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 - Hanik, Martin A1 - Ducke, Benjamin A1 - Hege, Hans-Christian A1 - Fless, Friederike A1 - von Tycowicz, Christoph T1 - Intrinsic shape analysis in archaeology: A case study on ancient sundials JF - Journal on Computing and Cultural Heritage N2 - The fact that the physical shapes of man-made objects are subject to overlapping influences—such as technological, economic, geographic, and stylistic progressions—holds great information potential. On the other hand, it is also a major analytical challenge to uncover these overlapping trends and to disentagle them in an unbiased way. This paper explores a novel mathematical approach to extract archaeological insights from ensembles of similar artifact shapes. We show that by considering all shape information in a find collection, it is possible to identify shape patterns that would be difficult to discern by considering the artifacts individually or by classifying shapes into predefined archaeological types and analyzing the associated distinguishing characteristics. Recently, series of high-resolution digital representations of artifacts have become available. Such data sets enable the application of extremely sensitive and flexible methods of shape analysis. We explore this potential on a set of 3D models of ancient Greek and Roman sundials, with the aim of providing alternatives to the traditional archaeological method of “trend extraction by ordination” (typology). In the proposed approach, each 3D shape is represented as a point in a shape space—a high-dimensional, curved, non-Euclidean space. Proper consideration of its mathematical properties reduces bias in data analysis and thus improves analytical power. By performing regression in shape space, we find that for Roman sundials, the bend of the shadow-receiving surface of the sundials changes with the latitude of the location. This suggests that, apart from the inscribed hour lines, also a sundial’s shape was adjusted to the place of installation. As an example of more advanced inference, we use the identified trend to infer the latitude at which a sundial, whose location of installation is unknown, was placed. We also derive a novel method for differentiated morphological trend assertion, building upon and extending the theory of geometric statistics and shape analysis. Specifically, we present a regression-based method for statistical normalization of shapes that serves as a means of disentangling parameter-dependent effects (trends) and unexplained variability. In addition, we show that this approach is robust to noise in the digital reconstructions of the artifact shapes. Y1 - 2023 U6 - https://doi.org/10.1145/3606698 VL - 16 IS - 4 SP - 1 EP - 26 ER - TY - JOUR A1 - Vohra, Sumit Kumar A1 - Eberle, Maren A1 - Boulanger-Weill, Jonathan A1 - Petkova, Mariela D. A1 - Schuhknecht, Gregor F. P. A1 - Herrera, Kristian J. A1 - Kämpf, Florian A1 - Ruetten, Virginia M. S. A1 - Lichtman, Jeff W. A1 - Engert, Florian A1 - Randlett, Owen A1 - Bahl, Armin A1 - Isoe, Yasuko A1 - Hege, Hans-Christian A1 - Baum, Daniel T1 - Fishexplorer: A multimodal cellular atlas platform for neuronal circuit dissection in larval zebrafish JF - bioRxiv N2 - Understanding how neural circuits give rise to behavior requires comprehensive knowledge of neuronal morphology, connectivity, and function. Atlas platforms play a critical role in enabling the visualization, exploration, and dissemination of such information. Here, we present FishExplorer, an interactive and expandable community platform designed to integrate and analyze multimodal brain data from larval zebrafish. FishExplorer supports datasets acquired through light microscopy (LM), electron microscopy (EM), and X-ray imaging, all co-registered within a unified spatial coordinate system which enables seamless comparison of neuronal morphologies and synaptic connections. To further assist circuit analysis, FishExplorer includes a suite of tools for querying and visualizing connectivity at the whole-brain scale. By integrating data from recent large-scale EM reconstructions (presented in companion studies), FishExplorer enables researchers to validate circuit models, explore wiring principles, and generate new hypotheses. As a continuously evolving resource, FishExplorer is designed to facilitate collaborative discovery and serve the growing needs of the teleost neuroscience community. Y1 - 2025 U6 - https://doi.org/10.1101/2025.07.14.664689 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 - JOUR 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 JF - IEEE Transactions on Visualization and Computer Graphics 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. Y1 - 2024 U6 - https://doi.org/10.1109/TVCG.2023.3243668 VL - 30 IS - 7 SP - 3945 EP - 3958 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 - Herter, Felix A1 - Hege, Hans-Christian A1 - Hadwiger, Markus A1 - Lepper, Verena A1 - Baum, Daniel T1 - Thin-Volume Visualization on Curved Domains JF - Computer Graphics Forum N2 - Thin, curved structures occur in many volumetric datasets. Their analysis using classical volume rendering is difficult because