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Meng J, Ahamed T, Yu B, Hung W, EI Mouridi S, Wang Z, Zhang Y, Wen Q, Boulin T, Gao S, Zhen M. A tonically active master neuron modulates mutually exclusive motor states at two timescales. SCIENCE ADVANCES 2024; 10:eadk0002. [PMID: 38598630 PMCID: PMC11006214 DOI: 10.1126/sciadv.adk0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 03/07/2024] [Indexed: 04/12/2024]
Abstract
Continuity of behaviors requires animals to make smooth transitions between mutually exclusive behavioral states. Neural principles that govern these transitions are not well understood. Caenorhabditis elegans spontaneously switch between two opposite motor states, forward and backward movement, a phenomenon thought to reflect the reciprocal inhibition between interneurons AVB and AVA. Here, we report that spontaneous locomotion and their corresponding motor circuits are not separately controlled. AVA and AVB are neither functionally equivalent nor strictly reciprocally inhibitory. AVA, but not AVB, maintains a depolarized membrane potential. While AVA phasically inhibits the forward promoting interneuron AVB at a fast timescale, it maintains a tonic, extrasynaptic excitation on AVB over the longer timescale. We propose that AVA, with tonic and phasic activity of opposite polarities on different timescales, acts as a master neuron to break the symmetry between the underlying forward and backward motor circuits. This master neuron model offers a parsimonious solution for sustained locomotion consisted of mutually exclusive motor states.
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Affiliation(s)
- Jun Meng
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Tosif Ahamed
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Bin Yu
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wesley Hung
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Sonia EI Mouridi
- University Claude Bernard Lyon 1, MeLiS, CNRS UMR 5284, INSERM U1314, 69008 Lyon, France
| | - Zezhen Wang
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yongning Zhang
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Quan Wen
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Thomas Boulin
- University Claude Bernard Lyon 1, MeLiS, CNRS UMR 5284, INSERM U1314, 69008 Lyon, France
| | - Shangbang Gao
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Mei Zhen
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
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2
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Geng Y, Yates C, Peterson RT. Social behavioral profiling by unsupervised deep learning reveals a stimulative effect of dopamine D3 agonists on zebrafish sociality. CELL REPORTS METHODS 2023; 3:100381. [PMID: 36814839 PMCID: PMC9939379 DOI: 10.1016/j.crmeth.2022.100381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 11/15/2022] [Accepted: 12/12/2022] [Indexed: 01/07/2023]
Abstract
It has been a major challenge to systematically evaluate and compare how pharmacological perturbations influence social behavioral outcomes. Although some pharmacological agents are known to alter social behavior, precise description and quantification of such effects have proven difficult. We developed a scalable social behavioral assay for zebrafish named ZeChat based on unsupervised deep learning to characterize sociality at high resolution. High-dimensional and dynamic social behavioral phenotypes are automatically classified using this method. By screening a neuroactive compound library, we found that different classes of chemicals evoke distinct patterns of social behavioral fingerprints. By examining these patterns, we discovered that dopamine D3 agonists possess a social stimulative effect on zebrafish. The D3 agonists pramipexole, piribedil, and 7-hydroxy-DPAT-HBr rescued social deficits in a valproic-acid-induced zebrafish autism model. The ZeChat platform provides a promising approach for dissecting the pharmacology of social behavior and discovering novel social-modulatory compounds.
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Affiliation(s)
- Yijie Geng
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher Yates
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Randall T. Peterson
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
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3
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Athira A, Dondorp D, Rudolf J, Peytral O, Chatzigeorgiou M. Comprehensive analysis of locomotion dynamics in the protochordate Ciona intestinalis reveals how neuromodulators flexibly shape its behavioral repertoire. PLoS Biol 2022; 20:e3001744. [PMID: 35925898 PMCID: PMC9352054 DOI: 10.1371/journal.pbio.3001744] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 07/06/2022] [Indexed: 11/19/2022] Open
Abstract
Vertebrate nervous systems can generate a remarkable diversity of behaviors. However, our understanding of how behaviors may have evolved in the chordate lineage is limited by the lack of neuroethological studies leveraging our closest invertebrate relatives. Here, we combine high-throughput video acquisition with pharmacological perturbations of bioamine signaling to systematically reveal the global structure of the motor behavioral repertoire in the Ciona intestinalis larvae. Most of Ciona’s postural variance can be captured by 6 basic shapes, which we term “eigencionas.” Motif analysis of postural time series revealed numerous stereotyped behavioral maneuvers including “startle-like” and “beat-and-glide.” Employing computational modeling of swimming dynamics and spatiotemporal embedding of postural features revealed that behavioral differences are generated at the levels of motor modules and the transitions between, which may in part be modulated by bioamines. Finally, we show that flexible motor module usage gives rise to diverse behaviors in response to different light stimuli. Vertebrate nervous systems can generate a remarkable diversity of behaviors, but how did these evolve in the chordate lineage? A study of the protochordate Ciona intestinalis reveals novel insights into how a simple chordate brain uses neuromodulators to control its behavioral repertoire.
