Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome
- Johns Hopkins University, United States
- University of Cambridge, United Kingdom
Abstract
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.
Data availability
All code used for analysis is available at https://github.com/neurodata/bilateral-connectome, and the version for this submission is archived at https://doi.org/10.5281/zenodo.7733481.All data analyzed in this study were generated in Winding, Pedigo et al. "The connectome of an insect brain," Science (2023) https://www.science.org/doi/10.1126/science.add9330.These data are also included for convenience in the code repository linked above and as Figure 1 - Source Data 1.
Article and author information
Author details
Joshua T Vogelstein
Funding
National Science Foundation (DGE1746891)
- Benjamin D Pedigo
National Science Foundation (1942963)
- Joshua T Vogelstein
National Science Foundation (2014862)
- Joshua T Vogelstein
National Institutes of Health (1RF1MH123233-01)
- Carey E Priebe
- Joshua T Vogelstein
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Srdjan Ostojic, Ecole Normale Superieure Paris, France
Version history
- Received: September 27, 2022
- Preprint posted: November 28, 2022 (view preprint)
- Accepted: March 27, 2023
- Accepted Manuscript published: March 28, 2023 (version 1)
- Version of Record published: April 19, 2023 (version 2)
Copyright
© 2023, Pedigo et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 910
- views
-
- 147
- downloads
-
- 2
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome
eLife 12:e83739.
https://doi.org/10.7554/eLife.83739
Further reading
-
- Genetics and Genomics
- Neuroscience
Single-cell RNA sequencing reveals the extent to which marmosets carry genetically distinct cells from their siblings.
-
- Neuroscience
We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words’ linguistic processing raised neural complexity. The words’ semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.
