1
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Zheng Z, Own CS, Wanner AA, Koene RA, Hammerschmith EW, Silversmith WM, Kemnitz N, Lu R, Tank DW, Seung HS. Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy. Nat Commun 2024; 15:6860. [PMID: 39127683 PMCID: PMC11316758 DOI: 10.1038/s41467-024-50846-4] [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: 12/15/2022] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larger and more brain volumes. We report here a threefold increase in the speed of TEM by using a beam deflecting mechanism to enable highly efficient acquisition of multiple image tiles (nine) for each motion of the mechanical stage. For millimeter-scale areas, the duty cycle of imaging doubles to more than 30%, yielding a net average imaging rate of 0.3 gigapixels per second. If fully utilized, an array of four beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in five weeks.
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Affiliation(s)
- Zhihao Zheng
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Adrian A Wanner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Paul Scherrer Institute, Villigen, Switzerland
| | | | | | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - David W Tank
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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2
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Guo T, Li S, Zhou YN, Lu WD, Yan Y, Wu YA. Interspecies-chimera machine vision with polarimetry for real-time navigation and anti-glare pattern recognition. Nat Commun 2024; 15:6731. [PMID: 39112546 PMCID: PMC11306562 DOI: 10.1038/s41467-024-51178-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 08/01/2024] [Indexed: 08/10/2024] Open
Abstract
Cutting-edge humanoid machine vision merely mimics human systems and lacks polarimetric functionalities that convey the information of navigation and authentic images. Interspecies-chimera vision reserving multiple hosts' capacities will lead to advanced machine vision. However, implementing the visual functions of multiple species (human and non-human) in one optoelectronic device is still elusive. Here, we develop an optically-controlled polarimetry memtransistor based on a van der Waals heterostructure (ReS2/GeSe2). The device provides polarization sensitivity, nonvolatility, and positive/negative photoconductance simultaneously. The polarimetric measurement can identify celestial polarizations for real-time navigation like a honeybee. Meanwhile, cognitive tasks can be completed like a human by sensing, memory, and synaptic functions. Particularly, the anti-glare recognition with polarimetry saves an order of magnitude energy compared to the traditional humanoid counterpart. This technique promotes the concept of interspecies-chimera visual systems that will leverage the advances of autonomous vehicles, medical diagnoses, intelligent robotics, etc.
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Affiliation(s)
- Tao Guo
- School of Physics, Henan Normal University, Henan, 453007, China
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, Materials Interfaces Foundry, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | - Shasha Li
- School of Physics, Henan Normal University, Henan, 453007, China
| | - Y Norman Zhou
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, Materials Interfaces Foundry, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | - Wei D Lu
- Department of Electrical and Computer Engineering, the University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yong Yan
- School of Physics, Henan Normal University, Henan, 453007, China.
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, Materials Interfaces Foundry, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
- iGaN Laboratory, School of Microelectronics, University of Science and Technology ofChina Hefei, Anhui, 230026, China.
| | - Yimin A Wu
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, Materials Interfaces Foundry, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
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3
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Asinof SK, Card GM. Neural Control of Naturalistic Behavior Choices. Annu Rev Neurosci 2024; 47:369-388. [PMID: 38724026 DOI: 10.1146/annurev-neuro-111020-094019] [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] [Indexed: 08/09/2024]
Abstract
In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology. In this approach, experimenters select animal models based on experimental accessibility and then utilize cutting-edge tools such as connectomes and genetically encoded reagents to analyze the flow of information through an animal's nervous system during naturalistic choice behaviors. We describe how the reverse neuroethology strategy has been applied to understand the neural underpinnings of innate, rapid decision making, with a focus on defensive behavioral choices in the vinegar fly Drosophila melanogaster.
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Affiliation(s)
- Samuel K Asinof
- Laboratory of Molecular Biology, National Institute of Mental Health, Bethesda, Maryland, USA
- Janelia Research Campus, Ashburn, Virginia, USA
| | - Gwyneth M Card
- Howard Hughes Medical Institute, Department of Neuroscience, and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA;
- Janelia Research Campus, Ashburn, Virginia, USA
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4
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Thornton-Kolbe EM, Ahmed M, Gordon FR, Sieriebriennikov B, Williams DL, Kurmangaliyev YZ, Clowney EJ. Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603956. [PMID: 39071296 PMCID: PMC11275898 DOI: 10.1101/2024.07.17.603956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The brain can represent almost limitless objects to "categorize an unlabeled world" (Edelman, 1989). This feat is supported by expansion layer circuit architectures, in which neurons carrying information about discrete sensory channels make combinatorial connections onto much larger postsynaptic populations. Combinatorial connections in expansion layers are modeled as randomized sets. The extent to which randomized wiring exists in vivo is debated, and how combinatorial connectivity patterns are generated during development is not understood. Non-deterministic wiring algorithms could program such connectivity using minimal genomic information. Here, we investigate anatomic and transcriptional patterns and perturb partner availability to ask how Kenyon cells, the expansion layer neurons of the insect mushroom body, obtain combinatorial input from olfactory projection neurons. Olfactory projection neurons form their presynaptic outputs in an orderly, predictable, and biased fashion. We find that Kenyon cells accept spatially co-located but molecularly heterogeneous inputs from this orderly map, and ask how Kenyon cell surface molecule expression impacts partner choice. Cell surface immunoglobulins are broadly depleted in Kenyon cells, and we propose that this allows them to form connections with molecularly heterogeneous partners. This model can explain how developmentally identical neurons acquire diverse wiring identities.
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Affiliation(s)
- Emma M. Thornton-Kolbe
- Neurosciences Graduate Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maria Ahmed
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Finley R. Gordon
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | | | - Donnell L. Williams
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | | | - E. Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
- Michigan Neuroscience Institute, Ann Arbor, MI, USA
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5
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Ganguly I, Heckman EL, Litwin-Kumar A, Clowney EJ, Behnia R. Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body. Nat Commun 2024; 15:5698. [PMID: 38972924 PMCID: PMC11228034 DOI: 10.1038/s41467-024-49616-z] [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: 01/24/2024] [Accepted: 06/11/2024] [Indexed: 07/09/2024] Open
Abstract
The arthropod mushroom body is well-studied as an expansion layer representing olfactory stimuli and linking them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their function remains unclear. Here, we identify inputs to visual Kenyon cells using the FlyWire adult whole-brain connectome. Input repertoires are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual neurons presynaptic to Kenyon cells have large receptive fields, while interneuron inputs receive spatially restricted signals that may be tuned to specific visual features. Individual visual Kenyon cells randomly sample sparse inputs from combinations of visual channels, including multiple optic lobe neuropils. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the specific input repertoire to the smaller population of visual Kenyon cells suggests a constrained encoding of visual stimuli.
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Affiliation(s)
- Ishani Ganguly
- Department of Neuroscience, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Emily L Heckman
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Ashok Litwin-Kumar
- Department of Neuroscience, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA.
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA.
| | - Rudy Behnia
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Institute, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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6
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Fulton KA, Zimmerman D, Samuel A, Vogt K, Datta SR. Common principles for odour coding across vertebrates and invertebrates. Nat Rev Neurosci 2024; 25:453-472. [PMID: 38806946 DOI: 10.1038/s41583-024-00822-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2024] [Indexed: 05/30/2024]
Abstract
The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.
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Affiliation(s)
- Kara A Fulton
- Department of Neuroscience, Harvard Medical School, Boston, MA, USA
| | - David Zimmerman
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Aravi Samuel
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Katrin Vogt
- Department of Physics, Harvard University, Cambridge, MA, USA.
- Department of Biology, University of Konstanz, Konstanz, Germany.
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany.
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7
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Klein C, Barron AB. Comparing cognition across major transitions using the hierarchy of formal automata. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2024; 15:e1680. [PMID: 38655871 DOI: 10.1002/wcs.1680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/26/2024]
Abstract
The evolution of cognition can be understood in terms of a few major transitions-changes in the computational architecture of nervous systems that changed what cognitive capacities could be evolved by downstream lineages. We demonstrate how the idea of a major cognitive transition can be modeled in terms of where a system's effective computational architecture falls on the well-studied hierarchy of formal automata (HFA). We then use recent work connecting artificial neural networks to the HFA, which provides a way to make the structure-architecture link in natural systems. We conclude with reflections on the power and the challenges of traditional thinking when applied to neural architectures. This article is categorized under: Cognitive Biology > Evolutionary Roots of Cognition Psychology > Comparative Philosophy > Foundations of Cognitive Science.
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Affiliation(s)
- Colin Klein
- School of Philosophy, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Andrew B Barron
- School of Natural Sciences, Macquarie University, North Ryde, New South Wales, Australia
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8
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Qi C, Qian C, Steijvers E, Colvin RA, Lee D. Single dopaminergic neuron DAN-c1 in Drosophila larval brain mediates aversive olfactory learning through D2-like receptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.575767. [PMID: 38293177 PMCID: PMC10827047 DOI: 10.1101/2024.01.15.575767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The intricate relationship between the dopaminergic system and olfactory associative learning in Drosophila has been an intense scientific inquiry. Leveraging the formidable genetic tools, we conducted a screening of 57 dopaminergic drivers, leading to the discovery of DAN-c1 driver, uniquely targeting the single dopaminergic neuron (DAN) in each brain hemisphere. While the involvement of excitatory D1-like receptors is well-established, the role of D2-like receptors (D2Rs) remains underexplored. Our investigation reveals the expression of D2Rs in both DANs and the mushroom body (MB) of third instar larval brains. Silencing D2Rs in DAN-c1 via microRNA disrupts aversive learning, further supported by optogenetic activation of DAN-c1 during training, affirming the inhibitory role of D2R autoreceptor. Intriguingly, D2R knockdown in the MB impairs both appetitive and aversive learning. These findings elucidate the distinct contributions of D2Rs in diverse brain structures, providing novel insights into the molecular mechanisms governing associative learning in Drosophila larvae.
