A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule
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Recent advances in neuroscience have engendered interest in large-scale brain networks. Using a consistent database of cortico-cortical connectivity, generated from hemisphere-wide, retrograde tracing experiments in the macaque, we analyzed interareal weights and distances to reveal an important organizational principle of brain connectivity. Using appropriate graph theoretical measures, we show that although very dense (66%), the interareal network has strong structural specificity. Connection weights exhibit a heavy-tailed lognormal distribution spanning five orders of magnitude and conform to a distance rule reflecting exponential decay with interareal separation. A single-parameter random graph model based on this rule predicts numerous features of the cortical network: (1) the existence of a network core and the distribution of cliques, (2) global and local binary properties, (3) global and local weight-based communication efficiencies modeled as network conductance, and (4) overall wire-length minimization. These findings underscore the importance of distance and weight-based heterogeneity in cortical architecture and processing.
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There is a true falloff of both structural and functional connectivity with distance in a number of mammalian species.
“true falloff of both structural and functional connectivity with distance has been shown with high-resolution invasive imaging as well as tractography in a number of mammalian species (Horvát et al., 2016; Markov et al., 2013; Perinelli et al., 2019; Roberts et al., 2016; Rubinov et al., 2015; Supekar et al., 2009).”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Markov et al., 2013
The Abstract and Introduction report that structural corticocortical connection weights and connection probability fall with distance in macaques, using retrograde-tracing data. Our copy does not show a comparable falloff in functional connectivity, demonstrate it across multiple mammalian species, or report tractography evidence. (LLM, conf=0.89)
Experimental — accuracy not yet measuredThe brain is a spatially embedded network with properties partially inherited from geometry.
“the brain is itself a spatially embedded network with properties partially inherited from geometry: there is a metabolic wiring cost in the establishment of long-range connections”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Markov et al., 2013
This extends previously reported findings that cortico-cortical connections follow a lognormal distribution in primate and mouse cortex to the entire mouse brain.
“This extends previously reported findings that cortico-cortical connections follow a lognormal distribution in the primate24,25 and mouse cortex26 to the entire mouse brain.”
From A mesoscale connectome of the mouse brain (2014) · cites this paper as [25]
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- Authors
- Ercsey-Ravasz, M., Markov, N. T., Lamy, C., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H.
- Affiliations
- University of Notre Dame (US)
- Lyon 1 Université (FR)
- Corresponding author
- Toroczkai, Zoltán
- Kennedy, Henry
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Issue
- 1
- Pages
- 184-197
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC3954498
- OpenAlex
- W2167845354
- Cited by
- 487
- References
- 89
- Citations by year
- 2026: 38 · 2025: 49 · 2024: 45 · 2023: 41 · 2022: 36 · 2021: 48 · 2020: 44 · 2019: 46 · 2018: 29 · 2017: 28 · 2016: 38 · 2015: 27 · 2014: 15 · 2013: 3
- FWCI
- 15.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-11-BSV4-0005, ANR-11-LABX-0042)
- National Institute of Mental Health (R01 MH60974, R01MH060974)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- corticocortical connections, cerebral cortex, random graph models, core network, cliques, communication efficiency, wirelength minimization, macaque, graph theory, brain network organization
- MeSH terms
- Animals, Brain Mapping, Cerebral Cortex, Humans, Macaca, Models, Neurological, Nerve Net
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