The hubs of the human connectome are generally implicated in the anatomy of brain disorders
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Brain networks or 'connectomes' include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P < 10(-4), permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P < 0.05, permutation test), including schizophrenia and Alzheimer's disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer's disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.
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Hubs identified with centrality measures are disproportionately implicated in brain disorders.
“Indeed, hubs identified with these measures have been found to entail high metabolic expenditures and be disproportionately implicated in brain disorders (Crossley et al., 2014; Fornito et al., 2015).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Crossley et al., 2014
Hubs identified with centrality measures have high metabolic expenditures.
“Indeed, hubs identified with these measures have been found to entail high metabolic expenditures and be disproportionately implicated in brain disorders (Crossley et al., 2014; Fornito et al., 2015).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Crossley et al., 2014
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- Authors
- Crossley, N. A., Mechelli, A., Scott, J., Carletti, F., Fox, P. T., McGuire, P., & Bullmore, E. T.
- Affiliations
- King's College London (GB)
- The University of Texas at San Antonio Health Science Center (US)
- GlaxoSmithKline (United Kingdom) (GB)
- Corresponding author
- Crossley, Nicolas A.
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-06-19
- Volume
- 137
- Issue
- 8
- Pages
- 2382-2395
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC4107735
- OpenAlex
- W2161493176
- Cited by
- 1,166
- References
- 13
- Citations by year
- 2026: 72 · 2025: 91 · 2024: 64 · 2023: 82 · 2022: 105 · 2021: 108 · 2020: 95 · 2019: 114 · 2018: 101 · 2017: 110 · 2016: 114 · 2015: 92 · 2014: 17
- FWCI
- 10.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Brain (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Funders
- GlaxoSmithKline
- Wellcome Trust (095844/Z/11/Z, 095844)
- National Institute for Health and Care Research (NF-SI-0513-10051)
- National Institutes of Health
- National Institute of Mental Health (NIMHR01, R01MH074457, MH074457)
- 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
- brain network hubs, human connectome, topological centrality, brain disorders, gray matter lesions, schizophrenia, Alzheimer's disease, diffusion tensor imaging, network efficiency, temporal lobe, frontal cortex, lesion distribution
- MeSH terms
- Adult, Brain, Computer Simulation, Female, Humans, Male, Nerve Net, Diffusion Tensor Imaging, Connectome
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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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