The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Graph theoretical modeling of brain connectivity
Abstract
PURPOSE OF REVIEW: In recent years, there has been an explosion of studies on network modeling of brain connectivity. This review will focus mainly on recent findings concerning graph theoretical analysis of human brain networks with a variety of imaging modalities, including structural MRI, diffusion MRI, functional MRI, and EEG/MEG. RECENT FINDINGS: Recent studies have utilized graph theoretical approaches to investigate the organizational principles of brain networks. These studies have consistently shown many important statistical properties underlying the topological organization of the human brain, including modularity, small-worldness, and the existence of highly connected network hubs. Importantly, these quantifiable network properties were found to change during normal development, aging, and various neurological and neuropsychiatric diseases such as Alzheimer's disease and schizophrenia. Moreover, several studies have also suggested that these network properties correlate with behavioral and genetic factors. SUMMARY: The exciting research regarding graph theoretical analysis of brain connectivity yields truly integrative and comprehensive descriptions of the structural and functional organization of the human brain, which provides important implications for health and disease. Future research will most likely involve integrative models of brain structural and functional connectivity with multimodal neuroimaging data, exploring whether graph-based brain network analysis could yield reliable biomarkers for disease diagnosis and treatment.
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.
Integrity signals
Registry
Cites
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.
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
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260
Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.5062-08.2009 · Cited by 2,862
Efficiency and Cost of Economical Brain Functional Networks
Achard, S., & Bullmore, E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030017 · Cited by 2,553
Functional brain networks develop from a local to distributed organization
Fair, D. A., Cohen, A. L., Power, J. D., Dosenbach, N. U. F., Church, J. A., Miezin, F. M., Schlaggar, B. L., & Petersen, S. E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000381 · Cited by 1,530
Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease
Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000100 · Cited by 1,148
Efficiency of Functional Brain Networks and Intellectual Performance
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1443-09.2009 · Cited by 1,078
He, Y., Chen, Z., & Evans, A. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.0141-08.2008 · Cited by 965
Self-citationDamoiseaux, J. S., & Greicius, M. D. (2009) · Brain Structure and Function · doi:10.1007/s00429-009-0208-6 · Cited by 897
Cited by
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106
Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657
The effects of childhood maltreatment on brain structure, function and connectivity
Teicher, M. H., Samson, J. A., Anderson, C. M., & Ohashi, K. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.111 · Cited by 1,856
Annual research review: enduring neurobiological effects of childhood abuse and neglect
Teicher, M. H., & Samson, J. A. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12507 · Cited by 1,473
Modular and hierarchically modular organization of brain networks
Meunier, D., Lambiotte, R., & Bullmore, E. T. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00200 · Cited by 1,156
Metadata
- Authors
- He, Y., & Evans, A.
- Venue
- Current Opinion in Neurology
- Published
- 2010-08
- Volume
- 23
- Issue
- 4
- Pages
- 341-350
- ISSN
- 1350-7540 (ISSN-L)
- OpenAlex
- W2092655084
- Cited by
- 700
- References
- 13
- FWCI
- 23.17
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Graph theoretical modeling of brain connectivity. https://app.cochat.ai/papers/10.1097/wco.0b013e32833aa567
- The paper
- He, Y., & Evans, A. (2010). Graph theoretical modeling of brain connectivity. Current Opinion in Neurology. https://doi.org/10.1097/wco.0b013e32833aa567
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.
