TY - JOUR A1 - Oberlaender, Marcel A1 - de Kock, Christiaan P. J. A1 - Bruno, Randy M. A1 - Ramirez, Alejandro A1 - Meyer, Hanno A1 - Dercksen, Vincent J. A1 - Helmstaedter, Moritz A1 - Sakmann, Bert T1 - Cell Type-Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex JF - Cerebral Cortex Y1 - 2012 U6 - https://doi.org/doi:10.1093/cercor/bhr317 VL - 22 IS - 10 SP - 2375 EP - 2391 ER - TY - GEN A1 - Oberlaender, Marcel A1 - Dercksen, Vincent J. A1 - Lang, Stefan A1 - Sakmann, Bert T1 - 3D mapping of synaptic connections within “in silico” microcircuits of full compartmental neurons in extended networks on the example of VPM axons projecting into S1 of rats. T2 - Conference Abstract, Neuroinformatics Y1 - 2009 U6 - https://doi.org/10.3389/conf.neuro.11.2009.08.092 ER - TY - GEN A1 - Oberlaender, Marcel A1 - Bruno, Randy M. A1 - de Kock, Christiaan P. J. A1 - Meyer, Hanno A1 - Dercksen, Vincent J. A1 - Sakmann, Bert T1 - 3D distribution and sub-cellular organization of thalamocortical VPM synapses for individual excitatory neuronal cell types in rat barrel cortex T2 - Conference Abstract No. 173.19/Y35, 39th Annual Meeting of the Society for Neuroscience (SfN) Y1 - 2009 ER - TY - GEN A1 - Oberlaender, Marcel A1 - Dercksen, Vincent J. A1 - Broser, Philip J. A1 - Bruno, Randy M. A1 - Sakmann, Bert T1 - NeuroMorph and NeuroCount: Automated tools for fast and objective acquisition of neuronal morphology for quantitative structural analysis T2 - Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics Y1 - 2008 U6 - https://doi.org/10.3389/conf.neuro.11.2008.01.065 ER - TY - CHAP A1 - Dercksen, Vincent J. A1 - Weber, Britta A1 - Günther, David A1 - Oberlaender, Marcel A1 - Prohaska, Steffen A1 - Hege, Hans-Christian T1 - Automatic alignment of stacks of filament data T2 - Proc. IEEE International Symposium on Biomedical Imaging Y1 - 2009 SP - 971 EP - 974 PB - IEEE press CY - Boston, USA ER - TY - JOUR A1 - Oberlaender, Marcel A1 - Dercksen, Vincent J. A1 - Egger, Robert A1 - Gensel, Maria A1 - Sakmann, Bert A1 - Hege, Hans-Christian T1 - Automated three-dimensional detection and counting of neuron somata JF - Journal of Neuroscience Methods Y1 - 2009 U6 - https://doi.org/10.1016/j.jneumeth.2009.03.008 VL - 180 IS - 1 SP - 147 EP - 160 ER - TY - JOUR A1 - Egger, Robert A1 - Dercksen, Vincent J. A1 - Udvary, Daniel A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - Generation of dense statistical connectomes from sparse morphological data JF - Frontiers in Neuroanatomy Y1 - 2014 U6 - https://doi.org/10.3389/fnana.2014.00129 VL - 8 IS - 129 ER - TY - GEN A1 - Egger, Robert A1 - Dercksen, Vincent J. A1 - Udvary, Daniel A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - Generation of dense statistical connectomes from sparse morphological data N2 - Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and sub-cellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results. T3 - ZIB-Report - 14-43 KW - 3D neural network KW - Dense connectome KW - Reconstruction Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-53075 SN - 1438-0064 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 - JOUR A1 - Harth, Philipp A1 - Udvary, Daniel A1 - Boelts, Jan A1 - Baum, Daniel A1 - Macke, Jakob H. A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - Dissecting origins of wiring specificity in dense cortical connectomes JF - bioRxiv N2 - 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. Y1 - 2024 U6 - https://doi.org/10.1101/2024.12.14.628490 ER -