@misc{DercksenHegeOberlaender2013, author = {Dercksen, Vincent J. and Hege, Hans-Christian and Oberlaender, Marcel}, title = {The Filament Editor: An Interactive Software Environment for Visualization, Proof-Editing and Analysis of 3D Neuron Morphology}, issn = {1438-0064}, doi = {10.1007/s12021-013-9213-2}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-43157}, year = {2013}, abstract = {Neuroanatomical analysis, such as classification of cell types, depends on reliable reconstruction of large numbers of complete 3D dendrite and axon morphologies. At present, the majority of neuron reconstructions are obtained from preparations in a single tissue slice in vitro, thus suffering from cut off dendrites and, more dramatically, cut off axons. In general, axons can innervate volumes of several cubic millimeters and may reach path lengths of tens of centimeters. Thus, their complete reconstruction requires in vivo labeling, histological sectioning and imaging of large fields of view. Unfortunately, anisotropic background conditions across such large tissue volumes, as well as faintly labeled thin neurites, result in incomplete or erroneous automated tracings and even lead experts to make annotation errors during manual reconstructions. Consequently, tracing reliability renders the major bottleneck for reconstructing complete 3D neuron morphologies. Here, we present a novel set of tools, integrated into a software environment named 'Filament Editor', for creating reliable neuron tracings from sparsely labeled in vivo datasets. The Filament Editor allows for simultaneous visualization of complex neuronal tracings and image data in a 3D viewer, proof-editing of neuronal tracings, alignment and interconnection across sections, and morphometric analysis in relation to 3D anatomical reference structures. We illustrate the functionality of the Filament Editor on the example of in vivo labeled axons and demonstrate that for the exemplary dataset the final tracing results after proof-editing are independent of the expertise of the human operator.}, language = {en} } @article{DercksenHegeOberlaender2014, author = {Dercksen, Vincent J. and Hege, Hans-Christian and Oberlaender, Marcel}, title = {The Filament Editor: An Interactive Software Environment for Visualization, Proof-Editing and Analysis of 3D Neuron Morphology}, volume = {12}, journal = {NeuroInformatics}, number = {2}, publisher = {Springer US}, doi = {10.1007/s12021-013-9213-2}, pages = {325 -- 339}, year = {2014}, language = {en} } @misc{EggerDercksenKocketal.2014, author = {Egger, Robert and Dercksen, Vincent J. and Kock, Christiaan P.J. and Oberlaender, Marcel}, title = {Reverse Engineering the 3D Structure and Sensory-Evoked Signal Flow of Rat Vibrissal Cortex}, volume = {11}, journal = {The Computing Dendrite}, editor = {Cuntz, Hermann and Remme, Michiel W.H. and Torben-Nielsen, Benjamin}, publisher = {Springer}, address = {New York}, doi = {10.1007/978-1-4614-8094-5_8}, pages = {127 -- 145}, year = {2014}, language = {en} } @masterthesis{Krabi2011, type = {Bachelor Thesis}, author = {Krabi, Marianne}, title = {Implementierung einer Methode zur automatischen Erkennung von Nervenzellk{\"o}rpern in 3D Mikroskopbildern}, year = {2011}, language = {de} } @article{LangDercksenSakmannetal.2011, author = {Lang, Stefan and Dercksen, Vincent J. and Sakmann, Bert and Oberlaender, Marcel}, title = {Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex}, volume = {24}, journal = {Neural Networks}, number = {9}, doi = {doi:10.1016/j.neunet.2011.06.013}, pages = {998 -- 1011}, year = {2011}, language = {en} } @misc{DercksenOberlaenderSakmannetal.2011, author = {Dercksen, Vincent J. and Oberlaender, Marcel and Sakmann, Bert and Hege, Hans-Christian}, title = {Light Microscopy-Based Reconstruction and Interactive Structural Analysis of Cortical Neural Networks}, journal = {BioVis 2011 Abstracts, 1st IEEE Symposium on Biological Data Visualization}, year = {2011}, language = {en} } @misc{DercksenOberlaenderSakmannetal.2012, author = {Dercksen, Vincent J. and Oberlaender, Marcel and Sakmann, Bert and Hege, Hans-Christian}, title = {Interactive Visualization - a Key Prerequisite for Reconstruction of Anatomically Realistic Neural Networks}, journal = {Visualization in Medicine and Life Sciences II}, editor = {Linsen, Lars and Hagen, Hans and Hamann, Bernd and Hege, Hans-Christian}, publisher = {Springer, Berlin}, pages = {27 -- 44}, year = {2012}, language = {en} } @inproceedings{DercksenEggerHegeetal.2012, author = {Dercksen, Vincent J. and Egger, Robert and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Synaptic Connectivity in Anatomically Realistic Neural Networks: Modeling and Visual Analysis}, booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, address = {Norrk{\"o}ping, Sweden}, doi = {10.2312/VCBM/VCBM12/017-024}, pages = {17 -- 24}, year = {2012}, language = {en} } @misc{PfisterKaynigBothaetal.2012, author = {Pfister, Hanspeter and Kaynig, Verena and