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Afkhami M. Neurobiology of egg-laying behavior in Drosophila: neural control of the female reproductive system. J Neurogenet 2024:1-15. [PMID: 39250036 DOI: 10.1080/01677063.2024.2396352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/20/2024] [Indexed: 09/10/2024]
Abstract
Egg-laying is one of the key aspects of female reproductive behavior in insects. Egg-laying has been studied since the dawn of Drosophila melanogaster as a model organism. The female's internal state, hormones, and external factors, such as nutrition, light, and social environment, affect egg-laying output. However, only recently, neurobiological features of egg-laying behavior have been studied in detail. fruitless and doublesex, two key players in the sex determination pathway, have become focal points in identifying neurons of reproductive significance in both central and peripheral nervous systems. The reproductive tract and external terminalia house sensory neurons that carry the sensory information of egg maturation, mating and egg-laying. These sensory signals include the presence of male accessory gland products and mechanical stimuli. The abdominal neuromere houses neurons that receive information from the reproductive tract, including sex peptide abdominal ganglion neurons (SAGs), and send their information to the brain. In the brain, neuronal groups like aDNs and pC1 clusters modulate egg-laying decision-making, and other neurons like oviINs and oviDNs are necessary for egg-laying itself. Lastly, motor neurons involved in egg-laying, which are mostly octopaminergic, reside in the abdominal neuromere and orchestrate the muscle movements required for laying the egg. Egg-laying neuronal control is important in various evolutionary processes like cryptic female choice, and using different Drosophila species can provide intriguing avenues for the future of the field.
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Affiliation(s)
- Mehrnaz Afkhami
- School of Biological Sciences, University of Oklahoma, Norman, OK, USA
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2
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Zhang L, Huang L, Yuan Z, Hang Y, Zeng Y, Li K, Wang L, Zeng H, Chen X, Zhang H, Xi J, Chen D, Gao Z, Le L, Chen J, Ye W, Liu L, Wang Y, Peng H. Collaborative augmented reconstruction of 3D neuron morphology in mouse and human brains. Nat Methods 2024:10.1038/s41592-024-02401-8. [PMID: 39232199 DOI: 10.1038/s41592-024-02401-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 07/30/2024] [Indexed: 09/06/2024]
Abstract
Digital reconstruction of the intricate 3D morphology of individual neurons from microscopic images is a crucial challenge in both individual laboratories and large-scale projects focusing on cell types and brain anatomy. This task often fails in both conventional manual reconstruction and state-of-the-art artificial intelligence (AI)-based automatic reconstruction algorithms. It is also challenging to organize multiple neuroanatomists to generate and cross-validate biologically relevant and mutually agreed upon reconstructions in large-scale data production. Based on collaborative group intelligence augmented by AI, we developed a collaborative augmented reconstruction (CAR) platform for neuron reconstruction at scale. This platform allows for immersive interaction and efficient collaborative editing of neuron anatomy using a variety of devices, such as desktop workstations, virtual reality headsets and mobile phones, enabling users to contribute anytime and anywhere and to take advantage of several AI-based automation tools. We tested CAR's applicability for challenging mouse and human neurons toward scaled and faithful data production.
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Affiliation(s)
- Lingli Zhang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lei Huang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Zexin Yuan
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
- School of Future Technology, Shanghai University, Shanghai, China
| | - Yuning Hang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Ying Zeng
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Kaixiang Li
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijun Wang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Haoyu Zeng
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Xin Chen
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Hairuo Zhang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Jiaqi Xi
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
| | - Danni Chen
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
| | - Ziqin Gao
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Longxin Le
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
- School of Future Technology, Shanghai University, Shanghai, China
| | - Jie Chen
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Wen Ye
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yimin Wang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China.
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China.
| | - Hanchuan Peng
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China.
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3
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Kim YJ, Packer O, Dacey DM. A circuit motif for color in the human foveal retina. Proc Natl Acad Sci U S A 2024; 121:e2405138121. [PMID: 39190352 PMCID: PMC11388358 DOI: 10.1073/pnas.2405138121] [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: 03/12/2024] [Accepted: 06/25/2024] [Indexed: 08/28/2024] Open
Abstract
The neural pathways that start human color vision begin in the complex synaptic network of the foveal retina where signals originating in long (L), middle (M), and short (S) wavelength-sensitive cone photoreceptor types are compared through antagonistic interactions, referred to as opponency. In nonhuman primates, two cone opponent pathways are well established: an L vs. M cone circuit linked to the midget ganglion cell type, often called the red-green pathway, and an S vs. L + M cone circuit linked to the small bistratified ganglion cell type, often called the blue-yellow pathway. These pathways have been taken to correspond in human vision to cardinal directions in a trichromatic color space, providing the parallel inputs to higher-level color processing. Yet linking cone opponency in the nonhuman primate retina to color mechanisms in human vision has proven particularly difficult. Here, we apply connectomic reconstruction to the human foveal retina to trace parallel excitatory synaptic outputs from the S-ON (or "blue-cone") bipolar cell to the small bistratified cell and two additional ganglion cell types: a large bistratified ganglion cell and a subpopulation of ON-midget ganglion cells, whose synaptic connections suggest a significant and unique role in color vision. These two ganglion cell types are postsynaptic to both S-ON and L vs. M opponent midget bipolar cells and thus define excitatory pathways in the foveal retina that merge the cardinal red-green and blue-yellow circuits, with the potential for trichromatic cone opponency at the first stage of human vision.
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Affiliation(s)
- Yeon Jin Kim
- Department of Biological Structure, University of Washington, Seattle, WA 98195
| | - Orin Packer
- Department of Biological Structure, University of Washington, Seattle, WA 98195
| | - Dennis M Dacey
- Department of Biological Structure, University of Washington, Seattle, WA 98195
- Washington National Primate Research Center, University of Washington, Seattle, WA 98195
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4
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McKim TH, Gera J, Gayban AJ, Reinhard N, Manoli G, Hilpert S, Helfrich-Förster C, Zandawala M. Synaptic connectome of a neurosecretory network in the Drosophila brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.28.609616. [PMID: 39257829 PMCID: PMC11384003 DOI: 10.1101/2024.08.28.609616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Hormones mediate inter-organ signaling which is crucial in orchestrating diverse behaviors and physiological processes including sleep and activity, feeding, growth, metabolism and reproduction. The pars intercerebralis and pars lateralis in insects represent major hubs which contain neurosecretory cells (NSC) that produce various hormones. To obtain insight into how hormonal signaling is regulated, we have characterized the synaptic connectome of NSC in the adult Drosophila brain. Identification of neurons providing inputs to multiple NSC subtypes implicates diuretic hormone 44-expressing NSC as a major coordinator of physiology and behavior. Surprisingly, despite most NSC having dendrites in the subesophageal zone (primary taste processing center), gustatory inputs to NSC are largely indirect. We also deciphered pathways via which diverse olfactory inputs are relayed to NSC. Further, our analyses revealed substantial inputs from descending neurons to NSC, suggesting that descending neurons regulate both endocrine and motor output to synchronize physiological changes with appropriate behaviors. In contrast to NSC inputs, synaptic output from NSC is sparse and mostly mediated by corazonin NSC. Therefore, we additionally determine putative paracrine interconnectivity between NSC subtypes and hormonal pathways from NSC to peripheral tissues by analyzing single-cell transcriptomic datasets. Our comprehensive characterization of the Drosophila neurosecretory network connectome provides a platform to understand complex hormonal networks and how they orchestrate animal behaviors and physiology.
