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48 Publications

Showing 1-10 of 48 results
09/21/23 | Nested neural circuits generate distinct acoustic signals during Drosophila courtship
Joshua L. Lillvis , Kaiyu Wang , Hiroshi M. Shiozaki , Min Xu , David L. Stern , Barry J. Dickson
bioRxiv. 2023 Sep 21:. doi: 10.1101/2023.08.30.555537

Many motor control systems generate multiple movements using a common set of muscles. How are premotor circuits able to flexibly generate diverse movement patterns? Here, we characterize the neuronal circuits that drive the distinct courtship songs of Drosophila melanogaster. Male flies vibrate their wings towards females to produce two different song modes – pulse and sine song – which signal species identity and male quality. Using cell-type specific genetic reagents and the connectome, we provide a cellular and synaptic map of the circuits in the male ventral nerve cord that generate these songs and examine how activating or inhibiting each cell type within these circuits affects the song. Our data reveal that the song circuit is organized into two nested feed-forward pathways, with extensive reciprocal and feed-back connections. The larger network produces pulse song, the more complex and ancestral song form. A subset of this network produces sine song, the simpler and more recent form. Such nested organization may be a common feature of motor control circuits in which evolution has layered increasing flexibility on to a basic movement pattern.

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09/01/23 | The Neural Basis of Drosophila Courtship Song
Joshua L. Lillvis , Kaiyu Wang , Hiroshi M. Shiozaki , Min Xu , David L. Stern , Barry J. Dickson
bioRxiv. 2023 Sep 01:. doi: 10.1101/2023.08.30.555537

Animal sounds are produced by patterned vibrations of specific organs, but the neural circuits that drive these vibrations are not well defined in any animal. Here we provide a functional and synaptic map of most of the neurons in the Drosophila male ventral nerve cord (the analog of the vertebrate spinal cord) that drive complex, patterned song during courtship. Male Drosophila vibrate their wings toward females during courtship to produce two distinct song modes – pulse and sine song – with characteristic features that signal species identity and male quality. We identified song-producing neural circuits by optogenetically activating and inhibiting identified cell types in the ventral nerve cord (VNC) and by tracing their patterns of synaptic connectivity in the male VNC connectome. The core song circuit consists of at least eight cell types organized into overlapping circuits, where all neurons are required for pulse song and a subset are required for sine song. The pulse and sine circuits each include a feed-forward pathway from brain descending neurons to wing motor neurons, with extensive reciprocal and feed-back connections. We also identify specific neurons that shape the individual features of each song mode. These results reveal commonalities amongst diverse animals in the neural mechanisms that generate diverse motor patterns from a single set of muscles.

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Card LabFlyEM
06/07/23 | Transforming descending input into behavior: The organization of premotor circuits in the Drosophila Male Adult Nerve Cord connectome
H. S. J. Cheong , K. Eichler , T. Stuerner , S. K. Asinof , A. S. Champion , E. C. Marin , T. B. Oram , M. Sumathipala , L. Venkatasubramanian , S. Namiki , I. Siwanowicz , M. Costa , S. Berg , Janelia FlyEM Project Team , G. S. X. E. Jefferis , G. M. Card
bioRxiv. 2023 Jun 07:. doi: 10.1101/2023.06.07.543976

In most animals, a relatively small number of descending neurons (DNs) connect higher brain centers in the animal’s head to motor neurons (MNs) in the nerve cord of the animal’s body that effect movement of the limbs. To understand how brain signals generate behavior, it is critical to understand how these descending pathways are organized onto the body MNs. In the fly, Drosophila melanogaster, MNs controlling muscles in the leg, wing, and other motor systems reside in a ventral nerve cord (VNC), analogous to the mammalian spinal cord. In companion papers, we introduced a densely-reconstructed connectome of the Drosophila Male Adult Nerve Cord (MANC, Takemura et al., 2023), including cell type and developmental lineage annotation (Marin et al., 2023), which provides complete VNC connectivity at synaptic resolution. Here, we present a first look at the organization of the VNC networks connecting DNs to MNs based on this new connectome information. We proofread and curated all DNs and MNs to ensure accuracy and reliability, then systematically matched DN axon terminals and MN dendrites with light microscopy data to link their VNC morphology with their brain inputs or muscle targets. We report both broad organizational patterns of the entire network and fine-scale analysis of selected circuits of interest. We discover that direct DN-MN connections are infrequent and identify communities of intrinsic neurons linked to control of different motor systems, including putative ventral circuits for walking, dorsal circuits for flight steering and power generation, and intermediate circuits in the lower tectulum for coordinated action of wings and legs. Our analysis generates hypotheses for future functional experiments and, together with the MANC connectome, empowers others to investigate these and other circuits of the Drosophila ventral nerve cord in richer mechanistic detail.

