1
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Huang Z, Sun Y, Liu S, Chen X, Ping J, Fei P, Gong Z, Zheng N. A machine learning based method for tracking of simultaneously imaged neural activity and body posture of freely moving maggot. Biochem Biophys Res Commun 2024; 727:150290. [PMID: 38941792 DOI: 10.1016/j.bbrc.2024.150290] [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: 03/12/2024] [Revised: 06/16/2024] [Accepted: 06/19/2024] [Indexed: 06/30/2024]
Abstract
To understand neural basis of animal behavior, it is necessary to monitor neural activity and behavior in freely moving animal before building relationship between them. Here we use light sheet fluorescence microscope (LSFM) combined with microfluidic chip to simultaneously capture neural activity and body movement in small freely behaving Drosophila larva. We develop a transfer learning based method to simultaneously track the continuously changing body posture and activity of neurons that move together using a sub-region tracking network with a precise landmark estimation network for the inference of target landmark trajectory. Based on the tracking of each labelled neuron, the activity of the neuron indicated by fluorescent intensity is calculated. For each video, annotation of only 20 frames in a video is sufficient to yield human-level accuracy for all other frames. The validity of this method is further confirmed by reproducing the activity pattern of PMSIs (period-positive median segmental interneurons) and larval movement as previously reported. Using this method, we disclosed the correlation between larval movement and left-right asymmetry in activity of a group of unidentified neurons labelled by R52H01-Gal4 and further confirmed the roles of these neurons in bilateral balance of body contraction during larval crawling by genetic inhibition of these neurons. Our method provides a new tool for accurate extraction of neural activities and movement of freely behaving small-size transparent animals.
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Affiliation(s)
- Zenan Huang
- Zhejiang Lab, Hangzhou, 311121, China; Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310007, China
| | - Yixuan Sun
- Zhejiang Lab, Hangzhou, 311121, China; Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou, 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310058, China
| | | | - Xiaopeng Chen
- Zhejiang Lab, Hangzhou, 311121, China; School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong, University of Science and Technology, Wuhan, 430074, China
| | - Junyu Ping
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong, University of Science and Technology, Wuhan, 430074, China
| | - Peng Fei
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong, University of Science and Technology, Wuhan, 430074, China
| | - Zhefeng Gong
- Zhejiang Lab, Hangzhou, 311121, China; Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou, 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310058, China.
| | - Nenggan Zheng
- Zhejiang Lab, Hangzhou, 311121, China; Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310007, China
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2
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Sprague DY, Rusch K, Dunn RL, Borchardt JM, Ban S, Bubnis G, Chiu GC, Wen C, Suzuki R, Chaudhary S, Lee HJ, Yu Z, Dichter B, Ly R, Onami S, Lu H, Kimura KD, Yemini E, Kato S. Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in C. elegans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.28.591397. [PMID: 38746302 PMCID: PMC11092512 DOI: 10.1101/2024.04.28.591397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
We develop a data harmonization approach for C. elegans volumetric microscopy data, still or video, consisting of a standardized format, data pre-processing techniques, and a set of human-in-the-loop machine learning based analysis software tools. We unify a diverse collection of 118 whole-brain neural activity imaging datasets from 5 labs, storing these and accompanying tools in an online repository called WormID (wormid.org). We use this repository to train three existing automated cell identification algorithms to, for the first time, enable accuracy in neural identification that generalizes across labs, approaching human performance in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. To facilitate communal use of this repository, we created open-source software, code, web-based tools, and tutorials to explore and curate datasets for contribution to the scientific community. This repository provides a growing resource for experimentalists, theorists, and toolmakers to (a) study neuroanatomical organization and neural activity across diverse experimental paradigms, (b) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (c) inform models of neurobiological development and function.
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Affiliation(s)
| | - Kevin Rusch
- Department of Neurobiology, UMass Chan Medical School
| | - Raymond L Dunn
- Department of Neurology, University of California San Francisco
| | | | - Steven Ban
- Department of Neurology, University of California San Francisco
| | - Greg Bubnis
- Department of Neurology, University of California San Francisco
| | - Grace C Chiu
- Department of Neurology, University of California San Francisco
| | - Chentao Wen
- RIKEN Center for Biosystems Dynamics Research
| | - Ryoga Suzuki
- Graduate School of Science, Nagoya City University
| | - Shivesh Chaudhary
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology
| | - Hyun Jee Lee
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology
| | - Zikai Yu
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology
| | | | - Ryan Ly
- Scientific Data Division, Lawrence Berkeley National Laboratory
| | | | - Hang Lu
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology
| | | | | | - Saul Kato
- Department of Neurology, University of California San Francisco
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3
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Boto T, Tomchik SM. Functional Imaging of Learning-Induced Plasticity in the Central Nervous System with Genetically Encoded Reporters in Drosophila. Cold Spring Harb Protoc 2024; 2024:pdb.top107799. [PMID: 37197830 DOI: 10.1101/pdb.top107799] [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] [Indexed: 05/19/2023]
Abstract
Learning and memory allow animals to adjust their behavior based on the predictive value of their past experiences. Memories often exist in complex representations, spread across numerous cells and synapses in the brain. Studying relatively simple forms of memory provides insights into the fundamental processes that underlie multiple forms of memory. Associative learning occurs when an animal learns the relationship between two previously unrelated sensory stimuli, such as when a hungry animal learns that a particular odor is followed by a tasty reward. Drosophila is a particularly powerful model to study how this type of memory works. The fundamental principles are widely shared among animals, and there is a wide range of genetic tools available to study circuit function in flies. In addition, the olfactory structures that mediate associative learning in flies, such as the mushroom body and its associated neurons, are anatomically organized, relatively well-characterized, and readily accessible to imaging. Here, we review the olfactory anatomy and physiology of the olfactory system, describe how plasticity in the olfactory pathway mediates learning and memory, and explain the general principles underlying calcium imaging approaches.
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Affiliation(s)
- Tamara Boto
- Department of Physiology, Trinity College Dublin, Dublin 2, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Seth M Tomchik
- Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA
- Stead Family Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA
- Iowa Neuroscience Institute, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA
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4
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Kramer TS, Flavell SW. Building and integrating brain-wide maps of nervous system function in invertebrates. Curr Opin Neurobiol 2024; 86:102868. [PMID: 38569231 DOI: 10.1016/j.conb.2024.102868] [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: 08/21/2023] [Revised: 02/13/2024] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
The selection and execution of context-appropriate behaviors is controlled by the integrated action of neural circuits throughout the brain. However, how activity is coordinated across brain regions, and how nervous system structure enables these functional interactions, remain open questions. Recent technical advances have made it feasible to build brain-wide maps of nervous system structure and function, such as brain activity maps, connectomes, and cell atlases. Here, we review recent progress in this area, focusing on C. elegans and D. melanogaster, as recent work has produced global maps of these nervous systems. We also describe neural circuit motifs elucidated in studies of specific networks, which highlight the complexities that must be captured to build accurate models of whole-brain function.
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Affiliation(s)
- Talya S Kramer
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; MIT Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven W Flavell
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
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5
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Hanson A, Reme R, Telerman N, Yamamoto W, Olivo-Marin JC, Lagache T, Yuste R. Automatic monitoring of neural activity with single-cell resolution in behaving Hydra. Sci Rep 2024; 14:5083. [PMID: 38429381 PMCID: PMC10907378 DOI: 10.1038/s41598-024-55608-2] [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: 09/25/2023] [Accepted: 02/26/2024] [Indexed: 03/03/2024] Open
Abstract
The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.
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Affiliation(s)
- Alison Hanson
- Department of Biological Sciences, Neurotechnology Center, Columbia University, New York, NY, USA.
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University, New York, NY, USA.
| | - Raphael Reme
- UMR3691, BioImage Analysis Unit, Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Noah Telerman
- Department of Biological Sciences, Neurotechnology Center, Columbia University, New York, NY, USA
| | - Wataru Yamamoto
- Department of Biological Sciences, Neurotechnology Center, Columbia University, New York, NY, USA
| | | | - Thibault Lagache
- UMR3691, BioImage Analysis Unit, Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Rafael Yuste
- Department of Biological Sciences, Neurotechnology Center, Columbia University, New York, NY, USA
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6
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Brezovec BE, Berger AB, Hao YA, Chen F, Druckmann S, Clandinin TR. Mapping the neural dynamics of locomotion across the Drosophila brain. Curr Biol 2024; 34:710-726.e4. [PMID: 38242122 DOI: 10.1016/j.cub.2023.12.063] [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: 08/16/2023] [Revised: 11/13/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024]
Abstract
Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.
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Affiliation(s)
- Bella E Brezovec
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Andrew B Berger
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Yukun A Hao
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Feng Chen
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Shaul Druckmann
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA.
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7
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Pospisil DA, Aragon MJ, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Jefferis GSXE, Murthy M, Pillow JW. From connectome to effectome: learning the causal interaction map of the fly brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.31.564922. [PMID: 37961285 PMCID: PMC10635032 DOI: 10.1101/2023.10.31.564922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.