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Affiliation(s)
- Cheng Qi
- Department of Biological Sciences, Ohio University, Athens, OH 45701, USA
| | | | | | - Robert A. Colvin
- Department of Biological Sciences, Ohio University, Athens, OH 45701, USA
| | - Daewoo Lee
- Department of Biological Sciences, Ohio University, Athens, OH 45701, USA
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9
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Kuan AT, Phan S, Kim KY, Mackey M, Kim M, Peltier ST, Ellisman M, Lee WCA. Scalable electron tomography for connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.05.597487. [PMID: 38895398 PMCID: PMC11185748 DOI: 10.1101/2024.06.05.597487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
We demonstrate limited-tilt, serial section electron tomography (ET), which can non-destructively map brain circuits over large 3D volumes and reveal high-resolution, supramolecular details within subvolumes of interest. We show accelerated ET imaging of thick sections (>500 nm) with the capacity to resolve key features of neuronal circuits including chemical synapses, endocytic structures, and gap junctions. Furthermore, we systematically assessed how imaging parameters affect image quality and speed to enable connectomic-scale projects.
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Affiliation(s)
- Aaron T Kuan
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Present address: Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Sébastien Phan
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California, San Diego, CA, USA
| | - Keun-Young Kim
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California, San Diego, CA, USA
| | - Mason Mackey
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California, San Diego, CA, USA
| | - Minsu Kim
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Present address: Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Steven T Peltier
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California, San Diego, CA, USA
| | - Mark Ellisman
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California, San Diego, CA, USA
- Departments of Neurosciences and Bioengineering, University of California, San Diego, CA, USA
| | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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10
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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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11
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Bullard MR, Cervantes JCM, Quaicoe NB, Jin A, Adams DA, Lin JM, Iliadis E, Seidler TM, Cervantes-Sandoval I, He HY. Accelerated protein retention expansion microscopy using microwave radiation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.11.593228. [PMID: 38766072 PMCID: PMC11100821 DOI: 10.1101/2024.05.11.593228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Protein retention expansion microscopy (ExM) retains genetically encoded fluorescent proteins or antibody-conjugated fluorescent probes in fixed tissue and isotropically expands the tissue through a swellable polymer network to allow nanoscale (<70 nm) resolution on diffraction-limited confocal microscopes. Despite numerous advantages ExM brings to biological studies, the full protocol is time-consuming and can take multiple days to complete. Here, we adapted the ExM protocol to the vibratome-sectioned brain tissue of Xenopus laevis tadpoles and implemented a microwave-assisted protocol to reduce the workflow from days to hours. In addition to the significantly accelerated processing time, our microwave-assisted ExM (M/WExM) protocol maintains the superior resolution and signal-to-noise ratio of the original ExM protocol. Furthermore, the M/WExM protocol yields higher magnitude of expansion, suggesting that in addition to accelerating the process through increased diffusion rate of reagents, microwave radiation may also facilitate the expansion process. To demonstrate the applicability of this method to other specimens and protocols, we adapted the microwave-accelerated protocol to whole mount adult brain tissue of Drosophila melanogaster fruit flies, and successfully reduced the total processing time of a widely-used Drosophila IHC-ExM protocol from 6 days to 2 days. Our results demonstrate that with appropriate adjustment of the microwave parameters (wattage, pulse duration, interval, and number of cycles), this protocol can be readily adapted to different model organisms and tissue types to greatly increase the efficiency of ExM experiments.
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Affiliation(s)
| | | | | | - Amanda Jin
- Department of Biology, Georgetown University, Washington, DC 20057
| | - Danya A. Adams
- Department of Biology, Georgetown University, Washington, DC 20057
| | - Jessica M. Lin
- Department of Biology, Georgetown University, Washington, DC 20057
| | - Elena Iliadis
- Department of Biology, Georgetown University, Washington, DC 20057
| | - Tess M. Seidler
- Department of Biology, Georgetown University, Washington, DC 20057
| | | | - Hai-yan He
- Department of Biology, Georgetown University, Washington, DC 20057
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12
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Nguyen TH, Vicidomini R, Choudhury SD, Han TH, Maric D, Brody T, Serpe M. scRNA-seq data from the larval Drosophila ventral cord provides a resource for studying motor systems function and development. Dev Cell 2024; 59:1210-1230.e9. [PMID: 38569548 PMCID: PMC11078614 DOI: 10.1016/j.devcel.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/05/2023] [Accepted: 03/06/2024] [Indexed: 04/05/2024]
Abstract
The Drosophila larval ventral nerve cord (VNC) shares many similarities with the spinal cord of vertebrates and has emerged as a major model for understanding the development and function of motor systems. Here, we use high-quality scRNA-seq, validated by anatomical identification, to create a comprehensive census of larval VNC cell types. We show that the neural lineages that comprise the adult VNC are already defined, but quiescent, at the larval stage. Using fluorescence-activated cell sorting (FACS)-enriched populations, we separate all motor neuron bundles and link individual neuron clusters to morphologically characterized known subtypes. We discovered a glutamate receptor subunit required for basal neurotransmission and homeostasis at the larval neuromuscular junction. We describe larval glia and endorse the general view that glia perform consistent activities throughout development. This census represents an extensive resource and a powerful platform for future discoveries of cellular and molecular mechanisms in repair, regeneration, plasticity, homeostasis, and behavioral coordination.
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Affiliation(s)
| | | | | | | | - Dragan Maric
- Flow and Imaging Cytometry Core, NINDS, NIH, Bethesda, MD 20892, USA
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13
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Avila B, Augusto P, Zimmer M, Serafino M, Makse HA. Fibration symmetries and cluster synchronization in the Caenorhabditis elegans connectome. ARXIV 2024:arXiv:2305.19367v2. [PMID: 37396607 PMCID: PMC10312817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the Caenorhabditi elegans worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
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Affiliation(s)
- Bryant Avila
- Levich Institute, Physics Department, City College of New York, New York, NY, USA
| | - Pedro Augusto
- Vienna Biocenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Manuel Zimmer
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Matteo Serafino
- Levich Institute, Physics Department, City College of New York, New York, NY, USA
| | - Hernán A. Makse
- Levich Institute, Physics Department, City College of New York, New York, NY, USA
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14
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Schmidt M, Motta A, Sievers M, Helmstaedter M. RoboEM: automated 3D flight tracing for synaptic-resolution connectomics. Nat Methods 2024; 21:908-913. [PMID: 38514779 PMCID: PMC11093750 DOI: 10.1038/s41592-024-02226-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.
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Affiliation(s)
- Martin Schmidt
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
| | - Alessandro Motta
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Meike Sievers
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
- Faculty of Science, Radboud University, Nijmegen, the Netherlands
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
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15
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Pribbenow C, Owald D. Skewing information flow through pre- and postsynaptic plasticity in the mushroom bodies of Drosophila. Learn Mem 2024; 31:a053919. [PMID: 38876487 PMCID: PMC11199954 DOI: 10.1101/lm.053919.124] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/26/2024] [Indexed: 06/16/2024]
Abstract
Animal brains need to store information to construct a representation of their environment. Knowledge of what happened in the past allows both vertebrates and invertebrates to predict future outcomes by recalling previous experience. Although invertebrate and vertebrate brains share common principles at the molecular, cellular, and circuit-architectural levels, there are also obvious differences as exemplified by the use of acetylcholine versus glutamate as the considered main excitatory neurotransmitters in the respective central nervous systems. Nonetheless, across central nervous systems, synaptic plasticity is thought to be a main substrate for memory storage. Therefore, how brain circuits and synaptic contacts change following learning is of fundamental interest for understanding brain computations tied to behavior in any animal. Recent progress has been made in understanding such plastic changes following olfactory associative learning in the mushroom bodies (MBs) of Drosophila A current framework of memory-guided behavioral selection is based on the MB skew model, in which antagonistic synaptic pathways are selectively changed in strength. Here, we review insights into plasticity at dedicated Drosophila MB output pathways and update what is known about the plasticity of both pre- and postsynaptic compartments of Drosophila MB neurons.
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Affiliation(s)
- Carlotta Pribbenow
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - David Owald
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany
- NeuroCure, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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16
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Wei T, Guo Q, Webb B. Learning with sparse reward in a gap junction network inspired by the insect mushroom body. PLoS Comput Biol 2024; 20:e1012086. [PMID: 38781280 DOI: 10.1371/journal.pcbi.1012086] [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: 02/21/2023] [Revised: 06/05/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.
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Affiliation(s)
- Tianqi Wei
- Institute of Perception, Action, and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
- School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Qinghai Guo
- Huawei Technologies Co., Ltd., Shenzhen, Guangdong, China
| | - Barbara Webb
- Institute of Perception, Action, and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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17
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Webb B. Beyond prediction error: 25 years of modeling the associations formed in the insect mushroom body. Learn Mem 2024; 31:a053824. [PMID: 38862164 PMCID: PMC11199945 DOI: 10.1101/lm.053824.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/01/2024] [Indexed: 06/13/2024]
Abstract
The insect mushroom body has gained increasing attention as a system in which the computational basis of neural learning circuits can be unraveled. We now understand in detail the key locations in this circuit where synaptic associations are formed between sensory patterns and values leading to actions. However, the actual learning rule (or rules) implemented by neural activity and leading to synaptic change is still an open question. Here, I survey the diversity of answers that have been offered in computational models of this system over the past decades, including the recurring assumption-in line with top-down theories of associative learning-that the core function is to reduce prediction error. However, I will argue, a more bottom-up approach may ultimately reveal a richer algorithmic capacity in this still enigmatic brain neuropil.
