@article {68800, title = {A Connectome of the Male Drosophila Ventral Nerve Cord}, journal = {bioRxiv}, year = {2023}, month = {2023 Jun 06}, abstract = {

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.

}, doi = {10.1101/2023.06.05.543757}, url = {https://doi.org/10.1101/2023.06.05.543757}, author = {Shin-ya Takemura and Kenneth J Hayworth and Gary B Huang and Michal Januszewski and Zhiyuan Lu and Elizabeth C Marin and Stephan Preibisch and C Shan Xu and John Bogovic and Andrew S Champion and Han S J Cheong and Marta Costa and Katharina Eichler and William Katz and Christopher Knecht and Feng Li and Billy J Morris and Christopher Ordish and Patricia K Rivlin and Philipp Schlegel and Kazunori Shinomiya and Tomke Sturner and Ting Zhao and Griffin Badalamente and Dennis Bailey and Paul Brooks and Brandon S Canino and Jody Clements and Michael Cook and Octave Duclos and Christopher R Dunne and Kelli Fairbanks and Siqi Fang and Samantha Finley-May and Audrey Francis and Reed George and Marina Gkantia and Kyle Harrington and Gary Patrick Hopkins and Joseph Hsu and Philip M Hubbard and Alexandre Javier and Dagmar Kainmueller and Wyatt Korff and Julie Kovalyak and Dominik Krzeminski and Shirley A Lauchie and Alanna Lohff and Charli Maldonado and Emily A Manley and Caroline Mooney and Erika Neace and Matthew Nichols and Omotara Ogundeyi and Nneoma Okeoma and Tyler Paterson and Elliott Phillips and Emily M Phillips and Caitlin Ribeiro and Sean M Ryan and Jon Thomson Rymer and Anne K Scott and Ashley L Scott and David Shepherd and Aya Shinomiya and Claire Smith and Alia Suleiman and Satoko Takemura and Iris Talebi and Imaan F M Tamimi and Eric T Trautman and Lowell Umayam and John J Walsh and Tansy Yang and Gerald M Rubin and Louis K Scheffer and Jan Funke and Stephan Saalfeld and Harald F Hess and Stephen M Plaza and Gwyneth M Card and Gregory S X E Jefferis and Stuart Berg} } @article {68801, title = {Systematic annotation of a complete adult male Drosophila nerve cord connectome reveals principles of functional organisation}, journal = {bioRxiv}, year = {2023}, month = {2023 Jun 06}, abstract = {

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 neuprint.janelia.org web application and also serve as the basis of programmatic analysis of the connectome through dedicated tools that we describe in this paper.

}, doi = {10.1101/2023.06.05.543407}, url = {https://doi.org/10.1101/2023.06.05.543407}, author = {Elizabeth C Marin and Billy J Morris and Tomke Stuerner and Andrew S Champion and Dominik Krzeminski and Griffin Badalamente and Marina Gkantia and Imaan F M Tamimi and Siqi Fang and Sung Soo Moon and Han S J Cheong and Feng Li and Philipp Schlegel and Stuart Berg and FlyEM Project Team and Gwyneth M Card and Marta Costa and David Shepherd and Gregory S X E Jefferis} } @article {68520, title = {The connectome of an insect brain}, journal = {bioRxiv}, year = {2022}, month = {2022 Nov 28}, abstract = {

Brains contain networks of interconnected neurons, so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an insect brain (Drosophila\ larva) with rich behavior, including learning, value-computation, and action-selection, comprising 3,013 neurons and 544,000 synapses. We characterized neuron-types, hubs, feedforward and feedback pathways, and cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain{\textquoteright}s most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled powerful machine learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

}, doi = {10.1101/2022.11.28.516756}, url = {https://doi.org/10.1101/2022.11.28.516756}, author = {Michael Winding and Benjamin D. Pedigo and Christopher L. Barnes and Heather G. Patsolic and Youngser Park and Tom Kazimiers and Akira Fushiki and Ingrid V. Andrade and Avinash Khandelwal and Javier Valdes-Aleman and Feng Li and Nadine Randel and Elizabeth Barsotti and Ana Correia and Richard D. Fetter and Volker Hartenstein and Carey E. Priebe and Joshua T. Vogelstein and Albert Cardona and Marta Zlatic} } @article {65001, title = {Automated reconstruction of a serial-section EM drosophila brain with flood-filling networks and local realignment.}, journal = {bioRxiv}, year = {2019}, month = {2019 Apr 11}, abstract = {

Reconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain\ Drosophila\ ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections and dynamically adjust image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM\ Drosophila\ brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.

}, doi = {10.1101/605634}, author = {Peter H. Li and Larry F. Lindsey and Micha{\l} Januszewski and Zhihao Zheng and Alexander Shakeel Bates and Istv{\'a}n Taisz and Mike Tyka and Matthew Nichols and Feng Li and Eric Perlman and Jeremy Maitin-Shepard and Tim Blakely and Laramie Leavitt and Gregory S.X.E. Jefferis and Davi Bock and Viren Jain} }