice have enhanced motor responding and reward learning. Electrically evoked striatal dopamine release, tested in one model, is greater. Brain development is otherwise grossly normal without signs of striatal interneuron loss. Altogether, mice expressing human mutations in high-confidence TD genes exhibit face and predictive validity. Reduced prepulse inhibition and repetitive motor behaviors are core behavioral phenotypes and are responsive to aripiprazole. Enhanced reward learning and motor responding occurs alongside greater evoked dopamine release. Phenotypes can also vary by sex and show stronger affection in females, an unexpected finding considering males are more frequently affected in TD. Significance Statement We generated mouse models that express mutations in high-confidence genes linked to Tourette disorder (TD). These models show sensorimotor and cognitive behavioral phenotypes resembling TD-like behaviors. Sensorimotor gating deficits and repetitive motor behaviors are attenuated by drugs that act on dopamine. Reward learning and striatal dopamine is enhanced. Brain development is grossly normal, including cortical layering and patterning of major axon tracts. Further, no signs of striatal interneuron loss are detected. Interestingly, behavioral phenotypes in affected females can be more pronounced than in males, despite male sex bias in the diagnosis of TD. These novel mouse models with construct, face, and predictive validity provide a new resource to study neural substrates that cause tics and related behavioral phenotypes in TD.
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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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Yang S, Chen ZY, Liang KW, Qin CJ, Yang Y, Fan WX, Jie CL, Ma XB. BARN: Behavior-Aware Relation Network for multi-label behavior detection in socially housed macaques. Zool Res 2023; 44:1026-1038. [PMID: 37804114 PMCID: PMC10802107 DOI: 10.24272/j.issn.2095-8137.2022.485] [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: 03/23/2023] [Accepted: 09/11/2023] [Indexed: 10/08/2023] Open
Abstract
Quantification of behaviors in macaques provides crucial support for various scientific disciplines, including pharmacology, neuroscience, and ethology. Despite recent advancements in the analysis of macaque behavior, research on multi-label behavior detection in socially housed macaques, including consideration of interactions among them, remains scarce. Given the lack of relevant approaches and datasets, we developed the Behavior-Aware Relation Network (BARN) for multi-label behavior detection of socially housed macaques. Our approach models the relationship of behavioral similarity between macaques, guided by a behavior-aware module and novel behavior classifier, which is suitable for multi-label classification. We also constructed a behavior dataset of rhesus macaques using ordinary RGB cameras mounted outside their cages. The dataset included 65 913 labels for 19 behaviors and 60 367 proposals, including identities and locations of the macaques. Experimental results showed that BARN significantly improved the baseline SlowFast network and outperformed existing relation networks. In conclusion, we successfully achieved multi-label behavior detection of socially housed macaques with both economic efficiency and high accuracy.
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Affiliation(s)
- Sen Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, China
- MAIS, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhi-Yuan Chen
- MAIS, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ke-Wei Liang
- MAIS, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Cai-Jie Qin
- MAIS, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yang Yang
- MAIS, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wen-Xuan Fan
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, China
- MAIS, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chen-Lu Jie
- MAIS, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- School of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China
| | - Xi-Bo Ma
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, China
- MAIS, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China. E-mail:
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Calhoun AJ, El Hady A. Everyone knows what behavior is but they just don't agree on it. iScience 2023; 26:108210. [PMID: 37953955 PMCID: PMC10638025 DOI: 10.1016/j.isci.2023.108210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/08/2023] [Accepted: 10/11/2023] [Indexed: 11/14/2023] Open
Abstract
Studying "behavior" lies at the heart of many disciplines. Nevertheless, academics rarely provide an explicit definition of what "behavior" actually is. What range of definitions do people use, and how does that vary across disciplines? To answer these questions, we have developed a survey to probe what constitutes "behavior." We find that academics adopt different definitions of behavior according to their academic discipline, animal model that they work with, and level of academic seniority. Using hierarchical clustering, we identify at least six distinct types of "behavior" which are used in seven distinct operational archetypes of "behavior." Individual respondents have clear consistent definitions of behavior, but these definitions are not consistent across the population. Our study is a call for academics to clarify what they mean by "behavior" wherever they study it, with the hope that this will foster interdisciplinary studies that will improve our understanding of behavioral phenomena.
