@article{VohraEberleBoulangerWeilletal.2025, author = {Vohra, Sumit Kumar and Eberle, Maren and Boulanger-Weill, Jonathan and Petkova, Mariela D. and Schuhknecht, Gregor F. P. and Herrera, Kristian J. and K{\"a}mpf, Florian and Ruetten, Virginia M. S. and Lichtman, Jeff W. and Engert, Florian and Randlett, Owen and Bahl, Armin and Isoe, Yasuko and Hege, Hans-Christian and Baum, Daniel}, title = {Fishexplorer: A multimodal cellular atlas platform for neuronal circuit dissection in larval zebrafish}, journal = {bioRxiv}, doi = {10.1101/2025.07.14.664689}, year = {2025}, abstract = {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.}, language = {en} } @article{VohraHerreraTavhelidseSucketal.2024, author = {Vohra, Sumit Kumar and Herrera, Kristian and Tavhelidse-Suck, Tinatini and Knoblich, Simon and Seleit, Ali and Boulanger-Weill, Jonathan and Chambule, Sydney and Aspiras, Ariel and Santoriello, Cristina and Randlett, Owen and Wittbrodt, Joachim and Aulehla, Alexander and Lichtman, Jeff W. and Fishman, Mark and Hege, Hans-Christian and Baum, Daniel and Engert, Florian and Isoe, Yasuko}, title = {Multi-species community platform for comparative neuroscience in teleost fish}, journal = {bioRxiv}, doi = {10.1101/2024.02.14.580400}, year = {2024}, abstract = {Studying neural mechanisms in complementary model organisms from different ecological niches in the same animal class can leverage the comparative brain analysis at the cellular level. To advance such a direction, we developed a unified brain atlas platform and specialized tools that allowed us to quantitatively compare neural structures in two teleost larvae, medaka (Oryzias latipes) and zebrafish (Danio rerio). Leveraging this quantitative approach we found that most brain regions are similar but some subpopulations are unique in each species. Specifically, we confirmed the existence of a clear dorsal pallial region in the telencephalon in medaka lacking in zebrafish. Further, our approach allows for extraction of differentially expressed genes in both species, and for quantitative comparison of neural activity at cellular resolution. The web-based and interactive nature of this atlas platform will facilitate the teleost community's research and its easy extensibility will encourage contributions to its continuous expansion.}, language = {en} } @article{HarthUdvaryBoeltsetal.2024, author = {Harth, Philipp and Udvary, Daniel and Boelts, Jan and Baum, Daniel and Macke, Jakob H. and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Dissecting origins of wiring specificity in dense cortical connectomes}, journal = {bioRxiv}, doi = {10.1101/2024.12.14.628490}, year = {2024}, abstract = {Wiring specificity in the cortex is observed across scales from the subcellular to the network level. It describes the deviations of connectivity patterns from those expected in randomly connected networks. Understanding the origins of wiring specificity in neural networks remains difficult as a variety of generative mechanisms could have contributed to the observed connectome. To take a step forward, we propose a generative modeling framework that operates directly on dense connectome data as provided by saturated reconstructions of neural tissue. The computational framework allows testing different assumptions of synaptic specificity while accounting for anatomical constraints posed by neuron morphology, which is a known confounding source of wiring specificity. We evaluated the framework on dense reconstructions of the mouse visual and the human temporal cortex. Our template model incorporates assumptions of synaptic specificity based on cell type, single-cell identity, and subcellular compartment. Combinations of these assumptions were sufficient to model various connectivity patterns that are indicative of wiring specificity. Moreover, the identified synaptic specificity parameters showed interesting similarities between both datasets, motivating further analysis of wiring specificity across species.