Low dimensional morphospace of topological motifs in human fMRI brain networks

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 corresponds to the small-world axis and correlates strongly with the networks global efficiency. There is also some evidence that PC1 correlates with the average length of the 5% of longest edges in the network. Hence this axis represents the trade-off between the cost of long distance edges and their topological benefits. The second PC correlates with the networks assortativity. Finally, we show that the economical clustering generative model proposed by Vertes et al. can approximately reproduce the motif PC space of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualise the relationships between network properties and to study the driving forces behind the topology of fMRI brain networks.