Abstract
Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions.
Highlights
The convergence of networks science to neuroscience has opened the way to the currently well-established network neuroscience framework (Bassett and Sporns, 2017), an emerging field that aims to investigate brain organizational principles by means of networks science tools
We report the differences between the community structure subtending the two phases obtained by using the investigated algorithms, with the aim to test their accordance with the guidelines provided by the simulation studies
As for the accuracy (Figure 3, first row), all the algorithms have performance that is inversely proportional to the level of noise and directly proportional to the number of clusters simulated in the network
Summary
The convergence of networks science to neuroscience has opened the way to the currently well-established network neuroscience framework (Bassett and Sporns, 2017), an emerging field that aims to investigate brain organizational principles by means of networks science tools. This shift was driven by two aspects. Previous studies pointed out how a modular structure represents a mean to reveal non-trivial relationships between topological and functional features of the complex networks (Guimerà and Amaral, 2005) This property of the brain network is located halfway between global and local scales, at a mesoscale level, which is informative of the network’s organization (Betzel and Bassett, 2017). Communities underpin the brain network’s organization: their composition shapes the communication patterns of the system and promotes well-balanced and efficient mechanisms of integration and segregation between brain sub-systems (Betzel et al, 2013; Sporns, 2013; Wig, 2017)
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