Event Abstract

Efficient reconstruction of large-scale neuronal morphologies

  • 1 University of Heidelberg, Interdisciplinary Center for Scientific Computing, Germany
  • 2 Max-Planck Institute of Neurobiology, Germany

The recently developed serial block face scanning electron microscopy (SBFSEM) allows for imaging of large volumes of brain tissue (~200x200x100 microns) with approximately 20 nm spatial resolution. Using this technique to reconstruct single biocytin-labeled neurons, will reveal new insights on widely spreading neuron morphologies at subcellular level. As a first step, we therefore aim to extract the number and three dimensional distribution of spines, to categorize spine morphologies and to determine membrane surface areas for dendrites of excitatory cortical neurons. This will yield key prerequisites for an authentic anatomical neuron classification and conversion into realistic full-compartmental models, which might as well be integrated within neuronal microcircuits. Hence, the presented work will help to reengineer the morphology and connectivity of large functional neuronal networks at subcellular resolution. However, imaging a few hundred microns of cortical tissue, with nanometer resolution, results in very large volumes of image data. Here, we present an efficient reconstruction pipeline that allows for a fast and reliable extraction of neuron geometry. The developed framework comprises specialized three dimensional segmentation and morphological operators, which result in tracings of the three and one dimensional skeleton structure of neurons.

The major algorithms of the presented reconstruction pipeline are parallelized, using the CUDA programming model. Exploiting the performance of current graphics hardware, the CUDA platform allows for an efficient multi-threaded parallelization of visualization algorithms, either at the level of pixels or voxels. It further offers possibilities to optimize the management of available hardware resources. In consequence, we achieved efficient processing of input data volumes of typical sizes of several Gigabytes. Further, time for image processing reduces from a few hours of CPU time to a few minutes. A resultant example, revealing highly resolved morphological characteristics and geometries of dendrites and spines, is shown Fig.1 (supplementary material). Thus, realistic anatomical description and classification of neuron types will become possible in the near future.

Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009.

Presentation Type: Oral Presentation

Topic: Information processing in neurons and networks

Citation: Drouvelis P, Bastian P, Oberlaender M, Kurz T, Sakmann B and Lang S (2009). Efficient reconstruction of large-scale neuronal morphologies. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.048

Received: 26 Aug 2009; Published Online: 26 Aug 2009.

* Correspondence: Panos Drouvelis, University of Heidelberg, Interdisciplinary Center for Scientific Computing, Heidelberg, Germany, panagiotis.drouvelis@iwr.uni-heidelberg.de

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