Comparative connectomics
Open accessAbstract
We introduce comparative connectomics, the quantitative study of cross-species commonalities and variations in brain network topology that aims to discover general principles of network architecture of nervous systems and the identification of species-specific features of brain connectivity. By comparing connectomes derived from simple to more advanced species, we identify two conserved themes of wiring: the tendency to organize network topology into communities that serve specialized functionality and the general drive to enable high topological integration by means of investment of neural resources in short communication paths, hubs, and rich clubs. Within the space of wiring possibilities that conform to these common principles, we argue that differences in connectome organization between closely related species support adaptations in cognition and behavior.
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Global integration is an important candidate mechanism for the evolution of complex brain networks and for explaining how the brain creates complex, adaptive behavior.
“As such, global integration is an important candidate mechanism responsible for the evolution of complex brain networks (van den Heuvel et al., 2016), and hence, for explaining the mechanism through which the brain creates complex, adaptive behavior.”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as van den Heuvel et al., 2016
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“The connectome is a unique property, consistent across multiple species (van den Heuvel et al., 2016) and thus has important analytical value in translational research.”
Background
- Authors
- van den Heuvel, M. P., Bullmore, E. T., & Sporns, O.
- Affiliations
- GlaxoSmithKline (United Kingdom) (GB)
- Indiana University Bloomington (US)
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 20
- Issue
- 5
- Pages
- 345-361
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W4211147837
- Cited by
- 328
- References
- 168
- Citations by year
- 2026: 12 · 2025: 24 · 2024: 19 · 2023: 41 · 2022: 24 · 2021: 38 · 2020: 39 · 2019: 35 · 2018: 38 · 2017: 39 · 2016: 18 · 2015: 1
- FWCI
- 4.57
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- Apollo (University of Cambridge) (acceptedVersion, cc-by-nc-nd)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Institute for Health and Care Research (NF-SI-0513-10051)
- 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
- brain network topology, cross-species comparison, network communities, hubs, rich club
- MeSH terms
- Brain, Brain Mapping, Cognition, Communication, Nerve Net, Species Specificity, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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