The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Association between Functional Connectivity Hubs and Brain Networks
Open accessAbstract
Functional networks are usually accessed with "resting-state" functional magnetic resonance imaging using preselected "seeds" regions. Frequently, however, the selection of the seed locations is arbitrary. Recently, we proposed local functional connectivity density mapping (FCDM), an ultrafast data-driven to locate highly connected brain regions (functional hubs). Here, we used the functional hubs obtained from local FCDM to determine the functional networks of the resting state in 979 healthy subjects without a priori hypotheses on seed locations. In addition, we computed the global functional connectivity hubs. Seven networks covering 80% of the gray matter volume were identified. Four major cortical hubs (ventral precuneus/posterior cingulate, inferior parietal cortex, cuneus, and postcentral gyrus) were linked to 4 cortical networks (default mode, dorsal attention, visual, and somatosensory). Three subcortical networks were associated to the major subcortical hubs (cerebellum, thalamus, and amygdala). The networks differed in their resting activity and topology. The higher coupling and overlap of subcortical networks was associated to higher contribution of short-range functional connectivity in thalamus and cerebellum. Whereas cortical local FCD hubs were also hubs of long-range connectivity, which corroborates the key role of cortical hubs in network architecture, subcortical hubs had minimal long-range connectivity. The significant variability among functional networks may underlie their sensitivity/resilience to neuropathology.
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
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
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
Wandering minds: the default network and stimulus-independent thought
Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007) · Science · doi:10.1126/science.1131295 · Cited by 2,792
Cited by
Network hubs in the human brain
van den Heuvel, M. P., & Sporns, O. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.012 · Cited by 2,123
The Angular Gyrus: Multiple Functions and Multiple Subdivisions
Seghier, M. L. (2013) · The Neuroscientist · doi:10.1177/1073858412440596 · Cited by 1,585
Aging and functional brain networks
Tomasi, D., & Volkow, N. D. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.81 · Cited by 578
Self-citationMorawetz, C., Bode, S., Derntl, B., & Heekeren, H. R. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.11.014 · Cited by 458
Brühl, A. B., Delsignore, A., Komossa, K., & Weidt, S. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.08.003 · Cited by 439
Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2013) · Translational Psychiatry · doi:10.1038/tp.2013.34 · Cited by 383
Abnormal Functional Connectivity in Children with Attention-Deficit/Hyperactivity Disorder
Tomasi, D., & Volkow, N. D. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.11.003 · Cited by 363
Self-citationKrienen, F. M., Yeo, B. T. T., & Buckner, R. L. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0526 · Cited by 372
The inferior parietal lobule and temporoparietal junction: A network perspective
Igelström, K. M., & Graziano, M. S. A. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.01.001 · Cited by 367
Functional connectivity hubs in the human brain
Tomasi, D., & Volkow, N. D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.024 · Cited by 367
Self-citationThe human connectome in Alzheimer disease — relationship to biomarkers and genetics
Yu, M., Sporns, O., & Saykin, A. J. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00529-1 · Cited by 265
Stern, E. R., Fitzgerald, K. D., Welsh, R. C., Abelson, J. L., & Taylor, S. F. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036356 · Cited by 245
Kapogiannis, D., Reiter, D. A., Willette, A. A., & Mattson, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.029 · Cited by 211
Yang, L., Yan, Y., Wang, Y., Hu, X., Lu, J., Chan, P., Yan, T., & Han, Y. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00975 · Cited by 182
Scholtens, L. H., Schmidt, R., de Reus, M. A., & van den Heuvel, M. P. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.0752-14.2014 · Cited by 160
Ongoing physiological processes in the cerebral cortex
Leopold, D. A., & Maier, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.059 · Cited by 127
fMRI functional connectivity of the periaqueductal gray in PTSD and its dissociative subtype
Harricharan, S., Rabellino, D., Frewen, P. A., Densmore, M., Théberge, J., McKinnon, M. C., Schore, A. N., & Lanius, R. A. (2016) · Brain and Behavior · doi:10.1002/brb3.579 · Cited by 127
Hubs in the human fetal brain network
van den Heuvel, M. I., Turk, E., Manning, J. H., Hect, J., Hernandez-Andrade, E., Hassan, S. S., Romero, R., van den Heuvel, M. P., & Thomason, M. E. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.02.001 · Cited by 108
“Recent work has also revealed that the fetal cerebellum may be particularly sensitive to premature birth (Limperopoulos et al., 2005; Pierson and Al Sufiani, 2016) and prenatal exposure to maternal psychological stress (Ulupinar and Yucel, 2005;).”
