Trial reportPloS one2014
Validation of network communicability metrics for the analysis of brain structural networks.
Trial report in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Multidimensional structural-functional coupling uncovers network dysregulation and predicts binge-eating severity in bulimia nervosa.BMC medicine · 2025Article
- Brain signaling becomes less integrated and more segregated with age.bioRxiv : the preprint server for biology · 2024Article
- Brain signaling becomes less integrated and more segregated with age.Network neuroscience (Cambridge, Mass.) · 2024Article
- Brain network communication: concepts, models and applications.Nature reviews. Neuroscience · 2023Review
- Abnormal Structural Network Communication Reflects Cognitive Deficits in Schizophrenia.Brain topography · 2023Article
- Resolving inter-regional communication capacity in the human connectome.Network neuroscience (Cambridge, Mass.) · 2023Article
- Percolation in networks with local homeostatic plasticity.Nature communications · 2022Article
- The reliability and heritability of cortical folds and their genetic correlations across hemispheres.Communications biology · 2020Article
- Multimodal mapping of the face connectome.Nature human behaviour · 2020Article
- Asymmetric high-order anatomical brain connectivity sculpts effective connectivity.Network neuroscience (Cambridge, Mass.) · 2020Article
- Article
- Network communication models improve the behavioral and functional predictive utility of the human structural connectome.Network neuroscience (Cambridge, Mass.) · 2020Article
- Consistency and differences between centrality measures across distinct classes of networks.PloS one · 2019Article
- Network-Based Asymmetry of the Human Auditory System.Cerebral cortex (New York, N.Y. : 1991) · 2018Article
- Navigation of brain networks.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Efficient communication dynamics on macro-connectome, and the propagation speed.Scientific reports · 2018Article
- Neural Reorganization Due to Neonatal Amygdala Lesions in the Rhesus Monkey: Changes in Morphology and Network Structure.Cerebral cortex (New York, N.Y. : 1991) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.
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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.