Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 630

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans

Open access
He, Yong, Wang, Jinhui, Wang, Liang, Chen, Zhang J., Yan, Chaogan, Yang, Hong, Tang, Hehan, Zhu, Chaozhe, Gong, Qiyong, Zang, Yufeng, Evans, Alan C.·2009·PLoS ONE

Abstract

The characterization of topological architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (<0.1 Hz), spontaneous fluctuations of the blood oxygen level dependent (BOLD) signal in functional magnetic resonance imaging are thought to be potentially important for the reflection of spontaneous neuronal activity. Many studies have shown that these fluctuations are highly coherent within anatomically or functionally linked areas of the brain. However, the underlying topological mechanisms responsible for these coherent intrinsic or spontaneous fluctuations are still poorly understood. Here, we apply modern network analysis techniques to investigate how spontaneous neuronal activities in the human brain derived from the resting-state BOLD signals are topologically organized at both the temporal and spatial scales. We first show that the spontaneous brain functional networks have an intrinsically cohesive modular structure in which the connections between regions are much denser within modules than between them. These identified modules are found to be closely associated with several well known functionally interconnected subsystems such as the somatosensory/motor, auditory, attention, visual, subcortical, and the "default" system. Specifically, we demonstrate that the module-specific topological features can not be captured by means of computing the corresponding global network parameters, suggesting a unique organization within each module. Finally, we identify several pivotal network connectors and paths (predominantly associated with the association and limbic/paralimbic cortex regions) that are vital for the global coordination of information flow over the whole network, and we find that their lesions (deletions) critically affect the stability and robustness of the brain functional system. Together, our results demonstrate the highly organized modular architecture and associated topological properties in the temporal and spatial brain functional networks of the human brain that underlie spontaneous neuronal dynamics, which provides important implications for our understanding of how intrinsically coherent spontaneous brain activity has evolved into an optimal neuronal architecture to support global computation and information integration in the absence of specific stimuli or behaviors.

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 · 10.1371/journal.pone.0005226