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06/06/23 | A Connectome of the Male Drosophila Ventral Nerve Cord
Shin-ya Takemura , Kenneth J Hayworth , Gary B Huang , Michal Januszewski , Zhiyuan Lu , Elizabeth C Marin , Stephan Preibisch , C Shan Xu , John Bogovic , Andrew S Champion , Han S J Cheong , Marta Costa , Katharina Eichler , William Katz , Christopher Knecht , Feng Li , Billy J Morris , Christopher Ordish , Patricia K Rivlin , Philipp Schlegel , Kazunori Shinomiya , Tomke Sturner , Ting Zhao , Griffin Badalamente , Dennis Bailey , Paul Brooks , Brandon S Canino , Jody Clements , Michael Cook , Octave Duclos , Christopher R Dunne , Kelli Fairbanks , Siqi Fang , Samantha Finley-May , Audrey Francis , Reed George , Marina Gkantia , Kyle Harrington , Gary Patrick Hopkins , Joseph Hsu , Philip M Hubbard , Alexandre Javier , Dagmar Kainmueller , Wyatt Korff , Julie Kovalyak , Dominik Krzeminski , Shirley A Lauchie , Alanna Lohff , Charli Maldonado , Emily A Manley , Caroline Mooney , Erika Neace , Matthew Nichols , Omotara Ogundeyi , Nneoma Okeoma , Tyler Paterson , Elliott Phillips , Emily M Phillips , Caitlin Ribeiro , Sean M Ryan , Jon Thomson Rymer , Anne K Scott , Ashley L Scott , David Shepherd , Aya Shinomiya , Claire Smith , Alia Suleiman , Satoko Takemura , Iris Talebi , Imaan F M Tamimi , Eric T Trautman , Lowell Umayam , John J Walsh , Tansy Yang , Gerald M Rubin , Louis K Scheffer , Jan Funke , Stephan Saalfeld , Harald F Hess , Stephen M Plaza , Gwyneth M Card , Gregory S X E Jefferis , Stuart Berg
bioRxiv. 2023 Jun 06:. doi: 10.1101/2023.06.05.543757

Animal behavior is principally expressed through neural control of muscles. Therefore understanding how the brain controls behavior requires mapping neuronal circuits all the way to motor neurons. We have previously established technology to collect large-volume electron microscopy data sets of neural tissue and fully reconstruct the morphology of the neurons and their chemical synaptic connections throughout the volume. Using these tools we generated a dense wiring diagram, or connectome, for a large portion of the Drosophila central brain. However, in most animals, including the fly, the majority of motor neurons are located outside the brain in a neural center closer to the body, i.e. the mammalian spinal cord or insect ventral nerve cord (VNC). In this paper, we extend our effort to map full neural circuits for behavior by generating a connectome of the VNC of a male fly.

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Card LabFlyEM
06/06/23 | Systematic annotation of a complete adult male Drosophila nerve cord connectome reveals principles of functional organisation
Elizabeth C Marin , Billy J Morris , Tomke Stuerner , Andrew S Champion , Dominik Krzeminski , Griffin Badalamente , Marina Gkantia , Imaan F M Tamimi , Siqi Fang , Sung Soo Moon , Han S J Cheong , Feng Li , Philipp Schlegel , Stuart Berg , FlyEM Project Team , Gwyneth M Card , Marta Costa , David Shepherd , Gregory S X E Jefferis
bioRxiv. 2023 Jun 06:. doi: 10.1101/2023.06.05.543407