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Affiliation(s)
- Athira Athira
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
| | - Daniel Dondorp
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
| | - Jerneja Rudolf
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
| | - Olivia Peytral
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
| | - Marios Chatzigeorgiou
- Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
- * E-mail:
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4
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Mazzucato L. Neural mechanisms underlying the temporal organization of naturalistic animal behavior. eLife 2022; 11:e76577. [PMID: 35792884 PMCID: PMC9259028 DOI: 10.7554/elife.76577] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/07/2022] [Indexed: 12/17/2022] Open
Abstract
Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.
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Affiliation(s)
- Luca Mazzucato
- Institute of Neuroscience, Departments of Biology, Mathematics and Physics, University of OregonEugeneUnited States
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5
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McCullough MH, Goodhill GJ. Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain. Curr Opin Neurobiol 2021; 70:89-100. [PMID: 34482006 DOI: 10.1016/j.conb.2021.07.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/20/2021] [Accepted: 07/21/2021] [Indexed: 01/02/2023]
Abstract
Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.
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Affiliation(s)
- Michael H McCullough
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Geoffrey J Goodhill
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia; School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, 4072, Australia.
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6
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Tracking changes in behavioural dynamics using prediction error. PLoS One 2021; 16:e0251053. [PMID: 33979384 PMCID: PMC8115816 DOI: 10.1371/journal.pone.0251053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/20/2021] [Indexed: 11/19/2022] Open
Abstract
Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded ‘attractor’ of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach—defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error—may be of broad interest and relevance to behavioural researchers working with video-derived time series.
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7
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Bian A, Jiang X, Berh D, Risse B. Resolving Colliding Larvae by Fitting ASM to Random Walker-Based Pre-Segmentations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1184-1194. [PMID: 31425121 DOI: 10.1109/tcbb.2019.2935718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Drosophila melanogaster is an important model organism for research in neuro- and behavioral biology. Automated studies of their locomotion are crucial to link sensory input and neural processing to motor output which has led to numerous vision-based tracking systems. However, most of these approaches share the inability to segment the contours of colliding animals causing identity losses, appearing and disappearing animals, and the absence of posture and motion related measurements during the time of the collision. We present a novel collision resolution algorithm enabling an accurate contour segmentation of multiple touching Drosophila larvae. Our algorithm utilizes an adapted active shape model (ASM) to learn a low dimensional posture space which is fitted to random-walker generated pre-segmentations. We evaluate our collision resolution algorithm using three publicly available datasets and compare it with the current state-of-the-art methods. In addition, we introduce a refined dataset enabling a segmentation evaluation on the level of pixel accuracy. The results demonstrate that our approach outperforms the state-of-the-art approaches in both accuracy and computational time. We will incorporate this algorithm into our widely used tracking program to improve the statistical strength of the behavioral quantification and allow marker-free studies of interacting Drosophila larvae.
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8
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Abstract
Our current understanding of manipulation is based on primate hands, resulting in a detailed but narrow perspective of ways to handle objects. Although most other animals lack hands, they are still capable of flexible manipulation of diverse objects, including food and nest materials, and depend on dexterity in object handling to survive and reproduce. Birds, for instance, use their bills and feet to forage and build nests, while insects carry food and construct nests with their mandibles and legs. Bird bills and insect mandibles are much simpler than a primate hand, resembling simple robotic grippers. A better understanding of manipulation in these and other species would provide a broader comparative perspective on the origins of dexterity. Here we contrast data from primates, birds and insects, describing how they sense and grasp objects, and the neural architectures that control manipulation. Finally, we outline techniques for collecting comparable manipulation data from animals with diverse morphologies and describe the practical applications of studying manipulation in a wide range of species, including providing inspiration for novel designs of robotic manipulators.