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Affiliation(s)
- Dean A Pospisil
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Max J Aragon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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8
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Park CF, Barzegar-Keshteli M, Korchagina K, Delrocq A, Susoy V, Jones CL, Samuel ADT, Rahi SJ. Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation. Nat Methods 2024; 21:142-149. [PMID: 38052988 DOI: 10.1038/s41592-023-02096-3] [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: 03/09/2022] [Accepted: 10/20/2023] [Indexed: 12/07/2023]
Abstract
Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional neural network with ground-truth (GT) annotations of images representing different brain postures. For 3D images, this is very labor intensive. We introduce 'targeted augmentation', a method to automatically synthesize artificial annotations from a few manual annotations. Our method ('Targettrack') learns the internal deformations of the brain to synthesize annotations for new postures by deforming GT annotations. This reduces the need for manual annotation and proofreading. A graphical user interface allows the application of the method end-to-end. We demonstrate Targettrack on recordings where neurons are labeled as key points or 3D volumes. Analyzing freely moving animals exposed to odor pulses, we uncover rich patterns in interneuron dynamics, including switching neuronal entrainment on and off.
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Affiliation(s)
- Core Francisco Park
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Mahsa Barzegar-Keshteli
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Kseniia Korchagina
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ariane Delrocq
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vladislav Susoy
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Corinne L Jones
- Swiss Data Science Center, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Aravinthan D T Samuel
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Sahand Jamal Rahi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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9
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Lee TJ, Briggman KL. Visually guided and context-dependent spatial navigation in the translucent fish Danionella cerebrum. Curr Biol 2023; 33:5467-5477.e4. [PMID: 38070503 DOI: 10.1016/j.cub.2023.11.030] [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: 06/19/2023] [Revised: 10/06/2023] [Accepted: 11/14/2023] [Indexed: 12/21/2023]
Abstract
Danionella cerebrum (DC) is a promising vertebrate animal model for systems neuroscience due to its small adult brain volume and inherent optical transparency, but the scope of their cognitive abilities remains an area of active research. In this work, we established a behavioral paradigm to study visual spatial navigation in DC and investigate their navigational capabilities and strategies. We initially observed that adult DC exhibit strong negative phototaxis in groups but less so as individuals. Using their dark preference as a motivator, we designed a spatial navigation task inspired by the Morris water maze. Through a series of environmental cue manipulations, we found that DC utilize visual cues to anticipate a reward location and found evidence for landmark-based navigational strategies wherein DC could use both proximal and distal visual cues. When subsets of proximal visual cues were occluded, DC were capable of using distant contextual visual information to solve the task, providing evidence for allocentric spatial navigation. Without proximal visual cues, DC tended to seek out a direct line of sight with at least one distal visual cue while maintaining a positional bias toward the reward location. In total, our behavioral results suggest that DC can be used to study the neural mechanisms underlying spatial navigation with cellular resolution imaging across an adult vertebrate brain.
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Affiliation(s)
- Timothy J Lee
- Max Planck Institute for Neurobiology of Behavior - caesar, Department of Computational Neuroethology, Ludwig-Erhard-Allee 2, Bonn, 53175 North Rhine-Westphalia, Germany.
| | - Kevin L Briggman
- Max Planck Institute for Neurobiology of Behavior - caesar, Department of Computational Neuroethology, Ludwig-Erhard-Allee 2, Bonn, 53175 North Rhine-Westphalia, Germany.
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10
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Schafer SF, Croke H, Kriete A, Ayaz H, Lewin PA, von Reyn CR, Schafer ME. A Miniature Ultrasound Source for Neural Modulation. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2023; 70:1544-1553. [PMID: 37812556 PMCID: PMC10751802 DOI: 10.1109/tuffc.2023.3322963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
This work describes a unique ultrasound (US) exposure system designed to create very localized ( [Formula: see text]) sound fields at operating frequencies that are currently being used for preclinical US neuromodulation. This system can expose small clusters of neuronal tissue, such as cell cultures or intact brain structures in target animal models, opening up opportunities to examine possible mechanisms of action. We modified a dental descaler and drove it at a resonance frequency of 96 kHz, well above its nominal operating point of 28 kHz. A ceramic microtip from an ultrasonic wire bonder was attached to the end of the applicator, creating a 100- [Formula: see text] point source. The device was calibrated with a polyvinylidene difluoride (PVDF) membrane hydrophone, in a novel, air-backed, configuration. The experimental results were confirmed by simulation using a monopole model. The results show a consistent decaying sound field from the tip, well-suited to neural stimulation. The system was tested on an existing neurological model, Drosophila melanogaster, which has not previously been used for US neuromodulation experiments. The results show brain-directed US stimulation induces or suppresses motor actions, demonstrated through synchronized tracking of fly limb movements. These results provide the basis for ongoing and future studies of US interaction with neuronal tissue, both at the level of single neurons and intact organisms.
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11
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Hanson A, Reme R, Telerman N, Yamamoto W, Olivo-Marin JC, Lagache T, Yuste R. Automatic monitoring of whole-body neural activity in behaving Hydra. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559063. [PMID: 37790332 PMCID: PMC10542483 DOI: 10.1101/2023.09.22.559063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.
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Affiliation(s)
- Alison Hanson
- Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University, New York, NY, USA
| | - Raphael Reme
- Institut Pasteur, Université Paris Cité, CNRS UMR3691, BioImage Analysis Unit, Paris, France
| | - Noah Telerman
- Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Wataru Yamamoto
- Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
| | | | - Thibault Lagache
- Institut Pasteur, Université Paris Cité, CNRS UMR3691, BioImage Analysis Unit, Paris, France
| | - Rafael Yuste
- Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
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12
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Schaffer ES, Mishra N, Whiteway MR, Li W, Vancura MB, Freedman J, Patel KB, Voleti V, Paninski L, Hillman EMC, Abbott LF, Axel R. The spatial and temporal structure of neural activity across the fly brain. Nat Commun 2023; 14:5572. [PMID: 37696814 PMCID: PMC10495430 DOI: 10.1038/s41467-023-41261-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 08/29/2023] [Indexed: 09/13/2023] Open
Abstract
What are the spatial and temporal scales of brainwide neuronal activity? We used swept, confocally-aligned planar excitation (SCAPE) microscopy to image all cells in a large volume of the brain of adult Drosophila with high spatiotemporal resolution while flies engaged in a variety of spontaneous behaviors. This revealed neural representations of behavior on multiple spatial and temporal scales. The activity of most neurons correlated (or anticorrelated) with running and flailing over timescales that ranged from seconds to a minute. Grooming elicited a weaker global response. Significant residual activity not directly correlated with behavior was high dimensional and reflected the activity of small clusters of spatially organized neurons that may correspond to genetically defined cell types. These clusters participate in the global dynamics, indicating that neural activity reflects a combination of local and broadly distributed components. This suggests that microcircuits with highly specified functions are provided with knowledge of the larger context in which they operate.
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Affiliation(s)
- Evan S Schaffer
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA.
| | - Neeli Mishra
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Matthew R Whiteway
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Statistics and the Grossman Center for the Statistics of Mind, Columbia University, New York, NY, 10027, USA
| | - Wenze Li
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Michelle B Vancura
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Jason Freedman
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Kripa B Patel
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Venkatakaushik Voleti
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Statistics and the Grossman Center for the Statistics of Mind, Columbia University, New York, NY, 10027, USA
| | - Elizabeth M C Hillman
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
- Department of Radiology, Columbia University, New York, NY, 10027, USA
| | - L F Abbott
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, 10032, USA
| | - Richard Axel
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, 10032, USA
- Howard Hughes Medical Institute, Columbia University, New York, NY, 10027, USA
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13
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Baker CM, Gong Y. Identifying properties of pattern completion neurons in a computational model of the visual cortex. PLoS Comput Biol 2023; 19:e1011167. [PMID: 37279242 DOI: 10.1371/journal.pcbi.1011167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 05/09/2023] [Indexed: 06/08/2023] Open
Abstract
Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles' role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection of pattern completion neurons in simulated ensembles. We developed a computational model that replicated the connectivity patterns and electrophysiological properties of layer 2/3 of mouse V1. We identified ensembles of excitatory model neurons using K-means clustering. We then stimulated pairs of neurons in identified ensembles while tracking the activity of the entire ensemble. Our analysis of ensemble activity quantified a neuron pair's power to activate an ensemble using a novel metric called pattern completion capability (PCC) based on the mean pre-stimulation voltage across the ensemble. We found that PCC was directly correlated with multiple graph theory parameters, such as degree and closeness centrality. To improve selection of pattern completion neurons in vivo, we computed a novel latency metric that was correlated with PCC and could potentially be estimated from modern physiological recordings. Lastly, we found that stimulation of five neurons could reliably activate ensembles. These findings can help researchers identify pattern completion neurons to stimulate in vivo during behavioral studies to control ensemble activation.
