Oct 30, 2018

Eigenvector-based community detection for identifying information hubs in neuronal networks

BioRxiv : the Preprint Server for Biology
Ruaridh Alexander Clark, Malcolm Macdonald

Abstract

Eigenvectors of networked systems are known to reveal central, well-connected, network vertices. Here we expand upon the known applications of eigenvectors to define well-connected communities where each is associated with a prominent vertex. This form of community detection provides an analytical approach for analysing the dynamics of information flow in a network. When applied to the neuronal network of the nematode Caenorhabditis elegans, known circuitry can be identified as separate eigenvector-based communities. For the macaque's neuronal network, community detection can expose the hippocampus as an information hub; this result contradicts current thinking that the analysis of static graphs cannot reveal such insights. The application of community detection on a large scale human connectome (around 1.8 million vertices) reveals the most prominent information carrying pathways present during a magnetic resonance imaging scan. We demonstrate that these pathways can act as an effective unique identifier for a subject's brain by assessing the number of matching pathways present in any two connectomes.

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Mentioned in this Paper

Biochemical Pathway
Biological Neural Networks
Connectome
Magnetic Resonance Imaging
Caenorhabditis elegans
Exercise, Isometric
Neurons
Brain
Human Connectome
Genus Hippocampus

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