parts of such structures can bend away or hide behind occluding elements. This problem cannot be fully compensated by effective navigation alone, because structure-adapted navigation in the volume is cumbersome and only parts of the structure are visible in each view. We solve this problem by rendering a spatially transformed view into the volume so that an unobscured visualization of the entire curved structure is obtained. As a result, simple and intuitive navigation becomes possible. The domain of the spatial transform is defined by a triangle mesh that is topologically equivalent to an open disc and that approximates the structure of interest. The rendering is based on ray-casting in which the rays traverse the original curved sub-volume. In order to carve out volumes of varying thickness, the lengths of the rays as well as the position of the mesh vertices can be easily modified in a view-controlled manner by interactive painting. We describe a prototypical implementation and demonstrate the interactive visual inspection of complex structures from digital humanities, biology, medicine, and materials science. Displaying the structure as a whole enables simple inspection of interesting substructures in their original spatial context. Overall, we show that transformed views utilizing ray-casting-based volume rendering supported by guiding surface meshes and supplemented by local, interactive modifications of ray lengths and vertex positions, represent a simple but versatile approach to effectively visualize thin, curved structures in volumetric data. Y1 - 2021 U6 - https://doi.org/10.1111/cgf.14296 VL - 40 IS - 3 SP - 147 EP - 157 PB - Wiley-Blackwell Publishing Ltd. CY - United Kingdom ER - TY - JOUR A1 - Fröhler, Bernhard A1 - Elberfeld, Tim A1 - Möller, Torsten A1 - Hege, Hans-Christian A1 - De Beenhouwer, Jan A1 - Sijbers, Jan A1 - Kastner, Johann A1 - Heinzl, Christoph T1 - Analysis and comparison of algorithms for the tomographic reconstruction of curved fibres JF - Nondestructive Testing and Evaluation N2 - We present visual methods for the analysis and comparison of the results of curved fibre reconstruction algorithms, i.e., of algorithms extracting characteristics of curved fibres from X-ray computed tomography scans. In this work, we extend previous methods for the analysis and comparison of results of different fibre reconstruction algorithms or parametrisations to the analysis of curved fibres. We propose fibre dissimilarity measures for such curved fibres and apply these to compare multiple results to a specified reference. We further propose visualisation methods to analyse differences between multiple results quantitatively and qualitatively. In two case studies, we show that the presented methods provide valuable insights for advancing and parametrising fibre reconstruction algorithms, and support in improving their results in characterising curved fibres. Y1 - 2020 U6 - https://doi.org/10.1080/10589759.2020.1774583 VL - 35 IS - 3 SP - 328 EP - 341 ER - TY - JOUR A1 - Mikula, Natalia A1 - Dörffel, Tom A1 - Baum, Daniel A1 - Hege, Hans-Christian T1 - An Interactive Approach for Identifying Structure Definitions JF - Computer Graphics Forum N2 - Our ability to grasp and understand complex phenomena is essentially based on recognizing structures and relating these to each other. For example, any meteorological description of a weather condition and explanation of its evolution recurs to meteorological structures, such as convection and circulation structures, cloud fields and rain fronts. All of these are spatiotemporal structures, defined by time-dependent patterns in the underlying fields. Typically, such a structure is defined by a verbal description that corresponds to the more or less uniform, often somewhat vague mental images of the experts. However, a precise, formal definition of the structures or, more generally, concepts is often desirable, e.g., to enable automated data analysis or the development of phenomenological models. Here, we present a systematic approach and an interactive tool to obtain formal definitions of spatiotemporal structures. The tool enables experts to evaluate and compare different structure definitions on the basis of data sets with time-dependent fields that contain the respective structure. Since structure definitions are typically parameterized, an essential part is to identify parameter ranges that lead to desired structures in all time steps. In addition, it is important to allow a quantitative assessment of the resulting structures simultaneously. We demonstrate the use of the tool by applying it to two meteorological examples: finding structure definitions for vortex cores and center lines of temporarily evolving tropical cyclones. Ideally, structure definitions should be objective and applicable to as many data sets as possible. However, finding such definitions, e.g., for the common atmospheric structures in meteorology, can only be a long-term goal. The proposed procedure, together with the presented tool, is just a first systematic approach aiming at facilitating this long and arduous way. Y1 - 2022 U6 - https://doi.org/10.1111/cgf.14543 