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Poster

Automated Detection of Putative Synaptic Contacts between in vivo Labelled Neurons

MPG-Autoren
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Seetharama,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Former Research Group Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Slabik,  D
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Former Research Group Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Smyth,  A
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Former Research Group Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Oberlaender,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Former Research Group Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Seetharama, M., Slabik, D., Smyth, A., & Oberlaender, M. (2016). Automated Detection of Putative Synaptic Contacts between in vivo Labelled Neurons. Poster presented at Barrel Cortex Function 2016, Amsterdam, The Netherlands.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-7B7C-C
Zusammenfassung
Understanding the structural organization of the neural networks requires reconstruction of the underlying neural circuitry in anatomical detail and mapping of the synaptic contacts. A typical in vivo labeled axon innervates a large volume and imaging such in vivo labeled pairs of neurons at high resolution yields a large set of images, typically in the order of Tera Bytes. Manual mapping of synaptic contacts between such cell pairs would be labor intensive and error prone. Here, we present a software pipeline that automatically detects putative synaptic contacts between the boutons and spines of in vivo labeled pairs of neurons. The pipeline has three phases. Firstly, the regions where the skeletons of axon and dendrites of different neurons come close to each other are detected. Secondly, in these proximity regions, the boutons along the axons and spines along the dendrites are detected. Thirdly, the overlaps between boutons and spines along with their locations are detected. The resulting putative contacts are visualized on the original image stack using a visualization tool and can be verified by the user. This semi-automated approach reduces the number of sites to be manually inspected for putative contacts from tens of thousands to tens of putative contact sites. Hence achieving about three orders of magnitude reduction in the manual effort required.