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Affiliation(s)
- Barbara Webb
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
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18
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Parnas M, Manoim JE, Lin AC. Sensory encoding and memory in the mushroom body: signals, noise, and variability. Learn Mem 2024; 31:a053825. [PMID: 38862174 PMCID: PMC11199953 DOI: 10.1101/lm.053825.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/21/2023] [Indexed: 06/13/2024]
Abstract
To survive in changing environments, animals need to learn to associate specific sensory stimuli with positive or negative valence. How do they form stimulus-specific memories to distinguish between positively/negatively associated stimuli and other irrelevant stimuli? Solving this task is one of the functions of the mushroom body, the associative memory center in insect brains. Here we summarize recent work on sensory encoding and memory in the Drosophila mushroom body, highlighting general principles such as pattern separation, sparse coding, noise and variability, coincidence detection, and spatially localized neuromodulation, and placing the mushroom body in comparative perspective with mammalian memory systems.
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Affiliation(s)
- Moshe Parnas
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Julia E Manoim
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Andrew C Lin
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, United Kingdom
- Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, United Kingdom
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19
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Fiala A, Kaun KR. What do the mushroom bodies do for the insect brain? Twenty-five years of progress. Learn Mem 2024; 31:a053827. [PMID: 38862175 PMCID: PMC11199942 DOI: 10.1101/lm.053827.123] [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: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 06/13/2024]
Abstract
In 1998, a special edition of Learning & Memory was published with a discrete focus of synthesizing the state of the field to provide an overview of the function of the insect mushroom body. While molecular neuroscience and optical imaging of larger brain areas were advancing, understanding the basic functioning of neuronal circuits, particularly in the context of the mushroom body, was rudimentary. In the past 25 years, technological innovations have allowed researchers to map and understand the in vivo function of the neuronal circuits of the mushroom body system, making it an ideal model for investigating the circuit basis of sensory encoding, memory formation, and behavioral decisions. Collaborative efforts within the community have played a crucial role, leading to an interactive connectome of the mushroom body and accessible genetic tools for studying mushroom body circuit function. Looking ahead, continued technological innovation and collaborative efforts are likely to further advance our understanding of the mushroom body and its role in behavior and cognition, providing insights that generalize to other brain structures and species.
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Affiliation(s)
- André Fiala
- Department of Molecular Neurobiology of Behaviour, University of Göttingen, Göttingen 37077, Germany
| | - Karla R Kaun
- Department of Neuroscience, Brown University, Providence, Rhode Island 02806, USA
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20
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Chan ICW, Chen N, Hernandez J, Meltzer H, Park A, Stahl A. Future avenues in Drosophila mushroom body research. Learn Mem 2024; 31:a053863. [PMID: 38862172 PMCID: PMC11199946 DOI: 10.1101/lm.053863.123] [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: 11/02/2023] [Accepted: 03/27/2024] [Indexed: 06/13/2024]
Abstract
How does the brain translate sensory information into complex behaviors? With relatively small neuronal numbers, readable behavioral outputs, and an unparalleled genetic toolkit, the Drosophila mushroom body (MB) offers an excellent model to address this question in the context of associative learning and memory. Recent technological breakthroughs, such as the freshly completed full-brain connectome, multiomics approaches, CRISPR-mediated gene editing, and machine learning techniques, led to major advancements in our understanding of the MB circuit at the molecular, structural, physiological, and functional levels. Despite significant progress in individual MB areas, the field still faces the fundamental challenge of resolving how these different levels combine and interact to ultimately control the behavior of an individual fly. In this review, we discuss various aspects of MB research, with a focus on the current knowledge gaps, and an outlook on the future methodological developments required to reach an overall view of the neurobiological basis of learning and memory.
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Affiliation(s)
- Ivy Chi Wai Chan
- Dynamics of Neuronal Circuits Group, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Developmental Biology, RWTH Aachen University, Aachen, Germany
| | - Nannan Chen
- School of Life Science and Technology, Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing 210096, China
| | - John Hernandez
- Neuroscience Department, Brown University, Providence, Rhode Island 02906, USA
| | - Hagar Meltzer
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Annie Park
- Department of Physiology, Anatomy and Genetics, Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
| | - Aaron Stahl
- Neuroscience and Pharmacology, University of Iowa, Iowa City, Iowa 52242, USA
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21
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Sen E, El-Keredy A, Jacob N, Mancini N, Asnaz G, Widmann A, Gerber B, Thoener J. Cognitive limits of larval Drosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning. Learn Mem 2024; 31:a053726. [PMID: 38862170 PMCID: PMC11199949 DOI: 10.1101/lm.053726.122] [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] [Accepted: 01/18/2024] [Indexed: 06/13/2024]
Abstract
Drosophila larvae are an established model system for studying the mechanisms of innate and simple forms of learned behavior. They have about 10 times fewer neurons than adult flies, and it was the low total number of their neurons that allowed for an electron microscopic reconstruction of their brain at synaptic resolution. Regarding the mushroom body, a central brain structure for many forms of associative learning in insects, it turned out that more than half of the classes of synaptic connection had previously escaped attention. Understanding the function of these circuit motifs, subsequently confirmed in adult flies, is an important current research topic. In this context, we test larval Drosophila for their cognitive abilities in three tasks that are characteristically more complex than those previously studied. Our data provide evidence for (i) conditioned inhibition, as has previously been reported for adult flies and honeybees. Unlike what is described for adult flies and honeybees, however, our data do not provide evidence for (ii) sensory preconditioning or (iii) second-order conditioning in Drosophila larvae. We discuss the methodological features of our experiments as well as four specific aspects of the organization of the larval brain that may explain why these two forms of learning are observed in adult flies and honeybees, but not in larval Drosophila.
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Affiliation(s)
- Edanur Sen
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Amira El-Keredy
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Department of Genetics, Faculty of Agriculture, Tanta University, 31111 Tanta, Egypt
| | - Nina Jacob
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Nino Mancini
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Gülüm Asnaz
- Department of Molecular Neurobiology of Behavior, University of Göttingen, 37077 Göttingen, Germany
| | - Annekathrin Widmann
- Department of Molecular Neurobiology of Behavior, University of Göttingen, 37077 Göttingen, Germany
| | - Bertram Gerber
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Otto von Guericke University Magdeburg, Institute of Biology, 39106 Magdeburg, Germany
- Center for Behavioral Brain Sciences, 39106 Magdeburg, Germany
| | - Juliane Thoener
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
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22
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Avila B, Serafino M, Augusto P, Zimmer M, Makse HA. Fibration symmetries and cluster synchronization in the Caenorhabditis elegans connectome. PLoS One 2024; 19:e0297669. [PMID: 38598455 PMCID: PMC11006206 DOI: 10.1371/journal.pone.0297669] [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: 05/19/2023] [Accepted: 01/11/2024] [Indexed: 04/12/2024] Open
Abstract
Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the Caenorhabditi elegans worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
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Affiliation(s)
- Bryant Avila
- Physics Department, Levich Institute, City College of New York, New York, NY, United Stated of America
| | - Matteo Serafino
- Physics Department, Levich Institute, City College of New York, New York, NY, United Stated of America
| | - Pedro Augusto
- Vienna Biocenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Manuel Zimmer
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Hernán A. Makse
- Physics Department, Levich Institute, City College of New York, New York, NY, United Stated of America
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23
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Mizunami M, Yamagata N. Editorial overview: Aroma nudges in bugs: Sensory perception and memory in insects. CURRENT OPINION IN INSECT SCIENCE 2024; 62:101165. [PMID: 38244691 DOI: 10.1016/j.cois.2024.101165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2024]
Affiliation(s)
- Makoto Mizunami
- Research Institute for Electronic Science, Hokkaido University, Sapporo, Japan.
| | - Nobuhiro Yamagata
- Faculty and Graduate School of Engineering Science, Akita University, Akita, Japan
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24
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Loreto JS, Ferreira SA, de Almeida P, da Rocha JBT, Barbosa NV. Screening for Differentially Expressed Memory Genes on a Diabetes Model Induced by High-Sugar Diet in Drosophila melanogaster: Potential Markers for Memory Deficits. Mol Neurobiol 2024; 61:1225-1236. [PMID: 37698834 DOI: 10.1007/s12035-023-03598-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023]
Abstract
Type 2 diabetes mellitus (T2DM) has been shown to affect a series of cognitive processes including memory, increasing the risk for dementia, particularly Alzheimer's disease (AD). Although increasing evidence has supported that both diseases share common features, the pathophysiological mechanisms connecting these two disorders remain to be fully elucidated. Herein, we used Drosophila melanogaster fed on a high-sugar diet (HSD) to mimic T2DM, and investigate its effects on memory as well as identify potential molecular players associated with the memory deficits induced by HSD. Flies hatched from and reared on HSD for 7 days had a substantial decrease in short-term memory (STM). The screening for memory-related genes using transcriptome data revealed that HSD altered the expression of 33% of memory genes in relation to the control. Among the differentially expressed genes (DEGs) with a fold change (FC) higher than two, we found five genes, related to synapse and memory trace formation, that could be considered strong candidates to underlie the STM deficits in HSD flies: Abl tyrosine kinase (Abl), bruchpilot (Brp), minibrain (Mnb), shaker (Sh), and gilgamesh (Gish). We also analyzed genes from the dopamine system, one of the most relevant signaling pathways for olfactory memory. Interestingly, the flies fed on HSD presented a decreased expression of the Tyrosine hydroxylase (Ple) and Dopa decarboxylase (Ddc) genes, signals of a possible dopamine deficiency. In this work, we present promising biomarkers to investigate molecular networks shared between T2DM and AD.