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Affiliation(s)
- Adam J. Calhoun
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ahmed El Hady
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Cluster for Advanced Study of Collective Behavior, University of Konstanz, Konstanz, Germany
- Max Planck Institute of Animal Behavior, Konstanz, Germany
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Hope J, Beckerle T, Cheng PH, Viavattine Z, Feldkamp M, Fausner S, Saxena K, Ko E, Hryb I, Carter R, Ebner T, Kodandaramaiah S. Brain-wide neural recordings in mice navigating physical spaces enabled by a cranial exoskeleton. RESEARCH SQUARE 2023:rs.3.rs-3491330. [PMID: 38014260 PMCID: PMC10680923 DOI: 10.21203/rs.3.rs-3491330/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Complex behaviors are mediated by neural computations occurring throughout the brain. In recent years, tremendous progress has been made in developing technologies that can record neural activity at cellular resolution at multiple spatial and temporal scales. However, these technologies are primarily designed for studying the mammalian brain during head fixation - wherein the behavior of the animal is highly constrained. Miniaturized devices for studying neural activity in freely behaving animals are largely confined to recording from small brain regions owing to performance limitations. We present a cranial exoskeleton that assists mice in maneuvering neural recording headstages that are orders of magnitude larger and heavier than the mice, while they navigate physical behavioral environments. Force sensors embedded within the headstage are used to detect the mouse's milli-Newton scale cranial forces which then control the x, y, and yaw motion of the exoskeleton via an admittance controller. We discovered optimal controller tuning parameters that enable mice to locomote at physiologically realistic velocities and accelerations while maintaining natural walking gait. Mice maneuvering headstages weighing up to 1.5 kg can make turns, navigate 2D arenas, and perform a navigational decision-making task with the same performance as when freely behaving. We designed an imaging headstage and an electrophysiology headstage for the cranial exoskeleton to record brain-wide neural activity in mice navigating 2D arenas. The imaging headstage enabled recordings of Ca2+ activity of 1000s of neurons distributed across the dorsal cortex. The electrophysiology headstage supported independent control of up to 4 silicon probes, enabling simultaneous recordings from 100s of neurons across multiple brain regions and multiple days. Cranial exoskeletons provide flexible platforms for largescale neural recording during the exploration of physical spaces, a critical new paradigm for unraveling the brain-wide neural mechanisms that control complex behavior.
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Affiliation(s)
- James Hope
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
| | - Travis Beckerle
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
| | - Pin-Hao Cheng
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
| | - Zoey Viavattine
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
| | - Michael Feldkamp
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
| | - Skylar Fausner
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
| | - Kapil Saxena
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
| | - Eunsong Ko
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
| | - Ihor Hryb
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
- Department of Neuroscience, University of Minnesota, Twin Cities
| | - Russell Carter
- Department of Biomedical Engineering, University of Minnesota, Twin Cities
| | - Timothy Ebner
- Department of Biomedical Engineering, University of Minnesota, Twin Cities
| | - Suhasa Kodandaramaiah
- Department of Mechanical Engineering, University of Minnesota, Twin Cities
- Department of Biomedical Engineering, University of Minnesota, Twin Cities
- Department of Neuroscience, University of Minnesota, Twin Cities
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Tariq MF, Sterrett SC, Moore S, Lane, Perkel DJ, Gire DH. Dynamics of odor-source localization: Insights from real-time odor plume recordings and head-motion tracking in freely moving mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566539. [PMID: 38014041 PMCID: PMC10680624 DOI: 10.1101/2023.11.10.566539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Animals navigating turbulent odor plumes exhibit a rich variety of behaviors, and employ efficient strategies to locate odor sources. A growing body of literature has started to probe this complex task of localizing airborne odor sources in walking mammals to further our understanding of neural encoding and decoding of naturalistic sensory stimuli. However, correlating the intermittent olfactory information with behavior has remained a long-standing challenge due to the stochastic nature of the odor stimulus. We recently reported a method to record real-time olfactory information available to freely moving mice during odor-guided navigation, hence overcoming that challenge. Here we combine our odor-recording method with head-motion tracking to establish correlations between plume encounters and head movements. We show that mice exhibit robust head-pitch motions in the 5-14Hz range during an odor-guided navigation task, and that these head motions are modulated by plume encounters. Furthermore, mice orient towards the odor source upon plume contact. Head motions may thus be an important part of the sensorimotor behavioral repertoire during naturalistic odor-source localization.
