ArticleScientific reports2019
Dependence of connectivity on geometric distance in brain networks.
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 12 papers.
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Who cites it
12 citing papers in PubMed, 23 citations in OpenAlex.
- Distance-dependent connectivity in the brain facilitates high dynamical and structural complexity.Cognitive neurodynamics · 2026Article
- A false discovery rate control method using a fully connected hidden Markov random field for neuroimaging data.Medical image analysis · 2026Article
- Spatial development of brain networks during the first six postnatal months.Communications biology · 2025Article
- Stationarity assessment of resting state condition via permutation entropy on EEG recordings.Scientific reports · 2025Article
- Machine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging.Sensors (Basel, Switzerland) · 2024Article
- Body mass index-dependent shifts along large-scale gradients in human cortical organization explain dietary regulatory success.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Parkinson disease psychosis: from phenomenology to neurobiological mechanisms.Nature reviews. Neurology · 2024Review
- Non-invasive mapping of epileptogenic networks predicts surgical outcome.Brain : a journal of neurology · 2023Article
- DART: Distance Assisted Recursive Testing.Journal of machine learning research : JMLR · 2023Article
- Brain rhythms define distinct interaction networks with differential dependence on anatomy.Neuron · 2021Article
- The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections.NeuroImage · 2021Article
- Dependence of Connectivity on the Logarithm of Geometric Distance in Brain Networks.Frontiers in physiology · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
In any network, the dependence of connectivity on physical distance between nodes is a direct consequence of trade-off mechanisms between costs of establishing and sustaining links, processing rates, propagation speed of signals between nodes. Despite its universality, there are still few studies addressing this issue. Here we apply a recently-developed method to infer links between nodes, and possibly subnetwork structures, to determine connectivity strength as a function of physical distance between nodes. The model system we investigate is brain activity reconstructed on the cortex out of magnetoencephalography recordings sampled on a set of healthy subjects in resting state. We found that the dependence of the time scale of observability of a link on its geometric length follows a power-law characterized by an exponent whose extent is inversely proportional to connectivity. Our method provides a new tool to highlight and investigate networks in neuroscience.
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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.