Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 896

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Human brain networks in health and disease

Open access
Bassett, Danielle S, Bullmore, Edward T·2009·Current Opinion in Neurology

Abstract

PURPOSE OF REVIEW: Recent developments in the statistical physics of complex networks have been translated to neuroimaging data in an effort to enhance our understanding of human brain structural and functional networks. This review focuses on studies using graph theoretical measures applied to structural MRI, diffusion MRI, functional MRI, electroencephalography, and magnetoencephalography data. RECENT FINDINGS: Complex network properties have been identified with some consistency in all modalities of neuroimaging data and over a range of spatial and time scales. Conserved properties include small worldness, high efficiency of information transfer for low wiring cost, modularity, and the existence of network hubs. Structural and functional network metrics have been found to be heritable and to change with normal aging. Clinical studies, principally in Alzheimer's disease and schizophrenia, have identified abnormalities of network configuration in patients. Future work will likely involve efforts to synthesize structural and functional networks in integrated models and to explore the interdependence of network configuration and cognitive performance. SUMMARY: Graph theoretical analysis of neuroimaging data is growing rapidly and could potentially provide a relatively simple but powerful quantitative framework to describe and compare whole human brain structural and functional networks under diverse experimental and clinical conditions.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1097/wco.0b013e32832d93dd

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 39

  1. Catani, 2008, What is a disconnection syndrome?, Cortex, 44, 911, 10.1016/j.cortex.2008.05.001 · DOI
  2. Geschwind, 1965, Disconnexion syndromes in animals and man. I, Brain, 88, 237, 10.1093/brain/88.2.237 · DOI · Paper page
  3. Geschwind, 1965, Disconnexion syndromes in animals and man. II, Brain, 88, 585, 10.1093/brain/88.3.585 · DOI · Paper page
  4. Albert, 2002, Statistical mechanics of complex networks, Rev Mod Phys, 74, 47, 10.1103/RevModPhys.74.47 · DOI · Paper page
  5. Bassett, 2006, Small-world brain networks, Neuroscientist, 12, 512, 10.1177/1073858406293182 · DOI · Paper page
  6. Chatterjee, 2008, Understanding the mind of a worm: hierarchical network structure underlying nervous system function in C. elegans, Prog Brain Res, 168, 145, 10.1016/S0079-6123(07)68012-1 · DOI
  7. Achard, 2006, A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs, J Neurosci, 26, 63, 10.1523/JNEUROSCI.3874-05.2006 · DOI · Paper page
  8. Salvador, 2005, Undirected graphs of frequency-dependent functional connectivity in whole brain networks, Philos Trans R Soc Lond B Biol Sci, 360, 937, 10.1098/rstb.2005.1645 · DOI · Paper page
  9. Latora, 2001, Efficient behavior of small-world networks, Phys Rev Lett, 87, 198701, 10.1103/PhysRevLett.87.198701 · DOI · Paper page
  10. Watts, 1998, Collective dynamics of ‘small-world’ networks, Nature, 393, 440, 10.1038/30918 · DOI · Paper page
  11. Sporns, 2004, The small world of the cerebral cortex, Neuroinformatics, 2, 145, 10.1385/NI:2:2:145 · DOI · Paper page
  12. Bassett, 2008, Hierarchical organization of human cortical networks in health and schizophrenia, J Neurosci, 28, 9239, 10.1523/JNEUROSCI.1929-08.2008 · DOI · Paper page
  13. Chen, 2008, Revealing modular architecture of human brain structural networks by using cortical thickness from MRI, Cereb Cortex, 18, 2374, 10.1093/cercor/bhn003 · DOI · Paper page
  14. Schmitt, 2008, Identification of genetically mediated cortical networks: a multivariate study of pediatric twins and siblings, Cereb Cortex, 18, 1737, 10.1093/cercor/bhm211 · DOI
  15. Gong, 2009, Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography, Cereb Cortex, 19, 524, 10.1093/cercor/bhn102 · DOI · Paper page
  16. Meunier, 2009, Age-related changes in modular organization of human brain functional networks, Neuroimage, 44, 715, 10.1016/j.neuroimage.2008.09.062 · DOI · Paper page
