@article {69997, title = {A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain}, journal = {bioRxiv}, year = {2025}, month = {2025 Jun 16}, abstract = {

We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing \>180,000 segmented soma, \>40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset{\textquoteright}s utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.

Preprint: https://www.biorxiv.org/content/early/2025/06/15/2025.06.10.658982

}, doi = {10.1101/2025.06.10.658982}, url = {https://www.biorxiv.org/content/early/2025/06/15/2025.06.10.658982}, author = {Petkova, Mariela D. and Januszewski, Micha{\l} and Blakely, Tim and Herrera, Kristian J. and Schuhknecht, Gregor F.P. and Tiller, Robert and Choi, Jinhan and Schalek, Richard L. and Boulanger-Weill, Jonathan and Peleg, Adi and Wu, Yuelong and Wang, Shuohong and Troidl, Jakob and Kumar Vohra, Sumit and Wei, Donglai and Lin, Zudi and Bahl, Armin and Tapia, Juan Carlos and Iyer, Nirmala and Miller, Zachary T. and Hebert, Kathryn B. and Pavarino, Elisa C. and Taylor, Milo and Deng, Zixuan and Stingl, Moritz and Hockling, Dana and Hebling, Alina and Wang, Ruohong C. and Zhang, Lauren L. and Dvorak, Sam and Faik, Zainab and King, Kareem I. and Goel, Pallavi and Wagner-Carena, Julian and Aley, David and Chalyshkan, Selimzhan and Contreas, Dominick and Li, Xiong and Muthukumar, Akila V. and Vernaglia, Marina S. and Carrasco, Teodoro Tapia and Melnychuck, Sofia and Yan, TingTing and Dalal, Ananya and DiMartino, James M. and Brown, Sam and Safo-Mensa, Nana and Greenberg, Ethan and Cook, Michael and Finley-May, Samantha and Flynn, Miriam A. and Hopkins, Gary Patrick and Kovalyak, Julie and Leonard, Meghan and Lohff, Alanna and Ordish, Christopher and Scott, Ashley L. and Takemura, Satoko and Walsh, Claire and Walsh, John J. and Berger, Daniel R. and Pfister, Hanspeter and Berg, Stuart and Knecht, Christopher and Meissner, Geoffrey W. and Korff, Wyatt and Ahrens, Misha B. and Jain, Viren and Lichtman, Jeff W. and Engert, Florian} } @article {70030, title = {Transforming descending input into motor output: An analysis of the Drosophila Male Adult Nerve Cord connectome}, journal = {eLife}, year = {2025}, month = {2025 Jul 21}, abstract = {

In most animals, a relatively small number of descending neurons (DNs) connect higher brain centers in the animal{\textquoteright}s head to circuits and motor neurons (MNs) in the nerve cord of the animal{\textquoteright}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., 2024)), including cell type and developmental lineage annotation (Marin et al., 2024), 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.

}, doi = {10.7554/elife.96084.2}, url = {http://dx.doi.org/10.7554/eLife.96084.2}, author = {Cheong, Han Sj and Eichler, Katharina and St{\"u}rner, Tomke and Asinof, Samuel K and Champion, Andrew S and Marin, Elizabeth C and Oram, Tess B and Sumathipala, Marissa and Venkatasubramanian, Lalanti and Namiki, Shigehiro and Siwanowicz, Igor and Costa, Marta and Berg, Stuart and Jefferis, Gregory Sxe and Card, Gwyneth M} } @article {68272, title = {neuPrint: An open access tool for EM connectomics.}, journal = {Frontiers in Neuroinformatics}, volume = {16}, year = {2022}, month = {2022 Jul 20}, pages = {896292}, abstract = {

Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication-it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components-a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research.