Our companion paper (Takemura et al., 2023) introduces the first completely proofread connectome of the nerve cord of an animal that can walk or fly. The base connectome consists of neuronal morphologies and the connections between them. However, in order to efficiently navigate and understand this connectome, it is crucial to have a system of annotations that systematically categorises and names neurons, linking them to the existing literature. In this paper we describe the comprehensive annotation of the VNC connectome, first by a system of hierarchical coarse annotations, then by grouping left-right and serially homologous neurons and eventually by defining systematic cell types for the intrinsic interneurons and sensory neurons of the VNC; descending and motor neurons are typed in (Cheong et al., 2023). We assign a sensory modality to over 5000 sensory neurons, cluster them by connectivity, and identify serially homologous cell types and a layered organisation likely corresponding to peripheral topography. We identify the developmental neuroblast of origin of the large majority of VNC neurons and confirm that (in most cases) all secondary neurons of each hemilineage express a single neurotransmitter. Neuroblast hemilineages are serially repeated along the segments of the nerve cord and generally exhibit consistent hemilineage-to-hemilineage connectivity across neuromeres, supporting the idea that hemilineages are a major organisational feature of the VNC. We also find that more than a third of individual neurons belong to serially homologous cell types, which were crucial for identifying motor neurons and sensory neurons across leg neuropils. Categorising interneurons by their neuropil innervation patterns provides an additional organisation axis. Over half of the intrinsic neurons of the VNC appear dedicated to the legs, with the majority restricted to single leg neuropils; in contrast, inhibitory interneurons connecting different leg neuropils, especially those crossing the midline, appear rarer than anticipated by standard models of locomotor circuitry. Our annotations are being released as part of the web application and also serve as the basis of programmatic analysis of the connectome through dedicated tools that we describe in this paper.

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07/21/22 | NeuronBridge: an intuitive web application for neuronal morphology search across large data sets
Jody Clements , Cristian Goina , Philip M. Hubbard , Takashi Kawase , Donald J. Olbris , Hideo Otsuna , Robert Svirskas , Konrad Rokicki
bioRxiv. 2022 Jul 21:. doi: 10.1101/2022.07.20.500311

Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. In order to exploit this knowledge base, researchers target individual neurons and study their function. Therefore, vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM). However, creating a fly line for driving gene expression within a single neuron found in the EM connectome remains a challenge, as it typically requires identifying a pair of fly lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities. Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large datasets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly GUI, data model, serverless cloud architecture, and massively parallel image search engine. NeuronBridge is openly accessible at

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12/22/20 | Latent Feature Representation via Unsupervised Learning for Pattern Discovery in Massive Electron Microscopy Image Volumes
Gary B Huang , Huei-Fang Yang , Shin-ya Takemura , Pat Rivlin , Stephen M Plaza
arXiv. 2020 Dec 22:. doi: 10.48550/arXiv.2012.12175

We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation that captures semantic similarity in the data set. The core idea is to use data augmentations that preserve semantic meaning to generate synthetic examples of elements whose feature representations should be close to one another.
We demonstrate the utility of our method applied to nano-scale electron microscopy data, where even relatively small portions of animal brains can require terabytes of image data. Although supervised methods can be used to predict and identify known patterns of interest, the scale of the data makes it difficult to mine and analyze patterns that are not known a priori. We show the ability of our learned representation to enable query by example, so that if a scientist notices an interesting pattern in the data, they can be presented with other locations with matching patterns. We also demonstrate that clustering of data in the learned space correlates with biologically-meaningful distinctions. Finally, we introduce a visualization tool and software ecosystem to facilitate user-friendly interactive analysis and uncover interesting biological patterns. In short, our work opens possible new avenues in understanding of and discovery in large data sets, arising in domains such as EM analysis.

