Structure-function clustering in multiplex brain networks

Abstract

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.

Multilayer modeling and analysis of human brain networks

“…The novel multilayer framework provides a unique opportunity to study, simultaneously, structural and functional information, and, in fact, it has been recently used for this purpose [59,60]. …”

Multilayer network modeling of integrated biological systems

“…In the case of the model organism C. elegans, a multilayer network with alternative modes of interaction (synaptic, gap junction, and neuromodulator) between neurons has been introduced [23,24], allowing for a better understanding of aminergic and peptidergic modulation of behaviour [25]. Anatomical and functional information has been integrated to gain insights about the macro-scale topology of the Macaque monkey [26] and the human brain [27]. Temporal [28,29] and multi-frequency [30,31,32,33] decompositions of human brain activity, followed by their successive integration into multilayer networks, have been used to improve our understanding of brain function in cognitive tasks and brain diseases, such as Alzheimer's, Parkinson's or Schizophrenia.…”

Joint Pairing and Structured Mapping of Convolutional Brain Morphological Multiplexes for Early Dementia Diagnosis

“…Previous research on dementia state classification showed that using multilayer networks (i.e., stacking different networks) improved the prediction accuracy for disease identification when compared with using single-view networks (Crofts et al, 2016;Giuliano Zippo and Castiglioni, 2016;La Rocca et al, 2017). However, none of these multilayer network-based methods explored the relationship between two consecutive layers in the network or cortical morphology (Brown and Hamarneh, 2016).…”