TY - JOUR A1 - Longren, Luke L. A1 - Eigen, Lennart A1 - Shubitidze, Ani A1 - Lieschnegg, Oliver A1 - Baum, Daniel A1 - Nyakatura, John A. A1 - Hildebrandt, Thomas A1 - Brecht, Michael T1 - Dense Reconstruction of Elephant Trunk Musculature JF - Current Biology N2 - The elephant trunk operates as a muscular hydrostat and is actuated by the most complex musculature known in animals. Because the number of trunk muscles is unclear, we performed dense reconstructions of trunk muscle fascicles, elementary muscle units, from microCT scans of an Asian baby elephant trunk. Muscle architecture changes markedly across the trunk. Trunk tip and finger consist of about 8,000 extraordinarily filigree fascicles. The dexterous finger consists exclusively of microscopic radial fascicles pointing to a role of muscle miniaturization in elephant dexterity. Radial fascicles also predominate (at 82% volume) the remainder of the trunk tip and we wonder if radial muscle fascicles are of particular significance for fine motor control of the dexterous trunk tip. By volume, trunk-shaft muscles comprise one-third of the numerous, small radial muscle fascicles, two-thirds of the three subtypes of large longitudinal fascicles (dorsal longitudinals, ventral outer obliques, and ventral inner obliques), and a small fraction of transversal fascicles. Shaft musculature is laterally, but not radially, symmetric. A predominance of dorsal over ventral radial muscles and of ventral over dorsal longitudinal muscles may result in a larger ability of the shaft to extend dorsally than ventrally and to bend inward rather than outward. There are around 90,000 trunk muscle fascicles. While primate hand control is based on fine control of contraction by the convergence of many motor neurons on a small set of relatively large muscles, evolution of elephant grasping has led to thousands of microscopic fascicles, which probably outnumber facial motor neurons. Y1 - 2023 U6 - https://doi.org/10.1016/j.cub.2023.09.007 VL - 33 SP - 1 EP - 8 ER - TY - JOUR A1 - Kiewisz, Robert A1 - Baum, Daniel A1 - Müller-Reichert, Thomas A1 - Fabig, Gunar T1 - Serial-section electron tomography and quantitative analysis of the microtubule organization in 3D-reconstructed mitotic spindles JF - Bio-protocol Y1 - 2023 U6 - https://doi.org/10.21769/BioProtoc.4849 VL - 13 IS - 20 ER - TY - JOUR A1 - Sterzik, Anna A1 - Lichtenberg, Nils A1 - Krone, Michael A1 - Baum, Daniel A1 - Cunningham, Douglas W. A1 - Lawonn, Kai T1 - Enhancing molecular visualization: Perceptual evaluation of line variables with application to uncertainty visualization JF - Computers & Graphics N2 - Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the user’s trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Thus, we conducted two perceptual studies to determine the suitability of the line variables blur, dashing, grayscale, sketchiness, and width for distinguishing several values in molecular visualizations. While our work was motivated by uncertainty visualization, our techniques and study results also apply to other types of scalar data. Y1 - 2023 U6 - https://doi.org/10.1016/j.cag.2023.06.006 VL - 114 SP - 401 EP - 413 ER - TY - JOUR A1 - Boelts, Jan A1 - Harth, Philipp A1 - Gao, Richard A1 - Udvary, Daniel A1 - Yanez, Felipe A1 - Baum, Daniel A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel A1 - Macke, Jakob H. T1 - Simulation-based inference for efficient identification of generative models in computational connectomics JF - PLOS Computational Biology N2 - Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data. Y1 - 2023 U6 - https://doi.org/10.1371/journal.pcbi.1011406 VL - 19 IS - 9 ER - TY - JOUR A1 - Fogalli, Giovani Bressan A1 - Peres Line, Sérgio Roberto A1 - Baum, Daniel T1 - Segmentation of tooth enamel microstructure images using classical image processing and U-Net approaches JF - Frontiers in Imaging N2 - Tooth enamel is the hardest tissue in human organism, formed by prism layers in regularly alternating directions. These prisms form the Hunter-Schreger Bands (HSB) pattern when under side illumination, which is composed of light and dark stripes resembling fingerprints. We have shown in previous works that HSB pattern is highly variable, seems to be unique for each tooth and can be used as a biometric method for human identification. Since this pattern cannot be acquired with sensors, the HSB region in the digital photograph must be identified and correctly segmented from the rest of the tooth and background. Although these areas can be manually removed, this process is not reliable as excluded areas can vary according to the individual‘s subjective impression. Therefore, the aim of this work was to develop an algorithm that automatically selects the region of interest (ROI), thus, making the entire biometric process straightforward. We used two different approaches: a classical image processing method which we called anisotropy-based segmentation (ABS) and a machine learning method known as U-Net, a fully convolutional neural network. Both approaches were applied to a set of extracted tooth images. U-Net with some post processing outperformed ABS in the segmentation task with an Intersection Over Union (IOU) of 0.837 against 0.766. Even with a small dataset, U-Net proved to be a potential candidate for fully automated in-mouth application. However, the ABS technique has several parameters which allow a more flexible segmentation with interactive adjustments specific to image properties. Y1 - 2023 U6 - https://doi.org/10.3389/fimag.2023.1215764 VL - 2 ER - TY - JOUR A1 - Zemann, Berit A1 - Le, Mai-Lee Van A1 - Sherlock, Rob E. A1 - Baum, Daniel A1 - Katija, Kakani A1 - Stach, Thomas T1 - Evolutionary traces of miniaturization in a giant – Comparative anatomy of brain and brain nerves in Bathochordaeus stygius (Tunicata, Appendicularia) JF - Journal of Morphology Y1 - 2023 U6 - https://doi.org/10.1002/jmor.21598 VL - 284 IS - 7 ER - TY - JOUR A1 - Boelts, Jan A1 - Harth, Philipp A1 - Gao, Richard A1 - Udvary, Daniel A1 - Yanez, Felipe