Floris, D. L., Lai, M., Auer, T., Lombardo, M. V., Ecker, C., Chakrabarti, B., Wheelwright, S. J., Bullmore, E. T., Murphy, D. G. M., Baron‐Cohen, S., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23023 · Cited by 108
Oxytocin effects on complex brain networks are moderated by experiences of maternal love withdrawal
Riem, M. M. E., van IJzendoorn, M. H., Tops, M., Boksem, M. A. S., Rombouts, S. A. R. B., & Bakermans-Kranenburg, M. J. (2013) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2013.01.011 · Cited by 91
Amygdala subnuclei resting-state functional connectivity sex and estrogen differences
Engman, J., Linnman, C., Van Dijk, K. R. A., & Milad, M. R. (2016) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2015.09.012 · Cited by 89
Tian, L., Meng, C., Jiang, Y., Tang, Q., Wang, S., Xie, X., Fu, X., Jin, C., Zhang, F., & Wang, J. (2016) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2015.12.003 · Cited by 63
Cortico-striatal circuits: novel therapeutic targets for substance use disorders
Kravitz, A. V., Tomasi, D., LeBlanc, K. H., Baler, R., Volkow, N. D., Bonci, A., & Ferré, S. (2015) · Brain Research · doi:10.1016/j.brainres.2015.03.048 · Cited by 63
Self-citationKraus, C., Ganger, S., Losak, J., Hahn, A., Savli, M., Kranz, G. S., Baldinger, P., Windischberger, C., Kasper, S., & Lanzenberger, R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.036 · Cited by 54
Xu, H., Ding, S., Hu, X., Yang, K., Xiao, C., Zou, Y., Chen, Y., Tao, L., Liu, H., & Qian, Z. (2013) · Neuroscience Letters · doi:10.1016/j.neulet.2013.02.062 · Cited by 54
An, J., Wang, L., Li, K., Zeng, Y., Su, Y., Jin, Z., Yu, X., & Si, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-10575-9 · Cited by 48
Zhang, S., & Li, C.-S. R. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00172 · Cited by 37
ADHD and Giftedness: A Neurocognitive Consideration of Twice Exceptionality
Budding, D., & Chidekel, D. (2012) · Applied Neuropsychology: Child · doi:10.1080/21622965.2012.699423 · Cited by 35
Wang, X., Cao, Q., Wang, J., Wu, Z., Wang, P., Sun, L., Cai, T., & Wang, Y. (2016) · Behaviour Research and Therapy · doi:10.1016/j.brat.2015.11.003 · Cited by 21
Shan, P. W., Liu, W., Liu, C., Han, Y., Wang, L., Chen, Q., Tian, H., Sun, X., Luan, S., Lin, X., et al. (2019) · Journal of International Medical Research · doi:10.1177/0300060518807058 · Cited by 12
Jensterle, M., Kovac, J., Vovk, A., Ferjan, S., Battelino, S., Battelino, T., & Janez, A. (2025) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaf278 · Cited by 12
Metadata
- Authors
- Tomasi, D., & Volkow, N. D.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-01-31
- Volume
- 21
- Issue
- 9
- Pages
- 2003-2013
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2150442066
- Cited by
- 439
- References
- 22
- FWCI
- 13.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
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.