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Affiliation(s)
- Shoko Sugasawa
- Centre for Biological Diversity, Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews KY16 9TH, UK
| | - Barbara Webb
- School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK
| | - Susan D Healy
- Centre for Biological Diversity, Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews KY16 9TH, UK
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9
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Sohrabi S, Mor DE, Kaletsky R, Keyes W, Murphy CT. High-throughput behavioral screen in C. elegans reveals Parkinson's disease drug candidates. Commun Biol 2021; 4:203. [PMID: 33589689 PMCID: PMC7884385 DOI: 10.1038/s42003-021-01731-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 12/18/2020] [Indexed: 12/17/2022] Open
Abstract
We recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson's disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like 'curling' behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.
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Affiliation(s)
- Salman Sohrabi
- Department of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Danielle E Mor
- Department of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Rachel Kaletsky
- Department of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - William Keyes
- Department of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Coleen T Murphy
- Department of Molecular Biology & Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA.
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10
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Fieseler C, Zimmer M, Kutz JN. Unsupervised learning of control signals and their encodings in Caenorhabditis elegans whole-brain recordings. J R Soc Interface 2020; 17:20200459. [PMID: 33292096 PMCID: PMC7811586 DOI: 10.1098/rsif.2020.0459] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 11/12/2020] [Indexed: 01/08/2023] Open
Abstract
A major goal of computational neuroscience is to understand the relationship between synapse-level structure and network-level functionality. Caenorhabditis elegans is a model organism to probe this relationship due to the historic availability of the synaptic structure (connectome) and recent advances in whole brain calcium imaging techniques. Recent work has applied the concept of network controllability to neuronal networks, discovering some neurons that are able to drive the network to a certain state. However, previous work uses a linear model of the network dynamics, and it is unclear if the real neuronal network conforms to this assumption. Here, we propose a method to build a global, low-dimensional model of the dynamics, whereby an underlying global linear dynamical system is actuated by temporally sparse control signals. A key novelty of this method is discovering candidate control signals that the network uses to control itself. We analyse these control signals in two ways, showing they are interpretable and biologically plausible. First, these control signals are associated with transitions between behaviours, which were previously annotated via expert-generated features. Second, these signals can be predicted both from neurons previously implicated in behavioural transitions but also additional neurons previously unassociated with these behaviours. The proposed mathematical framework is generic and can be generalized to other neurosensory systems, potentially revealing transitions and their encodings in a completely unsupervised way.
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Affiliation(s)
- Charles Fieseler
- Department of Physics, University of Washington, Seattle, WA 98195, USA
| | - Manuel Zimmer
- Department of Neurobiology, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1F030 Vienna, Austria
| | - J. Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA
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11
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Deconstructing Hunting Behavior Reveals a Tightly Coupled Stimulus-Response Loop. Curr Biol 2020; 30:54-69.e9. [DOI: 10.1016/j.cub.2019.11.022] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 09/30/2019] [Accepted: 11/06/2019] [Indexed: 01/02/2023]
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12
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Loveless J, Lagogiannis K, Webb B. Modelling the mechanics of exploration in larval Drosophila. PLoS Comput Biol 2019; 15:e1006635. [PMID: 31276489 PMCID: PMC6636753 DOI: 10.1371/journal.pcbi.1006635] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 07/17/2019] [Accepted: 11/08/2018] [Indexed: 12/03/2022] Open
Abstract
The Drosophila larva executes a stereotypical exploratory routine that appears to consist of stochastic alternation between straight peristaltic crawling and reorientation events through lateral bending. We present a model of larval mechanics for axial and transverse motion over a planar substrate, and use it to develop a simple, reflexive neuromuscular model from physical principles. The mechanical model represents the midline of the larva as a set of point masses which interact with each other via damped translational and torsional springs, and with the environment via sliding friction forces. The neuromuscular model consists of: 1. segmentally localised reflexes that amplify axial compression in order to counteract frictive energy losses, and 2. long-range mutual inhibition between reflexes in distant segments, enabling overall motion of the model larva relative to its substrate. In the absence of damping and driving, the mechanical model produces axial travelling waves, lateral oscillations, and unpredictable, chaotic deformations. The neuromuscular model counteracts friction to recover these motion patterns, giving rise to forward and backward peristalsis in addition to turning. Our model produces spontaneous