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09/07/20 | A connectome and analysis of the adult Drosophila central brain.
Scheffer LK, Xu CS, Januszewski M, Lu Z, Takemura S, Hayworth KJ, Huang GB, Shinomiya K, Maitlin-Shepard J, Berg S, Clements J, Hubbard PM, Katz WT, Umayam L, Zhao T, Ackerman D, Blakely T, Bogovic J, Dolafi T, Kainmueller D, Kawase T, Khairy KA, Leavitt L, Li PH, Lindsey L, Neubarth N, Olbris DJ, Otsuna H, Trautman ET, Ito M, Bates AS, Goldammer J, Wolff T, Svirskas R, Schlegel P, Neace E, Knecht CJ, Alvarado CX, Bailey DA, Ballinger S, Borycz JA, Canino BS, Cheatham N, Cook M, Dreher M, Duclos O, Eubanks B, Fairbanks K, Finley S, Forknall N, Francis A, Hopkins GP, Joyce EM, Kim S, Kirk NA, Kovalyak J, Lauchie SA, Lohff A, Maldonado C, Manley EA, McLin S, Mooney C, Ndama M, Ogundeyi O, Okeoma N, Ordish C, Padilla N, Patrick CM, Paterson T, Phillips EE, Phillips EM, Rampally N, Ribeiro C, Robertson MK, Rymer JT, Ryan SM, Sammons M, Scott AK, Scott AL, Shinomiya A, Smith C, Smith K, Smith NL, Sobeski MA, Suleiman A, Swift J, Takemura S, Talebi I, Tarnogorska D, Tenshaw E, Tokhi T, Walsh JJ, Yang T, Horne JA, Li F, Parekh R, Rivlin PK, Jayaraman V, Costa M, Jefferis GS, Ito K, Saalfeld S, George R, Meinertzhagen IA, Rubin GM, Hess HF, Jain V, Plaza SM
Elife. 2020 Sep 07;9:. doi: 10.7554/eLife.57443

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly . Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.

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01/17/20 | Accelerated EM Connectome Reconstruction using 3D Visualization and Segmentation Graphs
Philip M. Hubbard , Stuart Berg , Ting Zhao , Donald J. Olbris , Lowell Umayam , Jeremy Maitin-Shepard , Michal Januszewski , William T. Katz , Erika R. Neace , Stephen M. Plaza
bioRxiv. 2020 Jan 17:. doi: 10.1101/2020.01.17.909572

Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain enable more efficient reconstruction of neural connectivity. In these datasets, a single neuron can span thousands of images containing complex tree-like arbors with thousands of synapses. While image segmentation algorithms excel within narrow fields of views, the algorithms sometimes struggle to correctly segment large neurons, which require large context given their size and complexity. Conversely, humans are comparatively good at reasoning with large objects. In this paper, we introduce several semi-automated strategies that combine 3D visualization and machine guidance to accelerate connectome reconstruction. In particular, we introduce a strategy to quickly correct a segmentation through merging and cleaving, or splitting a segment along supervoxel boundaries, with both operations driven by affinity scores in the underlying segmentation. We deploy these algorithms as streamlined workflows in a tool called Neu3 and demonstrate superior performance compared to prior work, thus enabling efficient reconstruction of much larger datasets. The insights into proofreading from our work clarify the trade-offs to consider when tuning the parameters of image segmentation algorithms.

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10/11/19 | The organization of the second optic chiasm of the optic lobe.
Shinomiya K, Horne JA, McLin S, Wiederman M, Nern A, Plaza SM, Meinertzhagen IA
Frontiers in Neural Circuits. 2019 Oct 11;13:65. doi: 10.3389/fncir.2019.00065

Visual pathways from the compound eye of an insect relay to four neuropils, successively the lamina, medulla, lobula, and lobula plate in the underlying optic lobe. Among these neuropils, the medulla, lobula, and lobula plate are interconnected by the complex second optic chiasm, through which the anteroposterior axis undergoes an inversion between the medulla and lobula. Given their complex structure, the projection patterns through the second optic chiasm have so far lacked critical analysis. By densely reconstructing axon trajectories using a volumetric scanning electron microscopy (SEM) technique, we reveal the three-dimensional structure of the second optic chiasm of , which comprises interleaving bundles and sheets of axons insulated from each other by glial sheaths. These axon bundles invert their horizontal sequence in passing between the medulla and lobula. Axons connecting the medulla and lobula plate are also bundled together with them but do not decussate the sequence of their horizontal positions. They interleave with sheets of projection neuron axons between the lobula and lobula plate, which also lack decussations. We estimate that approximately 19,500 cells per hemisphere, about two thirds of the optic lobe neurons, contribute to the second chiasm, most being Tm cells, with an estimated additional 2,780 T4 and T5 cells each. The chiasm mostly comprises axons and cell body fibers, but also a few synaptic elements. Based on our anatomical findings, we propose that a chiasmal structure between the neuropils is potentially advantageous for processing complex visual information in parallel. The EM reconstruction shows not only the structure of the chiasm in the adult brain, the previously unreported main topic of our study, but also suggest that the projection patterns of the neurons comprising the chiasm may be determined by the proliferation centers from which the neurons develop. Such a complex wiring pattern could, we suggest, only have arisen in several evolutionary steps.

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