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44
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Jones H, Willis JA, Firth LC, Giachello CNG, Gilestro GF. A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster. eLife 2023; 12:RP86695. [PMID: 37938101 PMCID: PMC10631757 DOI: 10.7554/elife.86695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023] Open
Abstract
Understanding how the brain encodes behaviour is the ultimate goal of neuroscience and the ability to objectively and reproducibly describe and quantify behaviour is a necessary milestone on this path. Recent technological progresses in machine learning and computational power have boosted the development and adoption of systems leveraging on high-resolution video recording to track an animal pose and describe behaviour in all four dimensions. However, the high temporal and spatial resolution that these systems offer must come as a compromise with their throughput and accessibility. Here, we describe coccinella, an open-source reductionist framework combining high-throughput analysis of behaviour using real-time tracking on a distributed mesh of microcomputers (ethoscopes) with resource-lean statistical learning (HCTSA/Catch22). Coccinella is a reductionist system, yet outperforms state-of-the-art alternatives when exploring the pharmacobehaviour in Drosophila melanogaster.
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Affiliation(s)
- Hannah Jones
- Department of Life Sciences, Imperial College LondonLondonUnited Kingdom
| | - Jenny A Willis
- Syngenta, Jealott’s Hill International Research CentreBracknellUnited Kingdom
| | - Lucy C Firth
- Syngenta, Jealott’s Hill International Research CentreBracknellUnited Kingdom
| | - Carlo NG Giachello
- Syngenta, Jealott’s Hill International Research CentreBracknellUnited Kingdom
| | - Giorgio F Gilestro
- Department of Life Sciences, Imperial College LondonLondonUnited Kingdom
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Lovelace JW, Ma J, Yadav S, Chhabria K, Shen H, Pang Z, Qi T, Sehgal R, Zhang Y, Bali T, Vaissiere T, Tan S, Liu Y, Rumbaugh G, Ye L, Kleinfeld D, Stringer C, Augustine V. Vagal sensory neurons mediate the Bezold-Jarisch reflex and induce syncope. Nature 2023; 623:387-396. [PMID: 37914931 PMCID: PMC10632149 DOI: 10.1038/s41586-023-06680-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/26/2023] [Indexed: 11/03/2023]
Abstract
Visceral sensory pathways mediate homeostatic reflexes, the dysfunction of which leads to many neurological disorders1. The Bezold-Jarisch reflex (BJR), first described2,3 in 1867, is a cardioinhibitory reflex that is speculated to be mediated by vagal sensory neurons (VSNs) that also triggers syncope. However, the molecular identity, anatomical organization, physiological characteristics and behavioural influence of cardiac VSNs remain mostly unknown. Here we leveraged single-cell RNA-sequencing data and HYBRiD tissue clearing4 to show that VSNs that express neuropeptide Y receptor Y2 (NPY2R) predominately connect the heart ventricular wall to the area postrema. Optogenetic activation of NPY2R VSNs elicits the classic triad of BJR responses-hypotension, bradycardia and suppressed respiration-and causes an animal to faint. Photostimulation during high-resolution echocardiography and laser Doppler flowmetry with behavioural observation revealed a range of phenotypes reflected in clinical syncope, including reduced cardiac output, cerebral hypoperfusion, pupil dilation and eye-roll. Large-scale Neuropixels brain recordings and machine-learning-based modelling showed that this manipulation causes the suppression of activity across a large distributed neuronal population that is not explained by changes in spontaneous behavioural movements. Additionally, bidirectional manipulation of the periventricular zone had a push-pull effect, with inhibition leading to longer syncope periods and activation inducing arousal. Finally, ablating NPY2R VSNs specifically abolished the BJR. Combined, these results demonstrate a genetically defined cardiac reflex that recapitulates characteristics of human syncope at physiological, behavioural and neural network levels.
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Affiliation(s)
- Jonathan W Lovelace
- Department of Neurobiology, University of California, San Diego, CA, USA
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Jingrui Ma
- Department of Neurobiology, University of California, San Diego, CA, USA
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Saurabh Yadav
- Department of Neurobiology, University of California, San Diego, CA, USA
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | | | - Hanbing Shen
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Zhengyuan Pang
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Tianbo Qi
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Ruchi Sehgal
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Yunxiao Zhang
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Tushar Bali
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Thomas Vaissiere
- University of Florida-Scripps Biomedical Research, Jupiter, FL, USA
| | - Shawn Tan
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Yuejia Liu
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - Gavin Rumbaugh
- University of Florida-Scripps Biomedical Research, Jupiter, FL, USA
| | - Li Ye
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA
| | - David Kleinfeld
- Department of Neurobiology, University of California, San Diego, CA, USA
- Department of Physics, University of California, San Diego, CA, USA
| | | | - Vineet Augustine
- Department of Neurobiology, University of California, San Diego, CA, USA.