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Affiliation(s)
- Casey M Baker
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Yiyang Gong
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Neurobiology, Duke University, Durham, North Carolina, United States of America
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14
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Banerjee A, Chandra S, Ott E. Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics. Proc Natl Acad Sci U S A 2023; 120:e2216030120. [PMID: 36927154 PMCID: PMC10041139 DOI: 10.1073/pnas.2216030120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/04/2023] [Indexed: 03/18/2023] Open
Abstract
Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases, the measured time series data may be subject to limitations, including limited duration, low sampling rate, observational noise, and partial nodal state measurement. However, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality, transfer entropy, and, a machine learning-based method. Furthermore, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.
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Affiliation(s)
- Amitava Banerjee
- Department of Physics, University of Maryland, College Park, MD20742
- Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD20742
| | - Sarthak Chandra
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA02139
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Edward Ott
- Department of Physics, University of Maryland, College Park, MD20742
- Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD20742
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD20742
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15
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Kirk MJ, Gold A, Ravi A, Sterne GR, Scott K, Miller EW. Cell-Surface Targeting of Fluorophores in Drosophila for Rapid Neuroanatomy Visualization. ACS Chem Neurosci 2023; 14:909-916. [PMID: 36799505 PMCID: PMC10187464 DOI: 10.1021/acschemneuro.2c00745] [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: 02/18/2023] Open
Abstract
Visualizing neuronal anatomy often requires labor-intensive immunohistochemistry on fixed and dissected brains. To facilitate rapid anatomical staining in live brains, we used genetically targeted membrane tethers that covalently link fluorescent dyes for in vivo neuronal labeling. We generated a series of extracellularly trafficked small-molecule tethering proteins, HaloTag-CD4 (Kirk et al. Front. Neurosci. 2021, 15, 754027) and SNAPf-CD4, which directly label transgene-expressing cells with commercially available ligand-substituted fluorescent dyes. We created stable transgenic Drosophila reporter lines, which express extracellular HaloTag-CD4 and SNAPf-CD4 with LexA and Gal4 drivers. Expressing these enzymes in live Drosophila brains, we labeled the expression patterns of various Gal4 driver lines recapitulating histological staining in live-brain tissues. Pan-neural expression of SNAPf-CD4 enabled the registration of live brains to an existing template for anatomical comparisons. We predict that these extracellular platforms will not only become a valuable complement to existing anatomical methods but will also prove useful for future genetic targeting of other small-molecule probes, drugs, and actuators.
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Affiliation(s)
- Molly J. Kirk
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Arya Gold
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Ashvin Ravi
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Gabriella R. Sterne
- Department of Molecular & Cell Biology, University of California, Berkeley, California 94720, USA
| | - Kristin Scott
- Department of Molecular & Cell Biology, University of California, Berkeley, California 94720, USA
| | - Evan W. Miller
- Department of Molecular & Cell Biology, University of California, Berkeley, California 94720, USA
- Department of Chemistry, University of California, Berkeley, California 94720, USA
- Helen Wills Neuroscience Institute. University of California, Berkeley, California 94720, USA
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16
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Rusakov DA. A misadventure of the correlation coefficient. Trends Neurosci 2023; 46:94-96. [PMID: 36280457 DOI: 10.1016/j.tins.2022.09.009] [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: 08/05/2022] [Accepted: 09/30/2022] [Indexed: 01/25/2023]
Abstract
The correlation coefficient gauges linear association between two variables. However, interpreting its value depends on the question at hand. This article argues that relying on the correlation coefficient may be irrelevant for many neuroscience research tasks. When the experimental dataset is contextually suitable for binning-averaging, other indicators of statistical association could prove more suitable.
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Affiliation(s)
- Dmitri A Rusakov
- UCL Queen Square Institute of Neurology, University College London, London, UK.
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17
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Mitroshina EV, Pakhomov AM, Krivonosov MI, Yarkov RS, Gavrish MS, Shkirin AV, Ivanchenko MV, Vedunova MV. Novel Algorithm of Network Calcium Dynamics Analysis for Studying the Role of Astrocytes in Neuronal Activity in Alzheimer's Disease Models. Int J Mol Sci 2022; 23:ijms232415928. [PMID: 36555569 PMCID: PMC9781291 DOI: 10.3390/ijms232415928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/09/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022] Open
Abstract
Accumulated experimental data strongly suggest that astrocytes play an important role in the pathogenesis of neurodegeneration, including Alzheimer's disease (AD). The effect of astrocytes on the calcium activity of neuron-astroglia networks in AD modelling was the object of the present study. We have expanded and improved our approach's capabilities to analyze calcium activity. We have developed a novel algorithm to construct dynamic directed graphs of both astrocytic and neuronal networks. The proposed algorithm allows us not only to identify functional relationships between cells and determine the presence of network activity, but also to characterize the spread of the calcium signal from cell to cell. Our study showed that Alzheimer's astrocytes can change the functional pattern of the calcium activity of healthy nerve cells. When healthy nerve cells were cocultivated with astrocytes treated with Aβ42, activation of calcium signaling was found. When healthy nerve cells were cocultivated with 5xFAD astrocytes, inhibition of calcium signaling was observed. In this regard, it seems relevant to further study astrocytic-neuronal interactions as an important factor in the regulation of the functional activity of brain cells during neurodegenerative processes. The approach to the analysis of streaming imaging data developed by the authors is a promising tool for studying the collective calcium dynamics of nerve cells.
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Affiliation(s)
- Elena V. Mitroshina
- Department of Neurotechnology, Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
- Correspondence: ; Tel.: +7-950-604-5137
| | - Alexander M. Pakhomov
- Department of Neurotechnology, Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
- Institute of Applied Physics RAS, 46 Ulyanov Street, Nizhny Novgorod 603950, Russia
| | - Mikhail I. Krivonosov
- Department of Neurotechnology, Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
- Department of Applied Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
| | - Roman S. Yarkov
- Department of Neurotechnology, Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
| | - Maria S. Gavrish
- Department of Neurotechnology, Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
| | - Alexey V. Shkirin
- Prokhorov General Physics Institute of the Russian Academy of Sciences, Vavilova St. 38, Moscow 119991, Russia
- Laser Physics Department, National Research Nuclear University MEPhI, Kashirskoe Sh. 31, Moscow 115409, Russia
| | - Mikhail V. Ivanchenko
- Department of Applied Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
| | - Maria V. Vedunova
- Department of Neurotechnology, Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
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18
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Turner MH, Krieger A, Pang MM, Clandinin TR. Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila. eLife 2022; 11:e82587. [PMID: 36300621 PMCID: PMC9651947 DOI: 10.7554/elife.82587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/25/2022] [Indexed: 01/07/2023] Open
Abstract
Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here, we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal, and a visual motion signal associated with rotation of the animal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion.
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Affiliation(s)
- Maxwell H Turner
- Department of Neurobiology, Stanford UniversityStanfordUnited States
| | - Avery Krieger
- Department of Neurobiology, Stanford UniversityStanfordUnited States
| | - Michelle M Pang
- Department of Neurobiology, Stanford UniversityStanfordUnited States
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19
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Wang L, Wu C, Peng W, Zhou Z, Zeng J, Li X, Yang Y, Yu S, Zou Y, Huang M, Liu C, Chen Y, Li Y, Ti P, Liu W, Gao Y, Zheng W, Zhong H, Gao S, Lu Z, Ren PG, Ng HL, He J, Chen S, Xu M, Li Y, Chu J. A high-performance genetically encoded fluorescent indicator for in vivo cAMP imaging. Nat Commun 2022; 13:5363. [PMID: 36097007 PMCID: PMC9468011 DOI: 10.1038/s41467-022-32994-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
cAMP is a key second messenger that regulates diverse cellular functions including neural plasticity. However, the spatiotemporal dynamics of intracellular cAMP in intact organisms are largely unknown due to low sensitivity and/or brightness of current genetically encoded fluorescent cAMP indicators. Here, we report the development of the new circularly permuted GFP (cpGFP)-based cAMP indicator G-Flamp1, which exhibits a large fluorescence increase (a maximum ΔF/F0 of 1100% in HEK293T cells), decent brightness, appropriate affinity (a Kd of 2.17 μM) and fast response kinetics (an association and dissociation half-time of 0.20 and 0.087 s, respectively). Furthermore, the crystal structure of the cAMP-bound G-Flamp1 reveals one linker connecting the cAMP-binding domain to cpGFP adopts a distorted β-strand conformation that may serve as a fluorescence modulation switch. We demonstrate that G-Flamp1 enables sensitive monitoring of endogenous cAMP signals in brain regions that are implicated in learning and motor control in living organisms such as fruit flies and mice.
