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Structure-function clustering in multiplex brain networks (Vol. 48 No. 1)

New multiplex structure-function clustering under variation of neural model parameters.

A key question in neuroscience is to explain how the brain’s rich repertoire arises within relatively static anatomical networks: understanding the relationships between this structure and the ‘functional’ connections (inferred from the synchronisation of activity between brain areas) it supports has the potential to address this. We employ a multiplex approach, in which anatomical and functional networks are analysed simultaneously. In particular, we consider a network describing the structural connectivity of the Macaque cortex, and a functional network derived from simulated neural activity. By comparison with single-layer approaches, our results provide the first demonstration that multiplex analyses of structure-function networks are better placed to capture emergent features of neural systems. Moreover, we propose a novel multiplex structure-function clustering measure that allows us to highlight the dependence of functional structure on the particular neural dynamical regime, and to characterise the emergent disparity between functional and anatomical networks. This divergence is fundamental to higher brain function - our new measure, that quantifies precisely this disparity, and our multiplex approach more generally, represents a new avenue towards understanding structure-function relationships at a more fundamental level.

J. J. Crofts, M. Forrester and R. D. O’Dea, Structure-function clustering in multiplex brain networks, EPL 116, 18003 (2016)
[Abstract]