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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2023-03-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/83739 |
| _version_ | 1797844148237107200 |
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| author | Benjamin D Pedigo Mike Powell Eric W Bridgeford Michael Winding Carey E Priebe Joshua T Vogelstein |
| author_facet | Benjamin D Pedigo Mike Powell Eric W Bridgeford Michael Winding Carey E Priebe Joshua T Vogelstein |
| author_sort | Benjamin D Pedigo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T17:17:41Z |
| format | Article |
| id | doaj.art-cbf2a17f659b482fb9dd712226e51cad |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-09T17:17:41Z |
| publishDate | 2023-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-cbf2a17f659b482fb9dd712226e51cad2023-04-19T13:30:12ZengeLife Sciences Publications LtdeLife2050-084X2023-03-011210.7554/eLife.83739Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectomeBenjamin D Pedigo0https://orcid.org/0000-0002-9519-1190Mike Powell1https://orcid.org/0000-0003-2749-3725Eric W Bridgeford2https://orcid.org/0000-0001-6115-719XMichael Winding3https://orcid.org/0000-0003-1965-3266Carey E Priebe4https://orcid.org/0000-0002-0139-7201Joshua T Vogelstein5https://orcid.org/0000-0003-2487-6237Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United StatesDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, United StatesDepartment of Biostatistics, Johns Hopkins University, Baltimore, United StatesDepartment of Zoology, University of Cambridge, Cambridge, United Kingdom; Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United StatesDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, United StatesDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, United StatesComparing 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.https://elifesciences.org/articles/83739connectomicsstatistical modelingelectron microscopycircuit modeling |
| spellingShingle | Benjamin D Pedigo Mike Powell Eric W Bridgeford Michael Winding Carey E Priebe Joshua T Vogelstein Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome eLife connectomics statistical modeling electron microscopy circuit modeling |
| 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 | connectomics statistical modeling electron microscopy circuit modeling |
| url | https://elifesciences.org/articles/83739 |
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