ArticleHuman brain mapping2021
The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Benchmarking overlapping community detection methods for applications in human connectomics.Network neuroscience (Cambridge, Mass.) · 2026Article
- Decoding brain structure-function dynamics in health and in psychosis via an autoencoder.Scientific reports · 2025Article
- Community detection in the human connectome: Method types, differences and their impact on inference.Human brain mapping · 2024Article
- Dementia Risk Factors Modify Hubs but Leave Other Connectivity Measures Unchanged in Asymptomatic Individuals: A Graph Theoretical Analysis.Brain connectivity · 2022Article
- Test-retest reliability of modular-relevant analysis in brain functional network.Frontiers in neuroscience · 2022Article
- The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults.Frontiers in neuroscience · 2022Article
- The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks.Human brain mapping · 2021Article
- Composite reaction time and variability correlate with whole-brain white-matter characteristics.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
3 authors.
Funding
Abstract
A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test-retest data from the Human Connectome Project), the repeatability of thirty-three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi-scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme.
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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.