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5
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Guillemin J, Li J, Li V, McDowell SAT, Audette K, Davis G, Jelen M, Slamani S, Kelliher L, Gordon MD, Stanley M. Taste cells expressing Ionotropic Receptor 94e reciprocally impact feeding and egg laying in Drosophila. Cell Rep 2024; 43:114625. [PMID: 39141516 DOI: 10.1016/j.celrep.2024.114625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 06/01/2024] [Accepted: 07/30/2024] [Indexed: 08/16/2024] Open
Abstract
Chemosensory cells across the body of Drosophila melanogaster evaluate the environment to prioritize certain behaviors. Previous mapping of gustatory receptor neurons (GRNs) on the fly labellum identified a set of neurons in L-type sensilla that express Ionotropic Receptor 94e (IR94e), but the impact of IR94e GRNs on behavior remains unclear. We used optogenetics and chemogenetics to activate IR94e neurons and found that they drive mild feeding suppression but enhance egg laying. In vivo calcium imaging revealed that IR94e GRNs respond strongly to certain amino acids, including glutamate, and that IR94e plus co-receptors IR25a and IR76b are required for amino acid detection. Furthermore, IR94e mutants show behavioral changes to solutions containing amino acids, including increased consumption and decreased egg laying. Overall, our results suggest that IR94e GRNs on the fly labellum discourage feeding and encourage egg laying as part of an important behavioral switch in response to certain chemical cues.
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Affiliation(s)
| | - Jinfang Li
- Department of Zoology, Life Sciences Institute and Djavad Mowafaghian Centre for Brain Health, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Viktoriya Li
- Department of Zoology, Life Sciences Institute and Djavad Mowafaghian Centre for Brain Health, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Sasha A T McDowell
- Department of Zoology, Life Sciences Institute and Djavad Mowafaghian Centre for Brain Health, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Kayla Audette
- Department of Biology, The University of Vermont, Burlington, VT 05405, USA
| | - Grace Davis
- Department of Biology, The University of Vermont, Burlington, VT 05405, USA
| | - Meghan Jelen
- Department of Zoology, Life Sciences Institute and Djavad Mowafaghian Centre for Brain Health, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Samy Slamani
- Department of Biology, The University of Vermont, Burlington, VT 05405, USA
| | - Liam Kelliher
- Department of Biology, The University of Vermont, Burlington, VT 05405, USA
| | - Michael D Gordon
- Department of Zoology, Life Sciences Institute and Djavad Mowafaghian Centre for Brain Health, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
| | - Molly Stanley
- Department of Biology, The University of Vermont, Burlington, VT 05405, USA.
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6
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Chowdhury B, Shafer OT. Drosophila sleep homeostasis in sickness and in health. Sleep 2024; 47:zsae128. [PMID: 38899406 DOI: 10.1093/sleep/zsae128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Indexed: 06/21/2024] Open
Affiliation(s)
- Budhaditya Chowdhury
- The Advanced Science Research Center, The City University of New York; The Graduate Center at the City University of New York, New York, NY, USA
| | - Orie T Shafer
- The Advanced Science Research Center, The City University of New York; The Graduate Center at the City University of New York, New York, NY, USA
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7
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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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8
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Meschi E, Duquenoy L, Otto N, Dempsey G, Waddell S. Compensatory enhancement of input maintains aversive dopaminergic reinforcement in hungry Drosophila. Neuron 2024; 112:2315-2332.e8. [PMID: 38795709 DOI: 10.1016/j.neuron.2024.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/12/2024] [Accepted: 04/30/2024] [Indexed: 05/28/2024]
Abstract
Hungry animals need compensatory mechanisms to maintain flexible brain function, while modulation reconfigures circuits to prioritize resource seeking. In Drosophila, hunger inhibits aversively reinforcing dopaminergic neurons (DANs) to permit the expression of food-seeking memories. Multitasking the reinforcement system for motivation potentially undermines aversive learning. We find that chronic hunger mildly enhances aversive learning and that satiated-baseline and hunger-enhanced learning require endocrine adipokinetic hormone (AKH) signaling. Circulating AKH influences aversive learning via its receptor in four neurons in the ventral brain, two of which are octopaminergic. Connectomics revealed AKH receptor-expressing neurons to be upstream of several classes of ascending neurons, many of which are presynaptic to aversively reinforcing DANs. Octopaminergic modulation of and output from at least one of these ascending pathways is required for shock- and bitter-taste-reinforced aversive learning. We propose that coordinated enhancement of input compensates for hunger-directed inhibition of aversive DANs to preserve reinforcement when required.
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Affiliation(s)
- Eleonora Meschi
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Lucille Duquenoy
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Nils Otto
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Georgia Dempsey
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Scott Waddell
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK.
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9
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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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10
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Lesser E, Azevedo AW, Phelps JS, Elabbady L, Cook A, Syed DS, Mark B, Kuroda S, Sustar A, Moussa A, Dallmann CJ, Agrawal S, Lee SYJ, Pratt B, Skutt-Kakaria K, Gerhard S, Lu R, Kemnitz N, Lee K, Halageri A, Castro M, Ih D, Gager J, Tammam M, Dorkenwald S, Collman F, Schneider-Mizell C, Brittain D, Jordan CS, Macrina T, Dickinson M, Lee WCA, Tuthill JC. Synaptic architecture of leg and wing premotor control networks in Drosophila. Nature 2024; 631:369-377. [PMID: 38926579 PMCID: PMC11356479 DOI: 10.1038/s41586-024-07600-z] [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: 06/02/2023] [Accepted: 05/23/2024] [Indexed: 06/28/2024]
Abstract
Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles1. MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours2-6. Here we use connectomics7 to analyse the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. By contrast, wing premotor networks lack proportional synaptic connectivity, which may enable more flexible recruitment of wing steering muscles. Through comparison of the architecture of distinct motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.
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Affiliation(s)
- Ellen Lesser
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Anthony W Azevedo
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Jasper S Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Neuroengineering Laboratory, Brain Mind Institute and Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Leila Elabbady
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Andrew Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sumiya Kuroda
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Anthony Moussa
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Chris J Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Su-Yee J Lee
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | - Stephan Gerhard
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- UniDesign Solutions LLC, Zurich, Switzerland
| | - Ran Lu
- Zetta AI, LLC, Sherrill, NY, USA
| | | | - Kisuk Lee
- Zetta AI, LLC, Sherrill, NY, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | - Dodam Ih
- Zetta AI, LLC, Sherrill, NY, USA
| | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | | | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 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.
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
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11
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Azevedo A, Lesser E, Phelps JS, Mark B, Elabbady L, Kuroda S, Sustar A, Moussa A, Khandelwal A, Dallmann CJ, Agrawal S, Lee SYJ, Pratt B, Cook A, Skutt-Kakaria K, Gerhard S, Lu R, Kemnitz N, Lee K, Halageri A, Castro M, Ih D, Gager J, Tammam M, Dorkenwald S, Collman F, Schneider-Mizell C, Brittain D, Jordan CS, Dickinson M, Pacureanu A, Seung HS, Macrina T, Lee WCA, Tuthill JC. Connectomic reconstruction of a female Drosophila ventral nerve cord. Nature 2024; 631:360-368. [PMID: 38926570 PMCID: PMC11348827 DOI: 10.1038/s41586-024-07389-x] [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: 06/02/2023] [Accepted: 04/04/2024] [Indexed: 06/28/2024]
Abstract
A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)1, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines2 and X-ray holographic nanotomography3. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.
