ArticleFrontiers in systems neuroscience2021
A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks.
Article in Frontiers in systems neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Article
- Musicians' brains at rest: multilayer network analysis of magnetoencephalography data.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Multilayer network analysis in patients with end-stage kidney disease.Scientific reports · 2024Article
- Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.Sleep & breathing = Schlaf & Atmung · 2024Article
- Multilayer network analysis in patients with juvenile myoclonic epilepsy.Neuroradiology · 2024Article
- Article
- Involvement of the default mode network in patients with transient global amnesia: multilayer network.Neuroradiology · 2023Article
- Multilayer network changes in patients with migraine.Brain and behavior · 2023Article
- Community detection in multi-frequency EEG networks.Scientific reports · 2023Article
- Community detection in brain connectomes with hybrid quantum computing.Scientific reports · 2023Article
- A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest.Informatics in medicine unlocked · 2022Article
- Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold.Sensors (Basel, Switzerland) · 2021Article
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3 authors.
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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
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