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 - GEN A1 - Dercksen, Vincent J. A1 - Gensel, Maria A1 - Kuß, Anja T1 - Visual Accentuation of Spatial Relationships between Filamentous and Voluminous Surface Structures T2 - Conference Abstract, Eurographics / IEEE Symposium on Visualization Y1 - 2009 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 - CHAP A1 - Pfister, Hanspeter A1 - Kaynig, Verena A1 - Botha, Charl P. A1 - Bruckner, Stefan A1 - Dercksen, Vincent J. A1 - Hege, Hans-Christian A1 - Roerdink, Jos B.T.M. ED - Hansen, Charles D. ED - Chen, Min ED - Johnson, Christopher R. ED - Kaufman, Arie E. ED - Hagen, Hans T1 - Visualization in Connectomics T2 - Scientific Visualization - Uncertainty, Multifield, Biomedical, and Scalable Visualization N2 - Connectomics is a branch of neuroscience that attempts to create a connectome, i.e., a complete map of the neuronal system and all connections between neuronal structures. This representation can be used to understand how functional brain states emerge from their underlying anatomical structures and how dysfunction and neuronal diseases arise. We review the current state-of-the-art of visualization and image processing techniques in the field of connectomics and describe a number of challenges. After a brief summary of the biological background and an overview of relevant imaging modalities, we review current techniques to extract connectivity information from image data at macro-, meso- and microscales. We also discuss data integration and neural network modeling, as well as the visualization, analysis and comparison of brain networks. Y1 - 2014 SN - 978-1-4471-6496-8 U6 - https://doi.org/10.1007/978-1-4471-6497-5_21 SP - 221 EP - 245 PB - Springer 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 -