From Caenorhabditis elegans to the Human Connectome: A Specific Modular Organisation Increases Metabolic, Functional, and Developmental Efficiency

Abstract

The connectome, or the entire connectivity of a neural system represented by network, ranges various scales from synaptic connections between individual neurons to fibre tract connections between brain regions. Although the modularity they commonly show has been extensively studied, it is unclear whether connection specificity of such networks can already be fully explained by the modularity alone. To answer this question, we study two networks, the neuronal network of C. elegans and the fibre tract network of human brains yielded through diffusion spectrum imaging (DSI). We compare them to their respective benchmark networks with varying modularities, which are generated by link swapping to have desired modularity values but otherwise maximally random. We find several network properties that are specific to the neural networks and cannot be fully explained by the modularity alone. First, the clustering coefficient and the characteristic path length of C. elegans and human connectomes are both higher than those of the benchmark networks with similar modularity. High clustering coefficient indicates efficient local information distribution and high characteristic path length suggests reduced global integration. Second, the total wiring length is smaller than for the alternative configurations with similar modularity. This is due to lower dispersion of connections, which means each neuron in C. elegans connectome or each region of interest (ROI) in human connectome reaches fewer ganglia or cortical areas, respectively. Third, both neural networks show lower algorithmic entropy compared to the alternative arrangements. This implies that fewer rules are needed to encode for the organisation of neural systems.

References in corpus (15)

• Fast algorithm for detecting community structure in networks
• Hierarchical organization of modularity in metabolic networks
• Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems
• A Tutorial in Connectome Analysis: Topological and Spatial Features of Brain Networks
• Functional modularity of background activities in normal and epileptic brain networks
• Preferential Detachment During Human Brain Development: Age- and Sex-Specific Structural Connectivity in Diffusion Tensor Imaging (DTI) Data
• Optimal hierarchical modular topologies for producing limited sustained activation of neural networks
• A simple rule for axon outgrowth and synaptic competition generates realistic connection lengths and filling fractions
• Criticality of spreading dynamics in hierarchical cluster networks without inhibition
• Critical Networks Exhibit Maximal Information Diversity in Structure-Dynamics Relationships
• Neural development features: Spatio-temporal development of the Caenorhabditis elegans neuronal network
• Evolution and development of Brain Networks: From Caenorhabditis elegans to Homo sapiens
• Constancy and trade-offs in the neuroanatomical and metabolic design of the cerebral cortex
• Developmental time windows for spatial growth generate multiple-cluster small-world networks
• Graph Compression -- Save Information by Exploiting Redundancy

Cited by in corpus (5)

• Flow-based network analysis of the Caenorhabditis elegans connectome
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• Effect of clustering on Turing instability in complex networks
• Application of discrete Ricci curvature in pruning randomly wired neural networks: A case study with chest x-ray classification of COVID-19