TY - JOUR A1 - Shewarega, Michael A1 - Troidl, Jakob A1 - Alvarado Rodriguez, Oliver A1 - Dindoost, Mohammad A1 - Harth, Philipp A1 - Haberkern, Hannah A1 - Stegmaier, Johannes A1 - Bader, David A1 - Pfister, Hanspeter T1 - MoMo - Combining Neuron Morphology and Connectivity for Interactive Motif Analysis in Connectomes JF - IEEE Transactions on Visualization and Computer Graphics N2 - Connectomics, a subfield of neuroscience, reconstructs structural and functional brain maps at synapse-level resolution. These complex spatial maps consist of tree-like neurons interconnected by synapses. Motif analysis is a widely used method for identifying recurring subgraph patterns in connectomes. These motifs, thus, potentially represent fundamental units of information processing. However, existing computational tools often oversimplify neurons as mere nodes in a graph, disregarding their intricate morphologies. In this paper, we introduce MoMo, a novel interactive visualization framework for analyzing neuron morphology-aware motifs in large connectome graphs. First, we propose an advanced graph data structure that integrates both neuronal morphology and synaptic connectivity. This enables highly efficient, parallel subgraph isomorphism searches, allowing for interactive morphological motif queries. Second, we develop a sketch-based interface that facilitates the intuitive exploration of morphology-based motifs within our new data structure. Users can conduct interactive motif searches on state-of-the-art connectomes and visualize results as interactive 3D renderings. We present a detailed goal and task analysis for motif exploration in connectomes, incorporating neuron morphology. Finally, we evaluate MoMo through case studies with four domain experts, who asses the tool’s usefulness and effectiveness in motif exploration, and relevance to real-world neuroscience research. The source code for MoMo is available here: https://github.com/VCG/momo. Y1 - 2025 U6 - https://doi.org/10.1109/TVCG.2025.3634808 ER - TY - JOUR A1 - Petkova, Mariela D. A1 - Januszewski, Michał A1 - Blakely, Tim A1 - Herrera, Kristian J. A1 - Schuhknecht, Gregor F.P. A1 - Tiller, Robert A1 - Choi, Jinhan A1 - Schalek, Richard L. A1 - Boulanger-Weill, Jonathan A1 - Peleg, Adi A1 - Wu, Yuelong A1 - Wang, Shuohong A1 - Troidl, Jakob A1 - Vohra, Sumit Kumar A1 - Wei, Donglai A1 - Lin, Zudi A1 - Bahl, Armin A1 - Tapia, Juan Carlos A1 - Iyer, Nirmala A1 - Miller, Zachary T. A1 - Hebert, Kathryn B. A1 - Pavarino, Elisa C. A1 - Taylor, Milo A1 - Deng, Zixuan A1 - Stingl, Moritz A1 - Hockling, Dana A1 - Hebling, Alina A1 - Wang, Ruohong C. A1 - Zhang, Lauren L. A1 - Dvorak, Sam A1 - Faik, Zainab A1 - King, Jr., Kareem I. A1 - Goel, Pallavi A1 - Wagner-Carena, Julian A1 - Aley, David A1 - Chalyshkan, Selimzhan A1 - Contreas, Dominick A1 - Li, Xiong A1 - Muthukumar, Akila V. A1 - Vernaglia, Marina S. A1 - Carrasco, Teodoro Tapia A1 - Melnychuck, Sofia A1 - Yan, TingTing A1 - Dalal, Ananya A1 - DiMartino, James A1 - Brown, Sam A1 - Safo-Mensa, Nana A1 - Greenberg, Ethan A1 - Cook, Michael A1 - Finley, Samantha A1 - Flynn, Miriam A. A1 - Hopkins, Gary Patrick A1 - Kovalyak, Julie A1 - Leonard, Meghan A1 - Lohff, Alanna A1 - Ordish, Christopher A1 - Scott, Ashley L. A1 - Takemura, Satoko A1 - Smith, Claire A1 - Walsh, John J. A1 - Berger, Daniel R. A1 - Pfister, Hanspeter A1 - Berg, Stuart A1 - Knecht, Christopher A1 - Meissner, Geoffrey W. A1 - Korff, Wyatt A1 - Ahrens, Misha B A1 - Jain, Viren A1 - Lichtman, Jeff W. A1 - Engert, Florian T1 - A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain JF - bioRxiv N2 - 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’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. Y1 - 2025 U6 - https://doi.org/10.1101/2025.06.10.658982 ER - TY - CHAP A1 - Harth, Philipp A1 - Bast, Arco A1 - Troidl, Jakob A1 - Meulemeester, Bjorge A1 - Pfister, Hanspeter A1 - Beyer, Johanna A1 - Oberlaender, Marcel A1 - Hege, Hans-Christian A1 - Baum, Daniel T1 - Rapid Prototyping for Coordinated Views of Multi-scale Spatial and Abstract Data: A Grammar-based Approach T2 - Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM) N2 - Visualization grammars are gaining popularity as they allow visualization specialists and experienced users to quickly create static and interactive views. Existing grammars, however, mostly focus on abstract views, ignoring three-dimensional (3D) views, which are very important in fields such as natural sciences. We propose a generalized interaction grammar for the problem of coordinating heterogeneous view types, such as standard charts (e.g., based on Vega-Lite) and 3D anatomical views. An important aspect of our web-based framework is that user interactions with data items at various levels of detail can be systematically integrated and used to control the overall layout of the application workspace. With the help of a concise JSON-based specification of the intended workflow, we can handle complex interactive visual analysis scenarios. This enables rapid prototyping and iterative refinement of the visual analysis tool in collaboration with domain experts. We illustrate the usefulness of our framework in two real-world case studies from the field of neuroscience. Since the logic of the presented grammar-based approach for handling interactions between heterogeneous web-based views is free of any application specifics, it can also serve as a template for applications beyond biological research. Y1 - 2023 U6 - https://doi.org/10.2312/vcbm.20231218 ER - TY - CHAP A1 - Pfister, Hanspeter A1 - Kaynig, Verena A1 - Botha, Charl P. A1 - Bruckner, Stefan A1 - Dercksen, Vincent J. A1 - Hege, Hans-Christian A1 - Roerdink, Jos B.T.M. ED - Hansen, Charles D. ED - Chen, Min ED - Johnson, Christopher R. ED - Kaufman, Arie E. ED - Hagen, Hans T1 - Visualization in Connectomics T2 - Scientific Visualization - Uncertainty, Multifield, Biomedical, and Scalable Visualization N2 - Connectomics is a branch of neuroscience that attempts to create a connectome, i.e., a complete map of the neuronal system and all connections between neuronal structures. This representation can be used to understand how functional brain states emerge from their underlying anatomical structures and how dysfunction and neuronal diseases arise. We review the current state-of-the-art of visualization and image processing techniques in the field of connectomics and describe a number of challenges. After a brief summary of the biological background and an overview of relevant imaging modalities, we review current techniques to extract connectivity information from image data at macro-, meso- and microscales. We also discuss data integration and neural network modeling, as well as the visualization, analysis and comparison of brain networks. Y1 - 2014 SN - 978-1-4471-6496-8 U6 - https://doi.org/10.1007/978-1-4471-6497-5_21 SP - 221 EP - 245 PB - Springer ER - TY - GEN A1 - Pfister, Hanspeter A1 - Kaynig, Verena A1 - Botha, Charl A1 - Bruckner, Stefan A1 - Dercksen, Vincent J. A1 - Hege, Hans-Christian A1 - Roerdink, Jos T1 - Visualization in Connectomics Y1 - 2012 U6 - https://doi.org/10.1007/978-1-4471-6497-5_21 ER -