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neuPrint: Analysis Tools for EM Connectomics

Jody Clements, Tom Dolafi, Lowell Umayam, View ORCID ProfileNicole L. Neubarth, Stuart Berg, View ORCID ProfileLouis K. Scheffer, View ORCID ProfileStephen M. Plaza
doi: https://doi.org/10.1101/2020.01.16.909465
Jody Clements
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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Tom Dolafi
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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Lowell Umayam
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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Nicole L. Neubarth
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
2Two Six Labs, Arlington, VA, USA
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  • ORCID record for Nicole L. Neubarth
Stuart Berg
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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Louis K. Scheffer
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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Stephen M. Plaza
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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  • For correspondence: plazas@janelia.hhmi.org
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Abstract

Due to technological advances in electron microscopy (EM) and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the connections between them, for significant volumes of neural tissue. The limited scope of past reconstructions meant they were primarily used by domain experts, and performance was not a serious problem. But the new reconstructions, of common laboratory creatures such as the fruit fly Drosophila melanogaster, upend these assumptions. These natural neural networks now contain tens of thousands of neurons and tens of millions of connections between them, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. This requires new tools that are easy to use and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. neuPrint is a database and analysis ecosystem that organizes connectome data in a manner conducive to biological discovery. In particular, we propose a data model that allows users to access the connectome at different levels of abstraction primarily through a graph database, neo4j, and its powerfully expressive query language Cypher. neuPrint is compatible with modern connectome reconstruction workflows, providing tools for assessing reconstruction quality, and offering both batch and incremental updates to match modern connectome reconstruction flows. Finally, we introduce a web interface and programmer API that targets a diverse user skill set. We demonstrate the effectiveness and efficiency of neuPrint through example database queries.

Footnotes

  • https://neuprint-examples.janelia.org

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 17, 2020.
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neuPrint: Analysis Tools for EM Connectomics
Jody Clements, Tom Dolafi, Lowell Umayam, Nicole L. Neubarth, Stuart Berg, Louis K. Scheffer, Stephen M. Plaza
bioRxiv 2020.01.16.909465; doi: https://doi.org/10.1101/2020.01.16.909465
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neuPrint: Analysis Tools for EM Connectomics
Jody Clements, Tom Dolafi, Lowell Umayam, Nicole L. Neubarth, Stuart Berg, Louis K. Scheffer, Stephen M. Plaza
bioRxiv 2020.01.16.909465; doi: https://doi.org/10.1101/2020.01.16.909465

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