exploration, even though the nervous system has no intrinsic pattern generating or decision making ability, and neither senses nor drives bending motions. Ultimately, our model suggests a novel view of larval exploration as a deterministic superdiffusion process which is mechanistically grounded in the chaotic mechanics of the body. We discuss how this may provide new interpretations for existing observations at the level of tissue-scale activity patterns and neural circuitry, and provide some experimental predictions that would test the extent to which the mechanisms we present translate to the real larva. We investigate the relationship between brain, body and environment in the exploratory behaviour of fruitfly larva. A larva crawls forward by propagating a wave of compression through its segmented body, and changes its crawling direction by bending to one side or the other. We show first that a purely mechanical model of the larva’s body can produce travelling compression waves, sideways bending, and unpredictable, chaotic motions. For this body to locomote through its environment, it is necessary to add a neuromuscular system to counteract the loss of energy due to friction, and to limit the simultaneous compression of segments. These simple additions allow our model larva to generate life-like forward and backward crawling as well as spontaneous turns, which occur without any direct sensing or control of reorientation. The unpredictability inherent in the larva’s physics causes the model to explore its environment, despite the lack of any neural mechanism for rhythm generation or for deciding when to switch from crawling to turning. Our model thus demonstrates how understanding body mechanics can generate and simplify neurobiological hypotheses as to how behaviour arises.
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Affiliation(s)
- Jane Loveless
- Institute for Perception, Action, and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Konstantinos Lagogiannis
- Institute for Perception, Action, and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom
- MRC Centre for Developmental Neurobiology, New Hunt’s House, King’s College London, London, United Kingdom
| | - Barbara Webb
- Institute for Perception, Action, and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom
- * E-mail:
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13
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Abstract
The dynamics of complex systems generally include high-dimensional, nonstationary, and nonlinear behavior, all of which pose fundamental challenges to quantitative understanding. To address these difficulties, we detail an approach based on local linear models within windows determined adaptively from data. While the dynamics within each window are simple, consisting of exponential decay, growth, and oscillations, the collection of local parameters across all windows provides a principled characterization of the full time series. To explore the resulting model space, we develop a likelihood-based hierarchical clustering, and we examine the eigenvalues of the linear dynamics. We demonstrate our analysis with the Lorenz system undergoing stable spiral dynamics and in the standard chaotic regime. Applied to the posture dynamics of the nematode Caenorhabditis elegans, our approach identifies fine-grained behavioral states and model dynamics which fluctuate about an instability boundary, and we detail a bifurcation in a transition from forward to backward crawling. We analyze whole-brain imaging in C. elegans and show that global brain dynamics is damped away from the instability boundary by a decrease in oxygen concentration. We provide additional evidence for such near-critical dynamics from the analysis of electrocorticography in monkey and the imaging of a neural population from mouse visual cortex at single-cell resolution.
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Affiliation(s)
- Antonio C Costa
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081HV Amsterdam, The Netherlands
| | - Tosif Ahamed
- Biological Physics Theory Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
| | - Greg J Stephens
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081HV Amsterdam, The Netherlands;
- Biological Physics Theory Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
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14
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Tastekin I, Khandelwal A, Tadres D, Fessner ND, Truman JW, Zlatic M, Cardona A, Louis M. Sensorimotor pathway controlling stopping behavior during chemotaxis in the Drosophila melanogaster larva. eLife 2018; 7:e38740. [PMID: 30465650 PMCID: PMC6264072 DOI: 10.7554/elife.38740] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 11/07/2018] [Indexed: 02/02/2023] Open
Abstract
Sensory navigation results from coordinated transitions between distinct behavioral programs. During chemotaxis in the Drosophila melanogaster larva, the detection of positive odor gradients extends runs while negative gradients promote stops and turns. This algorithm represents a foundation for the control of sensory navigation across phyla. In the present work, we identified an olfactory descending neuron, PDM-DN, which plays a pivotal role in the organization of stops and turns in response to the detection of graded changes in odor concentrations. Artificial activation of this descending neuron induces deterministic stops followed by the initiation of turning maneuvers through head casts. Using electron microscopy, we reconstructed the main pathway that connects the PDM-DN neuron to the peripheral olfactory system and to the pre-motor circuit responsible for the actuation of forward peristalsis. Our results set the stage for a detailed mechanistic analysis of the sensorimotor conversion of graded olfactory inputs into action selection to perform goal-oriented navigation.