A1 - Baum, Daniel A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel A1 - Macke, Jakob H T1 - Simulation-based inference for efficient identification of generative models in connectomics JF - bioRxiv N2 - Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neural networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a single rule to fit the empirical data, SBI considers many parametrizations of a wiring rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rules and relies on machine learning methods to estimate a probability distribution (the `posterior distribution over rule parameters conditioned on the data') that characterizes all data-compatible rules. We demonstrate how to apply SBI in connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-89890 ER - TY - GEN A1 - Vohra, Sumit Kumar A1 - Harth, Philipp A1 - Isoe, Yasuko A1 - Bahl, Armin A1 - Fotowat, Haleh A1 - Engert, Florian A1 - Hege, Hans-Christian A1 - Baum, Daniel T1 - A Visual Interface for Exploring Hypotheses about Neural Circuits N2 - One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa. T3 - ZIB-Report - 23-07 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-89932 SN - 1438-0064 ER - TY - JOUR A1 - Tomholt, Lara A1 - Baum, Daniel A1 - Wood, Robert J. A1 - Weaver, James C. T1 - High-throughput segmentation, data visualization, and analysis of sea star skeletal networks JF - Journal of Structural Biology N2 - The remarkably complex skeletal systems of the sea stars (Echinodermata, Asteroidea), consisting of hundreds to thousands of individual elements (ossicles), have intrigued investigators for more than 150 years. While the general features and structural diversity of isolated asteroid ossicles have been well documented in the literature, the task of mapping the spatial organization of these constituent skeletal elements in a whole-animal context represents an incredibly laborious process, and as such, has remained largely unexplored. To address this unmet need, particularly in the context of understanding structure-function relationships in these complex skeletal systems, we present an integrated approach that combines micro-computed tomography, semi-automated ossicle segmentation, data visualization tools, and the production of additively manufactured tangible models to reveal biologically relevant structural data that can be rapidly analyzed in an intuitive manner. In the present study, we demonstrate this high-throughput workflow by segmenting and analyzing entire skeletal systems of the giant knobby star, Pisaster giganteus, at four different stages of growth. The in-depth analysis, presented herein, provides a fundamental understanding of the three-dimensional skeletal architecture of the sea star body wall, the process of skeletal maturation during growth, and the relationship between skeletal organization and morphological characteristics of individual ossicles. The widespread implementation of this approach for investigating other species, subspecies, and growth series has the potential to fundamentally improve our understanding of asteroid skeletal architecture and biodiversity in relation to mobility, feeding habits, and environmental specialization in this fascinating group of echinoderms. Y1 - 2023 U6 - https://doi.org/10.1016/j.jsb.2023.107955 VL - 215 IS - 2 SP - 107955 ER - TY - JOUR A1 - Schmitt, Kira A1 - Titschack, Jürgen A1 - Baum, Daniel T1 - CoDA: Interactive Segmentation and Morphological Analysis of Dendroid Structures Exemplified on Stony Cold-Water Corals N2 - Dendroid stony corals build highly complex colonies that develop from a single coral polyp sitting in a cup-like skeleton, called corallite, by asexual reproduction, resulting in a tree-like branching pattern of its skeleton. Despite their beauty and ecological importance as reef builders in tropical shallow-water reefs as well as in cold-water coral mounds in the deep ocean, systematic studies investigating the ontogenetic morphological development of such coral colonies are largely missing. One reason for this is the sheer number of corallites – up to several thousands in a single coral colony. Another limiting factor, especially for the analysis of dendroid cold-water corals, is the existence of many secondary joints in the ideally tree-like structure that make a reconstruction of the skeleton tree extremely tedious. Herein, we present CoDA, the Coral Dendroid structure Analyzer, a visual analytics suite that allows for the first time to investigate the ontogenetic morphological development of complex dendroid coral colonies, exemplified on three important framework-forming dendroid cold-water corals: Lophelia pertusa (Linnaeus, 1758), Madrepora oculata (Linnaeus, 1758), and Goniocorella dumosa (Alcock, 1902). Input to CoDA is an initial instance segmentation of the coral polyp cavities (calices), from which it estimates the skeleton tree of the colony and extracts classical morphological measurements and advanced shape features of the individual corallites. CoDA also works as a proofreading and error correction tool by helping to identify wrong parts in the skeleton tree and providing tools to quickly correct these errors. The final skeleton tree enables the derivation of additional information about the calices/corallite instances that otherwise could not be obtained, including their ontogenetic generation and branching patterns – the basis of a fully quantitative statistical analysis of the coral colony morphology. Part of CoDA is CoDA.Graph, a feature-rich link-and-brush user interface for visualizing the extracted features and 2D graph layouts of the skeleton tree, enabling the real-time exploration of complex coral colonies and their building blocks, the individual corallites and branches. In the future, we expect CoDA to greatly facilitate the analysis of large stony corals of different species and morphotypes, as well as other dendroid structures, enabling new insights into the influence of genetic and environmental factors on their ontogenetic morphological development. Y1 - 2024 ER -