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Affiliation(s)
- Julia Sepel Loreto
- Centro de Ciências Naturais E Exatas, Programa de Pós-Graduação Em Bioquímica Toxicológica, Universidade Federal de Santa Maria, Avenida Roraima, Santa Maria, RS, 1000, 97105-900, Brazil
| | - Sabrina Antunes Ferreira
- Centro de Ciências Naturais E Exatas, Programa de Pós-Graduação Em Bioquímica Toxicológica, Universidade Federal de Santa Maria, Avenida Roraima, Santa Maria, RS, 1000, 97105-900, Brazil
| | - Pâmela de Almeida
- Centro de Ciências Naturais E Exatas, Programa de Pós-Graduação Em Bioquímica Toxicológica, Universidade Federal de Santa Maria, Avenida Roraima, Santa Maria, RS, 1000, 97105-900, Brazil
| | - João Batista Teixeira da Rocha
- Centro de Ciências Naturais E Exatas, Programa de Pós-Graduação Em Bioquímica Toxicológica, Universidade Federal de Santa Maria, Avenida Roraima, Santa Maria, RS, 1000, 97105-900, Brazil
| | - Nilda Vargas Barbosa
- Centro de Ciências Naturais E Exatas, Programa de Pós-Graduação Em Bioquímica Toxicológica, Universidade Federal de Santa Maria, Avenida Roraima, Santa Maria, RS, 1000, 97105-900, Brazil.
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25
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Zhang T, Rosenberg M, Jing Z, Perona P, Meister M. Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling. eLife 2024; 12:RP84141. [PMID: 38420996 PMCID: PMC10911395 DOI: 10.7554/elife.84141] [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: 03/02/2024] Open
Abstract
An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Here we propose a neural algorithm that can solve all these problems and operates reliably in diverse and complex environments. At its core, the mechanism makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source. We show how the brain can learn to generate internal "virtual odors" that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.
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Affiliation(s)
- Tony Zhang
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Matthew Rosenberg
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
- Center for the Physics of Biological Function, Princeton UniversityPrincetonUnited States
| | - Zeyu Jing
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Pietro Perona
- Division of Engineering and Applied Science, California Institute of TechnologyPasadenaUnited States
| | - Markus Meister
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
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26
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Rubin GM, Aso Y. New genetic tools for mushroom body output neurons in Drosophila. eLife 2024; 12:RP90523. [PMID: 38270577 PMCID: PMC10945696 DOI: 10.7554/elife.90523] [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: 01/26/2024] Open
Abstract
How memories of past events influence behavior is a key question in neuroscience. The major associative learning center in Drosophila, the mushroom body (MB), communicates to the rest of the brain through mushroom body output neurons (MBONs). While 21 MBON cell types have their dendrites confined to small compartments of the MB lobes, analysis of EM connectomes revealed the presence of an additional 14 MBON cell types that are atypical in having dendritic input both within the MB lobes and in adjacent brain regions. Genetic reagents for manipulating atypical MBONs and experimental data on their functions have been lacking. In this report we describe new cell-type-specific GAL4 drivers for many MBONs, including the majority of atypical MBONs that extend the collection of MBON driver lines we have previously generated (Aso et al., 2014a; Aso et al., 2016; Aso et al., 2019). Using these genetic reagents, we conducted optogenetic activation screening to examine their ability to drive behaviors and learning. These reagents provide important new tools for the study of complex behaviors in Drosophila.
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Affiliation(s)
- Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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27
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Jürgensen AM, Sakagiannis P, Schleyer M, Gerber B, Nawrot MP. Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva. iScience 2024; 27:108640. [PMID: 38292165 PMCID: PMC10824792 DOI: 10.1016/j.isci.2023.108640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 11/10/2023] [Accepted: 12/01/2023] [Indexed: 02/01/2024] Open
Abstract
Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.
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Affiliation(s)
- Anna-Maria Jürgensen
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Panagiotis Sakagiannis
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Michael Schleyer
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for the Advancement of Higher Education, Faculty of Science, Hokkaido University, Sapporo 060-08080, Japan
| | - Bertram Gerber
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for Biology, Otto-von-Guericke University, 39120 Magdeburg, Germany
- Center for Brain and Behavioral Sciences (CBBS), Otto-von-Guericke University, 39118 Magdeburg, Germany
| | - Martin Paul Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
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28
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Jürgensen AM, Schmitt FJ, Nawrot MP. Minimal circuit motifs for second-order conditioning in the insect mushroom body. Front Physiol 2024; 14:1326307. [PMID: 38269060 PMCID: PMC10806035 DOI: 10.3389/fphys.2023.1326307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 12/22/2023] [Indexed: 01/26/2024] Open
Abstract
In well-established first-order conditioning experiments, the concurrence of a sensory cue with reinforcement forms an association, allowing the cue to predict future reinforcement. In the insect mushroom body, a brain region central to learning and memory, such associations are encoded in the synapses between its intrinsic and output neurons. This process is mediated by the activity of dopaminergic neurons that encode reinforcement signals. In second-order conditioning, a new sensory cue is paired with an already established one that presumably activates dopaminergic neurons due to its predictive power of the reinforcement. We explored minimal circuit motifs in the mushroom body for their ability to support second-order conditioning using mechanistic models. We found that dopaminergic neurons can either be activated directly by the mushroom body's intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrated that the circuit motifs differ in their computational efficiency and robustness. Beyond previous research, we suggest an additional motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters.
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Affiliation(s)
- Anna-Maria Jürgensen
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany
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29
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Zhou F, Tichy AM, Imambocus BN, Sakharwade S, Rodriguez Jimenez FJ, González Martínez M, Jahan I, Habib M, Wilhelmy N, Burre V, Lömker T, Sauter K, Helfrich-Förster C, Pielage J, Grunwald Kadow IC, Janovjak H, Soba P. Optimized design and in vivo application of optogenetically functionalized Drosophila dopamine receptors. Nat Commun 2023; 14:8434. [PMID: 38114457 PMCID: PMC10730509 DOI: 10.1038/s41467-023-43970-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/24/2023] [Indexed: 12/21/2023] Open
Abstract
Neuromodulatory signaling via G protein-coupled receptors (GPCRs) plays a pivotal role in regulating neural network function and animal behavior. The recent development of optogenetic tools to induce G protein-mediated signaling provides the promise of acute and cell type-specific manipulation of neuromodulatory signals. However, designing and deploying optogenetically functionalized GPCRs (optoXRs) with accurate specificity and activity to mimic endogenous signaling in vivo remains challenging. Here we optimize the design of optoXRs by considering evolutionary conserved GPCR-G protein interactions and demonstrate the feasibility of this approach using two Drosophila Dopamine receptors (optoDopRs). These optoDopRs exhibit high signaling specificity and light sensitivity in vitro. In vivo, we show receptor and cell type-specific effects of dopaminergic signaling in various behaviors, including the ability of optoDopRs to rescue the loss of the endogenous receptors. This work demonstrates that optoXRs can enable optical control of neuromodulatory receptor-specific signaling in functional and behavioral studies.
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Affiliation(s)
- Fangmin Zhou
- Institute of Physiology and Pathophysiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- LIMES Institute, Department of Molecular Brain Physiology and Behavior, University of Bonn, Carl-Troll-Str. 31, 53115, Bonn, Germany
- Neuronal Patterning and Connectivity laboratory, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Alexandra-Madelaine Tichy
- Australian Regenerative Medicine Institute (ARMI), Faculty of Medicine, Nursing and Health Sciences, Monash University, 3800, Clayton, Victoria, Australia
- European Molecular Biology Laboratory Australia (EMBL Australia), Monash University, 3800, Clayton, Victoria, Australia
| | - Bibi Nusreen Imambocus
- LIMES Institute, Department of Molecular Brain Physiology and Behavior, University of Bonn, Carl-Troll-Str. 31, 53115, Bonn, Germany
| | - Shreyas Sakharwade
- Institute of Physiology and Pathophysiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- LIMES Institute, Department of Molecular Brain Physiology and Behavior, University of Bonn, Carl-Troll-Str. 31, 53115, Bonn, Germany
| | - Francisco J Rodriguez Jimenez
- Institute of Physiology II, University Clinic Bonn (UKB), University of Bonn, 53115, Bonn, Germany
- ZIEL-Institute of Life and Health, Technical University of Munich, School of Life Sciences, 85354, Freising, Germany
| | - Marco González Martínez
- Institute of Physiology II, University Clinic Bonn (UKB), University of Bonn, 53115, Bonn, Germany
| | - Ishrat Jahan
- Institute of Physiology II, University Clinic Bonn (UKB), University of Bonn, 53115, Bonn, Germany
| | - Margarita Habib
- Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany
| | - Nina Wilhelmy
- Division of Neurobiology and Zoology, RPTU University of Kaiserslautern, 67663, Kaiserslautern, Germany
| | - Vanessa Burre
- Division of Neurobiology and Zoology, RPTU University of Kaiserslautern, 67663, Kaiserslautern, Germany
| | - Tatjana Lömker
- Neuronal Patterning and Connectivity laboratory, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Kathrin Sauter
- Neuronal Patterning and Connectivity laboratory, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | | | - Jan Pielage
- Division of Neurobiology and Zoology, RPTU University of Kaiserslautern, 67663, Kaiserslautern, Germany
| | - Ilona C Grunwald Kadow
- Institute of Physiology II, University Clinic Bonn (UKB), University of Bonn, 53115, Bonn, Germany
- ZIEL-Institute of Life and Health, Technical University of Munich, School of Life Sciences, 85354, Freising, Germany
| | - Harald Janovjak
- Australian Regenerative Medicine Institute (ARMI), Faculty of Medicine, Nursing and Health Sciences, Monash University, 3800, Clayton, Victoria, Australia
- European Molecular Biology Laboratory Australia (EMBL Australia), Monash University, 3800, Clayton, Victoria, Australia
- Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, 5042, Bedford Park, South Australia, Australia
| | - Peter Soba
- Institute of Physiology and Pathophysiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany.