}, language = {en} } @article{VohraHarthIsoeetal.2024, author = {Vohra, Sumit Kumar and Harth, Philipp and Isoe, Yasuko and Bahl, Armin and Fotowat, Haleh and Engert, Florian and Hege, Hans-Christian and Baum, Daniel}, title = {A Visual Interface for Exploring Hypotheses about Neural Circuits}, volume = {30}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {7}, doi = {10.1109/TVCG.2023.3243668}, pages = {3945 -- 3958}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{HarthBastTroidletal.2023, author = {Harth, Philipp and Bast, Arco and Troidl, Jakob and Meulemeester, Bjorge and Pfister, Hanspeter and Beyer, Johanna and Oberlaender, Marcel and Hege, Hans-Christian and Baum, Daniel}, title = {Rapid Prototyping for Coordinated Views of Multi-scale Spatial and Abstract Data: A Grammar-based Approach}, booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, doi = {10.2312/vcbm.20231218}, year = {2023}, abstract = {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.}, language = {en} } @article{BoeltsHarthGaoetal.2023, author = {Boelts, Jan and Harth, Philipp and Gao, Richard and Udvary, Daniel and Yanez, Felipe and Baum, Daniel and Hege, Hans-Christian and Oberlaender, Marcel and Macke, Jakob H.}, title = {Simulation-based inference for efficient identification of generative models in computational connectomics}, volume = {19}, journal = {PLOS Computational Biology}, number = {9}, doi = {10.1371/journal.pcbi.1011406}, year = {2023}, abstract = {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.}, language = {en} } @article{HanikDuckeHegeetal.2023, author = {Hanik, Martin and Ducke, Benjamin and Hege, Hans-Christian and Fless, Friederike and von Tycowicz, Christoph}, title = {Intrinsic shape analysis in archaeology: A case study on ancient sundials}, volume = {16}, journal = {Journal on Computing and Cultural Heritage}, number = {4}, arxiv = {http://arxiv.org/abs/2305.18960}, doi = {10.1145/3606698}, pages = {1 -- 26}, year = {2023}, abstract = {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.}, language = {en} } @article{BoeltsHarthGaoetal.2023, author = {Boelts, Jan and Harth, Philipp and Gao, Richard and Udvary, Daniel and Yanez, Felipe and Baum, Daniel and Hege, Hans-Christian and Oberlaender, Marcel and Macke, Jakob H}, title = {Simulation-based inference for efficient identification of generative models in connectomics}, journal = {bioRxiv}, doi = {10.1101/2023.01.31.526269}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-89890}, year = {2023}, abstract = {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.}, language = {en} } @misc{VohraHarthIsoeetal.2023, author = {Vohra, Sumit Kumar and Harth, Philipp and Isoe, Yasuko and Bahl, Armin and Fotowat, Haleh and Engert, Florian and Hege, Hans-Christian and Baum, Daniel}, title = {A Visual Interface for Exploring Hypotheses about Neural Circuits}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-89932}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{HanikHegevonTycowicz2022, author = {Hanik, Martin and Hege, Hans-Christian and von Tycowicz, Christoph}, title = {A Nonlinear Hierarchical Model for Longitudinal Data on Manifolds}, booktitle = {2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)}, arxiv = {http://arxiv.org/abs/2202.01180}, doi = {10.1109/ISBI52829.2022.9761465}, pages = {1 -- 5}, year = {2022}, abstract = {Large longitudinal studies provide lots of valuable information, especially in medical applications. A problem which must be taken care of in order to utilize their full potential is that of correlation between intra-subject measurements taken at different times. For data in Euclidean space this can be done with hierarchical models, that is, models that consider intra-subject and between-subject variability in two different stages. Nevertheless, data from medical studies often takes values in nonlinear manifolds. Here, as a first step, geodesic hierarchical models have been developed that generalize the linear ansatz by assuming that time-induced intra-subject variations occur along a generalized straight line in the manifold. However, this is often not the case (e.g., periodic motion or processes with saturation). We propose a hierarchical model for manifold-valued data that extends this to include trends along higher-order curves, namely B{\´e}zier splines in the manifold. To this end, we present a principled way of comparing shape trends in terms of a functional-based Riemannian metric. Remarkably, this metric allows efficient, yet simple computations by virtue of a variational time discretization requiring only the solution of regression problems. We validate our model on longitudinal data from the osteoarthritis initiative, including classification of disease progression.}, language = {en} }