Botha, Charl and Bruckner, Stefan and Dercksen, Vincent J. and Hege, Hans-Christian and Roerdink, Jos}, title = {Visualization in Connectomics}, arxiv = {http://arxiv.org/abs/1206.1428}, doi = {10.1007/978-1-4471-6497-5_21}, year = {2012}, language = {en} } @article{OberlaenderdeKockBrunoetal.2012, author = {Oberlaender, Marcel and de Kock, Christiaan P. J. and Bruno, Randy M. and Ramirez, Alejandro and Meyer, Hanno and Dercksen, Vincent J. and Helmstaedter, Moritz and Sakmann, Bert}, title = {Cell Type-Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex}, volume = {22}, journal = {Cerebral Cortex}, number = {10}, doi = {doi:10.1093/cercor/bhr317}, pages = {2375 -- 2391}, year = {2012}, language = {en} } @article{KussGenselMeyeretal.2010, author = {Kuß, Anja and Gensel, Maria and Meyer, Bj{\"o}rn and Dercksen, Vincent J. and Prohaska, Steffen}, title = {Effective Techniques to Visualize Filament-Surface Relationships}, volume = {29}, journal = {Comput. Graph. Forum}, pages = {1003 -- 1012}, year = {2010}, language = {en} } @misc{OberlaenderDercksenLangetal.2009, author = {Oberlaender, Marcel and Dercksen, Vincent J. and Lang, Stefan and Sakmann, Bert}, title = {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.}, journal = {Conference Abstract, Neuroinformatics}, doi = {10.3389/conf.neuro.11.2009.08.092}, year = {2009}, language = {en} } @misc{OberlaenderBrunodeKocketal.2009, author = {Oberlaender, Marcel and Bruno, Randy M. and de Kock, Christiaan P. J. and Meyer, Hanno and Dercksen, Vincent J. and Sakmann, Bert}, title = {3D distribution and sub-cellular organization of thalamocortical VPM synapses for individual excitatory neuronal cell types in rat barrel cortex}, journal = {Conference Abstract No. 173.19/Y35, 39th Annual Meeting of the Society for Neuroscience (SfN)}, year = {2009}, language = {en} } @misc{OberlaenderDercksenBroseretal.2008, author = {Oberlaender, Marcel and Dercksen, Vincent J. and Broser, Philip J. and Bruno, Randy M. and Sakmann, Bert}, title = {NeuroMorph and NeuroCount: Automated tools for fast and objective acquisition of neuronal morphology for quantitative structural analysis}, journal = {Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics}, doi = {10.3389/conf.neuro.11.2008.01.065}, year = {2008}, language = {en} } @inproceedings{DercksenWeberGuentheretal.2009, author = {Dercksen, Vincent J. and Weber, Britta and G{\"u}nther, David and Oberlaender, Marcel and Prohaska, Steffen and Hege, Hans-Christian}, title = {Automatic alignment of stacks of filament data}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging}, publisher = {IEEE press}, address = {Boston, USA}, pages = {971 -- 974}, year = {2009}, language = {en} } @article{OberlaenderDercksenEggeretal.2009, author = {Oberlaender, Marcel and Dercksen, Vincent J. and Egger, Robert and Gensel, Maria and Sakmann, Bert and Hege, Hans-Christian}, title = {Automated three-dimensional detection and counting of neuron somata}, volume = {180}, journal = {Journal of Neuroscience Methods}, number = {1}, doi = {10.1016/j.jneumeth.2009.03.008}, pages = {147 -- 160}, year = {2009}, language = {en} } @misc{DercksenGenselKuss2009, author = {Dercksen, Vincent J. and Gensel, Maria and Kuß, Anja}, title = {Visual Accentuation of Spatial Relationships between Filamentous and Voluminous Surface Structures}, journal = {Conference Abstract, Eurographics / IEEE Symposium on Visualization}, year = {2009}, language = {en} } @article{EggerDercksenUdvaryetal.2014, author = {Egger, Robert and Dercksen, Vincent J. and Udvary, Daniel and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Generation of dense statistical connectomes from sparse morphological data}, volume = {8}, journal = {Frontiers in Neuroanatomy}, number = {129}, doi = {10.3389/fnana.2014.00129}, year = {2014}, language = {en} } @incollection{PfisterKaynigBothaetal.2014, author = {Pfister, Hanspeter and Kaynig, Verena and Botha, Charl P. and Bruckner, Stefan and Dercksen, Vincent J. and Hege, Hans-Christian and Roerdink, Jos B.T.M.}, title = {Visualization in Connectomics}, booktitle = {Scientific Visualization - Uncertainty, Multifield, Biomedical, and Scalable Visualization}, editor = {Hansen, Charles D. and Chen, Min and Johnson, Christopher R. and Kaufman, Arie E. and Hagen, Hans}, publisher = {Springer}, isbn = {978-1-4471-6496-8}, arxiv = {http://arxiv.org/abs/1206.1428}, doi = {10.1007/978-1-4471-6497-5_21}, pages = {221 -- 245}, year = {2014}, abstract = {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.}, language = {en} } @misc{EggerDercksenUdvaryetal.2014, author = {Egger, Robert and Dercksen, Vincent J. and Udvary, Daniel and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Generation of dense statistical connectomes from sparse morphological data}, issn = {1438-0064}, doi = {10.3389/fnana.2014.00129}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-53075}, year = {2014}, abstract = {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.}, language = {en} }