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Affiliation(s)
- Ibrahim Tastekin
- EMBL-CRG Systems Biology Research UnitCentre for Genomic Regulation, The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - Avinash Khandelwal
- EMBL-CRG Systems Biology Research UnitCentre for Genomic Regulation, The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Janelia Research CampusHoward Hughes Medical InstituteAshburnUnited States
| | - David Tadres
- EMBL-CRG Systems Biology Research UnitCentre for Genomic Regulation, The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- Institute of Molecular Life SciencesUniversity of ZurichZurichSwitzerland
- Department of Molecular, Cellular and Developmental Biology & Neuroscience Research InstituteUniversity of CaliforniaSanta BarbaraUnited States
| | - Nico D Fessner
- EMBL-CRG Systems Biology Research UnitCentre for Genomic Regulation, The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - James W Truman
- Janelia Research CampusHoward Hughes Medical InstituteAshburnUnited States
| | - Marta Zlatic
- Janelia Research CampusHoward Hughes Medical InstituteAshburnUnited States
- Department of ZoologyUniversity of CambridgeCambridgeUnited Kingdom
| | - Albert Cardona
- Janelia Research CampusHoward Hughes Medical InstituteAshburnUnited States
- Department of Physiology, Development and NeuroscienceUniversity of CambridgeCambridgeUnited Kingdom
| | - Matthieu Louis
- EMBL-CRG Systems Biology Research UnitCentre for Genomic Regulation, The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- Department of Molecular, Cellular and Developmental Biology & Neuroscience Research InstituteUniversity of CaliforniaSanta BarbaraUnited States
- Department of PhysicsUniversity of California Santa BarbaraCaliforniaUnited States
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15
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Structure of the Zebrafish Locomotor Repertoire Revealed with Unsupervised Behavioral Clustering. Curr Biol 2018; 28:181-195.e5. [DOI: 10.1016/j.cub.2017.12.002] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 10/29/2017] [Accepted: 12/01/2017] [Indexed: 12/13/2022]
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16
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Calhoun AJ, Murthy M. Quantifying behavior to solve sensorimotor transformations: advances from worms and flies. Curr Opin Neurobiol 2017; 46:90-98. [PMID: 28850885 PMCID: PMC5765764 DOI: 10.1016/j.conb.2017.08.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Revised: 08/05/2017] [Accepted: 08/08/2017] [Indexed: 02/09/2023]
Abstract
The development of new computational tools has recently opened up the study of natural behaviors at a precision that was previously unachievable. These tools permit a highly quantitative analysis of behavioral dynamics at timescales that are well matched to the timescales of neural activity. Here we examine how combining these methods with established techniques for estimating an animal's sensory experience presents exciting new opportunities for dissecting the sensorimotor transformations performed by the nervous system. We focus this review primarily on examples from Caenorhabditis elegans and Drosophila melanogaster-for these model systems, computational approaches to characterize behavior, in combination with unparalleled genetic tools for neural activation, silencing, and recording, have already proven instrumental for illuminating underlying neural mechanisms.