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Affiliation(s)
- Anthony Azevedo
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Ellen Lesser
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Jasper S Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Neuroengineering Laboratory, Brain Mind Institute and Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Leila Elabbady
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sumiya Kuroda
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Anthony Moussa
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Avinash Khandelwal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Chris J Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Su-Yee J Lee
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Andrew Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | - Stephan Gerhard
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- UniDesign Solutions, Zurich, Switzerland
| | - Ran Lu
- Zetta AI, Sherrill, NJ, USA
| | | | - Kisuk Lee
- Zetta AI, Sherrill, NJ, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | | | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 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.
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
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12
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Thiyagarajan VV, Sheridan A, Harris KM, Manor U. A deep learning-based strategy for producing dense 3D segmentations from sparsely annotated 2D images. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.599135. [PMID: 38915491 PMCID: PMC11195258 DOI: 10.1101/2024.06.14.599135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Producing dense 3D reconstructions from biological imaging data is a challenging instance segmentation task that requires significant ground-truth training data for effective and accurate deep learning-based models. Generating training data requires intense human effort to annotate each instance of an object across serial section images. Our focus is on the especially complicated brain neuropil, comprising an extensive interdigitation of dendritic, axonal, and glial processes visualized through serial section electron microscopy. We developed a novel deep learning-based method to generate dense 3D segmentations rapidly from sparse 2D annotations of a few objects on single sections. Models trained on the rapidly generated segmentations achieved similar accuracy as those trained on expert dense ground-truth annotations. Human time to generate annotations was reduced by three orders of magnitude and could be produced by non-expert annotators. This capability will democratize generation of training data for large image volumes needed to achieve brain circuits and measures of circuit strengths.
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Affiliation(s)
- Vijay Venu Thiyagarajan
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin Texas, 78712
| | - Arlo Sheridan
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, 92037
| | - Kristen M. Harris
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin Texas, 78712
| | - Uri Manor
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, 92037
- Department of Cell & Developmental Biology, School of Biological Sciences, University of California, San Diego, CA 92093
- Halıcıoğlu Data Science Institute, University of California, San Diego, CA 92093
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13
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Smolin N, Dombrovski M, Hina BW, Moreno-Sanchez A, Gossart R, Carmona CR, Rehan A, Hussein RH, Mirshahidi P, Ausborn J, Kurmangaliyev YZ, von Reyn CR. Neuronal identity control at the resolution of a single transcription factor isoform. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.598883. [PMID: 38915533 PMCID: PMC11195191 DOI: 10.1101/2024.06.14.598883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain exhibits remarkable neuronal diversity which is critical for its functional integrity. From the sheer number of cell types emerging from extensive transcriptional, morphological, and connectome datasets, the question arises of how the brain is capable of generating so many unique identities. 'Terminal selectors' are transcription factors hypothesized to determine the final identity characteristics in post-mitotic cells. Which transcription factors function as terminal selectors and the level of control they exert over different terminal characteristics are not well defined. Here, we establish a novel role for the transcription factor broad as a terminal selector in Drosophila melanogaster. We capitalize on existing large sequencing and connectomics datasets and employ a comprehensive characterization of terminal characteristics including Perturb-seq and whole-cell electrophysiology. We find a single isoform broad-z4 serves as the switch between the identity of two visual projection neurons LPLC1 and LPLC2. Broad-z4 is natively expressed in LPLC1, and is capable of transforming the transcriptome, morphology, and functional connectivity of LPLC2 cells into LPLC1 cells when perturbed. Our comprehensive work establishes a single isoform as the smallest unit underlying an identity switch, which may serve as a conserved strategy replicated across developmental programs.
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Affiliation(s)
- Natalie Smolin
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
| | - Mark Dombrovski
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Bryce W. Hina
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
| | - Anthony Moreno-Sanchez
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA
| | - Ryan Gossart
- Brandeis University, Department of Biology, Waltham, MA
| | | | - Aadil Rehan
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Roni H. Hussein
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Parmis Mirshahidi
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Jessica Ausborn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA
| | | | - Catherine R. von Reyn
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA
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14
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Deluca A, Bascom B, Key Planas DA, Kocher MA, Torres M, Arbeitman MN. Contribution of neurons that express fruitless and Clock transcription factors to behavioral rhythms and courtship. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598537. [PMID: 38915619 PMCID: PMC11195222 DOI: 10.1101/2024.06.12.598537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Animals need to integrate information across neuronal networks that direct reproductive behaviors and circadian rhythms. In Drosophila, the master regulatory transcription factors that direct courtship behaviors and circadian rhythms are co-expressed in a small set of neurons. In this study we investigate the role of these neurons in both males and females. We find sex-differences in the number of these fruitless and Clock -expressing neurons ( fru ∩ Clk neurons) that is regulated by male-specific Fru. We assign the fru ∩ Clk neurons to the electron microscopy connectome that provides high resolution structural information. We also discover sex-differences in the number of fru -expressing neurons that are post-synaptic targets of Clk -expressing neurons, with more post-synaptic targets in males. When fru ∩ Clk neurons are activated or silenced, males have a shorter period length. Activation of fru ∩ Clk neurons also changes the rate a courtship behavior is performed. We find that activation and silencing fru ∩ Clk neurons impacts the molecular clock in the sLNv master pacemaker neurons, in a cell-nonautonomous manner. These results reveal how neurons that subserve the two processes, reproduction and circadian rhythms, can impact behavioral outcomes in a sex-specific manner.
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15
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Braun J, Hurtak F, Wang-Chen S, Ramdya P. Descending networks transform command signals into population motor control. Nature 2024; 630:686-694. [PMID: 38839968 PMCID: PMC11186778 DOI: 10.1038/s41586-024-07523-9] [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: 09/11/2023] [Accepted: 05/06/2024] [Indexed: 06/07/2024]
Abstract
To convert intentions into actions, movement instructions must pass from the brain to downstream motor circuits through descending neurons (DNs). These include small sets of command-like neurons that are sufficient to drive behaviours1-the circuit mechanisms for which remain unclear. Here we show that command-like DNs in Drosophila directly recruit networks of additional DNs to orchestrate behaviours that require the active control of numerous body parts. Specifically, we found that command-like DNs previously thought to drive behaviours alone2-4 in fact co-activate larger populations of DNs. Connectome analyses and experimental manipulations revealed that this functional recruitment can be explained by direct excitatory connections between command-like DNs and networks of interconnected DNs in the brain. Descending population recruitment is necessary for behavioural control: DNs with many downstream descending partners require network co-activation to drive complete behaviours and drive only simple stereotyped movements in their absence. These DN networks reside within behaviour-specific clusters that inhibit one another. These results support a mechanism for command-like descending control in which behaviours are generated through the recruitment of increasingly large DN networks that compose behaviours by combining multiple motor subroutines.
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Affiliation(s)
- Jonas Braun
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Femke Hurtak
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Sibo Wang-Chen
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Pavan Ramdya
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland.
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16
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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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17
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Eckstein N, Bates AS, Champion A, Du M, Yin Y, Schlegel P, Lu AKY, Rymer T, Finley-May S, Paterson T, Parekh R, Dorkenwald S, Matsliah A, Yu SC, McKellar C, Sterling A, Eichler K, Costa M, Seung S, Murthy M, Hartenstein V, Jefferis GSXE, Funke J. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Cell 2024; 187:2574-2594.e23. [PMID: 38729112 PMCID: PMC11106717 DOI: 10.1016/j.cell.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: 04/02/2023] [Revised: 10/04/2023] [Accepted: 03/13/2024] [Indexed: 05/12/2024]
Abstract
High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.
