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Lee SYJ, Dallmann CJ, Cook AP, Tuthill JC, Agrawal S. Divergent neural circuits for proprioceptive and exteroceptive sensing of the Drosophila leg. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590808. [PMID: 38712128 PMCID: PMC11071415 DOI: 10.1101/2024.04.23.590808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Somatosensory neurons provide the nervous system with information about mechanical forces originating inside and outside the body. Here, we use connectomics to reconstruct and analyze neural circuits downstream of the largest somatosensory organ in the Drosophila leg, the femoral chordotonal organ (FeCO). The FeCO has been proposed to support both proprioceptive sensing of the fly's femur-tibia joint and exteroceptive sensing of substrate vibrations, but it remains unknown which sensory neurons and central circuits contribute to each of these functions. We found that different subtypes of FeCO sensory neurons feed into distinct proprioceptive and exteroceptive pathways. Position- and movement-encoding FeCO neurons connect to local leg motor control circuits in the ventral nerve cord (VNC), indicating a proprioceptive function. In contrast, signals from the vibration-encoding FeCO neurons are integrated across legs and transmitted to auditory regions in the brain, indicating an exteroceptive function. Overall, our analyses reveal the structure of specialized circuits for processing proprioceptive and exteroceptive signals from the fly leg. They also demonstrate how analyzing patterns of synaptic connectivity can distill organizing principles from complex sensorimotor circuits.
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2
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Eichler K, Hampel S, Alejandro-García A, Calle-Schuler SA, Santana-Cruz A, Kmecova L, Blagburn JM, Hoopfer ED, Seeds AM. Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila. eLife 2024; 12:RP87602. [PMID: 38634460 PMCID: PMC11026096 DOI: 10.7554/elife.87602] [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] [Indexed: 04/19/2024] Open
Abstract
Mechanosensory neurons located across the body surface respond to tactile stimuli and elicit diverse behavioral responses, from relatively simple stimulus location-aimed movements to complex movement sequences. How mechanosensory neurons and their postsynaptic circuits influence such diverse behaviors remains unclear. We previously discovered that Drosophila perform a body location-prioritized grooming sequence when mechanosensory neurons at different locations on the head and body are simultaneously stimulated by dust (Hampel et al., 2017; Seeds et al., 2014). Here, we identify nearly all mechanosensory neurons on the Drosophila head that individually elicit aimed grooming of specific head locations, while collectively eliciting a whole head grooming sequence. Different tracing methods were used to reconstruct the projections of these neurons from different locations on the head to their distinct arborizations in the brain. This provides the first synaptic resolution somatotopic map of a head, and defines the parallel-projecting mechanosensory pathways that elicit head grooming.
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Affiliation(s)
- Katharina Eichler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Stefanie Hampel
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Adrián Alejandro-García
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Steven A Calle-Schuler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Alexis Santana-Cruz
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Lucia Kmecova
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Jonathan M Blagburn
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Eric D Hoopfer
- Neuroscience Program, Carleton CollegeNorthfieldUnited States
| | - Andrew M Seeds
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
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3
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Cheong HSJ, Boone KN, Bennett MM, Salman F, Ralston JD, Hatch K, Allen RF, Phelps AM, Cook AP, Phelps JS, Erginkaya M, Lee WCA, Card GM, Daly KC, Dacks AM. Organization of an ascending circuit that conveys flight motor state in Drosophila. Curr Biol 2024; 34:1059-1075.e5. [PMID: 38402616 PMCID: PMC10939832 DOI: 10.1016/j.cub.2024.01.071] [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: 06/08/2023] [Revised: 12/08/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024]
Abstract
Natural behaviors are a coordinated symphony of motor acts that drive reafferent (self-induced) sensory activation. Individual sensors cannot disambiguate exafferent (externally induced) from reafferent sources. Nevertheless, animals readily differentiate between these sources of sensory signals to carry out adaptive behaviors through corollary discharge circuits (CDCs), which provide predictive motor signals from motor pathways to sensory processing and other motor pathways. Yet, how CDCs comprehensively integrate into the nervous system remains unexplored. Here, we use connectomics, neuroanatomical, physiological, and behavioral approaches to resolve the network architecture of two pairs of ascending histaminergic neurons (AHNs) in Drosophila, which function as a predictive CDC in other insects. Both AHN pairs receive input primarily from a partially overlapping population of descending neurons, especially from DNg02, which controls wing motor output. Using Ca2+ imaging and behavioral recordings, we show that AHN activation is correlated to flight behavior and precedes wing motion. Optogenetic activation of DNg02 is sufficient to activate AHNs, indicating that AHNs are activated by descending commands in advance of behavior and not as a consequence of sensory input. Downstream, each AHN pair targets predominantly non-overlapping networks, including those that process visual, auditory, and mechanosensory information, as well as networks controlling wing, haltere, and leg sensorimotor control. These results support the conclusion that the AHNs provide a predictive motor signal about wing motor state to mostly non-overlapping sensory and motor networks. Future work will determine how AHN signaling is driven by other descending neurons and interpreted by AHN downstream targets to maintain adaptive sensorimotor performance.
