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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2021-06-01
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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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