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Affiliation(s)
- Nils Eckstein
- HHMI Janelia Research Campus, Ashburn, VA, USA; Institute of Neuroinformatics UZH/ETHZ, Zurich, Switzerland
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Centre for Neural Circuits and Behaviour, The University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK; Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Andrew Champion
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Michelle Du
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Volker Hartenstein
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, 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.
| | - Jan Funke
- HHMI Janelia Research Campus, Ashburn, VA, USA.
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18
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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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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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Selcho M. Octopamine in the mushroom body circuitry for learning and memory. Learn Mem 2024; 31:a053839. [PMID: 38862169 PMCID: PMC11199948 DOI: 10.1101/lm.053839.123] [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: 09/21/2023] [Accepted: 02/20/2024] [Indexed: 06/13/2024]
Abstract
Octopamine, the functional analog of noradrenaline, modulates many different behaviors and physiological processes in invertebrates. In the central nervous system, a few octopaminergic neurons project throughout the brain and innervate almost all neuropils. The center of memory formation in insects, the mushroom bodies, receive octopaminergic innervations in all insects investigated so far. Different octopamine receptors, either increasing or decreasing cAMP or calcium levels in the cell, are localized in Kenyon cells, further supporting the release of octopamine in the mushroom bodies. In addition, different mushroom body (MB) output neurons, projection neurons, and dopaminergic PAM cells are targets of octopaminergic neurons, enabling the modulation of learning circuits at different neural sites. For some years, the theory persisted that octopamine mediates rewarding stimuli, whereas dopamine (DA) represents aversive stimuli. This simple picture has been challenged by the finding that DA is required for both appetitive and aversive learning. Furthermore, octopamine is also involved in aversive learning and a rather complex interaction between these biogenic amines seems to modulate learning and memory. This review summarizes the role of octopamine in MB function, focusing on the anatomical principles and the role of the biogenic amine in learning and memory.
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Affiliation(s)
- Mareike Selcho
- Department of Animal Physiology, Institute of Biology, Leipzig University, 04103 Leipzig, Germany
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21
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Moreno-Sanchez A, Vasserman AN, Jang H, Hina BW, von Reyn CR, Ausborn J. Morphology and synapse topography optimize linear encoding of synapse numbers in Drosophila looming responsive descending neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.591016. [PMID: 38712267 PMCID: PMC11071487 DOI: 10.1101/2024.04.24.591016] [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
Synapses are often precisely organized on dendritic arbors, yet the role of synaptic topography in dendritic integration remains poorly understood. Utilizing electron microscopy (EM) connectomics we investigate synaptic topography in Drosophila melanogaster looming circuits, focusing on retinotopically tuned visual projection neurons (VPNs) that synapse onto descending neurons (DNs). Synapses of a given VPN type project to non-overlapping regions on DN dendrites. Within these spatially constrained clusters, synapses are not retinotopically organized, but instead adopt near random distributions. To investigate how this organization strategy impacts DN integration, we developed multicompartment models of DNs fitted to experimental data and using precise EM morphologies and synapse locations. We find that DN dendrite morphologies normalize EPSP amplitudes of individual synaptic inputs and that near random distributions of synapses ensure linear encoding of synapse numbers from individual VPNs. These findings illuminate how synaptic topography influences dendritic integration and suggest that linear encoding of synapse numbers may be a default strategy established through connectivity and passive neuron properties, upon which active properties and plasticity can then tune as needed.
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Affiliation(s)
- Anthony Moreno-Sanchez
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
| | - Alexander N. Vasserman
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
| | - HyoJong Jang
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Bryce W. Hina
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Catherine R. von Reyn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Jessica Ausborn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
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22
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Lesser E, Azevedo AW, Phelps JS, Elabbady L, Cook A, Sakeena Syed D, Mark B, Kuroda S, Sustar A, Moussa A, Dallmann CJ, Agrawal S, Lee SYJ, Pratt B, Skutt-Kakaria K, Gerhard S, Lu R, Kemnitz N, Lee K, Halageri A, Castro M, Ih D, Gager J, Tammam M, Dorkenwald S, Collman F, Schneider-Mizell C, Brittain D, Jordan CS, Macrina T, Dickinson M, Lee WCA, Tuthill JC. Synaptic architecture of leg and wing premotor control networks in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.30.542725. [PMID: 37398440 PMCID: PMC10312524 DOI: 10.1101/2023.05.30.542725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.
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Affiliation(s)
- Ellen Lesser
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Anthony W. Azevedo
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Jasper S. Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Leila Elabbady
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Andrew Cook
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | | | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Sumiya Kuroda
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Anthony Moussa
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Chris J. Dallmann
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Su-Yee J. Lee
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | | | - Stephan Gerhard
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- UniDesign Solutions LLC, Switzerland
| | | | | | - Kisuk Lee
- Zetta AI, LLC, USA
- Princeton Neuroscience Institute, Princeton University, NJ, USA
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, NJ, USA
- Computer Science Department, Princeton University, NJ, USA
| | | | | | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, NJ, 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, MA, USA
| | - John C. Tuthill
- Department of Physiology and Biophysics, University of Washington, WA, USA
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23
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Celii B, Papadopoulos S, Ding Z, Fahey PG, Wang E, Papadopoulos C, Kunin AB, Patel S, Bae JA, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yu SC, Yin W, Xenes D, Kitchell LM, Rivlin PK, Rose VA, Bishop CA, Wester B, Froudarakis E, Walker EY, Sinz F, Seung HS, Collman F, da Costa NM, Reid RC, Pitkow X, Tolias AS, Reimer J. NEURD: automated proofreading and feature extraction for connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.14.532674. [PMID: 36993282 PMCID: PMC10055177 DOI: 10.1101/2023.03.14.532674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows to automate a variety of tasks that would otherwise require extensive manual effort, such as state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and computation of other features. These features enable many downstream analyses of neural morphology and connectivity, making these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.
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24
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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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25
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Falahati H, Wu Y, De Camilli P. Ectopic Reconstitution of a Spine-Apparatus Like Structure Provides Insight into Mechanisms Underlying Its Formation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.16.589782. [PMID: 38659799 PMCID: PMC11042382 DOI: 10.1101/2024.04.16.589782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
The endoplasmic reticulum (ER) is a continuous cellular endomembrane network that displays focal specializations. Most notable examples of such specializations include the spine apparatus of neuronal dendrites, and the cisternal organelle of axonal initial segments. Both organelles exhibit stacks of smooth ER sheets with a narrow lumen and interconnected by a dense protein matrix. The actin-binding protein synaptopodin is required for their formation. Here, we report that expression in non-neuronal cells of a synaptopodin construct targeted to the ER is sufficient to generate stacked ER cisterns resembling the spine apparatus with molecular properties distinct from the surrounding ER. Cisterns within these stacks are connected to each other by an actin-based matrix that contains proteins also found at the spine apparatus of neuronal spines. These findings reveal a critical role of a synaptopodin-dependent actin matrix in generating cisternal stacks. These ectopically generated structures provide insight into spine apparatus morphogenesis.