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Affiliation(s)
- Adam J Calhoun
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, United States
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, United States; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, United States
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17
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Katsov AY, Freifeld L, Horowitz M, Kuehn S, Clandinin TR. Dynamic structure of locomotor behavior in walking fruit flies. eLife 2017; 6. [PMID: 28742018 PMCID: PMC5526672 DOI: 10.7554/elife.26410] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 06/08/2017] [Indexed: 12/21/2022] Open
Abstract
The function of the brain is unlikely to be understood without an accurate description of its output, yet the nature of movement elements and their organization remains an open problem. Here, movement elements are identified from dynamics of walking in flies, using unbiased criteria. On one time scale, dynamics of walking are consistent over hundreds of milliseconds, allowing elementary features to be defined. Over longer periods, walking is well described by a stochastic process composed of these elementary features, and a generative model of this process reproduces individual behavior sequences accurately over seconds or longer. Within elementary features, velocities diverge, suggesting that dynamical stability of movement elements is a weak behavioral constraint. Rather, long-term instability can be limited by the finite memory between these elementary features. This structure suggests how complex dynamics may arise in biological systems from elements whose combination need not be tuned for dynamic stability.
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Affiliation(s)
- Alexander Y Katsov
- Department of Neurobiology, Stanford University, Stanford, United States
| | - Limor Freifeld
- Department of Electrical Engineering, Stanford University, Stanford, United States.,Research Laboratory of Electronics, MIT Electrical Engineering and Computer Science Department, Cambridge, United States
| | - Mark Horowitz
- Department of Electrical Engineering, Stanford University, Stanford, United States
| | - Seppe Kuehn
- Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, United States.,Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, United States.,Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Stanford, United States
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18
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Todd JG, Kain JS, de Bivort BL. Systematic exploration of unsupervised methods for mapping behavior. Phys Biol 2017; 14:015002. [PMID: 28166059 DOI: 10.1088/1478-3975/14/1/015002] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
To fully understand the mechanisms giving rise to behavior, we need to be able to precisely measure it. When coupled with large behavioral data sets, unsupervised clustering methods offer the potential of unbiased mapping of behavioral spaces. However, unsupervised techniques to map behavioral spaces are in their infancy, and there have been few systematic considerations of all the methodological options. We compared the performance of seven distinct mapping methods in clustering a wavelet-transformed data set consisting of the x- and y-positions of the six legs of individual flies. Legs were automatically tracked by small pieces of fluorescent dye, while the fly was tethered and walking on an air-suspended ball. We find that there is considerable variation in the performance of these mapping methods, and that better performance is attained when clustering is done in higher dimensional spaces (which are otherwise less preferable because they are hard to visualize). High dimensionality means that some algorithms, including the non-parametric watershed cluster assignment algorithm, cannot be used. We developed an alternative watershed algorithm which can be used in high-dimensional spaces when a probability density estimate can be computed directly. With these tools in hand, we examined the behavioral space of fly leg postural dynamics and locomotion. We find a striking division of behavior into modes involving the fore legs and modes involving the hind legs, with few direct transitions between them. By computing behavioral clusters using the data from all flies simultaneously, we show that this division appears to be common to all flies. We also identify individual-to-individual differences in behavior and behavioral transitions. Lastly, we suggest a computational pipeline that can achieve satisfactory levels of performance without the taxing computational demands of a systematic combinatorial approach.
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Affiliation(s)
- Jeremy G Todd
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. Rowland Institute at Harvard, Cambridge, MA 02142, USA
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19
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Fukunaga T, Iwasaki W. Inactivity periods and postural change speed can explain atypical postural change patterns of Caenorhabditis elegans mutants. BMC Bioinformatics 2017; 18:46. [PMID: 28103804 PMCID: PMC5244558 DOI: 10.1186/s12859-016-1408-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 12/06/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND With rapid advances in genome sequencing and editing technologies, systematic and quantitative analysis of animal behavior is expected to be another key to facilitating data-driven behavioral genetics. The nematode Caenorhabditis elegans is a model organism in this field. Several video-tracking systems are available for automatically recording behavioral data for the nematode, but computational methods for analyzing these data are still under development. RESULTS In this study, we applied the Gaussian mixture model-based binning method to time-series postural data for 322 C. elegans strains. We revealed that the occurrence patterns of the postural states and the transition patterns among these states have a relationship as expected, and such a relationship must be taken into account to identify strains with atypical behaviors that are different from those of wild type. Based on this observation, we identified several strains that exhibit atypical transition patterns that cannot be fully explained by their occurrence patterns of postural states. Surprisingly, we found that two simple factors-overall acceleration of postural movement and elimination of inactivity periods-explained the behavioral characteristics of strains with very atypical transition patterns; therefore, computational analysis of animal behavior must be accompanied by evaluation of the effects of these simple factors. Finally, we found that the npr-1 and npr-3 mutants have similar behavioral patterns that were not predictable by sequence homology, proving that our data-driven approach can reveal the functions of genes that have not yet been characterized. CONCLUSION We propose that elimination of inactivity periods and overall acceleration of postural change speed can explain behavioral phenotypes of strains with very atypical postural transition patterns. Our methods and results constitute guidelines for effectively finding strains that show "truly" interesting behaviors and systematically uncovering novel gene functions by bioimage-informatic approaches.