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Affiliation(s)
- Han S J Cheong
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Kaitlyn N Boone
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Marryn M Bennett
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Farzaan Salman
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Jacob D Ralston
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Kaleb Hatch
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Raven F Allen
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Alec M Phelps
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Andrew P Cook
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Jasper S Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Swiss Federal Institute of Technology Lausanne, 1015 Lausanne, Switzerland
| | - Mert Erginkaya
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon 1400-038, Portugal
| | - Wei-Chung A Lee
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Gwyneth M Card
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Kevin C Daly
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA; Department of Neuroscience, West Virginia University, Morgantown, WV 26505, USA
| | - Andrew M Dacks
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA; Department of Neuroscience, West Virginia University, Morgantown, WV 26505, USA.
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4
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Eichler K, Hampel S, Alejandro-García A, Calle-Schuler SA, Santana-Cruz A, Kmecova L, Blagburn JM, Hoopfer ED, Seeds AM. Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.11.528119. [PMID: 36798384 PMCID: PMC9934617 DOI: 10.1101/2023.02.11.528119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Mechanosensory neurons located across the body surface respond to tactile stimuli and elicit diverse behavioral responses, from relatively simple stimulus location-aimed movements to complex movement sequences. How mechanosensory neurons and their postsynaptic circuits influence such diverse behaviors remains unclear. We previously discovered that Drosophila perform a body location-prioritized grooming sequence when mechanosensory neurons at different locations on the head and body are simultaneously stimulated by dust (Hampel et al., 2017; Seeds et al., 2014). Here, we identify nearly all mechanosensory neurons on the Drosophila head that individually elicit aimed grooming of specific head locations, while collectively eliciting a whole head grooming sequence. Different tracing methods were used to reconstruct the projections of these neurons from different locations on the head to their distinct arborizations in the brain. This provides the first synaptic resolution somatotopic map of a head, and defines the parallel-projecting mechanosensory pathways that elicit head grooming.
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Affiliation(s)
- Katharina Eichler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Contributed equally
| | - Stefanie Hampel
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Contributed equally
| | - Adrián Alejandro-García
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Contributed equally
| | - Steven A Calle-Schuler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Alexis Santana-Cruz
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Lucia Kmecova
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Neuroscience Program, Carleton College, Northfield, Minnesota
- Contributed equally
| | - Jonathan M Blagburn
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Eric D Hoopfer
- Neuroscience Program, Carleton College, Northfield, Minnesota
| | - Andrew M Seeds
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
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5
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Schlegel P, Yin Y, Bates AS, Dorkenwald S, Eichler K, Brooks P, Han DS, Gkantia M, Dos Santos M, Munnelly EJ, Badalamente G, Capdevila LS, Sane VA, Pleijzier MW, Tamimi IFM, Dunne CR, Salgarella I, Javier A, Fang S, Perlman E, Kazimiers T, Jagannathan SR, Matsliah A, Sterling AR, Yu SC, McKellar CE, Costa M, Seung HS, Murthy M, Hartenstein V, Bock DD, Jefferis GSXE. Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546055. [PMID: 37425808 PMCID: PMC10327018 DOI: 10.1101/2023.06.27.546055] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper 1 , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database 2 . Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the hemibrain connectome 3 . In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.