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Affiliation(s)
- Liang Wang
- grid.9227.e0000000119573309Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Chunling Wu
- grid.11135.370000 0001 2256 9319PKU-IDG–McGovern Institute for Brain Research, Beijing, 100871 China ,grid.9227.e0000000119573309State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101 China
| | - Wanling Peng
- grid.9227.e0000000119573309Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Ziliang Zhou
- grid.12981.330000 0001 2360 039XMolecular Imaging Center, Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000 China ,grid.410737.60000 0000 8653 1072Department of Oral Emergency and General Dentistry, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou, 510182 Guangdong China
| | - Jianzhi Zeng
- grid.11135.370000 0001 2256 9319PKU-IDG–McGovern Institute for Brain Research, Beijing, 100871 China
| | - Xuelin Li
- grid.11135.370000 0001 2256 9319PKU-IDG–McGovern Institute for Brain Research, Beijing, 100871 China
| | - Yini Yang
- grid.11135.370000 0001 2256 9319PKU-IDG–McGovern Institute for Brain Research, Beijing, 100871 China
| | - Shuguang Yu
- grid.9227.e0000000119573309State Key Laboratory of Neuroscience, Institute of Neuroscience, Shanghai Institutes for Biological Sciences, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Ye Zou
- grid.36567.310000 0001 0737 1259Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, 66506 KS USA
| | - Mian Huang
- grid.36567.310000 0001 0737 1259Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, 66506 KS USA
| | - Chang Liu
- grid.9227.e0000000119573309Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Yefei Chen
- grid.9227.e0000000119573309Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Yi Li
- grid.33199.310000 0004 0368 7223Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074 China
| | - Panpan Ti
- grid.33199.310000 0004 0368 7223Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074 China
| | - Wenfeng Liu
- grid.9227.e0000000119573309Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Yufeng Gao
- grid.9227.e0000000119573309Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Wei Zheng
- grid.9227.e0000000119573309Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Haining Zhong
- grid.5288.70000 0000 9758 5690Vollum Institute, Oregon Health and Science University, Portland, 97239 OR USA
| | - Shangbang Gao
- grid.33199.310000 0004 0368 7223Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074 China
| | - Zhonghua Lu
- grid.9227.e0000000119573309Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Pei-Gen Ren
- grid.9227.e0000000119573309Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | - Ho Leung Ng
- grid.36567.310000 0001 0737 1259Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, 66506 KS USA
| | - Jie He
- grid.9227.e0000000119573309State Key Laboratory of Neuroscience, Institute of Neuroscience, Shanghai Institutes for Biological Sciences, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Shoudeng Chen
- grid.12981.330000 0001 2360 039XMolecular Imaging Center, Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000 China ,grid.12981.330000 0001 2360 039XDepartment of Experimental Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000 China
| | - Min Xu
- grid.9227.e0000000119573309Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Yulong Li
- grid.11135.370000 0001 2256 9319PKU-IDG–McGovern Institute for Brain Research, Beijing, 100871 China
| | - Jun Chu
- grid.9227.e0000000119573309Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China ,grid.458489.c0000 0001 0483 7922Shenzhen-Hong Kong Institute of Brain Science, and Shenzhen Institute of Synthetic Biology, Shenzhen, 518055 China ,grid.9227.e0000000119573309CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
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20
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Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning. Nat Commun 2022; 13:5165. [PMID: 36056020 PMCID: PMC9440141 DOI: 10.1038/s41467-022-32886-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 08/18/2022] [Indexed: 11/08/2022] Open
Abstract
Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applied to denoise images, their applications to downstream analyses, such as recovering high-SNR calcium traces, have been limited. Further, these methods require temporally-sequential pre-registered data acquired at ultrafast rates. Here, we demonstrate a supervised deep-denoising method to circumvent these tradeoffs for several applications, including whole-brain imaging, large-field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans. Our framework has 30× smaller memory footprint, and is fast in training and inference (50–70 ms); it is highly accurate and generalizable, and further, trained with only small, non-temporally-sequential, independently-acquired training datasets (∼500 pairs of images). We envision that the framework will enable faster and long-term imaging experiments necessary to study neuronal mechanisms of many behaviors. Volumetric functional imaging is widely used for recording neuron activities in vivo for many experimental organisms. Here the authors report supervised deep-denoising methods for improved whole-brain imaging, large field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans.
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21
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The neuronal logic of how internal states control food choice. Nature 2022; 607:747-755. [DOI: 10.1038/s41586-022-04909-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 05/25/2022] [Indexed: 11/08/2022]
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22
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Sokolov RA, Mukhina IV. Spontaneous Ca 2+ events are linked to the development of neuronal firing during maturation in mice primary hippocampal culture cells. Arch Biochem Biophys 2022; 727:109330. [PMID: 35750097 DOI: 10.1016/j.abb.2022.109330] [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: 01/31/2022] [Revised: 04/21/2022] [Accepted: 06/19/2022] [Indexed: 11/30/2022]
Abstract
Calcium is one of the most vital intracellular secondary messengers that tightly regulates a variety of cell physiology processes, especially in the brain. Using a fluorescent Ca2+-sensitive Oregon Green probe, we revealed three different amplitude distributions of spontaneous Ca2+ events (SCEs) in neurons between 15 and 26 days in vitro (DIV) culture maturation. We detected a series of amplitude events: micro amplitude SCE (microSCE) 25% increase from the baseline, intermediate amplitude SCE (interSCE) as 25-75%, and macro amplitude SCE (macroSCE) - over 75%. The SCEs were fully dependent on extracellular Ca2+ and neuronal network activity and vanished in the Ca2+-free solution, 10 mM Mg2+-block, or in the presence of voltage-gated Na+-channel blocker, tetrodotoxin. Combined patch-clamp and Ca2+-imaging techniques revealed that microSCE match single action potential (AP), interSCE - burst of 3-12 APs, and macroSCE - 'superburst' of 10+ APs. MicroSCEs were blocked by a common α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)/kainic acid (KA) receptor antagonist, CNQX. The γ-aminobutyric acid (GABA) A-type receptor (GABAAR) picrotoxin blockade and L-type voltage-dependent Ca2+-channel inhibitor diltiazem significantly reduced microSCE frequency. InterSCEs were inhibited by CNQX, but picrotoxin treatment significantly increased its amplitude. The N-methyl-d-aspartate (NMDA) receptor antagonist, D-APV, voltage-gated K+-channel blocker, tetraethylammonium, noticeably suppressed interSCE amplitude. We also demonstrate that macroSCEs were AMPA/KA receptor-independent.
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Affiliation(s)
- Rostislav A Sokolov
- Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia; In Vivo Research Center, Sirius University of Science and Technology, Olympic Avenue, 1, Sochi, Russia.
| | - Irina V Mukhina
- Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia; Institute of Fundamental Medicine, Privolzhsky Research Medical University, Nizhny Novgorod, Russia.
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23
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Zanon M, Zanini D, Haase A. All-optical manipulation of the Drosophila olfactory system. Sci Rep 2022; 12:8506. [PMID: 35595846 PMCID: PMC9123005 DOI: 10.1038/s41598-022-12237-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/09/2022] [Indexed: 11/09/2022] Open
Abstract
Thanks to its well-known neuroanatomy, limited brain size, complex behaviour, and the extensive genetic methods, Drosophila has become an indispensable model in neuroscience. A vast number of studies have focused on its olfactory system and the processing of odour information. Optogenetics is one of the recently developed genetic tools that significantly advance this field of research, allowing to replace odour stimuli by direct neuronal activation with light. This becomes a universal all-optical toolkit when spatially selective optogenetic activation is combined with calcium imaging to read out neuronal responses. Initial experiments showed a successful implementation to study the olfactory system in fish and mice, but the olfactory system of Drosophila has been so far precluded from an application. To fill this gap, we present here optogenetic tools to selectively stimulate functional units in the Drosophila olfactory system, combined with two-photon calcium imaging to read out the activity patterns elicited by these stimuli at different levels of the brain. This method allows to study the spatial and temporal features of the information flow and reveals the functional connectivity in the olfactory network.
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Affiliation(s)
- Mirko Zanon
- Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy.
- Department of Physics, University of Trento, Trento, Italy.
| | - Damiano Zanini
- Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
- Neurobiology and Genetics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Albrecht Haase
- Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy.
- Department of Physics, University of Trento, Trento, Italy.
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24
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Lin A, Witvliet D, Hernandez-Nunez L, Linderman SW, Samuel ADT, Venkatachalam V. Imaging whole-brain activity to understand behavior. NATURE REVIEWS. PHYSICS 2022; 4:292-305. [PMID: 37409001 PMCID: PMC10320740 DOI: 10.1038/s42254-022-00430-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/25/2022] [Indexed: 07/07/2023]
Abstract
The brain evolved to produce behaviors that help an animal inhabit the natural world. During natural behaviors, the brain is engaged in many levels of activity from the detection of sensory inputs to decision-making to motor planning and execution. To date, most brain studies have focused on small numbers of neurons that interact in limited circuits. This allows analyzing individual computations or steps of neural processing. During behavior, however, brain activity must integrate multiple circuits in different brain regions. The activities of different brain regions are not isolated, but may be contingent on one another. Coordinated and concurrent activity within and across brain areas is organized by (1) sensory information from the environment, (2) the animal's internal behavioral state, and (3) recurrent networks of synaptic and non-synaptic connectivity. Whole-brain recording with cellular resolution provides a new opportunity to dissect the neural basis of behavior, but whole-brain activity is also mutually contingent on behavior itself. This is especially true for natural behaviors like navigation, mating, or hunting, which require dynamic interaction between the animal, its environment, and other animals. In such behaviors, the sensory experience of an unrestrained animal is actively shaped by its movements and decisions. Many of the signaling and feedback pathways that an animal uses to guide behavior only occur in freely moving animals. Recent technological advances have enabled whole-brain recording in small behaving animals including nematodes, flies, and zebrafish. These whole-brain experiments capture neural activity with cellular resolution spanning sensory, decision-making, and motor circuits, and thereby demand new theoretical approaches that integrate brain dynamics with behavioral dynamics. Here, we review the experimental and theoretical methods that are being employed to understand animal behavior and whole-brain activity, and the opportunities for physics to contribute to this emerging field of systems neuroscience.