- LIMES Institute, Department of Molecular Brain Physiology and Behavior, University of Bonn, Carl-Troll-Str. 31, 53115, Bonn, Germany.
- Neuronal Patterning and Connectivity laboratory, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany.
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Dorkenwald S, Li PH, Januszewski M, Berger DR, Maitin-Shepard J, Bodor AL, Collman F, Schneider-Mizell CM, da Costa NM, Lichtman JW, Jain V. Multi-layered maps of neuropil with segmentation-guided contrastive learning. Nat Methods 2023; 20:2011-2020. [PMID: 37985712 PMCID: PMC10703674 DOI: 10.1038/s41592-023-02059-8] [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: 11/18/2022] [Accepted: 10/02/2023] [Indexed: 11/22/2023]
Abstract
Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.
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Affiliation(s)
- Sven Dorkenwald
- Google Research, Mountain View, CA, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | - Daniel R Berger
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | | | | | | | | | | | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | - Viren Jain
- Google Research, Mountain View, CA, USA.
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31
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Ripoll-Sánchez L, Watteyne J, Sun H, Fernandez R, Taylor SR, Weinreb A, Bentley BL, Hammarlund M, Miller DM, Hobert O, Beets I, Vértes PE, Schafer WR. The neuropeptidergic connectome of C. elegans. Neuron 2023; 111:3570-3589.e5. [PMID: 37935195 PMCID: PMC7615469 DOI: 10.1016/j.neuron.2023.09.043] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 08/02/2023] [Accepted: 09/29/2023] [Indexed: 11/09/2023]
Abstract
Efforts are ongoing to map synaptic wiring diagrams, or connectomes, to understand the neural basis of brain function. However, chemical synapses represent only one type of functionally important neuronal connection; in particular, extrasynaptic, "wireless" signaling by neuropeptides is widespread and plays essential roles in all nervous systems. By integrating single-cell anatomical and gene-expression datasets with biochemical analysis of receptor-ligand interactions, we have generated a draft connectome of neuropeptide signaling in the C. elegans nervous system. This network is characterized by high connection density, extended signaling cascades, autocrine foci, and a decentralized topology, with a large, highly interconnected core containing three constituent communities sharing similar patterns of input connectivity. Intriguingly, several key network hubs are little-studied neurons that appear specialized for peptidergic neuromodulation. We anticipate that the C. elegans neuropeptidergic connectome will serve as a prototype to understand how networks of neuromodulatory signaling are organized.
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Affiliation(s)
- Lidia Ripoll-Sánchez
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Psychiatry, Cambridge University, Cambridge, UK
| | - Jan Watteyne
- Department of Biology, KU Leuven, Leuven, Belgium
| | - HaoSheng Sun
- Department of Biological Sciences/HHMI, Columbia University, New York, NY, USA; Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert Fernandez
- Department of Biological Sciences/HHMI, Columbia University, New York, NY, USA
| | - Seth R Taylor
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alexis Weinreb
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Barry L Bentley
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, UK
| | - Marc Hammarlund
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - David M Miller
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Oliver Hobert
- Department of Biological Sciences/HHMI, Columbia University, New York, NY, USA
| | - Isabel Beets
- Department of Biology, KU Leuven, Leuven, Belgium
| | - Petra E Vértes
- Department of Psychiatry, Cambridge University, Cambridge, UK
| | - William R Schafer
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Biology, KU Leuven, Leuven, Belgium.
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32
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Mancini N, Thoener J, Tafani E, Pauls D, Mayseless O, Strauch M, Eichler K, Champion A, Kobler O, Weber D, Sen E, Weiglein A, Hartenstein V, Chytoudis-Peroudis CC, Jovanic T, Thum AS, Rohwedder A, Schleyer M, Gerber B. Rewarding Capacity of Optogenetically Activating a Giant GABAergic Central-Brain Interneuron in Larval Drosophila. J Neurosci 2023; 43:7393-7428. [PMID: 37734947 PMCID: PMC10621887 DOI: 10.1523/jneurosci.2310-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 07/19/2023] [Accepted: 08/26/2023] [Indexed: 09/23/2023] Open
Abstract
Larvae of the fruit fly Drosophila melanogaster are a powerful study case for understanding the neural circuits underlying behavior. Indeed, the numerical simplicity of the larval brain has permitted the reconstruction of its synaptic connectome, and genetic tools for manipulating single, identified neurons allow neural circuit function to be investigated with relative ease and precision. We focus on one of the most complex neurons in the brain of the larva (of either sex), the GABAergic anterior paired lateral neuron (APL). Using behavioral and connectomic analyses, optogenetics, Ca2+ imaging, and pharmacology, we study how APL affects associative olfactory memory. We first provide a detailed account of the structure, regional polarity, connectivity, and metamorphic development of APL, and further confirm that optogenetic activation of APL has an inhibiting effect on its main targets, the mushroom body Kenyon cells. All these findings are consistent with the previously identified function of APL in the sparsening of sensory representations. To our surprise, however, we found that optogenetically activating APL can also have a strong rewarding effect. Specifically, APL activation together with odor presentation establishes an odor-specific, appetitive, associative short-term memory, whereas naive olfactory behavior remains unaffected. An acute, systemic inhibition of dopamine synthesis as well as an ablation of the dopaminergic pPAM neurons impair reward learning through APL activation. Our findings provide a study case of complex circuit function in a numerically simple brain, and suggest a previously unrecognized capacity of central-brain GABAergic neurons to engage in dopaminergic reinforcement.SIGNIFICANCE STATEMENT The single, identified giant anterior paired lateral (APL) neuron is one of the most complex neurons in the insect brain. It is GABAergic and contributes to the sparsening of neuronal activity in the mushroom body, the memory center of insects. We provide the most detailed account yet of the structure of APL in larval Drosophila as a neurogenetically accessible study case. We further reveal that, contrary to expectations, the experimental activation of APL can exert a rewarding effect, likely via dopaminergic reward pathways. The present study both provides an example of unexpected circuit complexity in a numerically simple brain, and reports an unexpected effect of activity in central-brain GABAergic circuits.
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Affiliation(s)
- Nino Mancini
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Juliane Thoener
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Esmeralda Tafani
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Dennis Pauls
- Department of Animal Physiology, Institute of Biology, Leipzig University, Leipzig, 04103, Germany
| | - Oded Mayseless
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Martin Strauch
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, 52074, Germany
| | - Katharina Eichler
- Institute of Neurobiology, University of Puerto Rico Medical Science Campus, Old San Juan, Puerto Rico, 00901
| | - Andrew Champion
- Department of Physiology, Development and Neuroscience, Cambridge University, Cambridge, CB2 3EL, United Kingdom
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, 20147, Virginia
| | - Oliver Kobler
- Leibniz Institute for Neurobiology, Combinatorial Neuroimaging Core Facility, Magdeburg, 39118, Germany
| | - Denise Weber
- Department of Genetics, Institute of Biology, Leipzig University, Leipzig, 04103, Germany
| | - Edanur Sen
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Aliće Weiglein
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Volker Hartenstein
- University of California, Department of Molecular, Cell and Developmental Biology, Los Angeles, California 90095-1606
| | | | - Tihana Jovanic
- Université Paris-Saclay, Centre National de la Recherche Scientifique, Institut des neurosciences Paris-Saclay, Saclay, 91400, France
| | - Andreas S Thum
- Department of Genetics, Institute of Biology, Leipzig University, Leipzig, 04103, Germany
| | - Astrid Rohwedder
- Department of Genetics, Institute of Biology, Leipzig University, Leipzig, 04103, Germany
| | - Michael Schleyer
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Bertram Gerber
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
- Center for Behavioral Brain Sciences, Magdeburg, 39106, Germany
- Institute for Biology, Otto von Guericke University, Magdeburg, 39120, Germany
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33
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Lei F, Peng Z, Liu M, Peng J, Cutsuridis V, Yue S. A Robust Visual System for Looming Cue Detection Against Translating Motion. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:8362-8376. [PMID: 35188895 DOI: 10.1109/tnnls.2022.3149832] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Collision detection is critical for autonomous vehicles or robots to serve human society safely. Detecting looming objects robustly and timely plays an important role in collision avoidance systems. The locust lobula giant movement detector (LGMD1) is specifically selective to looming objects which are on a direct collision course. However, the existing LGMD1 models cannot distinguish a looming object from a near and fast translatory moving object, because the latter can evoke a large amount of excitation that can lead to false LGMD1 spikes. This article presents a new visual neural system model (LGMD1) that applies a neural competition mechanism within a framework of separated ON and OFF pathways to shut off the translating response. The competition-based approach responds vigorously to monotonous ON/OFF responses resulting from a looming object. However, it does not respond to paired ON-OFF responses that result from a translating object, thereby enhancing collision selectivity. Moreover, a complementary denoising mechanism ensures reliable collision detection. To verify the effectiveness of the model, we have conducted systematic comparative experiments on synthetic and real datasets. The results show that our method exhibits more accurate discrimination between looming and translational events-the looming motion can be correctly detected. It also demonstrates that the proposed model is more robust than comparative models.