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Affiliation(s)
- Hanieh Falahati
- HHMI; Departments of Neuroscience and Cell Biology; Program in Cellular Neuroscience, Neurodegeneration, and Repair, Yale University School of Medicine, 100 College Street, New Haven, 06511, CT, USA
| | - Yumei Wu
- HHMI; Departments of Neuroscience and Cell Biology; Program in Cellular Neuroscience, Neurodegeneration, and Repair, Yale University School of Medicine, 100 College Street, New Haven, 06511, CT, USA
| | - Pietro De Camilli
- HHMI; Departments of Neuroscience and Cell Biology; Program in Cellular Neuroscience, Neurodegeneration, and Repair, Yale University School of Medicine, 100 College Street, New Haven, 06511, CT, USA
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26
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Arntsen C, Guillemin J, Audette K, Stanley M. Tastant-receptor interactions: insights from the fruit fly. Front Nutr 2024; 11:1394697. [PMID: 38665300 PMCID: PMC11043608 DOI: 10.3389/fnut.2024.1394697] [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: 03/01/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
Across species, taste provides important chemical information about potential food sources and the surrounding environment. As details about the chemicals and receptors responsible for gustation are discovered, a complex view of the taste system is emerging with significant contributions from research using the fruit fly, Drosophila melanogaster, as a model organism. In this brief review, we summarize recent advances in Drosophila gustation and their relevance to taste research more broadly. Our goal is to highlight the molecular mechanisms underlying the first step of gustatory circuits: ligand-receptor interactions in primary taste cells. After an introduction to the Drosophila taste system and how it encodes the canonical taste modalities sweet, bitter, and salty, we describe recent insights into the complex nature of carboxylic acid and amino acid detection in the context of sour and umami taste, respectively. Our analysis extends to non-canonical taste modalities including metals, fatty acids, and bacterial components, and highlights unexpected receptors and signaling pathways that have recently been identified in Drosophila taste cells. Comparing the intricate molecular and cellular underpinnings of how ligands are detected in vivo in fruit flies reveals both specific and promiscuous receptor selectivity for taste encoding. Throughout this review, we compare and contextualize these Drosophila findings with mammalian research to not only emphasize the conservation of these chemosensory systems, but to demonstrate the power of this model organism in elucidating the neurobiology of taste and feeding.
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Affiliation(s)
| | | | | | - Molly Stanley
- Department of Biology, University of Vermont, Burlington, VT, United States
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27
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Boergens KM, Wildenberg G, Li R, Lambert L, Moradi A, Stam G, Tromp R, van der Molen SJ, King SB, Kasthuri N. Photoemission electron microscopy for connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.05.556423. [PMID: 37771915 PMCID: PMC10525389 DOI: 10.1101/2023.09.05.556423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Detailing the physical basis of neural circuits with large-volume serial electron microscopy (EM), 'connectomics', has emerged as an invaluable tool in the neuroscience armamentarium. However, imaging synaptic resolution connectomes is currently limited to either transmission electron microscopy (TEM) or scanning electron microscopy (SEM). Here, we describe a third way, using photoemission electron microscopy (PEEM) which illuminates ultra-thin brain slices collected on solid substrates with UV light and images the photoelectron emission pattern with a wide-field electron microscope. PEEM works with existing sample preparations for EM and routinely provides sufficient resolution and contrast to reveal myelinated axons, somata, dendrites, and sub-cellular organelles. Under optimized conditions, PEEM provides synaptic resolution; and simulation and experiments show that PEEM can be transformatively fast, at Gigahertz pixel rates. We conclude that PEEM imaging leverages attractive aspects of SEM and TEM, namely reliable sample collection on robust substrates combined with fast wide-field imaging, and could enable faster data acquisition for next-generation circuit mapping.
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28
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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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29
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Lin A, Yang R, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Bates AS, Eckstein N, Funke J, Jefferis GSXE, Murthy M. Network Statistics of the Whole-Brain Connectome of Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.29.551086. [PMID: 37547019 PMCID: PMC10402125 DOI: 10.1101/2023.07.29.551086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.
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Affiliation(s)
- Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Centre for Neural Circuits and Behaviour, 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
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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30
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Tao L, Ayembem D, Barranca VJ, Bhandawat V. Neurons underlying aggressive actions that are shared by both males and females in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582148. [PMID: 38464020 PMCID: PMC10925114 DOI: 10.1101/2024.02.26.582148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Aggression involves both sexually monomorphic and dimorphic actions. How the brain implements these two types of actions is poorly understood. We found that a set of neurons, which we call CL062, previously shown to mediate male aggression also mediate female aggression. These neurons elicit aggression acutely and without the presence of a target. Although the same set of actions is elicited in males and females, the overall behavior is sexually dimorphic. The CL062 neurons do not express fruitless , a gene required for sexual dimorphism in flies, and expressed by most other neurons important for controlling fly aggression. Connectomic analysis suggests that these neurons have limited connections with fruitless expressing neurons that have been shown to be important for aggression, and signal to different descending neurons. Thus, CL062 is part of a monomorphic circuit for aggression that functions parallel to the known dimorphic circuits.
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31
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Brezovec BE, Berger AB, Hao YA, Chen F, Druckmann S, Clandinin TR. Mapping the neural dynamics of locomotion across the Drosophila brain. Curr Biol 2024; 34:710-726.e4. [PMID: 38242122 DOI: 10.1016/j.cub.2023.12.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/13/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024]
Abstract
Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.
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Affiliation(s)
- Bella E Brezovec
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Andrew B Berger
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Yukun A Hao
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Feng Chen
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Shaul Druckmann
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA.
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32
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Cornean J, Molina-Obando S, Gür B, Bast A, Ramos-Traslosheros G, Chojetzki J, Lörsch L, Ioannidou M, Taneja R, Schnaitmann C, Silies M. Heterogeneity of synaptic connectivity in the fly visual system. Nat Commun 2024; 15:1570. [PMID: 38383614 PMCID: PMC10882054 DOI: 10.1038/s41467-024-45971-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: 12/15/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
Visual systems are homogeneous structures, where repeating columnar units retinotopically cover the visual field. Each of these columns contain many of the same neuron types that are distinguished by anatomic, genetic and - generally - by functional properties. However, there are exceptions to this rule. In the 800 columns of the Drosophila eye, there is an anatomically and genetically identifiable cell type with variable functional properties, Tm9. Since anatomical connectivity shapes functional neuronal properties, we identified the presynaptic inputs of several hundred Tm9s across both optic lobes using the full adult female fly brain (FAFB) electron microscopic dataset and FlyWire connectome. Our work shows that Tm9 has three major and many sparsely distributed inputs. This differs from the presynaptic connectivity of other Tm neurons, which have only one major, and more stereotypic inputs than Tm9. Genetic synapse labeling showed that the heterogeneous wiring exists across individuals. Together, our data argue that the visual system uses heterogeneous, distributed circuit properties to achieve robust visual processing.
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Affiliation(s)
- Jacqueline Cornean
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Sebastian Molina-Obando
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Burak Gür
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Annika Bast
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Giordano Ramos-Traslosheros
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jonas Chojetzki
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Lena Lörsch
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Maria Ioannidou
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Rachita Taneja
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Christopher Schnaitmann
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany.
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33
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Liu YH, Baratin A, Cornford J, Mihalas S, Shea-Brown E, Lajoie G. How connectivity structure shapes rich and lazy learning in neural circuits. ARXIV 2024:arXiv:2310.08513v2. [PMID: 37873007 PMCID: PMC10593070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity could exhibit a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights -- in particular their effective rank -- influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting.
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34
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Pospisil DA, Aragon MJ, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Jefferis GSXE, Murthy M, Pillow JW. From connectome to effectome: learning the causal interaction map of the fly brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.31.564922. [PMID: 37961285 PMCID: PMC10635032 DOI: 10.1101/2023.10.31.564922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.