- Department of Neuroscience, Scripps Research, La Jolla, CA, USA.
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46
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Tseng YT, Schaefke B, Wei P, Wang L. Defensive responses: behaviour, the brain and the body. Nat Rev Neurosci 2023; 24:655-671. [PMID: 37730910 DOI: 10.1038/s41583-023-00736-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/11/2023] [Indexed: 09/22/2023]
Abstract
Most animals live under constant threat from predators, and predation has been a major selective force in shaping animal behaviour. Nevertheless, defence responses against predatory threats need to be balanced against other adaptive behaviours such as foraging, mating and recovering from infection. This behavioural balance in ethologically relevant contexts requires adequate integration of internal and external signals in a complex interplay between the brain and the body. Despite this complexity, research has often considered defensive behaviour as entirely mediated by the brain processing threat-related information obtained via perception of the external environment. However, accumulating evidence suggests that the endocrine, immune, gastrointestinal and reproductive systems have important roles in modulating behavioural responses to threat. In this Review, we focus on how predatory threat defence responses are shaped by threat imminence and review the circuitry between subcortical brain regions involved in mediating defensive behaviours. Then, we discuss the intersection of peripheral systems involved in internal states related to infection, hunger and mating with the neurocircuits that underlie defence responses against predatory threat. Through this process, we aim to elucidate the interconnections between the brain and body as an integrated network that facilitates appropriate defensive responses to threat and to discuss the implications for future behavioural research.
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Affiliation(s)
- Yu-Ting Tseng
- CAS Key Laboratory of Brain Connectome and Manipulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain Connectome and Behaviour, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Bernhard Schaefke
- CAS Key Laboratory of Brain Connectome and Manipulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Pengfei Wei
- CAS Key Laboratory of Brain Connectome and Manipulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liping Wang
- CAS Key Laboratory of Brain Connectome and Manipulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- Guangdong Provincial Key Laboratory of Brain Connectome and Behaviour, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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47
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Talluri BC, Kang I, Lazere A, Quinn KR, Kaliss N, Yates JL, Butts DA, Nienborg H. Activity in primate visual cortex is minimally driven by spontaneous movements. Nat Neurosci 2023; 26:1953-1959. [PMID: 37828227 PMCID: PMC10620084 DOI: 10.1038/s41593-023-01459-5] [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: 07/19/2022] [Accepted: 09/08/2023] [Indexed: 10/14/2023]
Abstract
Organisms process sensory information in the context of their own moving bodies, an idea referred to as embodiment. This idea is important for developmental neuroscience, robotics and systems neuroscience. The mechanisms supporting embodiment are unknown, but a manifestation could be the observation in mice of brain-wide neuromodulation, including in the primary visual cortex, driven by task-irrelevant spontaneous body movements. We tested this hypothesis in macaque monkeys (Macaca mulatta), a primate model for human vision, by simultaneously recording visual cortex activity and facial and body movements. We also sought a direct comparison using an analogous approach to those used in mouse studies. Here we found that activity in the primate visual cortex (V1, V2 and V3/V3A) was associated with the animals' own movements, but this modulation was largely explained by the impact of the movements on the retinal image, that is, by changes in visual input. These results indicate that visual cortex in primates is minimally driven by spontaneous movements and may reflect species-specific sensorimotor strategies.
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Affiliation(s)
- Bharath Chandra Talluri
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Incheol Kang
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Adam Lazere
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Katrina R Quinn
- Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
| | - Nicholas Kaliss
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jacob L Yates
- Herbert Wertheim School of Optometry & Vision Science, University of California, Berkeley, Berkeley, CA, USA
- Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Daniel A Butts
- Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Hendrikje Nienborg
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
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Brickson L, Zhang L, Vollrath F, Douglas-Hamilton I, Titus AJ. Elephants and algorithms: a review of the current and future role of AI in elephant monitoring. J R Soc Interface 2023; 20:20230367. [PMID: 37963556 PMCID: PMC10645515 DOI: 10.1098/rsif.2023.0367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/23/2023] [Indexed: 11/16/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.
