@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 {46325, title = {Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics.}, journal = {Nature Methods}, volume = {12}, year = {2015}, month = {2015 Feb 16}, pages = {319-22}, abstract = {

Focused-ion-beam scanning electron microscopy (FIB-SEM) has become an essential tool for studying neural tissue at resolutions below 10 nm {\texttimes} 10 nm {\texttimes} 10 nm, producing data sets optimized for automatic connectome tracing. We present a technical advance, ultrathick sectioning, which reliably subdivides embedded tissue samples into chunks (20 μm thick) optimally sized and mounted for efficient, parallel FIB-SEM imaging. These chunks are imaged separately and then {\textquoteright}volume stitched{\textquoteright} back together, producing a final three-dimensional data set suitable for connectome tracing.

}, issn = {1548-7105}, doi = {10.1038/nmeth.3292}, author = {Hayworth, Kenneth J and Xu, C Shan and Lu, Zhiyuan and Knott, Graham W and Fetter, Richard D and Tapia, Juan Carlos and Lichtman, Jeff W and Hess, Harald F} } @conference {3858, title = {Boundary learning by optimization with topological constraints.}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2010}, month = {2010}, abstract = {

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.

}, author = {Jain, Viren and Bollmann, Benjamin and Richardson, Mark and Berger, Daniel R. and Helmstaedter, Moritz N. and Briggman, Kevin L and Denk, Winfried and Bowden, Jared B. and Mendenhall, John M. and Abraham, Wickliffe C. and Harris, Kristen M and Kasthuri, Narayanan and Hayworth, Ken J. and Schalek, Richard and Tapia, Juan Carlos and Lichtman, Jeff W. and Seung, Hyunjune Sebastian} }