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Cited by 560

The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.

Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know

Open access
Lv, H., Wang, Z., Tong, E., Williams, L.M., Zaharchuk, G., Zeineh, M., Goldstein-Piekarski, A.N., Ball, T.M., Liao, C., Wintermark, M.·2018·American Journal of Neuroradiology

Abstract

Resting-state fMRI was first described by Biswal et al in 1995 and has since then been widely used in both healthy subjects and patients with various neurologic, neurosurgical, and psychiatric disorders. As opposed to paradigm- or task-based functional MR imaging, resting-state fMRI does not require subjects to perform any specific task. The low-frequency oscillations of the resting-state fMRI signal have been shown to relate to the spontaneous neural activity. There are many ways to analyze resting-state fMRI data. In this review article, we will briefly describe a few of these and highlight the advantages and limitations of each. This description is to facilitate the adoption and use of resting-state fMRI in the clinical setting, helping neuroradiologists become familiar with these techniques and applying them for the care of patients with neurologic and psychiatric diseases.

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Metadata

Authors
Lv, H., Wang, Z., Tong, E., Williams, L. M., Zaharchuk, G., Zeineh, M., Goldstein-Piekarski, A. N., Ball, T. M., Liao, C., & Wintermark, M.
Venue
American Journal of Neuroradiology
Published
2018-01-18
Volume
39
Issue
8
Pages
1390-1399
ISSN
  • 0195-6108 (ISSN-L)
  • 1936-959X
OpenAlex
W2784551971
Cited by
560
References
101
FWCI
22.82
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know. https://app.cochat.ai/papers/10.3174/ajnr.a5527
The paper
Lv, H., Wang, Z., Tong, E., Williams, L. M., Zaharchuk, G., Zeineh, M., Goldstein-Piekarski, A. N., Ball, T. M., Liao, C., & Wintermark, M. (2018). Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know. American Journal of Neuroradiology. https://doi.org/10.3174/ajnr.a5527

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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.