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Affiliation(s)
- Dean A Pospisil
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Max J Aragon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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35
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Ros IG, Omoto JJ, Dickinson MH. Descending control and regulation of spontaneous flight turns in Drosophila. Curr Biol 2024; 34:531-540.e5. [PMID: 38228148 PMCID: PMC10872223 DOI: 10.1016/j.cub.2023.12.047] [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: 10/13/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 01/18/2024]
Abstract
The clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of locomotion, selecting for a common search motif in which straight movements through resource-poor regions alternate with zig-zag exploration in resource-rich domains. For example, during local search, flying flies spontaneously execute rapid flight turns, called body saccades, but suppress these maneuvers during long-distance dispersal or when surging upstream toward an attractive odor. Here, we describe the key cellular components of a neural network in flies that generate spontaneous turns as well as a specialized pair of neurons that inhibits the network and suppresses turning. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional units-one for right turns and one for left-with each unit consisting of an excitatory (DNae014) and an inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly, we identified a pair of large, distinct interneurons (VES041) that form inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As predicted by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it promotes straight flight to regulate the transition between local search and long-distance dispersal. These results thus identify the key elements of a network that may play a crucial role in foraging ecology.
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Affiliation(s)
- Ivo G Ros
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
| | - Jaison J Omoto
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
| | - Michael H Dickinson
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA.
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36
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Borst A. Connectivity Matrix Seriation via Relaxation. PLoS Comput Biol 2024; 20:e1011904. [PMID: 38377134 PMCID: PMC10906871 DOI: 10.1371/journal.pcbi.1011904] [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: 08/24/2023] [Revised: 03/01/2024] [Accepted: 02/09/2024] [Indexed: 02/22/2024] Open
Abstract
Volume electron microscopy together with computer-based image analysis are yielding neural circuit diagrams of ever larger regions of the brain. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs allowing to directly test various theories on the computation in that brain structure. Of particular interest are the detection of cell assemblies and the quantification of feedback, which can profoundly change circuit properties. While the ordering of cells along the rows and columns doesn't change the connectivity, it can make special connectivity patterns recognizable. For example, ordering the cells along the flow of information, feedback and feedforward connections are segregated above and below the main matrix diagonal, respectively. Different algorithms are used to renumber matrices such as to minimize a given cost function, but either their performance becomes unsatisfying at a given size of the circuit or the CPU time needed to compute them scales in an unfavorable way with increasing number of neurons. Based on previous ideas, I describe an algorithm which is effective in matrix reordering with respect to both its performance as well as to its scaling in computing time. Rather than trying to reorder the matrix in discrete steps, the algorithm transiently relaxes the integer program by assigning a real-valued parameter to each cell describing its location on a continuous axis ('smooth-index') and finds the parameter set that minimizes the cost. I find that the smooth-index algorithm outperforms all algorithms I compared it to, including those based on topological sorting.
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Affiliation(s)
- Alexander Borst
- Max-Planck-Institute for Biological Intelligence, Martinsried, Germany
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37
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Pang R, Baker C, Murthy M, Pillow J. Inferring neural dynamics of memory during naturalistic social communication. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577404. [PMID: 38328156 PMCID: PMC10849655 DOI: 10.1101/2024.01.26.577404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Memory processes in complex behaviors like social communication require forming representations of the past that grow with time. The neural mechanisms that support such continually growing memory remain unknown. We address this gap in the context of fly courtship, a natural social behavior involving the production and perception of long, complex song sequences. To study female memory for male song history in unrestrained courtship, we present 'Natural Continuation' (NC)-a general, simulation-based model comparison procedure to evaluate candidate neural codes for complex stimuli using naturalistic behavioral data. Applying NC to fly courtship revealed strong evidence for an adaptive population mechanism for how female auditory neural dynamics could convert long song histories into a rich mnemonic format. Song temporal patterning is continually transformed by heterogeneous nonlinear adaptation dynamics, then integrated into persistent activity, enabling common neural mechanisms to retain continuously unfolding information over long periods and yielding state-of-the-art predictions of female courtship behavior. At a population level this coding model produces multi-dimensional advection-diffusion-like responses that separate songs over a continuum of timescales and can be linearly transformed into flexible output signals, illustrating its potential to create a generic, scalable mnemonic format for extended input signals poised to drive complex behavioral responses. This work thus shows how naturalistic behavior can directly inform neural population coding models, revealing here a novel process for memory formation.
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Affiliation(s)
- Rich Pang
- Princeton Neuroscience Institute, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton, NJ and New York, NY, USA
| | - Christa Baker
- Princeton Neuroscience Institute, Princeton, NJ, USA
- Present address: Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton, NJ, USA
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38
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Schneider-Mizell CM, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Elabbady L, Gamlin C, Kapner D, Kinn S, Mahalingam G, Seshamani S, Suckow S, Takeno M, Torres R, Yin W, Dorkenwald S, Bae JA, Castro MA, Halageri A, Jia Z, Jordan C, Kemnitz N, Lee K, Li K, Lu R, Macrina T, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Silversmith W, Turner NL, Wong W, Wu J, Reimer J, Tolias AS, Seung HS, Reid RC, Collman F, Maçarico da Costa N. Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.23.525290. [PMID: 36747710 PMCID: PMC9900837 DOI: 10.1101/2023.01.23.525290] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Sam Kinn
- Allen Institute for Brain Science, Seattle, WA
| | | | | | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA
| | | | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, WA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | | | | | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Kisuk Lee
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology
| | - Kai Li
- Computer Science Department, Princeton University
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | | | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
- Department of Electrical and Computer Engineering, Rice University
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA
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39
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Popovych S, Macrina T, Kemnitz N, Castro M, Nehoran B, Jia Z, Bae JA, Mitchell E, Mu S, Trautman ET, Saalfeld S, Li K, Seung HS. Petascale pipeline for precise alignment of images from serial section electron microscopy. Nat Commun 2024; 15:289. [PMID: 38177169 PMCID: PMC10767115 DOI: 10.1038/s41467-023-44354-0] [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: 04/08/2022] [Accepted: 12/07/2023] [Indexed: 01/06/2024] Open
Abstract
The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.
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Affiliation(s)
- Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, 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
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, 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 and Computer Engineering Department, Princeton University, Princeton, NJ, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | - Kai Li
- Computer Science Department, 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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40
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Gebehart C, Büschges A. The processing of proprioceptive signals in distributed networks: insights from insect motor control. J Exp Biol 2024; 227:jeb246182. [PMID: 38180228 DOI: 10.1242/jeb.246182] [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: 01/06/2024]
Abstract
The integration of sensory information is required to maintain body posture and to generate robust yet flexible locomotion through unpredictable environments. To anticipate required adaptations in limb posture and enable compensation of sudden perturbations, an animal's nervous system assembles external (exteroception) and internal (proprioception) cues. Coherent neuronal representations of the proprioceptive context of the body and the appendages arise from the concerted action of multiple sense organs monitoring body kinetics and kinematics. This multimodal proprioceptive information, together with exteroceptive signals and brain-derived descending motor commands, converges onto premotor networks - i.e. the local neuronal circuitry controlling motor output and movements - within the ventral nerve cord (VNC), the insect equivalent of the vertebrate spinal cord. This Review summarizes existing knowledge and recent advances in understanding how local premotor networks in the VNC use convergent information to generate contextually appropriate activity, focusing on the example of posture control. We compare the role and advantages of distributed sensory processing over dedicated neuronal pathways, and the challenges of multimodal integration in distributed networks. We discuss how the gain of distributed networks may be tuned to enable the behavioral repertoire of these systems, and argue that insect premotor networks might compensate for their limited neuronal population size by, in comparison to vertebrate networks, relying more heavily on the specificity of their connections. At a time in which connectomics and physiological recording techniques enable anatomical and functional circuit dissection at an unprecedented resolution, insect motor systems offer unique opportunities to identify the mechanisms underlying multimodal integration for flexible motor control.