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6
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Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Lin A, Costa M, Eichler K, Yin Y, Silversmith W, Schneider-Mizell C, Jordan CS, Brittain D, Halageri A, Kuehner K, Ogedengbe O, Morey R, Gager J, Kruk K, Perlman E, Yang R, Deutsch D, Bland D, Sorek M, Lu R, Macrina T, Lee K, Bae JA, Mu S, Nehoran B, Mitchell E, Popovych S, Wu J, Jia Z, Castro M, Kemnitz N, Ih D, Bates AS, Eckstein N, Funke J, Collman F, Bock DD, Jefferis GS, Seung HS, Murthy M. Neuronal wiring diagram of an adult brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546656. [PMID: 37425937 PMCID: PMC10327113 DOI: 10.1101/2023.06.27.546656] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Will Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Kai Kuehner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Ryan Morey
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jay Gager
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - David Deutsch
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Department of Neurobiology, University of Haifa, Haifa, Israel
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Marissa Sorek
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Harvard Medical School, Boston, USA
- Centre for Neural Circuits and Behaviour, The University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | | | - Davi D. Bock
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, USA
| | - Gregory S.X.E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
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7
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Cheong HSJ, Boone KN, Bennett MM, Salman F, Ralston JD, Hatch K, Allen RF, Phelps AM, Cook AP, Phelps JS, Erginkaya M, Lee WCA, Card GM, Daly KC, Dacks AM. Organization of an Ascending Circuit that Conveys Flight Motor State. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.07.544074. [PMID: 37333334 PMCID: PMC10274802 DOI: 10.1101/2023.06.07.544074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Natural behaviors are a coordinated symphony of motor acts which drive self-induced or reafferent sensory activation. Single sensors only signal presence and magnitude of a sensory cue; they cannot disambiguate exafferent (externally-induced) from reafferent sources. Nevertheless, animals readily differentiate between these sources of sensory signals to make appropriate decisions and initiate adaptive behavioral outcomes. This is mediated by predictive motor signaling mechanisms, which emanate from motor control pathways to sensory processing pathways, but how predictive motor signaling circuits function at the cellular and synaptic level is poorly understood. We use a variety of techniques, including connectomics from both male and female electron microscopy volumes, transcriptomics, neuroanatomical, physiological and behavioral approaches to resolve the network architecture of two pairs of ascending histaminergic neurons (AHNs), which putatively provide predictive motor signals to several sensory and motor neuropil. Both AHN pairs receive input primarily from an overlapping population of descending neurons, many of which drive wing motor output. The two AHN pairs target almost exclusively non-overlapping downstream neural networks including those that process visual, auditory and mechanosensory information as well as networks coordinating wing, haltere, and leg motor output. These results support the conclusion that the AHN pairs multi-task, integrating a large amount of common input, then tile their output in the brain, providing predictive motor signals to non-overlapping sensory networks affecting motor control both directly and indirectly.
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Affiliation(s)
- Han S. J. Cheong
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, United States of America
| | - Kaitlyn N. Boone
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
| | - Marryn M. Bennett
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
| | - Farzaan Salman
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
| | - Jacob D. Ralston
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
| | - Kaleb Hatch
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
| | - Raven F. Allen
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
| | - Alec M. Phelps
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
| | - Andrew P. Cook
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
| | - Jasper S. Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, United States of America
| | - Mert Erginkaya
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, 1400-038, Portugal
| | - Wei-Chung A. Lee
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States of America
| | - Gwyneth M. Card
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, United States of America
- Zuckerman Institute, Columbia University, New York, NY 10027, United States of America
| | - Kevin C. Daly
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
- Department of Neuroscience, West Virginia University, Morgantown, WV 26505, United States of America
| | - Andrew M. Dacks
- Department of Biology, West Virginia University, Morgantown, WV 26505, United States of America
- Department of Neuroscience, West Virginia University, Morgantown, WV 26505, United States of America
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8
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Loh YM, Su MP, Ellis DA, Andrés M. The auditory efferent system in mosquitoes. Front Cell Dev Biol 2023; 11:1123738. [PMID: 36923250 PMCID: PMC10009176 DOI: 10.3389/fcell.2023.1123738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/17/2023] [Indexed: 03/02/2023] Open
Abstract
Whilst acoustic communication forms an integral component of the mating behavior of many insect species, it is particularly crucial for disease-transmitting mosquitoes; swarming males rely on hearing the faint sounds of flying females for courtship initiation. That males can hear females within the din of a swarm is testament to their fabulous auditory systems. Mosquito hearing is highly frequency-selective, remarkably sensitive and, most strikingly, supported by an elaborate system of auditory efferent neurons that modulate the auditory function - the only documented example amongst insects. Peripheral release of octopamine, serotonin and GABA appears to differentially modulate hearing across major disease-carrying mosquito species, with receptors from other neurotransmitter families also identified in their ears. Because mosquito mating relies on hearing the flight tones of mating partners, the auditory efferent system offers new potential targets for mosquito control. It also represents a unique insect model for studying auditory efferent networks. Here we review current knowledge of the mosquito auditory efferent system, briefly compare it with its counterparts in other species and highlight future research directions to unravel its contribution to mosquito auditory perception.
