Abstract
We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs. The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs, and we observe that two principal components (PCs) can account for 97% of the motif variability. The first PC of the motif distribution is correlated with efficiency and inversely correlated with transitivity. Hence this axis approximately conforms to the well-known economical small-world trade-off between integration and segregation in brain networks. Finally, we show that the economical clustering generative model proposed by Vértes et al. (2012) can approximately reproduce the motif morphospace of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualize the relationships between network properties and to investigate generative or constraining factors in the formation of complex human brain functional networks.
Highlights
Understanding the factors influencing the global topology of Functional magnetic resonance imaging (fMRI) brain networks is a longheld ambition of network neuroscience
We combine the concepts of motifs and morphospaces to create the first motif morphospace of fMRI brain networks
We observe strong correlations between the networks’ positions in morphospace and their global topological properties, suggesting that motif morphospaces are a powerful way to capture the topology of networks in a low-dimensional space and to compare generative models of brain networks
Summary
Understanding the factors influencing the global topology of fMRI brain networks is a longheld ambition of network neuroscience. The energetic cost of forming and maintaining longdistance connections has long been established as an important factor shaping brain networks (Bullmore & Sporns, 2012; Ramon y Cajal, 1995; Young, 1992); for example, as early as 1899 Ramón y Cajal wrote about the importance of conservation of time, space and material in determining neuronal morphology and connections, (see chapter 5 of Ramon y Cajal, 1995). Low-dimensional morphospace of motifs in human fMRI brain networks. Functional magnetic resonance imaging (fMRI): An imaging technique that estimates brain activity by measuring changes in blood oxygenation levels locally, across the brain. Global efficiency: A measure of the network’s integration, inversely related to the average shortest path lengths between all pairs of nodes. Network motif: A small subgraph of the network, which can provide information about local connectivity patterns. Small-world network: A network with both high clustering and short path length compared with randomized networks
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