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Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome
Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115445/ https://www.ncbi.nlm.nih.gov/pubmed/36976249 http://dx.doi.org/10.7554/eLife.83739 |
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author | Pedigo, Benjamin D Powell, Mike Bridgeford, Eric W Winding, Michael Priebe, Carey E Vogelstein, Joshua T |
author_facet | Pedigo, Benjamin D Powell, Mike Bridgeford, Eric W Winding, Michael Priebe, Carey E Vogelstein, Joshua T |
author_sort | Pedigo, Benjamin D |
collection | PubMed |
description | Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensively applied to nanoscale connectomes. Here, we investigate this problem via a case study on the bilateral symmetry of a larval Drosophila brain connectome. We translate notions of ‘bilateral symmetry’ to generative models of the network structure of the left and right hemispheres, allowing us to test and refine our understanding of symmetry. We find significant differences in connection probabilities both across the entire left and right networks and between specific cell types. By rescaling connection probabilities or removing certain edges based on weight, we also present adjusted definitions of bilateral symmetry exhibited by this connectome. This work shows how statistical inferences from networks can inform the study of connectomes, facilitating future comparisons of neural structures. |
format | Online Article Text |
id | pubmed-10115445 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-101154452023-04-20 Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome Pedigo, Benjamin D Powell, Mike Bridgeford, Eric W Winding, Michael Priebe, Carey E Vogelstein, Joshua T eLife Neuroscience Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensively applied to nanoscale connectomes. Here, we investigate this problem via a case study on the bilateral symmetry of a larval Drosophila brain connectome. We translate notions of ‘bilateral symmetry’ to generative models of the network structure of the left and right hemispheres, allowing us to test and refine our understanding of symmetry. We find significant differences in connection probabilities both across the entire left and right networks and between specific cell types. By rescaling connection probabilities or removing certain edges based on weight, we also present adjusted definitions of bilateral symmetry exhibited by this connectome. This work shows how statistical inferences from networks can inform the study of connectomes, facilitating future comparisons of neural structures. eLife Sciences Publications, Ltd 2023-03-28 /pmc/articles/PMC10115445/ /pubmed/36976249 http://dx.doi.org/10.7554/eLife.83739 Text en © 2023, Pedigo et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Pedigo, Benjamin D Powell, Mike Bridgeford, Eric W Winding, Michael Priebe, Carey E Vogelstein, Joshua T Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome |
title | Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome |
title_full | Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome |
title_fullStr | Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome |
title_full_unstemmed | Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome |
title_short | Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome |
title_sort | generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115445/ https://www.ncbi.nlm.nih.gov/pubmed/36976249 http://dx.doi.org/10.7554/eLife.83739 |
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