Volume 116, Number 1, October 2016
|Number of page(s)||7|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||11 November 2016|
Structure-function clustering in multiplex brain networks
1 Nottingham Trent University - School of Science and Technology - Nottingham, NG11 8NS, UK
2 University of Nottingham - Centre for Mathematical Medicine and Biology, School of Mathematical Sciences Nottingham, NG7 2RD, UK
Received: 8 September 2016
Accepted: 20 October 2016
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.
PACS: 87.18.Sn – Neural networks and synaptic communication / 89.75.-k – Complex systems / 87.18.Vf – Systems biology
© EPLA, 2016
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