Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 180

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Advances in resting state fMRI acquisitions for functional connectomics

Open access
Raimondo, Luisa, Oliveira, ĺcaro A.F., Heij, Jurjen, Priovoulos, Nikos, Kundu, Prantik, Leoni, Renata Ferranti, van der Zwaag, Wietske·2021·NeuroImage

Abstract

Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.

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 · 10.1016/j.neuroimage.2021.118503

Cites

18 of 18 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

Show all 18 references

Cited by

See all 180

Metadata

Authors
Raimondo, L., Oliveira, Ĺ. A. F., Heij, J., Priovoulos, N., Kundu, P., Leoni, R. F., & van der Zwaag, W.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2021-11
Volume
243
Pages
118503
ISSN
1053-8119 (ISSN-L)
OpenAlex
W3196444363
Cited by
180
References
18
FWCI
12.44
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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Advances in resting state fMRI acquisitions for functional connectomics. https://app.cochat.ai/papers/10.1016/j.neuroimage.2021.118503
The paper
Raimondo, L., Oliveira, Ĺ. A. F., Heij, J., Priovoulos, N., Kundu, P., Leoni, R. F., & van der Zwaag, W. (2021). Advances in resting state fMRI acquisitions for functional connectomics. NeuroImage. https://doi.org/10.1016/j.neuroimage.2021.118503

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.