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34
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Ganguly I, Heckman EL, Litwin-Kumar A, Clowney EJ, Behnia R. Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.12.561793. [PMID: 37873086 PMCID: PMC10592809 DOI: 10.1101/2023.10.12.561793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The arthropod mushroom body is well-studied as an expansion layer that represents olfactory stimuli and links them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their tuning and function are poorly understood. Here, we use the FlyWire adult whole-brain connectome to identify inputs to visual Kenyon cells. The types of visual neurons we identify are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual projection neurons presynaptic to Kenyon cells receive input from large swathes of visual space, while local visual interneurons, providing smaller fractions of input, receive more spatially restricted signals that may be tuned to specific features of the visual scene. Like olfactory Kenyon cells, visual Kenyon cells receive sparse inputs from different combinations of visual channels, including inputs from multiple optic lobe neuropils. The sets of inputs to individual visual Kenyon cells are consistent with random sampling of available inputs. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the expansion coding properties appear different, with a specific repertoire of visual inputs projecting onto a relatively small number of visual Kenyon cells.
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Affiliation(s)
- Ishani Ganguly
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - Emily L Heckman
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ashok Litwin-Kumar
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Neuroscience Institute Affiliate
| | - Rudy Behnia
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
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35
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Salzberg Y, Haque R, Oren-Suissa M. The synaptic basis for sexual dimorphism in the invertebrate nervous system. Curr Opin Neurobiol 2023; 82:102757. [PMID: 37572555 PMCID: PMC10506627 DOI: 10.1016/j.conb.2023.102757] [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: 05/02/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 08/14/2023]
Abstract
Many animal behaviors are manifested differently in the two sexes of a given species, but how such sexual dimorphism is imprinted in the nervous system is not always clear. One mechanism involved is synaptic dimorphism, by which the same neurons exist in the two sexes, but form synapses that differ in features such as anatomy, molecular content or fate. While some evidence for synaptic dimorphism exists in humans and mammals, identifying these mechanisms in invertebrates has proven simpler, due to their smaller nervous systems and absence of external regulation by sex hormones. This review aims to present the current status of the field in invertebrates, the available toolkit for the study of synaptic dimorphism, and the standing questions that still remain incompletely answered.
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Affiliation(s)
- Yehuda Salzberg
- Department of Brain Science, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Rizwanul Haque
- Department of Brain Science, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Meital Oren-Suissa
- Department of Brain Science, Weizmann Institute of Science, Rehovot, 7610001, Israel.
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36
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Aso Y, Yamada D, Bushey D, Hibbard KL, Sammons M, Otsuna H, Shuai Y, Hige T. Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement. eLife 2023; 12:e85756. [PMID: 37721371 PMCID: PMC10588983 DOI: 10.7554/elife.85756] [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: 12/22/2022] [Accepted: 09/07/2023] [Indexed: 09/19/2023] Open
Abstract
How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign a valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After formation of appetitive memory, UpWiNs acquire enhanced response to reward-predicting odors as the response of the inhibitory presynaptic MBON undergoes depression. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was terminated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.
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Affiliation(s)
- Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Daichi Yamada
- Department of Biology, University of North Carolina at Chapel HillChapel HillUnited States
| | - Daniel Bushey
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Karen L Hibbard
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Megan Sammons
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Hideo Otsuna
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Yichun Shuai
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Toshihide Hige
- Department of Biology, University of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and Physiology, University of North Carolina at Chapel HillChapel HillUnited States
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel HillChapel HillUnited States
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37
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Zhu L, Mangan M, Webb B. Neuromorphic sequence learning with an event camera on routes through vegetation. Sci Robot 2023; 8:eadg3679. [PMID: 37756384 DOI: 10.1126/scirobotics.adg3679] [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: 12/30/2022] [Accepted: 08/29/2023] [Indexed: 09/29/2023]
Abstract
For many robotics applications, it is desirable to have relatively low-power and efficient onboard solutions. We took inspiration from insects, such as ants, that are capable of learning and following routes in complex natural environments using relatively constrained sensory and neural systems. Such capabilities are particularly relevant to applications such as agricultural robotics, where visual navigation through dense vegetation remains a challenging task. In this scenario, a route is likely to have high self-similarity and be subject to changing lighting conditions and motion over uneven terrain, and the effects of wind on leaves increase the variability of the input. We used a bioinspired event camera on a terrestrial robot to collect visual sequences along routes in natural outdoor environments and applied a neural algorithm for spatiotemporal memory that is closely based on a known neural circuit in the insect brain. We show that this method is plausible to support route recognition for visual navigation and more robust than SeqSLAM when evaluated on repeated runs on the same route or routes with small lateral offsets. By encoding memory in a spiking neural network running on a neuromorphic computer, our model can evaluate visual familiarity in real time from event camera footage.
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Affiliation(s)
- Le Zhu
- School of Informatics, University of Edinburgh, EH8 9AB Edinburgh, UK
| | - Michael Mangan
- Sheffield Robotics, Department of Computer Science, University of Sheffield, S1 4DP Sheffield, UK
| | - Barbara Webb
- School of Informatics, University of Edinburgh, EH8 9AB Edinburgh, UK
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38
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Kunin AB, Guo J, Bassler KE, Pitkow X, Josić K. Hierarchical Modular Structure of the Drosophila Connectome. J Neurosci 2023; 43:6384-6400. [PMID: 37591738 PMCID: PMC10501013 DOI: 10.1523/jneurosci.0134-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/19/2023] Open
Abstract
The structure of neural circuitry plays a crucial role in brain function. Previous studies of brain organization generally had to trade off between coarse descriptions at a large scale and fine descriptions on a small scale. Researchers have now reconstructed tens to hundreds of thousands of neurons at synaptic resolution, enabling investigations into the interplay between global, modular organization, and cell type-specific wiring. Analyzing data of this scale, however, presents unique challenges. To address this problem, we applied novel community detection methods to analyze the synapse-level reconstruction of an adult female Drosophila melanogaster brain containing >20,000 neurons and 10 million synapses. Using a machine-learning algorithm, we find the most densely connected communities of neurons by maximizing a generalized modularity density measure. We resolve the community structure at a range of scales, from large (on the order of thousands of neurons) to small (on the order of tens of neurons). We find that the network is organized hierarchically, and larger-scale communities are composed of smaller-scale structures. Our methods identify well-known features of the fly brain, including its sensory pathways. Moreover, focusing on specific brain regions, we are able to identify subnetworks with distinct connectivity types. For example, manual efforts have identified layered structures in the fan-shaped body. Our methods not only automatically recover this layered structure, but also resolve finer connectivity patterns to downstream and upstream areas. We also find a novel modular organization of the superior neuropil, with distinct clusters of upstream and downstream brain regions dividing the neuropil into several pathways. These methods show that the fine-scale, local network reconstruction made possible by modern experimental methods are sufficiently detailed to identify the organization of the brain across scales, and enable novel predictions about the structure and function of its parts.Significance Statement The Hemibrain is a partial connectome of an adult female Drosophila melanogaster brain containing >20,000 neurons and 10 million synapses. Analyzing the structure of a network of this size requires novel and efficient computational tools. We applied a new community detection method to automatically uncover the modular structure in the Hemibrain dataset by maximizing a generalized modularity measure. This allowed us to resolve the community structure of the fly hemibrain at a range of spatial scales revealing a hierarchical organization of the network, where larger-scale modules are composed of smaller-scale structures. The method also allowed us to identify subnetworks with distinct cell and connectivity structures, such as the layered structures in the fan-shaped body, and the modular organization of the superior neuropil. Thus, network analysis methods can be adopted to the connectomes being reconstructed using modern experimental methods to reveal the organization of the brain across scales. This supports the view that such connectomes will allow us to uncover the organizational structure of the brain, which can ultimately lead to a better understanding of its function.
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Affiliation(s)
- Alexander B Kunin
- Department of Mathematics, Creighton University, Omaha, Nebraska 68178
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
| | - Jiahao Guo
- Department of Physics, University of Houston, Houston, Texas 77204
- Texas Center for Superconductivity, University of Houston, Houston, Texas 77204
| | - Kevin E Bassler
- Department of Physics, University of Houston, Houston, Texas 77204
- Texas Center for Superconductivity, University of Houston, Houston, Texas 77204
- Department of Mathematics, University of Houston, Houston, Texas 77204
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas 77204
- Department of Biology and Biochemistry, University of Houston, Houston, Texas 77204
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39
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Zheng Y, Wang Y, Wu G, Li H, Peng J. Enhancing LGMD-based model for collision prediction via binocular structure. Front Neurosci 2023; 17:1247227. [PMID: 37732308 PMCID: PMC10507862 DOI: 10.3389/fnins.2023.1247227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 08/21/2023] [Indexed: 09/22/2023] Open
Abstract
Introduction Lobular giant motion detector (LGMD) neurons, renowned for their distinctive response to looming stimuli, inspire the development of visual neural network models for collision prediction. However, the existing LGMD-based models could not yet incorporate the invaluable feature of depth distance and still suffer from the following two primary drawbacks. Firstly, they struggle to effectively distinguish the three fundamental motion patterns of approaching, receding, and translating, in contrast to the natural abilities of LGMD neurons. Secondly, due to their reliance on a general determination process employing an activation function and fixed threshold for output, these models exhibit dramatic fluctuations in prediction effectiveness across different scenarios. Methods To address these issues, we propose a novel LGMD-based model with a binocular structure (Bi-LGMD). The depth distance of the moving object is extracted by calculating the binocular disparity facilitating a clear differentiation of the motion patterns, after obtaining the moving object's contour through the basic components of the LGMD network. In addition, we introduce a self-adaptive warning depth-distance, enhancing the model's robustness in various motion scenarios. Results The effectiveness of the proposed model is verified using computer-simulated and real-world videos. Discussion Furthermore, the experimental results demonstrate that the proposed model is robust to contrast and noise.
