ArticleScientific reports2017
The Robustness and the Doubly-Preferential Attachment Simulation of the Consensus Connectome Dynamics of the Human Brain.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Introducing and applying Newtonian blurring: an augmented dataset of 126,000 human connectomes at braingraph.org.Scientific reports · 2022Article
- Identifying super-feminine, super-masculine and sex-defining connections in the human braingraph.Cognitive neurodynamics · 2021Article
- The braingraph.org database with more than 1000 robust human connectomes in five resolutions.Cognitive neurodynamics · 2021Article
- The Graph of Our Mind.Brain sciences · 2021Article
- Good neighbors, bad neighbors: the frequent network neighborhood mapping of the hippocampus enlightens several structural factors of the human intelligence on a 414-subject cohort.Scientific reports · 2020Article
- Scaling in Colloidal and Biological Networks.Entropy (Basel, Switzerland) · 2020Review
- The frequent complete subgraphs in the human connectome.PloS one · 2020Article
- Raising the Connectome: The Emergence of Neuronal Activity and Behavior inFrontiers in cellular neuroscience · 2020Article
- The Frequent Network Neighborhood Mapping of the human hippocampus shows much more frequent neighbor sets in males than in females.PloS one · 2020Article
- The frequent subgraphs of the connectome of the human brain.Cognitive neurodynamics · 2019Article
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3 authors.
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Abstract
Consensus Connectome Dynamics (CCD) is a remarkable phenomenon of the human connectomes (braingraphs) that was discovered by continuously decreasing the minimum confidence-parameter at the graphical interface of the Budapest Reference Connectome Server, which depicts the cerebral connections of n = 418 subjects with a frequency-parameter k: For any k = 1, 2, …, n one can view the graph of the edges that are present in at least k connectomes. If parameter k is decreased one-by-one from k = n through k = 1 then more and more edges appear in the graph, since the inclusion condition is relaxed. The surprising observation is that the appearance of the edges is far from random: it resembles a growing, complex structure. We hypothesize that this growing structure copies the axonal development of the human brain. Here we show the robustness of the CCD phenomenon: it is almost independent of the particular choice of the set of underlying connectomes. This result shows that the CCD phenomenon is most likely a biological property of the human brain and not just a property of the data sets examined. We also present a simulation that well-describes the growth of the CCD structure: in our random graph model a doubly-preferential attachment distribution is found to mimic the CCD.
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