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Affiliation(s)
- Albert Lin
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Daniel Witvliet
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Luis Hernandez-Nunez
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Scott W Linderman
- Department of Statistics, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Aravinthan D T Samuel
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Vivek Venkatachalam
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
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Urai AE, Doiron B, Leifer AM, Churchland AK. Large-scale neural recordings call for new insights to link brain and behavior. Nat Neurosci 2022; 25:11-19. [PMID: 34980926 DOI: 10.1038/s41593-021-00980-9] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/08/2021] [Indexed: 12/17/2022]
Abstract
Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.
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Affiliation(s)
- Anne E Urai
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.,Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands
| | | | | | - Anne K Churchland
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. .,University of California Los Angeles, Los Angeles, CA, USA.
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26
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Aymanns F, Chen CL, Ramdya P. Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors. eLife 2022; 11:81527. [PMID: 36286408 PMCID: PMC9605690 DOI: 10.7554/elife.81527] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/13/2022] [Indexed: 11/21/2022] Open
Abstract
Deciphering how the brain regulates motor circuits to control complex behaviors is an important, long-standing challenge in neuroscience. In the fly, Drosophila melanogaster, this is coordinated by a population of ~ 1100 descending neurons (DNs). Activating only a few DNs is known to be sufficient to drive complex behaviors like walking and grooming. However, what additional role the larger population of DNs plays during natural behaviors remains largely unknown. For example, they may modulate core behavioral commands or comprise parallel pathways that are engaged depending on sensory context. We evaluated these possibilities by recording populations of nearly 100 DNs in individual tethered flies while they generated limb-dependent behaviors, including walking and grooming. We found that the largest fraction of recorded DNs encode walking while fewer are active during head grooming and resting. A large fraction of walk-encoding DNs encode turning and far fewer weakly encode speed. Although odor context does not determine which behavior-encoding DNs are recruited, a few DNs encode odors rather than behaviors. Lastly, we illustrate how one can identify individual neurons from DN population recordings by using their spatial, functional, and morphological properties. These results set the stage for a comprehensive, population-level understanding of how the brain’s descending signals regulate complex motor actions.
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Affiliation(s)
- Florian Aymanns
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFLLausanneSwitzerland
| | - Chin-Lin Chen
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFLLausanneSwitzerland
| | - Pavan Ramdya
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFLLausanneSwitzerland
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27
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Climer JR, Dombeck DA. Information Theoretic Approaches to Deciphering the Neural Code with Functional Fluorescence Imaging. eNeuro 2021; 8:ENEURO.0266-21.2021. [PMID: 34433574 PMCID: PMC8474651 DOI: 10.1523/eneuro.0266-21.2021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/22/2021] [Accepted: 08/04/2021] [Indexed: 11/21/2022] Open
Abstract
Information theoretic metrics have proven useful in quantifying the relationship between behaviorally relevant parameters and neuronal activity with relatively few assumptions. However, these metrics are typically applied to action potential (AP) recordings and were not designed for the slow timescales and variable amplitudes typical of functional fluorescence recordings (e.g., calcium imaging). The lack of research guidelines on how to apply and interpret these metrics with fluorescence traces means the neuroscience community has yet to realize the power of information theoretic metrics. Here, we used computational methods to create mock AP traces with known amounts of information. From these, we generated fluorescence traces and examined the ability of different information metrics to recover the known information values. We provide guidelines for how to use information metrics when applying them to functional fluorescence and demonstrate their appropriate application to GCaMP6f population recordings from mouse hippocampal neurons imaged during virtual navigation.
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Affiliation(s)
- Jason R Climer
- Department of Neurobiology, Northwestern University, Evanston, 60208 IL
| | - Daniel A Dombeck
- Department of Neurobiology, Northwestern University, Evanston, 60208 IL
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28
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Redolfi N, García-Casas P, Fornetto C, Sonda S, Pizzo P, Pendin D. Lighting Up Ca 2+ Dynamics in Animal Models. Cells 2021; 10:2133. [PMID: 34440902 PMCID: PMC8392631 DOI: 10.3390/cells10082133] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/08/2021] [Accepted: 08/16/2021] [Indexed: 12/11/2022] Open
Abstract
Calcium (Ca2+) signaling coordinates are crucial processes in brain physiology. Particularly, fundamental aspects of neuronal function such as synaptic transmission and neuronal plasticity are regulated by Ca2+, and neuronal survival itself relies on Ca2+-dependent cascades. Indeed, impaired Ca2+ homeostasis has been reported in aging as well as in the onset and progression of neurodegeneration. Understanding the physiology of brain function and the key processes leading to its derangement is a core challenge for neuroscience. In this context, Ca2+ imaging represents a powerful tool, effectively fostered by the continuous amelioration of Ca2+ sensors in parallel with the improvement of imaging instrumentation. In this review, we explore the potentiality of the most used animal models employed for Ca2+ imaging, highlighting their application in brain research to explore the pathogenesis of neurodegenerative diseases.
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Affiliation(s)
- Nelly Redolfi
- Department of Biomedical Sciences, University of Padua, 35131 Padua, Italy; (N.R.); (P.G.-C.); (C.F.); (S.S.); (P.P.)
| | - Paloma García-Casas
- Department of Biomedical Sciences, University of Padua, 35131 Padua, Italy; (N.R.); (P.G.-C.); (C.F.); (S.S.); (P.P.)
| | - Chiara Fornetto
- Department of Biomedical Sciences, University of Padua, 35131 Padua, Italy; (N.R.); (P.G.-C.); (C.F.); (S.S.); (P.P.)
| | - Sonia Sonda
- Department of Biomedical Sciences, University of Padua, 35131 Padua, Italy; (N.R.); (P.G.-C.); (C.F.); (S.S.); (P.P.)
| | - Paola Pizzo
- Department of Biomedical Sciences, University of Padua, 35131 Padua, Italy; (N.R.); (P.G.-C.); (C.F.); (S.S.); (P.P.)
- Neuroscience Institute, National Research Council (CNR), 35131 Padua, Italy
| | - Diana Pendin
- Department of Biomedical Sciences, University of Padua, 35131 Padua, Italy; (N.R.); (P.G.-C.); (C.F.); (S.S.); (P.P.)
- Neuroscience Institute, National Research Council (CNR), 35131 Padua, Italy
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29
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Turner MH, Mann K, Clandinin TR. The connectome predicts resting-state functional connectivity across the Drosophila brain. Curr Biol 2021; 31:2386-2394.e3. [PMID: 33770490 PMCID: PMC8519013 DOI: 10.1016/j.cub.2021.03.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/08/2021] [Accepted: 03/01/2021] [Indexed: 12/26/2022]
Abstract
Anatomical connectivity can constrain both a neural circuit's function and its underlying computation. This principle has been demonstrated for many small, defined neural circuits. For example, connectome reconstructions have informed models for direction selectivity in the vertebrate retina1,2 as well as the Drosophila visual system.3 In these cases, the circuit in question is relatively compact, well-defined, and has known functions. However, how the connectome constrains global properties of large-scale networks, across multiple brain regions or the entire brain, is incompletely understood. As the availability of partial or complete connectomes expands to more systems and species4-8 it becomes critical to understand how this detailed anatomical information can inform our understanding of large-scale circuit function.9,10 Here, we use data from the Drosophila connectome4 in conjunction with whole-brain in vivo imaging11 to relate structural and functional connectivity in the central brain. We find a strong relationship between resting-state functional correlations and direct region-to-region structural connectivity. We find that the relationship between structure and function varies across the brain, with some regions displaying a tight correspondence between structural and functional connectivity whereas others, including the mushroom body, are more strongly dependent on indirect connections. Throughout this work, we observe features of structural and functional networks in Drosophila that are strikingly similar to those seen in mammalian cortex, including in the human brain. Given the vast anatomical and functional differences between Drosophila and mammalian nervous systems, these observations suggest general principles that govern brain structure, function, and the relationship between the two.
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Affiliation(s)
- Maxwell H Turner
- Department of Neurobiology, Stanford University, Stanford, CA 94103, USA
| | - Kevin Mann
- Department of Neurobiology, Stanford University, Stanford, CA 94103, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Stanford, CA 94103, USA.