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Affiliation(s)
- Yi Zheng
- School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
- Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou, China
| | - Yusi Wang
- School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
- Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou, China
| | - Guangrong Wu
- School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
- Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou, China
| | - Haiyang Li
- School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
- Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou, China
| | - Jigen Peng
- School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
- Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou, China
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40
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Zhao H, Shao C, Shi Z, He S, Gong Z. The Intrinsic Similarity of Topological Structure in Biological Neural Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3292-3305. [PMID: 37224366 DOI: 10.1109/tcbb.2023.3279443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Most previous studies mainly have focused on the analysis of structural properties of individual neuronal networks from C. elegans. In recent years, an increasing number of synapse-level neural maps, also known as biological neural networks, have been reconstructed. However, it is not clear whether there are intrinsic similarities of structural properties of biological neural networks from different brain compartments or species. To explore this issue, we collected nine connectomes at synaptic resolution including C. elegans, and analyzed their structural properties. We found that these biological neural networks possess small-world properties and modules. Excluding the Drosophila larval visual system, these networks have rich clubs. The distributions of synaptic connection strength for these networks can be fitted by the truncated pow-law distributions. Additionally, compared with the power-law model, a log-normal distribution is a better model to fit the complementary cumulative distribution function (CCDF) of degree for these neuronal networks. Moreover, we also observed that these neural networks belong to the same superfamily based on the significance profile (SP) of small subgraphs in the network. Taken together, these findings suggest that biological neural networks share intrinsic similarities in their topological structure, revealing some principles underlying the formation of biological neural networks within and across species.
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41
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Barabási DL, Bianconi G, Bullmore E, Burgess M, Chung S, Eliassi-Rad T, George D, Kovács IA, Makse H, Nichols TE, Papadimitriou C, Sporns O, Stachenfeld K, Toroczkai Z, Towlson EK, Zador AM, Zeng H, Barabási AL, Bernard A, Buzsáki G. Neuroscience Needs Network Science. J Neurosci 2023; 43:5989-5995. [PMID: 37612141 PMCID: PMC10451115 DOI: 10.1523/jneurosci.1014-23.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 08/25/2023] Open
Abstract
The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.
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Affiliation(s)
- Dániel L Barabási
- Biophysics Program, Harvard University, Cambridge, 02138, Massachusetts
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, 02138, Massachusetts
| | - Ginestra Bianconi
- School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, United Kingdom
- Alan Turing Institute, The British Library, London, NW1 2DB, United Kingdom
| | - Ed Bullmore
- Department of Psychiatry and Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, New York 10003
- Center for Computational Neuroscience, Flatiron Institute, Simons Foundation, New York, New York 10010
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, 02115, Massachusetts
- Khoury College of Computer Sciences, Northeastern University, Boston, 02115, Massachusetts
- Santa Fe Institute, Santa Fe, New Mexico 87501
| | | | - István A Kovács
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois 60208
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois 60208
| | - Hernán Makse
- Levich Institute and Physics Department, City College of New York, New York, New York 10031
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | | | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405
| | - Kim Stachenfeld
- DeepMind, London, EC4A 3TW, United Kingdom
- Columbia University, New York, New York 10027
| | - Zoltán Toroczkai
- Department of Physics, University of Notre Dame, Notre Dame, Indiana 46556
| | - Emma K Towlson
- Department of Computer Science, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
| | - Anthony M Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, 98109, Washington
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, 02115, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary
| | - Amy Bernard
- The Kavli Foundation, Los Angeles, 90230, California
| | - György Buzsáki
- Center for Neural Science, New York University, New York, New York 10003
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, New York 10016
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42
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Kohsaka H. Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion. Front Neural Circuits 2023; 17:1175899. [PMID: 37711343 PMCID: PMC10499525 DOI: 10.3389/fncir.2023.1175899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/13/2023] [Indexed: 09/16/2023] Open
Abstract
The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.
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Affiliation(s)
- Hiroshi Kohsaka
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan
- Department of Complexity Science and Engineering, Graduate School of Frontier Science, The University of Tokyo, Chiba, Japan
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43
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Davis RL. Learning and memory using Drosophila melanogaster: a focus on advances made in the fifth decade of research. Genetics 2023; 224:iyad085. [PMID: 37212449 PMCID: PMC10411608 DOI: 10.1093/genetics/iyad085] [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: 02/16/2023] [Accepted: 05/03/2023] [Indexed: 05/23/2023] Open
Abstract
In the last decade, researchers using Drosophila melanogaster have made extraordinary progress in uncovering the mysteries underlying learning and memory. This progress has been propelled by the amazing toolkit available that affords combined behavioral, molecular, electrophysiological, and systems neuroscience approaches. The arduous reconstruction of electron microscopic images resulted in a first-generation connectome of the adult and larval brain, revealing complex structural interconnections between memory-related neurons. This serves as substrate for future investigations on these connections and for building complete circuits from sensory cue detection to changes in motor behavior. Mushroom body output neurons (MBOn) were discovered, which individually forward information from discrete and non-overlapping compartments of the axons of mushroom body neurons (MBn). These neurons mirror the previously discovered tiling of mushroom body axons by inputs from dopamine neurons and have led to a model that ascribes the valence of the learning event, either appetitive or aversive, to the activity of different populations of dopamine neurons and the balance of MBOn activity in promoting avoidance or approach behavior. Studies of the calyx, which houses the MBn dendrites, have revealed a beautiful microglomeruluar organization and structural changes of synapses that occur with long-term memory (LTM) formation. Larval learning has advanced, positioning it to possibly lead in producing new conceptual insights due to its markedly simpler structure over the adult brain. Advances were made in how cAMP response element-binding protein interacts with protein kinases and other transcription factors to promote the formation of LTM. New insights were made on Orb2, a prion-like protein that forms oligomers to enhance synaptic protein synthesis required for LTM formation. Finally, Drosophila research has pioneered our understanding of the mechanisms that mediate permanent and transient active forgetting, an important function of the brain along with acquisition, consolidation, and retrieval. This was catalyzed partly by the identification of memory suppressor genes-genes whose normal function is to limit memory formation.
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Affiliation(s)
- Ronald L Davis
- Department of Neuroscience, Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, University of Florida, 130 Scripps Way, Jupiter, FL 33458, USA
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44
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Patel AA, Cardona A, Cox DN. Neural substrates of cold nociception in Drosophila larva. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.31.551339. [PMID: 37577520 PMCID: PMC10418107 DOI: 10.1101/2023.07.31.551339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Metazoans detect and differentiate between innocuous (non-painful) and/or noxious (harmful) environmental cues using primary sensory neurons, which serve as the first node in a neural network that computes stimulus specific behaviors to either navigate away from injury-causing conditions or to perform protective behaviors that mitigate extensive injury. The ability of an animal to detect and respond to various sensory stimuli depends upon molecular diversity in the primary sensors and the underlying neural circuitry responsible for the relevant behavioral action selection. Recent studies in Drosophila larvae have revealed that somatosensory class III multidendritic (CIII md) neurons function as multimodal sensors regulating distinct behavioral responses to innocuous mechanical and nociceptive thermal stimuli. Recent advances in circuit bases of behavior have identified and functionally validated Drosophila larval somatosensory circuitry involved in innocuous (mechanical) and noxious (heat and mechanical) cues. However, central processing of cold nociceptive cues remained unexplored. We implicate multisensory integrators (Basins), premotor (Down-and-Back) and projection (A09e and TePns) neurons as neural substrates required for cold-evoked behavioral and calcium responses. Neural silencing of cell types downstream of CIII md neurons led to significant reductions in cold-evoked behaviors and neural co-activation of CIII md neurons plus additional cell types facilitated larval contraction (CT) responses. We further demonstrate that optogenetic activation of CIII md neurons evokes calcium increases in these neurons. Collectively, we demonstrate how Drosophila larvae process cold stimuli through functionally diverse somatosensory circuitry responsible for generating stimulus specific behaviors.
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Affiliation(s)
- Atit A. Patel
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Albert Cardona
- HHMI Janelia Research Campus, Ashburn, VA, USA
- MRC Laboratory of Molecular Biology, Cambridge, UK
- Department of Physiology, Development, and Neuroscience, University of Cambridge, UK
| | - Daniel N. Cox
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
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45
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Chapochnikov NM, Pehlevan C, Chklovskii DB. Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction. Proc Natl Acad Sci U S A 2023; 120:e2117484120. [PMID: 37428907 PMCID: PMC10629579 DOI: 10.1073/pnas.2117484120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 05/08/2023] [Indexed: 07/12/2023] Open
Abstract
One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we formulate biologically plausible mechanistic models of the circuit. In particular, we consider a linear circuit model, for which we derive an exact theoretical solution, and a nonnegative circuit model, which we examine through simulations. The latter largely predicts the ORN [Formula: see text] LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN [Formula: see text] LN and LN-LN synaptic counts and the emergence of different LN types. Functionally, we propose that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. We thus uncover a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, our study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations.