}, issn = {1662-5196}, doi = {10.3389/fninf.2022.896292}, author = {Plaza, Stephen M and Clements, Jody and Dolafi, Tom and Umayam, Lowell and Neubarth, Nicole N and Scheffer, Louis K and Berg, Stuart} } @article {68507, title = {A connectome and analysis of the adult Drosophila central brain.}, journal = {Elife}, volume = {9}, year = {2020}, month = {2020 Sep 07}, abstract = {

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{\textquoteright}s brain.

}, keywords = {Animals, Brain, Connectome, Drosophila melanogaster, Female, Male, Neurons, Synapses}, issn = {2050-084X}, doi = {10.7554/eLife.57443}, author = {Scheffer, Louis K and Xu, C Shan and Januszewski, Michal and Lu, Zhiyuan and Takemura, Shin-ya and Hayworth, Kenneth J and Huang, Gary B and Shinomiya, Kazunori and Maitlin-Shepard, Jeremy and Berg, Stuart and Clements, Jody and Hubbard, Philip M and Katz, William T and Umayam, Lowell and Zhao, Ting and Ackerman, David and Blakely, Tim and Bogovic, John and Dolafi, Tom and Kainmueller, Dagmar and Kawase, Takashi and Khairy, Khaled A and Leavitt, Laramie and Li, Peter H and Lindsey, Larry and Neubarth, Nicole and Olbris, Donald J and Otsuna, Hideo and Trautman, Eric T and Ito, Masayoshi and Bates, Alexander S and Goldammer, Jens and Wolff, Tanya and Svirskas, Robert and Schlegel, Philipp and Neace, Erika and Knecht, Christopher J and Alvarado, Chelsea X and Bailey, Dennis A and Ballinger, Samantha and Borycz, Jolanta A and Canino, Brandon S and Cheatham, Natasha and Cook, Michael and Dreher, Marisa and Duclos, Octave and Eubanks, Bryon and Fairbanks, Kelli and Finley, Samantha and Forknall, Nora and Francis, Audrey and Hopkins, Gary Patrick and Joyce, Emily M and Kim, SungJin and Kirk, Nicole A and Kovalyak, Julie and Lauchie, Shirley A and Lohff, Alanna and Maldonado, Charli and Manley, Emily A and McLin, Sari and Mooney, Caroline and Ndama, Miatta and Ogundeyi, Omotara and Okeoma, Nneoma and Ordish, Christopher and Padilla, Nicholas and Patrick, Christopher M and Paterson, Tyler and Phillips, Elliott E and Phillips, Emily M and Rampally, Neha and Ribeiro, Caitlin and Robertson, Madelaine K and Rymer, Jon Thomson and Ryan, Sean M and Sammons, Megan and Scott, Anne K and Scott, Ashley L and Shinomiya, Aya and Smith, Claire and Smith, Kelsey and Smith, Natalie L and Sobeski, Margaret A and Suleiman, Alia and Swift, Jackie and Takemura, Satoko and Talebi, Iris and Tarnogorska, Dorota and Tenshaw, Emily and Tokhi, Temour and Walsh, John J and Yang, Tansy and Horne, Jane Anne and Li, Feng and Parekh, Ruchi and Rivlin, Patricia K and Jayaraman, Vivek and Costa, Marta and Jefferis, Gregory Sxe and Ito, Kei and Saalfeld, Stephan and George, Reed and Meinertzhagen, Ian A and Rubin, Gerald M and Hess, Harald F and Jain, Viren and Plaza, Stephen M} } @article {65178, title = {ilastik: interactive machine learning for (bio)image analysis.}, journal = {Nature Methods}, volume = {16}, year = {2019}, month = {2019 Sep 30}, pages = {1226-32}, abstract = {

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

}, issn = {1548-7105}, doi = {10.1038/s41592-019-0582-9}, author = {Berg, Stuart and Kutra, Dominik and Kroeger, Thorben and Straehle, Christoph N and Kausler, Bernhard X and Haubold, Carsten and Schiegg, Martin and Ales, Janez and Beier, Thorsten and Rudy, Markus and Eren, Kemal and Cervantes, Jaime I and Xu, Buote and Beuttenmueller, Fynn and Wolny, Adrian and Zhang, Chong and Koethe, Ullrich and Hamprecht, Fred A and Kreshuk, Anna} } @article {48658, title = {A connectome of a learning and memory center in the adult Drosophila brain.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 Jul 18}, pages = {e26975}, abstract = {

