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Cited by 614Functional Brain Connectivity StudiesComplex Network Analysis TechniquesAdvanced Neuroimaging Techniques and Applications

Graph-based network analysis of resting-state functional MRI

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Wang, Jinhui·2010·Frontiers in Systems Neuroscience
DOI 10.3389/fnsys.2010.00016PMC PMC2893007
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Abstract

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future.

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  • Sparse small-world attributes are robust in that they are reproducible across imaging modalities, disease states, and species.

    “Sparse small-world attributes are robust in that they are reproducible across imaging modalities (diffusion MRI, 45,46 resting fMRI, 2,78,102 structural MRI, 48 EEG, 82 MEG, 90 and PET 30,79 ), disease states, and species (such as the cat, macaque, and nematode Caenorhabditis elegans).20”

    From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [102]

    The Basic Conceptions section says small-world topology has been demonstrated across multiple species in healthy and diseased states, and Technical Challenge and Future Perspectives reports common small-world attributes across imaging modalities. Our copy does not identify the cat, macaque, or Caenorhabditis elegans as examples, so it does not support those species-specific details in the citing sentence. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Wang, J.
Affiliations
Beijing Normal University (CN)
Corresponding author
Wang, Jinhui
ORCID
Wang, Jinhui
Venue
Frontiers in Systems Neuroscience
Publisher
Frontiers Media SA
Published
2010
Volume
4
Pages
16-16
ISSN
1662-5137 (ISSN-L)
DOI
10.3389/fnsys.2010.00016
PMC
PMC2893007
OpenAlex
W2057504626
Cited by
614
References
19
Citations by year
2026: 13 · 2025: 41 · 2024: 33 · 2023: 48 · 2022: 51 · 2021: 60 · 2020: 48 · 2019: 59 · 2018: 38 · 2017: 46 · 2016: 27 · 2015: 36 · 2014: 32 · 2013: 30 · 2012: 22
FWCI
15.52
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Systems Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
resting-state fMRI, brain networks, graph theory, small-world networks, network modularity, hub regions, functional connectivity, brain development, brain aging, neuropsychiatric disorders, network topology

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