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30
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Mann K, Deny S, Ganguli S, Clandinin TR. Coupling of activity, metabolism and behaviour across the Drosophila brain. Nature 2021; 593:244-248. [PMID: 33911283 PMCID: PMC10544789 DOI: 10.1038/s41586-021-03497-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 03/26/2021] [Indexed: 12/21/2022]
Abstract
Coordinated activity across networks of neurons is a hallmark of both resting and active behavioural states in many species1-5. These global patterns alter energy metabolism over seconds to hours, which underpins the widespread use of oxygen consumption and glucose uptake as proxies of neural activity6,7. However, whether changes in neural activity are causally related to metabolic flux in intact circuits on the timescales associated with behaviour is unclear. Here we combine two-photon microscopy of the fly brain with sensors that enable the simultaneous measurement of neural activity and metabolic flux, across both resting and active behavioural states. We demonstrate that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across brain networks. Using local optogenetic perturbation, we demonstrate that even transient increases in neural activity result in rapid and persistent increases in cytosolic ATP, which suggests that neuronal metabolism predictively allocates resources to anticipate the energy demands of future activity. Finally, our studies reveal that the initiation of even minimal behavioural movements causes large-scale changes in the pattern of neural activity and energy metabolism, which reveals a widespread engagement of the brain. As the relationship between neural activity and energy metabolism is probably evolutionarily ancient and highly conserved, our studies provide a critical foundation for using metabolic proxies to capture changes in neural activity.
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Affiliation(s)
- Kevin Mann
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Stephane Deny
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Surya Ganguli
- Department of Neurobiology, Stanford University, Stanford, CA, USA.
- Department of Applied Physics, Stanford University, Stanford, CA, USA.
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31
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Hanson A. Spontaneous electrical low-frequency oscillations: a possible role in Hydra and all living systems. Philos Trans R Soc Lond B Biol Sci 2021; 376:20190763. [PMID: 33487108 PMCID: PMC7934974 DOI: 10.1098/rstb.2019.0763] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2020] [Indexed: 12/20/2022] Open
Abstract
As one of the first model systems in biology, the basal metazoan Hydra has been revealing fundamental features of living systems since it was first discovered by Antonie van Leeuwenhoek in the early eighteenth century. While it has become well-established within cell and developmental biology, this tiny freshwater polyp is only now being re-introduced to modern neuroscience where it has already produced a curious finding: the presence of low-frequency spontaneous neural oscillations at the same frequency as those found in the default mode network in the human brain. Surprisingly, increasing evidence suggests such spontaneous electrical low-frequency oscillations (SELFOs) are found across the wide diversity of life on Earth, from bacteria to humans. This paper reviews the evidence for SELFOs in diverse phyla, beginning with the importance of their discovery in Hydra, and hypothesizes a potential role as electrical organism organizers, which supports a growing literature on the role of bioelectricity as a 'template' for developmental memory in organism regeneration. This article is part of the theme issue 'Basal cognition: conceptual tools and the view from the single cell'.
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Affiliation(s)
- Alison Hanson
- Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University, New York, NY, USA
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32
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Li ES, Saha MS. Optimizing Calcium Detection Methods in Animal Systems: A Sandbox for Synthetic Biology. Biomolecules 2021; 11:343. [PMID: 33668387 PMCID: PMC7996158 DOI: 10.3390/biom11030343] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/19/2021] [Accepted: 02/21/2021] [Indexed: 12/16/2022] Open
Abstract
Since the 1970s, the emergence and expansion of novel methods for calcium ion (Ca2+) detection have found diverse applications in vitro and in vivo across a series of model animal systems. Matched with advances in fluorescence imaging techniques, the improvements in the functional range and stability of various calcium indicators have significantly enhanced more accurate study of intracellular Ca2+ dynamics and its effects on cell signaling, growth, differentiation, and regulation. Nonetheless, the current limitations broadly presented by organic calcium dyes, genetically encoded calcium indicators, and calcium-responsive nanoparticles suggest a potential path toward more rapid optimization by taking advantage of a synthetic biology approach. This engineering-oriented discipline applies principles of modularity and standardization to redesign and interrogate endogenous biological systems. This review will elucidate how novel synthetic biology technologies constructed for eukaryotic systems can offer a promising toolkit for interfacing with calcium signaling and overcoming barriers in order to accelerate the process of Ca2+ detection optimization.
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Affiliation(s)
| | - Margaret S. Saha
- Department of Biology, College of William and Mary, Williamsburg, VA 23185, USA;
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33
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Yemini E, Lin A, Nejatbakhsh A, Varol E, Sun R, Mena GE, Samuel ADT, Paninski L, Venkatachalam V, Hobert O. NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans. Cell 2021; 184:272-288.e11. [PMID: 33378642 PMCID: PMC10494711 DOI: 10.1016/j.cell.2020.12.012] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 06/30/2020] [Accepted: 12/08/2020] [Indexed: 12/31/2022]
Abstract
Comprehensively resolving neuronal identities in whole-brain images is a major challenge. We achieve this in C. elegans by engineering a multicolor transgene called NeuroPAL (a neuronal polychromatic atlas of landmarks). NeuroPAL worms share a stereotypical multicolor fluorescence map for the entire hermaphrodite nervous system that resolves all neuronal identities. Neurons labeled with NeuroPAL do not exhibit fluorescence in the green, cyan, or yellow emission channels, allowing the transgene to be used with numerous reporters of gene expression or neuronal dynamics. We showcase three applications that leverage NeuroPAL for nervous-system-wide neuronal identification. First, we determine the brainwide expression patterns of all metabotropic receptors for acetylcholine, GABA, and glutamate, completing a map of this communication network. Second, we uncover changes in cell fate caused by transcription factor mutations. Third, we record brainwide activity in response to attractive and repulsive chemosensory cues, characterizing multimodal coding for these stimuli.
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Affiliation(s)
- Eviatar Yemini
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA.
| | - Albert Lin
- Department of Physics, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Amin Nejatbakhsh
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Erdem Varol
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Ruoxi Sun
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Gonzalo E Mena
- Department of Statistics and Data Science Initiative, Harvard University, Cambridge, MA 02138, USA
| | - Aravinthan D T Samuel
- Department of Physics, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Liam Paninski
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | | | - Oliver Hobert
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
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34
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Pacheco DA, Thiberge SY, Pnevmatikakis E, Murthy M. Auditory activity is diverse and widespread throughout the central brain of Drosophila. Nat Neurosci 2021; 24:93-104. [PMID: 33230320 PMCID: PMC7783861 DOI: 10.1038/s41593-020-00743-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 10/19/2020] [Indexed: 11/09/2022]
Abstract
Sensory pathways are typically studied by starting at receptor neurons and following postsynaptic neurons into the brain. However, this leads to a bias in analyses of activity toward the earliest layers of processing. Here, we present new methods for volumetric neural imaging with precise across-brain registration to characterize auditory activity throughout the entire central brain of Drosophila and make comparisons across trials, individuals and sexes. We discover that auditory activity is present in most central brain regions and in neurons responsive to other modalities. Auditory responses are temporally diverse, but the majority of activity is tuned to courtship song features. Auditory responses are stereotyped across trials and animals in early mechanosensory regions, becoming more variable at higher layers of the putative pathway, and this variability is largely independent of ongoing movements. This study highlights the power of using an unbiased, brain-wide approach for mapping the functional organization of sensory activity.
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Affiliation(s)
- Diego A Pacheco
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Stephan Y Thiberge
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Bezos Center for Neural Circuit Dynamics, Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Eftychios Pnevmatikakis
- Center for Computational Mathematics, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Bezos Center for Neural Circuit Dynamics, Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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35
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Roth RH, Ding JB. From Neurons to Cognition: Technologies for Precise Recording of Neural Activity Underlying Behavior. BME FRONTIERS 2020; 2020:7190517. [PMID: 37849967 PMCID: PMC10521756 DOI: 10.34133/2020/7190517] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/27/2020] [Indexed: 10/19/2023] Open
Abstract
Understanding how brain activity encodes information and controls behavior is a long-standing question in neuroscience. This complex problem requires converging efforts from neuroscience and engineering, including technological solutions to perform high-precision and large-scale recordings of neuronal activity in vivo as well as unbiased methods to reliably measure and quantify behavior. Thanks to advances in genetics, molecular biology, engineering, and neuroscience, in recent decades, a variety of optical imaging and electrophysiological approaches for recording neuronal activity in awake animals have been developed and widely applied in the field. Moreover, sophisticated computer vision and machine learning algorithms have been developed to analyze animal behavior. In this review, we provide an overview of the current state of technology for neuronal recordings with a focus on optical and electrophysiological methods in rodents. In addition, we discuss areas that future technological development will need to cover in order to further our understanding of the neural activity underlying behavior.
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Affiliation(s)
- Richard H Roth
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Jun B Ding
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
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36
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Biswas T, Bishop WE, Fitzgerald JE. Theoretical principles for illuminating sensorimotor processing with brain-wide neuronal recordings. Curr Opin Neurobiol 2020; 65:138-145. [PMID: 33248437 PMCID: PMC8754199 DOI: 10.1016/j.conb.2020.10.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/28/2020] [Accepted: 10/29/2020] [Indexed: 11/24/2022]
Abstract
Modern recording techniques now permit brain-wide sensorimotor circuits to be observed at single neuron resolution in small animals. Extracting theoretical understanding from these recordings requires principles that organize findings and guide future experiments. Here we review theoretical principles that shed light onto brain-wide sensorimotor processing. We begin with an analogy that conceptualizes principles as streetlamps that illuminate the empirical terrain, and we illustrate the analogy by showing how two familiar principles apply in new ways to brain-wide phenomena. We then focus the bulk of the review on describing three more principles that have wide utility for mapping brain-wide neural activity, making testable predictions from highly parameterized mechanistic models, and investigating the computational determinants of neuronal response patterns across the brain.
