The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
The contribution of geometry to the human connectome
Open accessAbstract
The human connectome is a topologically complex, spatially embedded network. While its topological properties have been richly characterized, the constraints imposed by its spatial embedding are poorly understood. By applying a novel resampling method to tractography data, we show that the brain's spatial embedding makes a major, but not definitive, contribution to the topology of the human connectome. We first identify where the brain's structural hubs would likely be located if geometry was the sole determinant of brain topology. Empirical networks show a widespread shift away from this geometric center toward more peripheral interconnected skeletons in each hemisphere, with discrete clusters around the anterior insula, and the anterior and posterior midline regions of the cortex. A relatively small number of strong inter-hemispheric connections assimilate these intra-hemispheric structures into a rich club, whose connections are locally more clustered but globally longer than predicted by geometry. We also quantify the extent to which the segregation, integration, and modularity of the human brain are passively inherited from its geometry. These analyses reveal novel insights into the influence of spatial geometry on the human connectome, highlighting specific topological features that likely confer functional advantages but carry an additional metabolic cost.
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In the citing paper
“For example, Roberts et al. (2016) demonstrated that empirical human tractography graphs exhibit a more peripheral hub network structure than predicted by their corresponding geometric surrogate graphs, similar to our tracer results in Fig. 6 and in contrast to what we observed with tractography networks.”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Roberts et al. (2016)
In the citing paper
“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 Roberts et al., 2016
In the citing paper
“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 Roberts et al., 2016
In the citing paper
“Previous work with human tractography datasets (Roberts et al., 2016) has revealed that the human brain places its strongest nodes further into geometrically peripheral regions than would be expected by weight-distance effects alone.”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Roberts et al., 2016
In the citing paper
“The number of streamlines (NOS) is one widely used measure to quantify the connectivity strength (Hagmann et al., 2008b; Ingalhalikar et al., 2014; Roberts et al., 2016; Sporns et al., 2005; van Dellen et al., 2018).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Roberts et al., 2016
In the citing paper
“The most popular scalar value for connectivity matrix analysis is the NOS (Hagmann et al., 2008b; Ingalhalikar et al., 2014; Roberts et al., 2016; Sporns et al., 2005; van Dellen et al., 2018), where higher values are considered to indicate stronger connectivity between pairs of gray matter regions.”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Roberts et al., 2016
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Metadata
- Authors
- Roberts, J. A., Perry, A., Lord, A. R., Roberts, G., Mitchell, P. B., Smith, R. E., Calamante, F., & Breakspear, M.
- Affiliations
- QIMR Berghofer Medical Research Institute (AU)
- Black Dog Institute (AU)
- Florey Institute of Neuroscience and Mental Health (AU)
- The University of Melbourne (AU)
- Corresponding author
- Roberts, James A.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 124
- Issue
- Pt A
- Pages
- 379-393
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1449393047
- Cited by
- 213
- References
- 81
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 21 · 2023: 22 · 2022: 24 · 2021: 23 · 2020: 16 · 2019: 18 · 2018: 20 · 2017: 14 · 2016: 16
- FWCI
- 10.43
- Type
- journal article
- Language
- en
- Open access
- green
- handle.unsw.edu.au
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Funders
- National Health and Medical Research Council (1037196, 628952)
- Australian Research Council (CE140100007, FT110100726)
- Victorian Government's Operational Infrastructure Support Grant
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- human connectome, brain network topology, spatial embedding, brain geometry, rich club, integration and segregation, network modularity, anterior insula, metabolic cost
- MeSH terms
- Adolescent, Adult, Algorithms, Brain, Data Interpretation, Statistical, Female, Humans, Image Processing, Computer-Assisted, Male, Models, Neurological, Neural Pathways, Diffusion Magnetic Resonance Imaging, Mathematical Concepts, Young Adult, Connectome
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