Evidence map›Paper›PMID 27956209›Full record

ReviewNeuroImage2017

Methods for cleaning the BOLD fMRI signal.

César Caballero-Gaudes, Richard C Reynolds

Open access · hybridAbstract readReview
In one paragraph

Review in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 293 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
293citing papers in PubMed, 2 pooled it
25.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

293 citing papers in PubMed, 2 syntheses or guidelines pooled it, 595 citations in OpenAlex.

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  15. Benchmarking fMRI Denoising Pipelines.Human brain mapping · 2026
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233 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 2 countries.

César Caballero-GaudesBasque Center of Cognition, Brain and Language, San Sebastian, Spain. Electronic address: c.caballero@bcbl.eu.
Richard C ReynoldsScientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, USA.
Basque Center on Cognition, Brain and Language · ESNational Institute of Mental Health · US

Funding

Scientific and Statistical Computing CoreZICMH002888 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI TAYLOR, PAUL · 2009 to 2025
$30.9M
Intramural NIH HHS Z99 MH999999Intramural NIH HHS ZIC MH002888
6 · The paper itself

Abstract

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

Indexed as

Functional NeuroimagingHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingBOLD fMRIDenoising methodsMotion artifactsMulti-echoPhase-based methodsPhysiological noise

Identifiers

PMID27956209
PMCPMC5466511
OpenAlexW2560079766

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.