@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} } @misc{HajarolasvadiBaum2024, author = {Hajarolasvadi, Noushin and Baum, Daniel}, title = {Data for Training the DeepOrientation Model: Simulated cryo-ET tomogram patches}, doi = {10.12752/9686}, year = {2024}, abstract = {A major restriction to applying deep learning methods in cryo-electron tomography is the lack of annotated data. Many large learning-based models cannot be applied to these images due to the lack of adequate experimental ground truth. One appealing alternative solution to the time-consuming and expensive experimental data acquisition and annotation is the generation of simulated cryo-ET images. In this context, we exploit a public cryo-ET simulator called PolNet to generate three datasets of two macromolecular structures, namely the ribosomal complex 4v4r and Thermoplasma acidophilum 20S proteasome, 3j9i. We select these two specific particles to test whether our models work for macromolecular structures with and without rotational symmetry. The three datasets contain 50, 150, and 450 tomograms with a voxel size of 10 ̊A, respectively. Here, we publish patches of size 40 × 40 × 40 extracted from the medium-sized dataset with 26,703 samples of 4v4r and 40,671 samples of 3j9i. The original tomograms from which the samples were extracted are of size 500 × 500 × 250. Finally, it should be noted that the currently published test dataset is employed for reporting the results of our paper titled "DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography" paper.}, language = {en} } @article{YangKnoetelCiecierskaHolmesetal.2024, author = {Yang, Binru and Kn{\"o}tel, David and Ciecierska-Holmes, Jana and W{\"o}lfer, Jan and Chaumel, J{\´u}lia and Zaslansky, Paul and Baum, Daniel and Fratzl, Peter and Dean, Mason N.}, title = {Growth of a tessellation: geometric rules for the development of stingray skeletal patterns}, volume = {11}, journal = {Advanced Science}, number = {48}, doi = {10.1002/advs.202407641}, year = {2024}, language = {en} } @article{EigenWoelferBaumetal.2024, author = {Eigen, Lennart and W{\"o}lfer, Jan and Baum, Daniel and Van Le, Mai-Lee and Werner, Daniel and Dean, Mason N. and Nyakatura, John A.}, title = {Comparative architecture of the tessellated boxfish (Ostracioidea) carapace}, volume = {7}, journal = {Communications Biology}, doi = {10.1038/s42003-024-07119-z}, year = {2024}, language = {en} } @article{MayerBaumAmbellanetal.2024, author = {Mayer, Julius and Baum, Daniel and Ambellan, Felix and von Tycowicz, Christoph and for the Alzheimer's Disease Neuroimaging Initiative,}, title = {Shape-based Disease Grading via Functional Maps and Graph Convolutional Networks with Application to Alzheimer's Disease}, volume = {24}, journal = {BMC Medical Imaging}, doi = {10.1186/s12880-024-01513-z}, year = {2024}, abstract = {Shape analysis provides methods for understanding anatomical structures extracted from medical images. However, the underlying notions of shape spaces that are frequently employed come with strict assumptions prohibiting the analysis of incomplete and/or topologically varying shapes. This work aims to alleviate these limitations by adapting the concept of functional maps. Further, we present a graph-based learning approach for morphometric classification of disease states that uses novel shape descriptors based on this concept. We demonstrate the performance of the derived classifier on the open-access ADNI database differentiating normal controls and subjects with Alzheimer's disease. Notably, the experiments show that our approach can improve over state-of-the-art from geometric deep learning.}, language = {en} } @inproceedings{GossingBeckertFischeretal.2024, author = {Gossing, Anne and Beckert, Andreas and Fischer, Christoph and Klenert, Nicolas and Natarajan, Vijay and Pacey, George and Vogt, Thorwin and Rautenhaus, Marc and Baum, Daniel}, title = {A Ridge-based Approach for Extraction and Visualization of 3D Atmospheric Fronts}, booktitle = {2024 IEEE Visualization and Visual Analytics (VIS)}, doi = {10.1109/VIS55277.2024.00043}, pages = {176 -- 180}, year = {2024}, abstract = {An atmospheric front is an imaginary surface that separates two distinct air masses and is commonly defined as the warm-air side of a frontal zone with high gradients of atmospheric temperature and humidity. These fronts are a widely used conceptual model in meteorology, which are often encountered in the literature as two-dimensional (2D) front lines on surface analysis charts. This paper presents a method for computing three-dimensional (3D) atmospheric fronts as surfaces that is capable of extracting continuous and well-confined features suitable for 3D visual analysis, spatio-temporal tracking, and statistical analyses. Recently developed contour-based methods for 3D front extraction rely on computing the third derivative of a moist potential temperature field. Additionally, they require the field to be smoothed to obtain continuous large-scale structures. This paper demonstrates the feasibility of an alternative method to front extraction using ridge surface computation. The proposed method requires only the sec- ond derivative of the input field and produces accurate structures even from unsmoothed data. An application of the ridge-based method to a data set corresponding to Cyclone Friederike demonstrates its benefits and utility towards visual analysis of the full 3D structure of fronts.}, 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{KlenertLepperBaum2024, author = {Klenert, Nicolas and Lepper, Verena and Baum, Daniel}, title = {A Local Iterative Approach for the Extraction of 2D Manifolds from Strongly Curved and Folded Thin-Layer Structures}, volume = {30}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {1}, doi = {10.1109/TVCG.2023.3327403}, pages = {1260 -- 1270}, year = {2024}, abstract = {Ridge surfaces represent important features for the analysis of 3-dimensional (3D) datasets in diverse applications and are often derived from varying underlying data including flow fields, geological fault data, and point data, but they can also be present in the original scalar images acquired using a plethora of imaging techniques. Our work is motivated by the analysis of image data acquired using micro-computed tomography (μCT) of ancient, rolled and folded thin-layer structures such as papyrus, parchment, and paper as well as silver and lead sheets. From these documents we know that they are 2-dimensional (2D) in nature. Hence, we are particularly interested in reconstructing 2D manifolds that approximate the document's structure. The image data from which we want to reconstruct the 2D manifolds are often very noisy and represent folded, densely-layered structures with many artifacts, such as ruptures or layer splitting and merging. Previous ridge-surface extraction methods fail to extract the desired 2D manifold for such challenging data. We have therefore developed a novel method to extract 2D manifolds. The proposed method uses a local fast marching scheme in combination with a separation of the region covered by fast marching into two sub-regions. The 2D manifold of interest is then extracted as the surface separating the two sub-regions. The local scheme can be applied for both automatic propagation as well as interactive analysis. We demonstrate the applicability and robustness of our method on both artificial data as well as real-world data including folded silver and papyrus sheets.}, 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} } @article{LuetzkendorfMatkovicRachidLiuetal.2025, author = {L{\"u}tzkendorf, Janine and Matkovic-Rachid, Tanja and Liu, Sunbin and G{\"o}tz, Torsten and Gao, Lili and Turrel, Oriane and Maglione, Marta and Grieger, Melanie and Putignano, Sabrina and Ramesh, Niraja and Ghelani, Tina and Neumann, Alexander and Gimber, Niclas and Schmoranzer, Jan and Stawrakakis, Anastasia and Brence, Blaž and Baum, Daniel and Ludwig, Kai and Heine, Martin and Mielke, Thorsten and Liu, Fan and Walter, Alexander and Wahl, Markus and Sigrist, Stephan}, title = {Blobby is a synaptic active zone assembly protein required for memory in Drosophila}, volume = {16}, journal = {Nature Communications}, doi = {10.1038/s41467-024-55382-9}, year = {2025}, language = {en} }