Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Pedigo, Benjamin D, Powell, Mike, Bridgeford, Eric W, Winding, Michael, Priebe, Carey E, Vogelstein, Joshua T
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
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
_version_ 1785028215448797184
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
work_keys_str_mv AT pedigobenjamind generativenetworkmodelingrevealsquantitativedefinitionsofbilateralsymmetryexhibitedbyawholeinsectbrainconnectome
AT powellmike generativenetworkmodelingrevealsquantitativedefinitionsofbilateralsymmetryexhibitedbyawholeinsectbrainconnectome
AT bridgefordericw generativenetworkmodelingrevealsquantitativedefinitionsofbilateralsymmetryexhibitedbyawholeinsectbrainconnectome
AT windingmichael generativenetworkmodelingrevealsquantitativedefinitionsofbilateralsymmetryexhibitedbyawholeinsectbrainconnectome
AT priebecareye generativenetworkmodelingrevealsquantitativedefinitionsofbilateralsymmetryexhibitedbyawholeinsectbrainconnectome
AT vogelsteinjoshuat generativenetworkmodelingrevealsquantitativedefinitionsofbilateralsymmetryexhibitedbyawholeinsectbrainconnectome