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Affiliation(s)
- Tirthabir Biswas
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - William E Bishop
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - James E Fitzgerald
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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37
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Kang B, Druckmann S. Approaches to inferring multi-regional interactions from simultaneous population recordings: Inferring multi-regional interactions from simultaneous population recordings. Curr Opin Neurobiol 2020; 65:108-119. [PMID: 33227602 PMCID: PMC7853322 DOI: 10.1016/j.conb.2020.10.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 09/28/2020] [Accepted: 10/03/2020] [Indexed: 12/20/2022]
Abstract
Most past studies of neural representations and dynamics have focused on recordings from single brain areas. However, growing evidence of brain-wide, parallel representations of cognitive variables suggests that analyzing neural representations and dynamics in individual brain areas can benefit from understanding the context of multi-regional interactions that support them. Moreover, perturbation experiments revealed that the manner in which these parallel representations interact with each other can differ dramatically across different pairs of brain areas. Recent advances in recording technology offer a potentially powerful substrate to study how multi-regional interactions coordinate neural representations in individual brain areas and dictate behavior on a single-trial basis through simultaneous recordings of multiple brain areas. We review pragmatic approaches to studying multi-regional interactions and illustrate them in the concrete context of a rodent delayed response task paradigm.
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Affiliation(s)
- Byungwoo Kang
- Dept. of Neurobiology, Stanford University, Stanford, CA, United States; Physics Department, Stanford University, Stanford, CA, United States
| | - Shaul Druckmann
- Dept. of Neurobiology, Stanford University, Stanford, CA, United States.
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38
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Deutsch D, Pacheco D, Encarnacion-Rivera L, Pereira T, Fathy R, Clemens J, Girardin C, Calhoun A, Ireland E, Burke A, Dorkenwald S, McKellar C, Macrina T, Lu R, Lee K, Kemnitz N, Ih D, Castro M, Halageri A, Jordan C, Silversmith W, Wu J, Seung HS, Murthy M. The neural basis for a persistent internal state in Drosophila females. eLife 2020; 9:e59502. [PMID: 33225998 PMCID: PMC7787663 DOI: 10.7554/elife.59502] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 11/18/2020] [Indexed: 12/13/2022] Open
Abstract
Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify the neural circuit mechanisms that underlie persistent activity and contribute to long-lasting changes in behavior. Here, we show that a subset of Doublesex+ pC1 neurons in the Drosophila female brain, called pC1d/e, can drive minutes-long changes in female behavior in the presence of males. Using automated reconstruction of a volume electron microscopic (EM) image of the female brain, we map all inputs and outputs to both pC1d and pC1e. This reveals strong recurrent connectivity between, in particular, pC1d/e neurons and a specific subset of Fruitless+ neurons called aIPg. We additionally find that pC1d/e activation drives long-lasting persistent neural activity in brain areas and cells overlapping with the pC1d/e neural network, including both Doublesex+ and Fruitless+ neurons. Our work thus links minutes-long persistent changes in behavior with persistent neural activity and recurrent circuit architecture in the female brain.
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Affiliation(s)
- David Deutsch
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Diego Pacheco
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | | | - Talmo Pereira
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Ramie Fathy
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jan Clemens
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Cyrille Girardin
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Adam Calhoun
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Elise Ireland
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Austin Burke
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Department of Computer Science, Princeton UniversityPrincetonUnited States
| | - Claire McKellar
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Department of Computer Science, Princeton UniversityPrincetonUnited States
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Brain & Cognitive Science Department, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - William Silversmith
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Department of Computer Science, Princeton UniversityPrincetonUnited States
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
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39
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Zamani Esfahlani F, Jo Y, Faskowitz J, Byrge L, Kennedy DP, Sporns O, Betzel RF. High-amplitude cofluctuations in cortical activity drive functional connectivity. Proc Natl Acad Sci U S A 2020; 117:28393-28401. [PMID: 33093200 PMCID: PMC7668041 DOI: 10.1073/pnas.2005531117] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to correlated activity are, however, poorly understood. Here we decompose resting-state functional connectivity using a temporal unwrapping procedure to assess the contributions of moment-to-moment activity cofluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of cofluctuations of network organization with fluctuations in the blood oxygen level-dependent (BOLD) time series. We show that surprisingly, only a small fraction of frames exhibiting the strongest cofluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the network's modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Finally, we demonstrate that cofluctuation amplitude synchronizes across subjects during movie watching and that high-amplitude frames carry detailed information about individual subjects (whereas low-amplitude frames carry little). Our approach reveals fine-scale temporal structure of resting-state functional connectivity and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of directions for future research.
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Affiliation(s)
| | - Youngheun Jo
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
| | - Lisa Byrge
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
| | - Daniel P Kennedy
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
- Cognitive Science Program, Indiana University, Bloomington, IN 47405
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
- Cognitive Science Program, Indiana University, Bloomington, IN 47405
- Network Science Institute, Indiana University, Bloomington, IN 47405
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405;
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
- Cognitive Science Program, Indiana University, Bloomington, IN 47405
- Network Science Institute, Indiana University, Bloomington, IN 47405
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40
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Hu Y, Wang C, Yang L, Pan G, Liu H, Yu G, Ye B. A Neural Basis for Categorizing Sensory Stimuli to Enhance Decision Accuracy. Curr Biol 2020; 30:4896-4909.e6. [PMID: 33065003 DOI: 10.1016/j.cub.2020.09.045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 08/08/2020] [Accepted: 09/14/2020] [Indexed: 11/15/2022]
Abstract
Sensory stimuli with graded intensities often lead to yes-or-no decisions on whether to respond to the stimuli. How this graded-to-binary conversion is implemented in the central nervous system (CNS) remains poorly understood. Here, we show that graded encodings of noxious stimuli are categorized in a decision-associated CNS region in Drosophila larvae, and then decoded by a group of peptidergic neurons for executing binary escape decisions. GABAergic inhibition gates weak nociceptive encodings from being decoded, whereas escalated amplification through the recruitment of second-order neurons boosts nociceptive encodings at intermediate intensities. These two modulations increase the detection accuracy by reducing responses to negligible stimuli whereas enhancing responses to intense stimuli. Our findings thus unravel a circuit mechanism that underlies accurate detection of harmful stimuli.
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Affiliation(s)
- Yujia Hu
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Congchao Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Limin Yang
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; School of Medicine, Dalian University, Dalian, Liaoning 116622, China
| | - Geng Pan
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hao Liu
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Guoqiang Yu
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
| | - Bing Ye
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA.
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41
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Bates AS, Manton JD, Jagannathan SR, Costa M, Schlegel P, Rohlfing T, Jefferis GSXE. The natverse, a versatile toolbox for combining and analysing neuroanatomical data. eLife 2020; 9:e53350. [PMID: 32286229 PMCID: PMC7242028 DOI: 10.7554/elife.53350] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 04/11/2020] [Indexed: 11/18/2022] Open
Abstract
To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community.
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Affiliation(s)
| | - James D Manton
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Sridhar R Jagannathan
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Marta Costa
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Torsten Rohlfing
- SRI International, Neuroscience Program, Center for Health SciencesMenlo ParkUnited States
| | - Gregory SXE Jefferis
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
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42
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Betzel RF. Organizing principles of whole-brain functional connectivity in zebrafish larvae. Netw Neurosci 2020; 4:234-256. [PMID: 32166210 PMCID: PMC7055648 DOI: 10.1162/netn_a_00121] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 12/04/2019] [Indexed: 12/13/2022] Open
Abstract
Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and functionally flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchical modular structure and hubs that span module boundaries. We go on to show that spontaneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal basic organizing principles of whole-brain functional brain networks at the mesoscale. Our overarching methodological framework provides a blueprint for studying correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and mesoscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.
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Affiliation(s)
- Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- IU Network Science Institute, Indiana University, Bloomington, IN, USA
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43
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Abstract
Drosophila melanogaster, colloquially known as the fruit fly, is one of the most commonly used model organisms in scientific research. Although the final architecture of a fly and a human differs greatly, most of the fundamental biological mechanisms and pathways controlling development and survival are conserved through evolution between the two species. For this reason, Drosophila has been productively used as a model organism for over a century, to study a diverse range of biological processes, including development, learning, behavior and aging. Ca2+ signaling comprises complex pathways that impact on virtually every aspect of cellular physiology. Within such a complex field of study, Drosophila offers the advantages of consolidated molecular and genetic techniques, lack of genetic redundancy and a completely annotated genome since 2000. These and other characteristics provided the basis for the identification of many genes encoding Ca2+ signaling molecules and the disclosure of conserved Ca2+ signaling pathways. In this review, we will analyze the applications of Ca2+ imaging in the fruit fly model, highlighting in particular their impact on the study of normal brain function and pathogenesis of neurodegenerative diseases.
