ArticleScientific reports2018
Topological structures are consistently overestimated in functional complex networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives.Frontiers in network physiology · 2023Review
- Statistical inference links data and theory in network science.Nature communications · 2022Review
- Principles and open questions in functional brain network reconstruction.Human brain mapping · 2021Review
- An Early Stage Researcher's Primer on Systems Medicine Terminology.Network and systems medicine · 2021Review
- Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients.Brain topography · 2021Article
- The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges.Frontiers in physiology · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Functional complex networks have meant a pivotal change in the way we understand complex systems, being the most outstanding one the human brain. These networks have classically been reconstructed using a frequentist approach that, while simple, completely disregards the uncertainty that derives from data finiteness. We provide here an alternative solution based on Bayesian inference, with link weights treated as random variables described by probability distributions, from which ensembles of networks are sampled. By using both statistical and topological considerations, we prove that the role played by links' uncertainty is equivalent to the introduction of a random rewiring, whose omission leads to a consistent overestimation of topological structures. We further show that this bias is enhanced in short time series, suggesting the existence of a theoretical time resolution limit for obtaining reliable structures. We also propose a simple sampling process for correcting topological values obtained in frequentist networks. We finally validate these concepts through synthetic and real network examples, the latter representing the brain electrical activity of a group of people during a cognitive task.
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Registered trials
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