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Affiliation(s)
- YuMin M. Loh
- Graduate School of Science, Nagoya University, Nagoya, Aichi, Japan
| | - Matthew P. Su
- Graduate School of Science, Nagoya University, Nagoya, Aichi, Japan
- Institute for Advanced Research, Nagoya University, Nagoya, Aichi, Japan
| | - David A. Ellis
- UCL Ear Institute, University College London, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
| | - Marta Andrés
- UCL Ear Institute, University College London, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
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9
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Ohashi TS, Ishikawa Y, Awasaki T, Su MP, Yoneyama Y, Morimoto N, Kamikouchi A. Evolutionary conservation and diversification of auditory neural circuits that process courtship songs in Drosophila. Sci Rep 2023; 13:383. [PMID: 36611081 PMCID: PMC9825394 DOI: 10.1038/s41598-022-27349-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 12/30/2022] [Indexed: 01/09/2023] Open
Abstract
Acoustic communication signals diversify even on short evolutionary time scales. To understand how the auditory system underlying acoustic communication could evolve, we conducted a systematic comparison of the early stages of the auditory neural circuit involved in song information processing between closely-related fruit-fly species. Male Drosophila melanogaster and D. simulans produce different sound signals during mating rituals, known as courtship songs. Female flies from these species selectively increase their receptivity when they hear songs with conspecific temporal patterns. Here, we firstly confirmed interspecific differences in temporal pattern preferences; D. simulans preferred pulse songs with longer intervals than D. melanogaster. Primary and secondary song-relay neurons, JO neurons and AMMC-B1 neurons, shared similar morphology and neurotransmitters between species. The temporal pattern preferences of AMMC-B1 neurons were also relatively similar between species, with slight but significant differences in their band-pass properties. Although the shift direction of the response property matched that of the behavior, these differences are not large enough to explain behavioral differences in song preferences. This study enhances our understanding of the conservation and diversification of the architecture of the early-stage neural circuit which processes acoustic communication signals.
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Affiliation(s)
- Takuro S. Ohashi
- grid.27476.300000 0001 0943 978XGraduate School of Science, Nagoya University, Nagoya, Aichi 464-8602 Japan
| | - Yuki Ishikawa
- Graduate School of Science, Nagoya University, Nagoya, Aichi, 464-8602, Japan.
| | - Takeshi Awasaki
- grid.411205.30000 0000 9340 2869School of Medicine, Kyorin University, Tokyo, 181-8611 Japan
| | - Matthew P. Su
- grid.27476.300000 0001 0943 978XGraduate School of Science, Nagoya University, Nagoya, Aichi 464-8602 Japan ,grid.27476.300000 0001 0943 978XInstitute for Advanced Research, Nagoya University, Nagoya, Aichi 464-8601 Japan
| | - Yusuke Yoneyama
- grid.27476.300000 0001 0943 978XGraduate School of Science, Nagoya University, Nagoya, Aichi 464-8602 Japan
| | - Nao Morimoto
- grid.39158.360000 0001 2173 7691Institute for Genetic Medicine, Hokkaido University, Sapporo, Hokkaido 060-0815 Japan
| | - Azusa Kamikouchi
- Graduate School of Science, Nagoya University, Nagoya, Aichi, 464-8602, Japan. .,Graduate School of Life Sciences, Tohoku University, Sendai, Miyagi, 980-8577, Japan.