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Affiliation(s)
- Tsukasa Fukunaga
- Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8568, Japan. .,Faculty of Science and Engineering, Waseda University, Tokyo, 169-0072, Japan. .,Research Fellow of Japan Society for the Promotion of Science, Tokyo, Japan.
| | - Wataru Iwasaki
- Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8568, Japan. .,Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, 277-8564, Japan. .,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, 113-0032, Japan.
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20
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Wystrach A, Lagogiannis K, Webb B. Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae. eLife 2016; 5. [PMID: 27751233 PMCID: PMC5117870 DOI: 10.7554/elife.15504] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 10/17/2016] [Indexed: 12/19/2022] Open
Abstract
Taxis behaviour in Drosophila larva is thought to consist of distinct control mechanisms triggering specific actions. Here, we support a simpler hypothesis: that taxis results from direct sensory modulation of continuous lateral oscillations of the anterior body, sparing the need for ‘action selection’. Our analysis of larvae motion reveals a rhythmic, continuous lateral oscillation of the anterior body, encompassing all head-sweeps, small or large, without breaking the oscillatory rhythm. Further, we show that an agent-model that embeds this hypothesis reproduces a surprising number of taxis signatures observed in larvae. Also, by coupling the sensory input to a neural oscillator in continuous time, we show that the mechanism is robust and biologically plausible. The mechanism provides a simple architecture for combining information across modalities, and explaining how learnt associations modulate taxis. We discuss the results in the light of larval neural circuitry and make testable predictions. DOI:http://dx.doi.org/10.7554/eLife.15504.001
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Affiliation(s)
- Antoine Wystrach
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.,Centre de recherche sur la cognition animal, CNRS, Universite de Toulouse, Toulouse, United Kingdom
| | | | - Barbara Webb
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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21
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Gyenes B, Brown AEX. Deriving Shape-Based Features for C. elegans Locomotion Using Dimensionality Reduction Methods. Front Behav Neurosci 2016; 10:159. [PMID: 27582697 PMCID: PMC4987360 DOI: 10.3389/fnbeh.2016.00159] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 08/03/2016] [Indexed: 11/24/2022] Open
Abstract
High-throughput analysis of animal behavior is increasingly common following the advances of recording technology, leading to large high-dimensional data sets. This dimensionality can sometimes be reduced while still retaining relevant information. In the case of the nematode worm Caenorhabditis elegans, more than 90% of the shape variance can be captured using just four principal components. However, it remains unclear if other methods can achieve a more compact representation or contribute further biological insight to worm locomotion. Here we take a data-driven approach to worm shape analysis using independent component analysis (ICA), non-negative matrix factorization (NMF), a cosine series, and jPCA (a dynamic variant of principal component analysis [PCA]) and confirm that the dimensionality of worm shape space is close to four. Projecting worm shapes onto the bases derived using each method gives interpretable features ranging from head movements to tail oscillation. We use these as a comparison method to find differences between the wild type N2 worms and various mutants. For example, we find that the neuropeptide mutant nlp-1(ok1469) has an exaggerated head movement suggesting a mode of action for the previously described increased turning rate. The different bases provide complementary views of worm behavior and we expect that closer examination of the time series of projected amplitudes will lead to new results in the future.
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Affiliation(s)
- Bertalan Gyenes
- MRC Clinical Sciences CentreLondon, UK; Institute of Clinical Sciences, Faculty of Medicine, Imperial College LondonLondon, UK; Department of Mathematics, Imperial College LondonLondon, UK
| | - André E X Brown
- MRC Clinical Sciences CentreLondon, UK; Institute of Clinical Sciences, Faculty of Medicine, Imperial College LondonLondon, UK
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