From 728e49f5fd64bd77e9a23dbbc032aa1d37f5593f Mon Sep 17 00:00:00 2001 From: Benjamin Pedigo Date: Mon, 13 Mar 2023 18:51:43 -0400 Subject: [PATCH] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4b81a55..b34b2fa 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ # Generative network modeling reveals a first quantitative definition of bilateral symmetry exhibited by a whole insect brain connectome -[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](http://docs.neurodata.io/bilateral-connectome/) +[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](http://docs.neurodata.io/bilateral-connectome/) [![DOI](https://zenodo.org/badge/410043679.svg)](https://zenodo.org/badge/latestdoi/410043679) + ## Abstract Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, or learning. 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.