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Affiliation(s)
| | | | - Fritz Vollrath
- Save the Elephants, Nairobi, Kenya
- Department of Biology, University of Oxford, Oxford, UK
| | | | - Alexander J. Titus
- Colossal Biosciences, Dallas, TX, USA
- Information Sciences Institute, University of Southern California, Los Angeles, USA
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49
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Costa AC, Vergassola M. Fluctuating landscapes and heavy tails in animal behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.03.522580. [PMID: 36747746 PMCID: PMC9900741 DOI: 10.1101/2023.01.03.522580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Animal behavior is shaped by a myriad of mechanisms acting on a wide range of scales. This immense variability hampers quantitative reasoning and renders the identification of universal principles elusive. Through data analysis and theory, we here show that slow non-ergodic drives generally give rise to heavy-tailed statistics in behaving animals. We leverage high-resolution recordings of C. elegans locomotion to extract a self-consistent reduced order model for an inferred reaction coordinate, bridging from sub-second chaotic dynamics to long-lived stochastic transitions among metastable states. The slow mode dynamics exhibits heavy-tailed first passage time distributions and correlation functions, and we show that such heavy tails can be explained by dynamics on a time-dependent potential landscape. Inspired by these results, we introduce a generic model in which we separate faster mixing modes that evolve on a quasi-stationary potential, from slower non-ergodic modes that drive the potential landscape, and reflect slowly varying internal states. We show that, even for simple potential landscapes, heavy tails emerge when barrier heights fluctuate slowly and strongly enough. In particular, the distribution of first passage times and the correlation function can asymptote to a power law, with related exponents that depend on the strength and nature of the fluctuations. We support our theoretical findings through direct numerical simulations.
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Affiliation(s)
- Antonio Carlos Costa
- Laboratoire de Physique de l’Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Massimo Vergassola
- Laboratoire de Physique de l’Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
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50
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García MT, Tran DN, Peterson RE, Stegmann SK, Hanson SM, Reid CM, Xie Y, Vu S, Harwell CC. A developmentally defined population of neurons in the lateral septum controls responses to aversive stimuli. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.24.559205. [PMID: 37873286 PMCID: PMC10592641 DOI: 10.1101/2023.09.24.559205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
When interacting with their environment, animals must balance exploratory and defensive behavior to evaluate and respond to potential threats. The lateral septum (LS) is a structure in the ventral forebrain that calibrates the magnitude of behavioral responses to stress-related external stimuli, including the regulation of threat avoidance. The complex connectivity between the LS and other parts of the brain, together with its largely unexplored neuronal diversity, makes it difficult to understand how defined LS circuits control specific behaviors. Here, we describe a mouse model in which a population of neurons with a common developmental origin (Nkx2.1-lineage neurons) are absent from the LS. Using a combination of circuit tracing and behavioral analyses, we found that these neurons receive inputs from the perifornical area of the anterior hypothalamus (PeFAH) and are specifically activated in stressful contexts. Mice lacking Nkx2.1-lineage LS neurons display increased exploratory behavior even under stressful conditions. Our study extends the current knowledge about how defined neuronal populations within the LS can evaluate contextual information to select appropriate behavioral responses. This is a necessary step towards understanding the crucial role that the LS plays in neuropsychiatric conditions where defensive behavior is dysregulated, such as anxiety and aggression disorders.
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Affiliation(s)
- Miguel Turrero García
- Department of Neurology, University of California, San Francisco; San Francisco, CA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research; San Francisco, CA
| | - Diana N. Tran
- Department of Neurobiology, Harvard Medical School; Boston, MA
| | | | | | - Sarah M. Hanson
- Department of Neurology, University of California, San Francisco; San Francisco, CA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research; San Francisco, CA
| | - Christopher M. Reid
- Department of Neurology, University of California, San Francisco; San Francisco, CA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research; San Francisco, CA
- Ph.D. Program in Neuroscience, Harvard University; Boston, MA
| | - Yajun Xie
- Department of Neurology, University of California, San Francisco; San Francisco, CA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research; San Francisco, CA
| | - Steve Vu
- Department of Neurobiology, Harvard Medical School; Boston, MA
| | - Corey C. Harwell
- Department of Neurology, University of California, San Francisco; San Francisco, CA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research; San Francisco, CA
- Chan Zuckerberg Biohub San Francisco; San Francisco, CA
- Lead contact
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