@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 {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 {6261, title = {Connectomic reconstruction of the inner plexiform layer in the mouse retina.}, journal = {Nature}, volume = {500}, year = {2013}, month = {2013 Aug 8}, pages = {168-74}, abstract = {

Comprehensive high-resolution structural maps are central to functional exploration and understanding in biology. For the nervous system, in which high resolution and large spatial extent are both needed, such maps are scarce as they challenge data acquisition and analysis capabilities. Here we present for the mouse inner plexiform layer{\textendash}the main computational neuropil region in the mammalian retina{\textendash}the dense reconstruction of 950 neurons and their mutual contacts. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. We characterize a new type of retinal bipolar interneuron and show that we can subdivide a known type based on connectivity. Circuit motifs that emerge from our data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive.

}, keywords = {Amacrine Cells, Animals, Biological, Cell Communication, Computer-Assisted, Connectome, Electron, Image Processing, Inbred C57BL, Mice, Microscopy, Models, Neuropil, Retina, Retinal Ganglion Cells}, issn = {1476-4687}, doi = {10.1038/nature12346}, author = {Helmstaedter, Moritz and Briggman, Kevin L and Turaga, Srinivas C and Jain, Viren and Seung, H Sebastian and Denk, Winfried} } @article {3844, title = {Convolutional networks can learn to generate affinity graphs for image segmentation.}, journal = {Neural Computation}, volume = {22}, year = {2010}, month = {2010 Feb}, pages = {511-38}, abstract = {

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Electron, Image Processing, Mathematical Computing, Mathematical Concepts, Mathematics, Microscopy, Neural Networks (Computer), Pattern Recognition}, issn = {1530-888X}, doi = {10.1162/neco.2009.10-08-881}, author = {Turaga, Srinivas C and Murray, Joseph F and Jain, Viren and Roth, Fabian and Helmstaedter, Moritz and Briggman, Kevin and Denk, Winfried and Seung, Hyunjune Sebastian} }