A joint graph inference case study: the C. elegans chemical and electrical connectomes

Li Chen; Joshua T Vogelstein; Vince Lyzinski; Carey E Priebe

doi:10.1080/21624054.2016.1142041

A joint graph inference case study: the C. elegans chemical and electrical connectomes

Worm. 2016 Mar 16;5(2):e1142041. doi: 10.1080/21624054.2016.1142041. eCollection 2016 Apr-Jun.

Authors

Li Chen¹, Joshua T Vogelstein², Vince Lyzinski³, Carey E Priebe⁴

Affiliations

¹ Software and Service Group, Intel Corporation , Hillsboro, OR, USA.
² Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University , Baltimore, MD, USA.
³ Johns Hopkins University Human Language Technology Center of Excellence , Baltimore, MD, USA.
⁴ Department of Applied Mathematics and Statistics, Johns Hopkins University , Baltimore, MD, USA.

Abstract

We investigate joint graph inference for the chemical and electrical connectomes of the Caenorhabditis elegans roundworm. The C. elegans connectomes consist of [Formula: see text] non-isolated neurons with known functional attributes, and there are two types of synaptic connectomes, resulting in a pair of graphs. We formulate our joint graph inference from the perspectives of seeded graph matching and joint vertex classification. Our results suggest that connectomic inference should proceed in the joint space of the two connectomes, which has significant neuroscientific implications.

Keywords: fusion inference; joint graph inference; joint vertex classification; neural connectome; omnibus embedding; seeded graph matching.