DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries

Abstract Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to q...

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Main Authors: Jordan K. Matelsky, Elizabeth P. Reilly, Erik C. Johnson, Jennifer Stiso, Danielle S. Bassett, Brock A. Wester, William Gray-Roncal
Format: Article
Language:English
Published: Nature Publishing Group 2021-06-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-91025-5
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spelling doaj-5af554d11b0f4e378002f4870edab4512021-06-27T11:30:35ZengNature Publishing GroupScientific Reports2045-23222021-06-0111111410.1038/s41598-021-91025-5DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queriesJordan K. Matelsky0Elizabeth P. Reilly1Erik C. Johnson2Jennifer Stiso3Danielle S. Bassett4Brock A. Wester5William Gray-Roncal6The Johns Hopkins University Applied Physics LaboratoryThe Johns Hopkins University Applied Physics LaboratoryThe Johns Hopkins University Applied Physics LaboratoryDepartment of Bioengineering, School of Engineering and Applied Science, University of PennsylvaniaDepartment of Bioengineering, School of Engineering and Applied Science, University of PennsylvaniaThe Johns Hopkins University Applied Physics LaboratoryThe Johns Hopkins University Applied Physics LaboratoryAbstract Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.https://doi.org/10.1038/s41598-021-91025-5
collection DOAJ
language English
format Article
sources DOAJ
author Jordan K. Matelsky
Elizabeth P. Reilly
Erik C. Johnson
Jennifer Stiso
Danielle S. Bassett
Brock A. Wester
William Gray-Roncal
spellingShingle Jordan K. Matelsky
Elizabeth P. Reilly
Erik C. Johnson
Jennifer Stiso
Danielle S. Bassett
Brock A. Wester
William Gray-Roncal
DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries
Scientific Reports
author_facet Jordan K. Matelsky
Elizabeth P. Reilly
Erik C. Johnson
Jennifer Stiso
Danielle S. Bassett
Brock A. Wester
William Gray-Roncal
author_sort Jordan K. Matelsky
title DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries
title_short DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries
title_full DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries
title_fullStr DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries
title_full_unstemmed DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries
title_sort dotmotif: an open-source tool for connectome subgraph isomorphism search and graph queries
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-06-01
description Abstract Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.
url https://doi.org/10.1038/s41598-021-91025-5
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