Cites

31 of 31 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: 52

  1. Y He, 2007, Regional coherence changes in the early stages of Alzheimer's disease: a combined structural and resting-state functional MRI study., Neuroimage, 35, 488, 10.1016/j.neuroimage.2006.11.042 · DOI · Paper page
  2. AM Wink, 2006, Age and cholinergic effects on hemodynamics and functional coherence of human hippocampus., Neurobiology of Aging, 27, 1395, 10.1016/j.neurobiolaging.2005.08.011 · DOI
  3. KJ Friston, 1993, Functional connectivity: the principal-component analysis of large (PET) data sets., J Cereb Blood Flow Metab, 13, 5, 10.1038/jcbfm.1993.4 · DOI · Paper page
  4. T Jiang, 2004, Modulation of functional connectivity during the resting state and the motor task., Hum Brain Mapp, 22, 63, 10.1002/hbm.20012 · DOI
  5. MJ Lowe, 1998, Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations., Neuroimage, 7, 119, 10.1006/nimg.1997.0315 · DOI · Paper page
  6. D Cordes, 2001, Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data., AJNR Am J Neuroradiol, 22, 1326
  7. MD Fox, 2005, The human brain is intrinsically organized into dynamic, anticorrelated functional networks., Proc Natl Acad Sci U S A, 102, 9673, 10.1073/pnas.0504136102 · DOI · Paper page
  8. R Salvador, 2005, Neurophysiological architecture of functional magnetic resonance images of human brain., Cereb Cortex, 15, 1332, 10.1093/cercor/bhi016 · DOI · Paper page
  9. V Kiviniemi, 2003, Independent component analysis of nondeterministic fMRI signal sources., Neuroimage, 19, 253, 10.1016/S1053-8119(03)00097-1 · DOI · Paper page
  10. M De Luca, 2006, fMRI resting state networks define distinct modes of long-distance interactions in the human brain., NeuroImage, 29, 1359, 10.1016/j.neuroimage.2005.08.035 · DOI · Paper page
  11. SH Strogatz, 2001, Exploring complex networks., Nature, 410, 268, 10.1038/35065725 · DOI · Paper page
  12. S Boccaletti, 2006, Complex networks: structure and dynamics., Phys Reports, 175, 10.1016/j.physrep.2005.10.009 · DOI · Paper page
  13. S 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
  14. J Wang, 2008, Parcellation-dependent small-world brain functional networks: A resting-state fMRI study., Hum Brain Mapp
  15. Y Liu, 2008, Disrupted small-world networks in schizophrenia., Brain, 131, 945, 10.1093/brain/awn018 · DOI · Paper page
  16. L Wang, 2008, Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder., Hum Brain Mapp
  17. Y 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
  18. G Gong, 2008, Mapping Anatomical Connectivity Patterns of Human Cerebral Cortex Using In Vivo Diffusion Tensor Imaging Tractography., Cereb Cortex
  19. R Guimera, 2005, The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles., Proc Natl Acad Sci U S A, 102, 7794, 10.1073/pnas.0407994102 · DOI · Paper page
  20. JD Han, 2004, Evidence for dynamically organized modularity in the yeast protein-protein interaction network., Nature, 430, 88, 10.1038/nature02555 · DOI · Paper page
  21. LH Hartwell, 1999, From molecular to modular cell biology., Nature, 402, C47, 10.1038/35011540 · DOI · Paper page
  22. JW Scannell, 1999, The connectional organization of the cortico-thalamic system of the cat., Cereb Cortex, 9, 277, 10.1093/cercor/9.3.277 · DOI · Paper page
  23. C Zhou, 2006, Hierarchical organization unveiled by functional connectivity in complex brain networks., Phys Rev Lett, 97, 238103, 10.1103/PhysRevLett.97.238103 · DOI · Paper page
  24. ZJ Chen, 2008, Revealing Modular Architecture of Human Brain Structural Networks by Using Cortical Thickness from MRI., Cereb Cortex
  25. AJ Schwarz, 2008, Community structure and modularity in networks of correlated brain activity., Magn Reson Imaging, 26, 914, 10.1016/j.mri.2008.01.048 · DOI
  26. L Ferrarini, 2008, Hierarchical functional modularity in the resting-state human brain., Hum Brain Mapp
  27. D 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
  28. N Tzourio-Mazoyer, 2002, Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain., Neuroimage, 15, 273, 10.1006/nimg.2001.0978 · DOI · Paper page
  29. R Guimera, 2005, Cartography of complex networks: modules and universal roles., J Stat Mech Theor Exp, P02001
  30. R Guimera, 2004, Modularity from fluctuations in random graphs and complex networks., Phys Rev E Stat Nonlin Soft Matter Phys, 70, 025101, 10.1103/PhysRevE.70.025101 · DOI · Paper page
  31. P Fransson, 2005, Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis., Hum Brain Mapp, 26, 15, 10.1002/hbm.20113 · DOI · Paper page
  32. CJ Stam, 2007, Small-world networks and functional connectivity in Alzheimer's disease., Cereb Cortex, 17, 92, 10.1093/cercor/bhj127 · DOI · Paper page
  33. DJ Watts, 1998, Collective dynamics of ‘small-world’ networks., Nature, 393, 440, 10.1038/30918 · DOI · Paper page
  34. V Latora, 2001, Efficient behavior of small-world networks., Phys Rev Lett, 87, 198701, 10.1103/PhysRevLett.87.198701 · DOI · Paper page
  35. R Guimera, 2007, Classes of complex networks defined by role-to-role connectivity profiles., Nat Phys, 3, 63, 10.1038/nphys489 · DOI · Paper page
  36. LC Freeman, 1977, A set of measures of centrality based upon betweenness., Sociometry, 35, 10.2307/3033543 · DOI · Paper page
  37. M Kaiser, 2004, Edge vulnerability in neural and metabolic networks., Biol Cybern, 90, 311, 10.1007/s00422-004-0479-1 · DOI
  38. JA Bullinaria, 2007, Understanding the emergence of modularity in neural systems., Cognitive Science, 31, 673, 10.1080/15326900701399939 · DOI
  39. O Sporns, 2000, Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices., Cereb Cortex, 10, 127, 10.1093/cercor/10.2.127 · DOI · Paper page
  40. R Kotter, 2003, Network participation indices: characterizing component roles for information processing in neural networks., Neural Netw, 16, 1261, 10.1016/j.neunet.2003.06.002 · DOI
  41. O Sporns, 2007, Identification and classification of hubs in brain networks., PLoS ONE, 2, e1049, 10.1371/journal.pone.0001049 · DOI · Paper page
  42. HB Fraser, 2005, Modularity and evolutionary constraint on proteins., Nat Genet, 37, 351, 10.1038/ng1530 · DOI
  43. V Spirin, 2006, A metabolic network in the evolutionary context: multiscale structure and modularity., Proc Natl Acad Sci U S A, 103, 8774, 10.1073/pnas.0510258103 · DOI
  44. A Hintze, 2008, Evolution of complex modular biological networks., PLoS Comput Biol, 4, e23, 10.1371/journal.pcbi.0040023 · DOI
  45. CJ Honey, 2007, Network structure of cerebral cortex shapes functional connectivity on multiple time scales., Proc Natl Acad Sci U S A, 104, 10240, 10.1073/pnas.0701519104 · DOI · Paper page
  46. R 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
  47. MX Cohen, 2008, Covariance-based subdivision of the human striatum using T1-weighted MRI., Eur J Neurosci, 27, 1534, 10.1111/j.1460-9568.2008.06117.x · DOI
  48. AL Cohen, 2008, Defining functional areas in individual human brains using resting functional connectivity MRI., Neuroimage, 41, 45, 10.1016/j.neuroimage.2008.01.066 · DOI · Paper page
  49. KE Stephan, 2003, Lateralized cognitive processes and lateralized task control in the human brain., Science, 301, 384, 10.1126/science.1086025 · DOI · Paper page
  50. RM Birn, 2006, Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI., Neuroimage, 31, 1536, 10.1016/j.neuroimage.2006.02.048 · DOI · Paper page
  51. LD-G Danon, 2005, Comparing community structure identification., J Stat mech Theor Exp, P09008
  52. DC Van Essen, 2005, A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex., Neuroimage, 28, 635, 10.1016/j.neuroimage.2005.06.058 · DOI · Paper page

Cited by

See all 630

Metadata

Authors
He, Y., Wang, J., Wang, L., Chen, Z. J., Yan, C., Yang, H., Tang, H., Zhu, C., Gong, Q., Zang, Y., & Evans, A. C.
Venue
PLoS ONE
Published
2009-04-21
Volume
4
Issue
4
Pages
e5226
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2028645759
Cited by
630
References
31
FWCI
19.03
Type
journal article
Language
en
Open access
gold

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.