Article

Low dimensional morphospace of topological motifs in human fMRI brain networks

2017-06-21

Abstract excerpt

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 correlat...

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Literature Corpus work: 11f06ca0-b15f-53df-872d-b969fb20e789
DOI: 10.1101/153320

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