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Affiliation(s)
- Nikolai M. Chapochnikov
- Center for Computation Neuroscience, Flatiron Institute, New York, NY10010
- Department of Neurology, New York University School of Medicine, New York, NY10016
| | - Cengiz Pehlevan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA02138
- Center for Brain Science, Harvard University, Cambridge, MA02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA02138
| | - Dmitri B. Chklovskii
- Center for Computation Neuroscience, Flatiron Institute, New York, NY10010
- Neuroscience Institute, New York University School of Medicine, New York, NY10016
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46
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Ahmed M, Rajagopalan AE, Pan Y, Li Y, Williams DL, Pedersen EA, Thakral M, Previero A, Close KC, Christoforou CP, Cai D, Turner GC, Clowney EJ. Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body. Curr Biol 2023; 33:2742-2760.e12. [PMID: 37348501 PMCID: PMC10529417 DOI: 10.1016/j.cub.2023.05.064] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 05/02/2023] [Accepted: 05/26/2023] [Indexed: 06/24/2023]
Abstract
The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.
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Affiliation(s)
- Maria Ahmed
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Adithya E Rajagopalan
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA; The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Yijie Pan
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ye Li
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48104, USA
| | - Donnell L Williams
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Erik A Pedersen
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Manav Thakral
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Angelica Previero
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kari C Close
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA
| | | | - Dawen Cai
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48104, USA; Biophysics LS&A, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute Affiliate, University of Michigan, Ann Arbor, MI 48109, USA
| | - Glenn C Turner
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute Affiliate, University of Michigan, Ann Arbor, MI 48109, USA.
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47
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Mohamed A, Malekou I, Sim T, O'Kane CJ, Maait Y, Scullion B, Masuda-Nakagawa LM. Mushroom body output neurons MBON-a1/a2 define an odor intensity channel that regulates behavioral odor discrimination learning in larval Drosophila. Front Physiol 2023; 14:1111244. [PMID: 37256074 PMCID: PMC10225628 DOI: 10.3389/fphys.2023.1111244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/02/2023] [Indexed: 06/01/2023] Open
Abstract
The sensitivity of animals to sensory input must be regulated to ensure that signals are detected and also discriminable. However, how circuits regulate the dynamic range of sensitivity to sensory stimuli is not well understood. A given odor is represented in the insect mushroom bodies (MBs) by sparse combinatorial coding by Kenyon cells (KCs), forming an odor quality representation. To address how intensity of sensory stimuli is processed at the level of the MB input region, the calyx, we characterized a set of novel mushroom body output neurons that respond preferentially to high odor concentrations. We show that a pair of MB calyx output neurons, MBON-a1/2, are postsynaptic in the MB calyx, where they receive extensive synaptic inputs from KC dendrites, the inhibitory feedback neuron APL, and octopaminergic sVUM1 neurons, but relatively few inputs from projection neurons. This pattern is broadly consistent in the third-instar larva as well as in the first instar connectome. MBON-a1/a2 presynaptic terminals innervate a region immediately surrounding the MB medial lobe output region in the ipsilateral and contralateral brain hemispheres. By monitoring calcium activity using jRCamP1b, we find that MBON-a1/a2 responses are odor-concentration dependent, responding only to ethyl acetate (EA) concentrations higher than a 200-fold dilution, in contrast to MB neurons which are more concentration-invariant and respond to EA dilutions as low as 10-4. Optogenetic activation of the calyx-innervating sVUM1 modulatory neurons originating in the SEZ (Subesophageal zone), did not show a detectable effect on MBON-a1/a2 odor responses. Optogenetic activation of MBON-a1/a2 using CsChrimson impaired odor discrimination learning compared to controls. We propose that MBON-a1/a2 form an output channel of the calyx, summing convergent sensory and modulatory input, firing preferentially to high odor concentration, and might affect the activity of downstream MB targets.
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Barabási DL, Bianconi G, Bullmore E, Burgess M, Chung S, Eliassi-Rad T, George D, Kovács IA, Makse H, Papadimitriou C, Nichols TE, Sporns O, Stachenfeld K, Toroczkai Z, Towlson EK, Zador AM, Zeng H, Barabási AL, Bernard A, Buzsáki G. Neuroscience needs Network Science. ARXIV 2023:arXiv:2305.06160v2. [PMID: 37214134 PMCID: PMC10197734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.
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Affiliation(s)
- Dániel L Barabási
- Biophysics Program, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA
| | - Ginestra Bianconi
- School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK
- The Alan Turing Institute, The British Library, London, NW1 2DB, UK
| | - Ed Bullmore
- Department of Psychiatry and Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, NY, USA
- Center for Computational Neuroscience, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, MA, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | | | - István A. Kovács
- Department of Physics and Astronomy, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Chambers Hall, 600 Foster St, Northwestern University, Evanston, IL 60208
| | - Hernán Makse
- Levich Institute and Physics Department, City College of New York, New York, NY 10031 US
| | | | - Thomas E. Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47405
| | | | - Zoltán Toroczkai
- Department of Physics, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame IN 46556, USA
| | - Emma K. Towlson
- Department of Computer Science, Department of Physics and Astronomy, Hotchkiss Brain Institute, Children’s Research Hospital, University of Calgary, Calgary, Alberta, Canada 22
| | - Anthony M Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary
| | | | - György Buzsáki
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
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49
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Sorkaç A, Moșneanu RA, Crown AM, Savaş D, Okoro AM, Memiş E, Talay M, Barnea G. retro-Tango enables versatile retrograde circuit tracing in Drosophila. eLife 2023; 12:e85041. [PMID: 37166114 PMCID: PMC10208638 DOI: 10.7554/elife.85041] [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: 11/19/2022] [Accepted: 05/11/2023] [Indexed: 05/12/2023] Open
Abstract
Transsynaptic tracing methods are crucial tools in studying neural circuits. Although a couple of anterograde tracing methods and a targeted retrograde tool have been developed in Drosophila melanogaster, there is still need for an unbiased, user-friendly, and flexible retrograde tracing system. Here, we describe retro-Tango, a method for transsynaptic, retrograde circuit tracing and manipulation in Drosophila. In this genetically encoded system, a ligand-receptor interaction at the synapse triggers an intracellular signaling cascade that results in reporter gene expression in presynaptic neurons. Importantly, panneuronal expression of the elements of the cascade renders this method versatile, enabling its use not only to test hypotheses but also to generate them. We validate retro-Tango in various circuits and benchmark it by comparing our findings with the electron microscopy reconstruction of the Drosophila hemibrain. Our experiments establish retro-Tango as a key method for circuit tracing in neuroscience research.
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Affiliation(s)
- Altar Sorkaç
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Rareș A Moșneanu
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Anthony M Crown
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Doruk Savaş
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Angel M Okoro
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Ezgi Memiş
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Mustafa Talay
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Gilad Barnea
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
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50
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Zeng J, Li X, Zhang R, Lv M, Wang Y, Tan K, Xia X, Wan J, Jing M, Zhang X, Li Y, Yang Y, Wang L, Chu J, Li Y, Li Y. Local 5-HT signaling bi-directionally regulates the coincidence time window for associative learning. Neuron 2023; 111:1118-1135.e5. [PMID: 36706757 PMCID: PMC11152601 DOI: 10.1016/j.neuron.2022.12.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/03/2022] [Accepted: 12/30/2022] [Indexed: 01/27/2023]
Abstract
The coincidence between conditioned stimulus (CS) and unconditioned stimulus (US) is essential for associative learning; however, the mechanism regulating the duration of this temporal window remains unclear. Here, we found that serotonin (5-HT) bi-directionally regulates the coincidence time window of olfactory learning in Drosophila and affects synaptic plasticity of Kenyon cells (KCs) in the mushroom body (MB). Utilizing GPCR-activation-based (GRAB) neurotransmitter sensors, we found that KC-released acetylcholine (ACh) activates a serotonergic dorsal paired medial (DPM) neuron, which in turn provides inhibitory feedback to KCs. Physiological stimuli induce spatially heterogeneous 5-HT signals, which proportionally gate the intrinsic coincidence time windows of different MB compartments. Artificially reducing or increasing the DPM neuron-released 5-HT shortens or prolongs the coincidence window, respectively. In a sequential trace conditioning paradigm, this serotonergic neuromodulation helps to bridge the CS-US temporal gap. Altogether, we report a model circuitry for perceiving the temporal coincidence and determining the causal relationship between environmental events.
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Affiliation(s)
- Jianzhi Zeng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Hefei National Laboratory for Physical Sciences at the Microscale, CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, Anhui, China.
| | - Xuelin Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Renzimo Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Yuanpei College, Peking University, Beijing 100871, China
| | - Mingyue Lv
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Yipan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Ke Tan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Xiju Xia
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; PKU-THU-NIBS Joint Graduate Program, Beijing 100871, China
| | - Jinxia Wan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Miao Jing
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Xiuning Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Yu Li
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yang Yang
- Institute of Biophysics, State Key Laboratory of Brain and Cognitive Science, Center for Excellence in Biomacromolecules, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Wang
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology & Center for Biomedical Optics and Molecular Imaging & CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jun Chu
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology & Center for Biomedical Optics and Molecular Imaging & CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yan Li
- Institute of Biophysics, State Key Laboratory of Brain and Cognitive Science, Center for Excellence in Biomacromolecules, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Yuanpei College, Peking University, Beijing 100871, China; PKU-THU-NIBS Joint Graduate Program, Beijing 100871, China; Chinese Institute for Brain Research, Beijing 102206, China.
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