Colored motifs reveal computational building blocks in the C. elegans brain

TL;DR

This paper introduces a method combining structural network motifs with node function information in the C. elegans brain, revealing functionally significant motifs that highlight information processing pathways and are more abundant than expected by chance.

Contribution

It presents a novel approach to analyze complex networks by integrating topological motifs with functional data, enabling identification of biologically meaningful sub-network patterns.

Findings

Colored motifs are significantly more abundant than expected by chance.

Motifs emphasize feed-forward information processing and avoid feedback loops.

Interneurons are over-represented in common motifs, supporting their role in information transduction.

Abstract

Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex network into its functional (rather than topological) motifs as long as both wiring and functional information is available.