ArticleScientific reports2019
A Physarum Centrality Measure of the Human Brain Network.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Therapeutic efficacy and pharmacological mechanism of Yindan Xinnaotong soft capsule on acute ischemic stroke: a meta-analysis and network pharmacology analysis.Metabolic brain disease · 2024Pooled it
- Metabolism-weighted brain connectome reveals synaptic integration and vulnerability to neurodegeneration.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- Influence of individual's age on the characteristics of brain effective connectivity.GeroScience · 2025Article
- Electroacupuncture modulates abnormal brain connectivity after ischemia reperfusion injury in rats: A graph theory-based approach.Brain and behavior · 2024Article
- Oxytocin promotes prefrontal population activity via the PVN-PFC pathway to regulate pain.Neuron · 2023Article
- Review
- Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs.Brain communications · 2022Article
- Article
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3 authors.
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Abstract
The most important goals of brain network analyses are to (a) detect pivotal regions and connections that contribute to disproportionate communication flow, (b) integrate global information, and (c) increase the brain network efficiency. Most centrality measures assume that information propagates in networks with the shortest connection paths, but this assumption is not true for most real networks given that information in the brain propagates through all possible paths. This study presents a methodological pipeline for identifying influential nodes and edges in human brain networks based on the self-regulating biological concept adopted from the Physarum model, thereby allowing the identification of optimal paths that are independent of the stated assumption. Network hubs and bridges were investigated in structural brain networks using the Physarum model. The optimal paths and fluid flow were used to formulate the Physarum centrality measure. Most network hubs and bridges are overlapped to some extent, but those based on Physarum centrality contain local and global information in the superior frontal, anterior cingulate, middle temporal gyrus, and precuneus regions. This approach also reduced individual variation. Our results suggest that the Physarum centrality presents a trade-off between the degree and betweenness centrality measures.
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