Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes

Graph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have previously been used to match individual neurons in nanoscale connectomes - in particular, to find pairings of neurons across hemispheres. However, since graph matching techniques deal specifically with two isolated networks, they have only utilized the ipsilateral (same hemisphere) subgraphs when performing the matching. Here, we present a modification to a state-of-the-art graph matching algorithm which allows it to solve what we call the bisected graph matching problem. This modification allows us to also use connections between the brain hemispheres when predicting neuron pairs. Via simulations and real connectome datasets we show that when edge correlation is present between the contralateral (between hemisphere) subgraphs, this approach improves matching accuracy. We expect that our proposed method will improve future endeavors to accurately match neurons across hemispheres in connectomes, and be useful in other applications where the bisected graph matching problem arises. ### Competing Interest Statement The authors have declared no competing interest.