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Affiliation(s)
- Corinna Gebehart
- Champalimaud Foundation, Champalimaud Research, 1400-038 Lisbon, Portugal
| | - Ansgar Büschges
- Department of Animal Physiology, Institute of Zoology, Biocenter Cologne, University of Cologne, Zülpicher Strasse 47b, 50674 Cologne, Germany
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41
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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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42
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Franco-Barranco D, Lin Z, Jang WD, Wang X, Shen Q, Yin W, Fan Y, Li M, Chen C, Xiong Z, Xin R, Liu H, Chen H, Li Z, Zhao J, Chen X, Pape C, Conrad R, Nightingale L, de Folter J, Jones ML, Liu Y, Ziaei D, Huschauer S, Arganda-Carreras I, Pfister H, Wei D. Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3956-3971. [PMID: 37768797 PMCID: PMC10753957 DOI: 10.1109/tmi.2023.3320497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 1,986 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that: 1) challenge metric was permissive with the false positive predictions; and 2) size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download.
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Affiliation(s)
- Daniel Franco-Barranco
- Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), 20018 San Sebastián, Spain, and also with the Donostia International Physics Center (DIPC), 20018 San Sebastián, Spain
| | - Zudi Lin
- Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard University, Allston, MA 02134 USA
| | - Won-Dong Jang
- Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard University, Allston, MA 02134 USA
| | - Xueying Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138 USA
| | - Qijia Shen
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, U.K
| | - Wenjie Yin
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138 USA
| | - Yutian Fan
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138 USA
| | - Mingxing Li
- Department of Electronic Engineering and Information Science (EEIS), University of Science and Technology of China, Anhui 230026, China
| | - Chang Chen
- Department of Electronic Engineering and Information Science (EEIS), University of Science and Technology of China, Anhui 230026, China
| | - Zhiwei Xiong
- Department of Electronic Engineering and Information Science (EEIS), University of Science and Technology of China, Anhui 230026, China
| | - Rui Xin
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hao Liu
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Huai Chen
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhili Li
- National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application, University of Science and Technology of China, Anhui 230026, China
| | - Jie Zhao
- National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application, University of Science and Technology of China, Anhui 230026, China
| | - Xuejin Chen
- National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application, University of Science and Technology of China, Anhui 230026, China
| | - Constantin Pape
- European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany. He is now with the Institute for Computer Science, Georg-August-Universität Göttingen, Göttingen, Germany
| | - Ryan Conrad
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892 USA, and also with the Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21701 USA
| | | | | | | | - Yanling Liu
- Advanced Biomedical Computational Science Group, Frederick National Laboratory for Cancer Research, Frederick, MD 21701 USA
| | - Dorsa Ziaei
- Advanced Biomedical Computational Science Group, Frederick National Laboratory for Cancer Research, Frederick, MD 21701 USA
| | | | - Ignacio Arganda-Carreras
- Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), 20018 San Sebastián, Spain, also with the Donostia International Physics Center (DIPC), 20018 San Sebastián, Spain, also with the IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain, and also with the Biofisika Institute, 48940 Leioa, Spain
| | - Hanspeter Pfister
- Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard University, Allston, MA 02134 USA
| | - Donglai Wei
- Computer Science Department, Boston College, Chestnut Hill, MA 02467 USA
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43
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Garner D, Kind E, Nern A, Houghton L, Zhao A, Sancer G, Rubin GM, Wernet MF, Kim SS. Connectomic reconstruction predicts the functional organization of visual inputs to the navigation center of the Drosophila brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569241. [PMID: 38076786 PMCID: PMC10705420 DOI: 10.1101/2023.11.29.569241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Many animals, including humans, navigate their surroundings by visual input, yet we understand little about how visual information is transformed and integrated by the navigation system. In Drosophila melanogaster, compass neurons in the donut-shaped ellipsoid body of the central complex generate a sense of direction by integrating visual input from ring neurons, a part of the anterior visual pathway (AVP). Here, we densely reconstruct all neurons in the AVP using FlyWire, an AI-assisted tool for analyzing electron-microscopy data. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons, which connect the medulla in the optic lobe to the small unit of anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons, which connect the anterior optic tubercle to the bulb neuropil; and ring neurons, which connect the bulb to the ellipsoid body. Based on neuronal morphologies, connectivity between different neural classes, and the locations of synapses, we identified non-overlapping channels originating from four types of MeTu neurons, which we further divided into ten subtypes based on the presynaptic connections in medulla and postsynaptic connections in AOTUsu. To gain an objective measure of the natural variation within the pathway, we quantified the differences between anterior visual pathways from both hemispheres and between two electron-microscopy datasets. Furthermore, we infer potential visual features and the visual area from which any given ring neuron receives input by combining the connectivity of the entire AVP, the MeTu neurons' dendritic fields, and presynaptic connectivity in the optic lobes. These results provide a strong foundation for understanding how distinct visual features are extracted and transformed across multiple processing stages to provide critical information for computing the fly's sense of direction.
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Affiliation(s)
- Dustin Garner
- Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Emil Kind
- Department of Biology, Freie Universität Berlin, Berlin, Germany
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lucy Houghton
- Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Arthur Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gizem Sancer
- Department of Biology, Freie Universität Berlin, Berlin, Germany
| | - Gerald M. Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Sung Soo Kim
- Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
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44
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Mehta K, Ljungquist B, Ogden J, Nanda S, Ascoli RG, Ng L, Ascoli GA. Online conversion of reconstructed neural morphologies into standardized SWC format. Nat Commun 2023; 14:7429. [PMID: 37973857 PMCID: PMC10654402 DOI: 10.1038/s41467-023-42931-x] [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: 03/23/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Digital reconstructions provide an accurate and reliable way to store, share, model, quantify, and analyze neural morphology. Continuous advances in cellular labeling, tissue processing, microscopic imaging, and automated tracing catalyzed a proliferation of software applications to reconstruct neural morphology. These computer programs typically encode the data in custom file formats. The resulting format heterogeneity severely hampers the interoperability and reusability of these valuable data. Among these many alternatives, the SWC file format has emerged as a popular community choice, coalescing a rich ecosystem of related neuroinformatics resources for tracing, visualization, analysis, and simulation. This report presents a standardized specification of the SWC file format. In addition, we introduce xyz2swc, a free online service that converts all 26 reconstruction formats (and 72 variations) described in the scientific literature into the SWC standard. The xyz2swc service is available open source through a user-friendly browser interface ( https://neuromorpho.org/xyz2swc/ui/ ) and an Application Programming Interface (API).
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Affiliation(s)
- Ketan Mehta
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - James Ogden
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Sumit Nanda
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Ruben G Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA.
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45
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Troidl J, Warchol S, Choi J, Matelsky J, Dhanyasi N, Wang X, Wester B, Wei D, Lichtman JW, Pfister H, Beyer J. Vimo - Visual Analysis of Neuronal Connectivity Motifs. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; PP:10.1109/TVCG.2023.3327388. [PMID: 37883279 PMCID: PMC11045665 DOI: 10.1109/tvcg.2023.3327388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Recent advances in high-resolution connectomics provide researchers with access to accurate petascale reconstructions of neuronal circuits and brain networks for the first time. Neuroscientists are analyzing these networks to better understand information processing in the brain. In particular, scientists are interested in identifying specific small network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. Although such motifs are typically small (e.g., 2 - 6 neurons), the vast data sizes and intricate data complexity present significant challenges to the search and analysis process. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings. To simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by visual abstractions. This allows users to transition from a highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly (e.g., 2 - 4 times) to form a larger network structure. We evaluate Vimo in a user study and an in-depth case study with seven domain experts on motifs in a large connectome of the fruit fly, including more than 21,000 neurons and 20 million synapses. We find that Vimo enables hypothesis generation and confirmation through fast analysis iterations and connectivity highlighting.