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In\ Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB{\textquoteright}s α lobe, using a dataset of isotropic 8-nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple\ en passant\ synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only six percent of KC\>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC\>DAN and DAN\>MBON. DAN activation produces a slow depolarization of the MBON in these DAN\>MBON synapses and can weaken memory recall.

}, doi = {10.7554/eLife.26975}, author = {Takemura, Shin-ya and Aso, Yoshinori and Hige, Toshihide and Wong, Allan M and Lu, Zhiyuan and Xu, C Shan and Rivlin, Patricia K and Hess, Harald F and Zhao, Ting and Parag, Toufiq and Berg, Stuart and Huang, Gary and Katz, William T and Olbris, Donald J and Plaza, Stephen M and Umayam, Lowell A and Aniceto, Roxanne and Chang, Lei-Ann and Lauchie, Shirley and et al} } @article {48254, title = {Multicut brings automated neurite segmentation closer to human performance.}, journal = {Nature Methods}, volume = {14}, year = {2017}, month = {2017 Jan 31}, pages = {101-102}, issn = {1548-7105}, doi = {10.1038/nmeth.4151}, author = {Beier, Thorsten and Pape, Constantin and Rahaman, Nasim and Prange, Timo and Berg, Stuart and Bock, Davi D and Cardona, Albert and Knott, Graham W and Plaza, Stephen M and Scheffer, Louis K and Koethe, Ullrich and Kreshuk, Anna and Hamprecht, Fred A} } @article {47537, title = {Rapid and semi-automated extraction of neuronal cell bodies and nuclei from electron microscopy image stacks.}, journal = {Methods in molecular biology (Clifton, N.J.)}, volume = {1427}, year = {2016}, month = {2016}, pages = {277-90}, abstract = {

Connectomics-the study of how neurons wire together in the brain-is at the forefront of modern neuroscience research. However, many connectomics studies are limited by the time and precision needed to correctly segment large volumes of electron microscopy (EM) image data. We present here a semi-automated segmentation pipeline using freely available software that can significantly decrease segmentation time for extracting both nuclei and cell bodies from EM image volumes.

}, issn = {1940-6029}, doi = {10.1007/978-1-4939-3615-1_16}, author = {Holcomb, Paul S and Morehead, Michael and Doretto, Gianfranco and Chen, Peter and Berg, Stuart and Plaza, Stephen and Spirou, George} } @article {47511, title = {Segmenting and Tracking Multiple Dividing Targets Using ilastik.}, journal = {Advances in anatomy, embryology, and cell biology}, volume = {219}, year = {2016}, month = {2016 May 21}, pages = {199-229}, abstract = {

Tracking crowded cells or other targets in biology is often a challenging task due to poor signal-to-noise ratio, mutual occlusion, large displacements, little discernibility, and the ability of cells to divide. We here present an open source implementation of conservation tracking (Schiegg et\ al., IEEE international conference on computer vision (ICCV). IEEE, New York, pp\ 2928-2935, 2013) in the ilastik software framework. This robust tracking-by-assignment algorithm explicitly makes allowance for false positive detections, undersegmentation, and cell division. We give an overview over the underlying algorithm and parameters, and explain the use for a light sheet microscopy sequence of a Drosophila embryo. Equipped with this knowledge, users will be able to track targets of interest in their own data.

}, issn = {0301-5556}, doi = {10.1007/978-3-319-28549-8_8}, author = {Haubold, Carsten and Schiegg, Martin and Kreshuk, Anna and Berg, Stuart and Koethe, Ullrich and Hamprecht, Fred A} }