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Structure-function clustering in multiplex brain networks

Crofts, Jonathan J.; Forrester, Michael; O'Dea, Reuben D.


Jonathan J. Crofts

Michael Forrester


A key question in neuroscience is to understand how a rich functional repertoire of brain activity arises within relatively static networks of structurally connected neural populations: elucidating the subtle interactions between evoked "functional connectivity" and the underlying "structural connectivity" has the potential to address this. These structural-functional networks (and neural networks more generally) are more naturally described using a multilayer or multiplex network approach, in favour of standard single-layer network analyses that are more typically applied to such systems. In this letter, we address such issues by exploring important structure-function relations in the Macaque cortical network by modelling it as a duplex network that comprises an anatomical layer, describing the known (macro-scale) network topology of the Macaque monkey, and a functional layer derived from simulated neural activity. We investigate and characterize correlations between structural and functional layers, as system parameters controlling simulated neural activity are varied, by employing recently described multiplex network measures. Moreover, we propose a novel measure of multiplex structure-function clustering which allows us to investigate the emergence of functional connections that are distinct from the underlying cortical structure, and to highlight the dependence of multiplex structure on the neural dynamical regime.


Crofts, J. J., Forrester, M., & O'Dea, R. D. (2016). Structure-function clustering in multiplex brain networks. EPL, 116(1), Article 18003.

Journal Article Type Article
Acceptance Date Oct 20, 2016
Publication Date Nov 11, 2016
Deposit Date Sep 8, 2017
Publicly Available Date Sep 8, 2017
Journal Europhysics Letters
Print ISSN 0295-5075
Electronic ISSN 1286-4854
Publisher EPL Association
Peer Reviewed Peer Reviewed
Volume 116
Issue 1
Article Number 18003
Public URL
Publisher URL


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