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Sato K, Yamamoto D. Molecular and cellular origins of behavioral sex differences: a tiny little fly tells a lot. Front Mol Neurosci 2023; 16:1284367. [PMID: 37928065 PMCID: PMC10622783 DOI: 10.3389/fnmol.2023.1284367] [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: 08/28/2023] [Accepted: 10/02/2023] [Indexed: 11/07/2023] Open
Abstract
Behavioral sex differences primarily derive from the sexually dimorphic organization of neural circuits that direct the behavior. In Drosophila melanogaster, the sex-determination genes fruitless (fru) and doublesex (dsx) play pivotal roles in producing the sexual dimorphism of neural circuits for behavior. Here we examine three neural groups expressing fru and/or dsx, i.e., the P1 cluster, aSP-f and aSP-g cluster pairs and aDN cluster, in which causal relationships between the dimorphic behavior and dimorphic neural characteristics are best illustrated. aSP-f, aSP-g and aDN clusters represent examples where fru or dsx switches cell-autonomously their neurite structures between the female-type and male-type. Processed sensory inputs impinging on these neurons may result in outputs that encode different valences, which culminate in the execution of distinct behavior according to the sex. In contrast, the P1 cluster is male-specific as its female counterpart undergoes dsx-driven cell death, which lowers the threshold for the induction of male-specific behaviors. We propose that the products of fru and dsx genes, as terminal selectors in sexually dimorphic neuronal wiring, induce and maintain the sex-typical chromatin state at postembryonic stages, orchestrating the transcription of effector genes that shape single neuron structures and govern cell survival and death.
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Affiliation(s)
- Kosei Sato
- Neuro-ICT Laboratory, Advanced ICT Research Institute, National Institute of Information and Communications Technology, Kobe, Japan
| | - Daisuke Yamamoto
- Neuro-ICT Laboratory, Advanced ICT Research Institute, National Institute of Information and Communications Technology, Kobe, Japan
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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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Roemschied FA, Pacheco DA, Aragon MJ, Ireland EC, Li X, Thieringer K, Pang R, Murthy M. Flexible circuit mechanisms for context-dependent song sequencing. Nature 2023; 622:794-801. [PMID: 37821705 PMCID: PMC10600009 DOI: 10.1038/s41586-023-06632-1] [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: 10/31/2021] [Accepted: 09/11/2023] [Indexed: 10/13/2023]
Abstract
Sequenced behaviours, including locomotion, reaching and vocalization, are patterned differently in different contexts, enabling animals to adjust to their environments. How contextual information shapes neural activity to flexibly alter the patterning of actions is not fully understood. Previous work has indicated that this could be achieved via parallel motor circuits, with differing sensitivities to context1,2. Here we demonstrate that a single pathway operates in two regimes dependent on recent sensory history. We leverage the Drosophila song production system3 to investigate the role of several neuron types4-7 in song patterning near versus far from the female fly. Male flies sing 'simple' trains of only one mode far from the female fly but complex song sequences comprising alternations between modes when near her. We find that ventral nerve cord (VNC) circuits are shaped by mutual inhibition and rebound excitability8 between nodes driving the two song modes. Brief sensory input to a direct brain-to-VNC excitatory pathway drives simple song far from the female, whereas prolonged input enables complex song production via simultaneous recruitment of functional disinhibition of VNC circuitry. Thus, female proximity unlocks motor circuit dynamics in the correct context. We construct a compact circuit model to demonstrate that the identified mechanisms suffice to replicate natural song dynamics. These results highlight how canonical circuit motifs8,9 can be combined to enable circuit flexibility required for dynamic communication.
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Affiliation(s)
- Frederic A Roemschied
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- European Neuroscience Institute, Göttingen, Germany
| | - Diego A Pacheco
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Harvard Medical School, Boston, MA, USA
| | - Max J Aragon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Elise C Ireland
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Xinping Li
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Kyle Thieringer
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Rich Pang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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Légaré A, Lemieux M, Desrosiers P, De Koninck P. Zebrafish brain atlases: a collective effort for a tiny vertebrate brain. NEUROPHOTONICS 2023; 10:044409. [PMID: 37786400 PMCID: PMC10541682 DOI: 10.1117/1.nph.10.4.044409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/03/2023] [Accepted: 09/13/2023] [Indexed: 10/04/2023]
Abstract
In the past two decades, digital brain atlases have emerged as essential tools for sharing and integrating complex neuroscience datasets. Concurrently, the larval zebrafish has become a prominent vertebrate model offering a strategic compromise for brain size, complexity, transparency, optogenetic access, and behavior. We provide a brief overview of digital atlases recently developed for the larval zebrafish brain, intersecting neuroanatomical information, gene expression patterns, and connectivity. These atlases are becoming pivotal by centralizing large datasets while supporting the generation of circuit hypotheses as functional measurements can be registered into an atlas' standard coordinate system to interrogate its structural database. As challenges persist in mapping neural circuits and incorporating functional measurements into zebrafish atlases, we emphasize the importance of collaborative efforts and standardized protocols to expand these resources to crack the complex codes of neuronal activity guiding behavior in this tiny vertebrate brain.
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Affiliation(s)
| | - Mado Lemieux
- CERVO Brain Research Center, Québec, Québec, Canada
| | - Patrick Desrosiers
- CERVO Brain Research Center, Québec, Québec, Canada
- Université Laval, Department of Physics, Engineering Physics and Optics, Québec, Québec, Canada
| | - Paul De Koninck
- CERVO Brain Research Center, Québec, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology and Bio-informatics, Québec, Québec, Canada
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Mabuchi Y, Cui X, Xie L, Kim H, Jiang T, Yapici N. Visual feedback neurons fine-tune Drosophila male courtship via GABA-mediated inhibition. Curr Biol 2023; 33:3896-3910.e7. [PMID: 37673068 PMCID: PMC10529139 DOI: 10.1016/j.cub.2023.08.034] [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: 02/09/2023] [Revised: 06/27/2023] [Accepted: 08/11/2023] [Indexed: 09/08/2023]
Abstract
Many species of animals use vision to regulate their social behaviors. However, the molecular and circuit mechanisms underlying visually guided social interactions remain largely unknown. Here, we show that the Drosophila ortholog of the human GABAA-receptor-associated protein (GABARAP) is required in a class of visual feedback neurons, lamina tangential (Lat) cells, to fine-tune male courtship. GABARAP is a ubiquitin-like protein that maintains cell-surface levels of GABAA receptors. We demonstrate that knocking down GABARAP or GABAAreceptors in Lat neurons or hyperactivating them induces male courtship toward other males. Inhibiting Lat neurons, on the other hand, delays copulation by impairing the ability of males to follow females. Remarkably, the fly GABARAP protein and its human ortholog share a strong sequence identity, and the fly GABARAP function in Lat neurons can be rescued by its human ortholog. Using in vivo two-photon imaging and optogenetics, we reveal that Lat neurons are functionally connected to neural circuits that mediate visually guided courtship pursuits in males. Our work identifies a novel physiological function for GABARAP in regulating visually guided courtship pursuits in Drosophila males. Reduced GABAA signaling has been linked to social deficits observed in the autism spectrum and bipolar disorders. The functional similarity between the human and the fly GABARAP raises the possibility of a conserved role for this gene in regulating social behaviors across insects and mammals.
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Affiliation(s)
- Yuta Mabuchi
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Xinyue Cui
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Lily Xie
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Haein Kim
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Tianxing Jiang
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Nilay Yapici
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA.
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