ArticleNature communications2020
Robust dynamic community detection with applications to human brain functional networks.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 61 citations in OpenAlex.
- TFFBN-HDLF: a hybrid deep learning framework based on time-frequency functional brain networks for epileptic seizure detection.Frontiers in medicine · 2026Article
- Article
- Uncovering migration systems through spatio-temporal tensor co-clustering.Scientific reports · 2024Article
- Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures.PLoS computational biology · 2023Article
- Brain network dynamics codify heterogeneity in seizure evolution.Brain communications · 2022Article
- An improved two-stage label propagation algorithm based on LeaderRank.PeerJ. Computer science · 2022Article
- Ranking of communities in multiplex spatiotemporal models of brain dynamics.Applied network science · 2022Article
- NMDA receptor inhibition increases, synchronizes, and stabilizes the collective pancreatic beta cell activity: Insights through multilayer network analysis.PLoS computational biology · 2021Article
- Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle.Network neuroscience (Cambridge, Mass.) · 2021Article
- Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
While current technology permits inference of dynamic brain networks over long time periods at high temporal resolution, the detailed structure of dynamic network communities during human seizures remains poorly understood. We introduce a new methodology that addresses critical aspects unique to the analysis of dynamic functional networks inferred from noisy data. We propose a dynamic plex percolation method (DPPM) that is robust to edge noise, and yields well-defined spatiotemporal communities that span forward and backwards in time. We show in simulation that DPPM outperforms existing methods in accurately capturing certain stereotypical dynamic community behaviors in noisy situations. We then illustrate the ability of this method to track dynamic community organization during human seizures, using invasive brain voltage recordings at seizure onset. We conjecture that application of this method will yield new targets for surgical treatment of epilepsy, and more generally could provide new insights in other network neuroscience applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.