Cortical high-density counterstream architectures
Open accessAbstract
Small-world networks provide an appealing description of cortical architecture owing to their capacity for integration and segregation combined with an economy of connectivity. Previous reports of low-density interareal graphs and apparent small-world properties are challenged by data that reveal high-density cortical graphs in which economy of connections is achieved by weight heterogeneity and distance-weight correlations. These properties define a model that predicts many binary and weighted features of the cortical network including a core-periphery, a typical feature of self-organizing information processing systems. Feedback and feedforward pathways between areas exhibit a dual counterstream organization, and their integration into local circuits constrains cortical computation. Here, we propose a bow-tie representation of interareal architecture derived from the hierarchical laminar weights of pathways between the high-efficiency dense core and periphery.
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Tracer studies in non-human primates show that these areas are connected as a high-efficiency cortical core with high-density connectivity.
“Recent tracer studies in non-human primates indicate that these areas indeed connect together as a “high-efficiency cortical core” with high-density connectivity (Markov et al., 2013) (Figure 1C).”
From Conscious Processing and the Global Neuronal Workspace Hypothesis (2020) · cites this paper as Markov et al., 2013
Recent neuroanatomical studies point to additional pathways besides classical forward and backward connections.
“This is interesting, given that recent neuroanatomical studies point to additional pathways besides the classical forward (from lower-level supragranular to higher-level granular layers) and backward (from higher-level infragranular to lower-level extragranular layers) connections (Markov et al., 2013, 2014).”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Markov et al., 2013
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Self-citationCriteria for optimizing cortical hierarchies with continuous ranges
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The generalized Hierarchical Gaussian Filter
Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., & Mathys, C. (2026) · eLife · doi:10.7554/elife.110174
- Authors
- Markov, N. T., Ercsey-Ravasz, M., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H.
- Affiliations
- Lyon 1 Université (FR)
- Babeș-Bolyai University (RO)
- Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110–1093, USA.
- Stem cell and Brain Research Institute, INSERM U846, 18 Avenue Doyen Lépine, 69500 Bron, France.
- University of Notre Dame (US)
- Corresponding author
- Toroczkai, Zoltán
- Kennedy, Henry
- Venue
- Science
- Published
- 2013-11
- Volume
- 342
- Issue
- 6158
- Pages
- 1238406-1238406
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3905047
- OpenAlex
- W1993665893
- Cited by
- 616
- References
- 133
- Citations by year
- 2026: 29 · 2025: 52 · 2024: 53 · 2023: 48 · 2022: 61 · 2021: 48 · 2020: 47 · 2019: 57 · 2018: 41 · 2017: 43 · 2016: 51 · 2015: 47 · 2014: 37 · 2013: 2
- FWCI
- 3.88
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- 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
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- cortical connectivity, small-world networks, core-periphery structure
- MeSH terms
- Cerebral Cortex, Humans, Mental Processes, Models, Neurological, Nerve Net
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