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Low-dimensional morphospace of topological motifs in human fMRI brain networks.

Accepted version
Peer-reviewed

Type

Article

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Authors

Morgan, Sarah E 
Achard, Sophie 
Termenon, Maite 
Bullmore, Edward T 
Vértes, Petra E 

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.

Description

Keywords

Functional connectivity, Graph theory, Human brain networks, Morphospace, Network motifs, fMRI

Journal Title

Netw Neurosci

Conference Name

Journal ISSN

2472-1751
2472-1751

Volume Title

2

Publisher

MIT Press
Sponsorship
Medical Research Council (MR/K020706/1)
MQ: Transforming Mental Health (MQ17-24 Vertes)
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