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10
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The 3D ultrastructure of the chordotonal organs in the antenna of a microwasp remains complex although simplified. Sci Rep 2022; 12:20172. [PMID: 36424494 PMCID: PMC9691716 DOI: 10.1038/s41598-022-24390-4] [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: 07/08/2022] [Accepted: 11/15/2022] [Indexed: 11/26/2022] Open
Abstract
Insect antennae are astonishingly versatile and have multiple sensory modalities. Audition, detection of airflow, and graviception are combined in the antennal chordotonal organs. The miniaturization of these complex multisensory organs has never been investigated. Here we present a comprehensive study of the structure and scaling of the antennal chordotonal organs of the extremely miniaturized parasitoid wasp Megaphragma viggianii based on 3D electron microscopy. Johnston's organ of M. viggianii consists of 19 amphinematic scolopidia (95 cells); the central organ consists of five scolopidia (20 cells). Plesiomorphic composition includes one accessory cell per scolopidium, but in M. viggianii this ratio is only 0.3. Scolopale rods in Johnston's organ have a unique structure. Allometric analyses demonstrate the effects of scaling on the antennal chordotonal organs in insects. Our results not only shed light on the universal principles of miniaturization of sense organs, but also provide context for future interpretation of the M. viggianii connectome.
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11
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Baker CA, McKellar C, Pang R, Nern A, Dorkenwald S, Pacheco DA, Eckstein N, Funke J, Dickson BJ, Murthy M. Neural network organization for courtship-song feature detection in Drosophila. Curr Biol 2022; 32:3317-3333.e7. [PMID: 35793679 PMCID: PMC9378594 DOI: 10.1016/j.cub.2022.06.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 05/18/2022] [Accepted: 06/08/2022] [Indexed: 10/17/2022]
Abstract
Animals communicate using sounds in a wide range of contexts, and auditory systems must encode behaviorally relevant acoustic features to drive appropriate reactions. How feature detection emerges along auditory pathways has been difficult to solve due to challenges in mapping the underlying circuits and characterizing responses to behaviorally relevant features. Here, we study auditory activity in the Drosophila melanogaster brain and investigate feature selectivity for the two main modes of fly courtship song, sinusoids and pulse trains. We identify 24 new cell types of the intermediate layers of the auditory pathway, and using a new connectomic resource, FlyWire, we map all synaptic connections between these cell types, in addition to connections to known early and higher-order auditory neurons-this represents the first circuit-level map of the auditory pathway. We additionally determine the sign (excitatory or inhibitory) of most synapses in this auditory connectome. We find that auditory neurons display a continuum of preferences for courtship song modes and that neurons with different song-mode preferences and response timescales are highly interconnected in a network that lacks hierarchical structure. Nonetheless, we find that the response properties of individual cell types within the connectome are predictable from their inputs. Our study thus provides new insights into the organization of auditory coding within the Drosophila brain.
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Affiliation(s)
- Christa A Baker
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Janelia Research Campus, HHMI, Ashburn, VA, USA
| | - Rich Pang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Computer Science, Princeton University, Princeton, NJ, USA
| | - Diego A Pacheco
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Nils Eckstein
- Janelia Research Campus, HHMI, Ashburn, VA, USA; Institute of Neuroinformatics UZH/ETHZ, Zurich, Switzerland
| | - Jan Funke
- Janelia Research Campus, HHMI, Ashburn, VA, USA
| | | | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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12
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Dorkenwald S, McKellar CE, Macrina T, Kemnitz N, Lee K, Lu R, Wu J, Popovych S, Mitchell E, Nehoran B, Jia Z, Bae JA, Mu S, Ih D, Castro M, Ogedengbe O, Halageri A, Kuehner K, Sterling AR, Ashwood Z, Zung J, Brittain D, Collman F, Schneider-Mizell C, Jordan C, Silversmith W, Baker C, Deutsch D, Encarnacion-Rivera L, Kumar S, Burke A, Bland D, Gager J, Hebditch J, Koolman S, Moore M, Morejohn S, Silverman B, Willie K, Willie R, Yu SC, Murthy M, Seung HS. FlyWire: online community for whole-brain connectomics. Nat Methods 2021; 19:119-128. [PMID: 34949809 PMCID: PMC8903166 DOI: 10.1038/s41592-021-01330-0] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 10/25/2021] [Indexed: 11/09/2022]
Abstract
Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Computer Science Department, Princeton University, Princeton, NJ, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Electrical Engineering Department, Princeton University, Princeton, NJ, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Kai Kuehner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Zoe Ashwood
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | | | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Christa Baker
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - David Deutsch
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Sandeep Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Austin Burke
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jay Gager
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - James Hebditch
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Selden Koolman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Merlin Moore
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sarah Morejohn
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ben Silverman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Kyle Willie
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ryan Willie
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA. .,Computer Science Department, Princeton University, Princeton, NJ, USA.