  17. Liu, 2008, Disrupted small-world networks in schizophrenia, Brain, 131, 945, 10.1093/brain/awn018 · DOI · Paper page
  18. Wang, 2009, Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder, Hum Brain Mapp, 30, 638, 10.1002/hbm.20530 · DOI · Paper page
  19. van den Heuvel, 2008, Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain, Neuroimage, 43, 528, 10.1016/j.neuroimage.2008.08.010 · DOI · Paper page
  20. Wang, 2009, Parcellation-dependent small-world brain functional networks: a resting-state fMRI study, Hum Brain Mapp, 30, 1511, 10.1002/hbm.20623 · DOI · Paper page
  21. Micheloyannis, 2009, The influence of ageing on complex brain networks: a graph theoretical analysis, Hum Brain Mapp, 30, 200, 10.1002/hbm.20492 · DOI
  22. Rubinov, 2009, Small-world properties of nonlinear brain activity in schizophrenia, Hum Brain Mapp, 30, 403, 10.1002/hbm.20517 · DOI · Paper page
  23. Pachou, 2008, Working memory in schizophrenia: an EEG study using power spectrum and coherence analysis to estimate cortical activation and network behavior, Brain Topogr, 21, 128, 10.1007/s10548-008-0062-5 · DOI
  24. Ferri, 2008, The functional connectivity of different EEG bands moves towards small-world network organization during sleep, Clin Neurophysiol, 119, 2026, 10.1016/j.clinph.2008.04.294 · DOI
  25. De Vico Fallani, 2008, Cortical network dynamics during foot movements, Neuroinformatics, 6, 23, 10.1007/s12021-007-9006-6 · DOI
  26. Astolfi, 2008, Neural basis for brain responses to TV commercials: a high-resolution EEG study, IEEE Trans Neural Syst Rehabil Eng, 16, 522, 10.1109/TNSRE.2008.2009784 · DOI
  27. Smit, 2008, Heritability of ‘small-world’ networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity, Hum Brain Mapp, 29, 1368, 10.1002/hbm.20468 · DOI · Paper page
  28. Ponten, 2009, Indications for network regularization during absence seizures: Weighted and unweighted graph theoretical analyses, Exp Neurol, 217, 197, 10.1016/j.expneurol.2009.02.001 · DOI
  29. Achard, 2008, Fractal connectivity of long-memory networks, Phys Rev E, 77, 036104, 10.1103/PhysRevE.77.036104 · DOI
  30. Stam, 2009, Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease, Brain, 132, 213, 10.1093/brain/awn262 · DOI · Paper page
  31. Valencia, 2008, Dynamic small-world behavior in functional brain networks unveiled by an event-related networks approach, Phys Rev E, 77, 050905, 10.1103/PhysRevE.77.050905 · DOI
  32. He, 2007, Small-world anatomical networks in the human brain revealed by cortical thickness from MRI, Cereb Cortex, 17, 2407, 10.1093/cercor/bhl149 · DOI · Paper page
  33. Lerch, 2006, Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI, Neuroimage, 31, 993, 10.1016/j.neuroimage.2006.01.042 · DOI · Paper page
  34. Riedl, 2008, Alzheimer's disease: a search for broken links, J Neurosci, 28, 8148, 10.1523/JNEUROSCI.2433-08.2008 · DOI
  35. Honey, 2009, Predicting human resting-state functional connectivity from structural connectivity, Proc Natl Acad Sci U S A, 106, 2035, 10.1073/pnas.0811168106 · DOI · Paper page
  36. Rykhlevskaia, 2008, Combining structural and functional neuroimaging data for studying brain connectivity: a review, Psychophysiology, 45, 173, 10.1111/j.1469-8986.2007.00621.x · DOI
  37. David, 2004, Evaluation of different measures of functional connectivity using a neural mass model, Neuroimage, 21, 659, 10.1016/j.neuroimage.2003.10.006 · DOI · Paper page
  38. Dosenbach, 2008, A dual-networks architecture of top-down control, Trends Cogn Sci, 12, 99, 10.1016/j.tics.2008.01.001 · DOI · Paper page
  39. Fair, 2007, A method for using blocked and event-related fMRI data to study ‘resting state’ functional connectivity, Neuroimage, 35, 396, 10.1016/j.neuroimage.2006.11.051 · DOI · Paper page

Cited by

See all 896

Metadata

Authors
Bassett, D. S., & Bullmore, E. T.
Venue
Current Opinion in Neurology
Published
2009-08
Volume
22
Issue
4
Pages
340-347
ISSN
1350-7540 (ISSN-L)
OpenAlex
W1967931162
Cited by
896
References
10
FWCI
19.34
Type
journal article
Language
en
Open access
green

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.