ArticleScientific reports2016
The topology of large Open Connectome networks for the human brain.
Article in Scientific reports, 2016. 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.
- Benchmarking overlapping community detection methods for applications in human connectomics.Network neuroscience (Cambridge, Mass.) · 2026Article
- Learning and criticality in a self-organizing model of connectome growth.Scientific reports · 2025Article
- A generative model of the connectome with dynamic axon growth.bioRxiv : the preprint server for biology · 2024Article
- A generative model of the connectome with dynamic axon growth.Network neuroscience (Cambridge, Mass.) · 2024Article
- Hyperbolic disc embedding of functional human brain connectomes using resting-state fMRI.Network neuroscience (Cambridge, Mass.) · 2022Article
- Navigable maps of structural brain networks across species.PLoS computational biology · 2020Article
- The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges.Frontiers in physiology · 2020Article
- Dependence of Connectivity on the Logarithm of Geometric Distance in Brain Networks.Frontiers in physiology · 2020Article
- Critical synchronization dynamics of the Kuramoto model on connectome and small world graphs.Scientific reports · 2019Article
- Dependence of connectivity on geometric distance in brain networks.Scientific reports · 2019Article
- How Memory Conforms to Brain Development.Frontiers in computational neuroscience · 2019Article
- Griffiths phases in infinite-dimensional, non-hierarchical modular networks.Scientific reports · 2018Article
- Concurrence of form and function in developing networks and its role in synaptic pruning.Nature communications · 2018Article
- Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?Applied network science · 2017Article
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2 authors.
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Abstract
The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.
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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.