ArticleNetwork neuroscience (Cambridge, Mass.)2020
Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks.
Article in Network neuroscience (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Topological insights into the neural basis of flexible behavior.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Alterations in white matter network dynamics in patients with schizophrenia and bipolar disorder.Human brain mapping · 2022Article
- The hidden community architecture of human brain networks.Scientific reports · 2022Article
- Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model.Proceedings of machine learning research · 2021Article
- Editorial: Network Communication in the Brain.Network neuroscience (Cambridge, Mass.) · 2020Article
- Article
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4 authors.
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Abstract
The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step toward the ability to manipulate the brain's large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that
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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.