SyConn2: dense synaptic connectivity inference for volume electron microscopy

The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute...

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Autores principales: Schubert, Philipp J., Dorkenwald, Sven, Januszewski, Michał, Klimesch, Jonathan, Svara, Fabian, Mancu, Andrei, Ahmad, Hashir, Fee, Michale S., Jain, Viren, Kornfeld, Joergen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636020/
https://www.ncbi.nlm.nih.gov/pubmed/36280715
http://dx.doi.org/10.1038/s41592-022-01624-x
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author Schubert, Philipp J.
Dorkenwald, Sven
Januszewski, Michał
Klimesch, Jonathan
Svara, Fabian
Mancu, Andrei
Ahmad, Hashir
Fee, Michale S.
Jain, Viren
Kornfeld, Joergen
author_facet Schubert, Philipp J.
Dorkenwald, Sven
Januszewski, Michał
Klimesch, Jonathan
Svara, Fabian
Mancu, Andrei
Ahmad, Hashir
Fee, Michale S.
Jain, Viren
Kornfeld, Joergen
author_sort Schubert, Philipp J.
collection PubMed
description The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries.
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spelling pubmed-96360202022-11-06 SyConn2: dense synaptic connectivity inference for volume electron microscopy Schubert, Philipp J. Dorkenwald, Sven Januszewski, Michał Klimesch, Jonathan Svara, Fabian Mancu, Andrei Ahmad, Hashir Fee, Michale S. Jain, Viren Kornfeld, Joergen Nat Methods Brief Communication The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries. Nature Publishing Group US 2022-10-24 2022 /pmc/articles/PMC9636020/ /pubmed/36280715 http://dx.doi.org/10.1038/s41592-022-01624-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Brief Communication
Schubert, Philipp J.
Dorkenwald, Sven
Januszewski, Michał
Klimesch, Jonathan
Svara, Fabian
Mancu, Andrei
Ahmad, Hashir
Fee, Michale S.
Jain, Viren
Kornfeld, Joergen
SyConn2: dense synaptic connectivity inference for volume electron microscopy
title SyConn2: dense synaptic connectivity inference for volume electron microscopy
title_full SyConn2: dense synaptic connectivity inference for volume electron microscopy
title_fullStr SyConn2: dense synaptic connectivity inference for volume electron microscopy
title_full_unstemmed SyConn2: dense synaptic connectivity inference for volume electron microscopy
title_short SyConn2: dense synaptic connectivity inference for volume electron microscopy
title_sort syconn2: dense synaptic connectivity inference for volume electron microscopy
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636020/
https://www.ncbi.nlm.nih.gov/pubmed/36280715
http://dx.doi.org/10.1038/s41592-022-01624-x
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