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44
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Abstract
The field has successfully used Drosophila genetic tools to identify neurons and sub-circuits important for specific functions. However, for an organism with complex and changing internal states to succeed in a complex and changing natural environment, many neurons and circuits need to interact dynamically. Drosophila's many advantages, combined with new imaging tools, offer unique opportunities to study how the brain functions as a complex dynamical system. We give an overview of complex activity patterns and how they can be observed, as well as modeling strategies, adding proof of principle in some cases.
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Affiliation(s)
- Sophie Aimon
- School of Life Sciences, Technical University of Munich, Freising, Germany
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45
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Betzel RF, Wood KC, Angeloni C, Neimark Geffen M, Bassett DS. Stability of spontaneous, correlated activity in mouse auditory cortex. PLoS Comput Biol 2019; 15:e1007360. [PMID: 31815941 PMCID: PMC6968873 DOI: 10.1371/journal.pcbi.1007360] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 01/17/2020] [Accepted: 08/24/2019] [Indexed: 12/31/2022] Open
Abstract
Neural systems can be modeled as complex networks in which neural elements are represented as nodes linked to one another through structural or functional connections. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the system’s topological organization and to better understand its function. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work indicates a framework for studying spontaneous activity measured by two-photon calcium imaging using computational methods and graphical models from network science. The methods are flexible and easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease. Neurons coordinate their activity with one another, forming networks that help support adaptive, flexible behavior. Still, little is known about the organization of these networks at the cellular scale and their stability over time. Here, we reconstruct networks from calcium imaging data recorded in mouse primary auditory cortex. We show that these networks exhibit spatially constrained, hierarchical modular structure, which may facilitate specialized information processing. However, we show that connection weights and modular structure are also variable over time, changing on a timescale of days and adopting novel network configurations. Despite this, a small subset of neurons maintain their connections to one another and preserve their modular organization across time, forming a stable temporal core surrounded by a flexible periphery. These findings represent a conceptual bridge linking network analyses of macroscale and cellular-level neuroimaging data. They also represent a complementary approach to existing circuits- and systems-based interrogation of nervous system function, opening the door for deeper and more targeted analysis in the future.
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Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America.,Cognitive Science Program, Indiana University, Bloomington, Indiana, United States of America.,Program in Neuroscience, Indiana University, Bloomington, Indiana, United States of America.,Network Science Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Katherine C Wood
- Department of Otorhinolaryngology: HNS, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Christopher Angeloni
- Department of Otorhinolaryngology: HNS, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Maria Neimark Geffen
- Department of Otorhinolaryngology: HNS, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Santa Fe Institute, Santa Fa, New Mexico, United States of America
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46
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Xie T, Ho MCW, Liu Q, Horiuchi W, Lin CC, Task D, Luan H, White BH, Potter CJ, Wu MN. A Genetic Toolkit for Dissecting Dopamine Circuit Function in Drosophila. Cell Rep 2019; 23:652-665. [PMID: 29642019 PMCID: PMC5962273 DOI: 10.1016/j.celrep.2018.03.068] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 02/19/2018] [Accepted: 03/15/2018] [Indexed: 01/23/2023] Open
Abstract
The neuromodulator dopamine (DA) plays a key role in motor control, motivated behaviors, and higher-order cognitive processes. Dissecting how these DA neural networks tune the activity of local neural circuits to regulate behavior requires tools for manipulating small groups of DA neurons. To address this need, we assembled a genetic toolkit that allows for an exquisite level of control over the DA neural network in Drosophila. To further refine targeting of specific DA neurons, we also created reagents that allow for the conversion of any existing GAL4 line into Split GAL4 or GAL80 lines. We demonstrated how this toolkit can be used with recently developed computational methods to rapidly generate additional reagents for manipulating small subsets or individual DA neurons. Finally, we used the toolkit to reveal a dynamic interaction between a small subset of DA neurons and rearing conditions in a social space behavioral assay.
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Affiliation(s)
- Tingting Xie
- School of Life Sciences, Peking University, Beijing 100871, China; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Margaret C W Ho
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Qili Liu
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Wakako Horiuchi
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Chun-Chieh Lin
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Darya Task
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Haojiang Luan
- Laboratory of Molecular Biology, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA
| | - Benjamin H White
- Laboratory of Molecular Biology, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA
| | - Christopher J Potter
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Mark N Wu
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
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47
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Betzel RF, Medaglia JD, Kahn AE, Soffer J, Schonhaut DR, Bassett DS. Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography. Nat Biomed Eng 2019; 3:902-916. [DOI: 10.1038/s41551-019-0404-5] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 04/15/2019] [Indexed: 01/05/2023]
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48
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Sych Y, Chernysheva M, Sumanovski LT, Helmchen F. High-density multi-fiber photometry for studying large-scale brain circuit dynamics. Nat Methods 2019; 16:553-560. [PMID: 31086339 DOI: 10.1038/s41592-019-0400-4] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 03/28/2019] [Indexed: 11/09/2022]
Abstract
Animal behavior originates from neuronal activity distributed across brain-wide networks. However, techniques available to assess large-scale neural dynamics in behaving animals remain limited. Here we present compact, chronically implantable, high-density arrays of optical fibers that enable multi-fiber photometry and optogenetic perturbations across many regions in the mammalian brain. In mice engaged in a texture discrimination task, we achieved simultaneous photometric calcium recordings from networks of 12-48 brain regions, including striatal, thalamic, hippocampal and cortical areas. Furthermore, we optically perturbed subsets of regions in VGAT-ChR2 mice by targeting specific fiber channels with a spatial light modulator. Perturbation of ventral thalamic nuclei caused distributed network modulation and behavioral deficits. Finally, we demonstrate multi-fiber photometry in freely moving animals, including simultaneous recordings from two mice during social interaction. High-density multi-fiber arrays are versatile tools for the investigation of large-scale brain dynamics during behavior.
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Affiliation(s)
- Yaroslav Sych
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
| | - Maria Chernysheva
- Brain Research Institute, University of Zurich, Zurich, Switzerland.,Neuroscience Center Zurich, Zurich, Switzerland
| | | | - Fritjof Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland. .,Neuroscience Center Zurich, Zurich, Switzerland.
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49
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Hsu KJ, Lin YY, Chiang AS, Chu SW. Optical properties of adult Drosophila brains in one-, two-, and three-photon microscopy. BIOMEDICAL OPTICS EXPRESS 2019; 10:1627-1637. [PMID: 31086697 PMCID: PMC6484994 DOI: 10.1364/boe.10.001627] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 01/23/2019] [Accepted: 02/17/2019] [Indexed: 06/09/2023]
Abstract
Drosophila is widely used in connectome studies due to its small brain size, sophisticated genetic tools, and the most complete single-neuron-based anatomical brain map. Surprisingly, even the brain thickness is only 200-μm, common Ti:sapphire-based two-photon excitation cannot penetrate, possibly due to light aberration/scattering of trachea. Here we quantitatively characterized scattering and light distortion of trachea-filled tissues, and found that trachea-induced light distortion dominates at long wavelength by comparing one-photon (488-nm), two-photon (920-nm), and three-photon (1300-nm) excitations. Whole-Drosophila-brain imaging is achieved by reducing tracheal light aberration/scattering via brain-degassing or long-wavelength excitation at 1300-nm. Our work paves the way toward constructing whole-brain connectome in a living Drosophila.
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Affiliation(s)
- Kuo-Jen Hsu
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
- Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Yen-Yin Lin
- Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Ann-Shyn Chiang
- Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu 30013, Taiwan
- Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80780, Taiwan
- Kavli Institute for Brain and Mind, University of California at San Diego, La Jolla, CA 92093-0526, USA
| | - Shi-Wei Chu
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
- Molecular Imaging Center, National Taiwan University, Taipei 10617, Taiwan
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50
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Baker CA, Clemens J, Murthy M. Acoustic Pattern Recognition and Courtship Songs: Insights from Insects. Annu Rev Neurosci 2019; 42:129-147. [PMID: 30786225 DOI: 10.1146/annurev-neuro-080317-061839] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Across the animal kingdom, social interactions rely on sound production and perception. From simple cricket chirps to more elaborate bird songs, animals go to great lengths to communicate information critical for reproduction and survival via acoustic signals. Insects produce a wide array of songs to attract a mate, and the intended receivers must differentiate these calls from competing sounds, analyze the quality of the sender from spectrotemporal signal properties, and then determine how to react. Insects use numerically simple nervous systems to analyze and respond to courtship songs, making them ideal model systems for uncovering the neural mechanisms underlying acoustic pattern recognition. We highlight here how the combination of behavioral studies and neural recordings in three groups of insects-crickets, grasshoppers, and fruit flies-reveals common strategies for extracting ethologically relevant information from acoustic patterns and how these findings might translate to other systems.
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Affiliation(s)
- Christa A Baker
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA;
| | - Jan Clemens
- University Medical Center Goettingen, Max-Planck-Society, European Neuroscience Institute, D-37077 Goettingen, Germany;
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA;
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