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13
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Ni L. Genetic Transsynaptic Techniques for Mapping Neural Circuits in Drosophila. Front Neural Circuits 2021; 15:749586. [PMID: 34675781 PMCID: PMC8524129 DOI: 10.3389/fncir.2021.749586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/13/2021] [Indexed: 11/23/2022] Open
Abstract
A neural circuit is composed of a population of neurons that are interconnected by synapses and carry out a specific function when activated. It is the structural framework for all brain functions. Its impairments often cause diseases in the nervous system. To understand computations and functions in a brain circuit, it is of crucial importance to identify how neurons in this circuit are connected. Genetic transsynaptic techniques provide opportunities to efficiently answer this question. These techniques label synapses or across synapses to unbiasedly label synaptic partners. They allow for mapping neural circuits with high reproducibility and throughput, as well as provide genetic access to synaptically connected neurons that enables visualization and manipulation of these neurons simultaneously. This review focuses on three recently developed Drosophila genetic transsynaptic tools for detecting chemical synapses, highlights their advantages and potential pitfalls, and discusses the future development needs of these techniques.
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14
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Hampel S, Eichler K, Yamada D, Bock DD, Kamikouchi A, Seeds AM. Distinct subpopulations of mechanosensory chordotonal organ neurons elicit grooming of the fruit fly antennae. eLife 2020; 9:e59976. [PMID: 33103999 PMCID: PMC7652415 DOI: 10.7554/elife.59976] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 10/25/2020] [Indexed: 11/13/2022] Open
Abstract
Diverse mechanosensory neurons detect different mechanical forces that can impact animal behavior. Yet our understanding of the anatomical and physiological diversity of these neurons and the behaviors that they influence is limited. We previously discovered that grooming of the Drosophila melanogaster antennae is elicited by an antennal mechanosensory chordotonal organ, the Johnston's organ (JO) (Hampel et al., 2015). Here, we describe anatomically and physiologically distinct JO mechanosensory neuron subpopulations that each elicit antennal grooming. We show that the subpopulations project to different, discrete zones in the brain and differ in their responses to mechanical stimulation of the antennae. Although activation of each subpopulation elicits antennal grooming, distinct subpopulations also elicit the additional behaviors of wing flapping or backward locomotion. Our results provide a comprehensive description of the diversity of mechanosensory neurons in the JO, and reveal that distinct JO subpopulations can elicit both common and distinct behavioral responses.
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Affiliation(s)
- Stefanie Hampel
- Institute of Neurobiology, University of Puerto Rico Medical Sciences CampusSan JuanPuerto Rico
| | - Katharina Eichler
- Institute of Neurobiology, University of Puerto Rico Medical Sciences CampusSan JuanPuerto Rico
| | - Daichi Yamada
- Division of Biological Science, Graduate School of Science, Nagoya UniversityNagoyaJapan
| | - Davi D Bock
- Department of Neurological Sciences, Larner College of Medicine, University of VermontBurlingtonUnited States
| | - Azusa Kamikouchi
- Division of Biological Science, Graduate School of Science, Nagoya UniversityNagoyaJapan
| | - Andrew M Seeds
- Institute of Neurobiology, University of Puerto Rico Medical Sciences CampusSan JuanPuerto Rico
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