VL - 41 IS - 3 SP - 321 EP - 332 ER - TY - JOUR A1 - Nava-Yazdani, Esfandiar A1 - Hege, Hans-Christian A1 - von Tycowicz, Christoph T1 - A Hierarchical Geodesic Model for Longitudinal Analysis on Manifolds JF - Journal of Mathematical Imaging and Vision N2 - In many applications, geodesic hierarchical models are adequate for the study of temporal observations. We employ such a model derived for manifold-valued data to Kendall's shape space. In particular, instead of the Sasaki metric, we adapt a functional-based metric, which increases the computational efficiency and does not require the implementation of the curvature tensor. We propose the corresponding variational time discretization of geodesics and employ the approach for longitudinal analysis of 2D rat skulls shapes as well as 3D shapes derived from an imaging study on osteoarthritis. Particularly, we perform hypothesis test and estimate the mean trends. Y1 - 2022 U6 - https://doi.org/10.1007/s10851-022-01079-x VL - 64 IS - 4 SP - 395 EP - 407 ER - TY - JOUR A1 - Hanik, Martin A1 - Hege, Hans-Christian A1 - von Tycowicz, Christoph T1 - Bi-invariant Dissimilarity Measures for Sample Distributions in Lie Groups JF - SIAM Journal on Mathematics of Data Science N2 - Data sets sampled in Lie groups are widespread, and as with multivariate data, it is important for many applications to assess the differences between the sets in terms of their distributions. Indices for this task are usually derived by considering the Lie group as a Riemannian manifold. Then, however, compatibility with the group operation is guaranteed only if a bi-invariant metric exists, which is not the case for most non-compact and non-commutative groups. We show here that if one considers an affine connection structure instead, one obtains bi-invariant generalizations of well-known dissimilarity measures: a Hotelling $T^2$ statistic, Bhattacharyya distance and Hellinger distance. Each of the dissimilarity measures matches its multivariate counterpart for Euclidean data and is translation-invariant, so that biases, e.g., through an arbitrary choice of reference, are avoided. We further derive non-parametric two-sample tests that are bi-invariant and consistent. We demonstrate the potential of these dissimilarity measures by performing group tests on data of knee configurations and epidemiological shape data. Significant differences are revealed in both cases. Y1 - 2022 U6 - https://doi.org/10.1137/21M1410373 VL - 4 IS - 4 SP - 1223 EP - 1249 ER - TY - CHAP A1 - Harth, Philipp A1 - Vohra, Sumit A1 - Udvary, Daniel A1 - Oberlaender, Marcel A1 - Hege, Hans-Christian A1 - Baum, Daniel T1 - A Stratification Matrix Viewer for Analysis of Neural Network Data T2 - Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM) N2 - The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex. Y1 - 2022 U6 - https://doi.org/10.2312/vcbm.20221194 CY - Vienna, Austria ER - TY - JOUR A1 - Udvary, Daniel A1 - Harth, Philipp A1 - Macke, Jakob H. A1 - Hege, Hans-Christian A1 - de Kock, Christiaan P. J. A1 - Sakmann, Bert A1 - Oberlaender, Marcel T1 - The Impact of Neuron Morphology on Cortical Network Architecture JF - Cell Reports N2 - The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons’ axo-dendritic projections patterns, the packing density and cellular diversity of the neuropil. The higher these three factors are the more recurrent is the topology of the network. Conversely, the lower these factors are the more feedforward is the network’s topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular and network scales from the specific morphological properties of its neuronal constituents. Y1 - 2022 U6 - https://doi.org/10.1016/j.celrep.2022.110677 VL - 39 IS - 2 ER - TY - GEN A1 - Sagnol, Guillaume A1 - Hege, Hans-Christian A1 - Weiser, Martin T1 - Using sparse kernels to design computer experiments with tunable precision N2 - Statistical methods to design computer experiments usually rely on a Gaussian process (GP) surrogate model, and typically aim at selecting design points (combinations of algorithmic and model parameters) that minimize the average prediction variance, or maximize the prediction accuracy for the hyperparameters of the GP surrogate. In many applications, experiments have a tunable precision, in the sense that one software parameter controls the tradeoff between accuracy and computing time (e.g., mesh size in FEM simulations or number of Monte-Carlo samples). We formulate the problem of allocating a budget of computing time over a finite set of candidate points for the goals mentioned above. This is a continuous optimization problem, which is moreover convex whenever the tradeoff function accuracy vs. computing time is concave. On the other hand, using non-concave weight functions can help to identify sparse designs. In addition, using sparse kernel approximations drastically reduce the cost per iteration of the multiplicative weights updates that can be used to solve this problem. T3 - ZIB-Report - 16-33 KW - Optimal design of computer experiments KW - Sparse kernels KW - Gaussian Process Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-59605 SN - 1438-0064 ER -