Evidence map›Paper›PMID 30818024›Full record

ArticleNeuroImage2019

No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI.

R Kopel, R Sladky, P Laub, Y Koush, F Robineau, C Hutton, N Weiskopf, P Vuilleumier, D Van De Ville, F Scharnowski

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

  1. Article
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  3. Review
  4. Article
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  7. Review
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

10 authors.

R KopelDepartment of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
R SladkyDepartment of Psychiatric, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland; Social, Cognitive and Affective Neuroscience Unit, Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria. Electronic address: ronald.sladky@univie.ac.at.
P LaubInstitute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Y KoushDepartment of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Imaging, Yale University, New Haven, USA.
F RobineauLaboratory for Behavioral Neurology and Imaging of Cognition, Department of Neuroscience, University Medical Center, Geneva, Switzerland; Geneva Neuroscience Center, Geneva, Switzerland.
C HuttonWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
N WeiskopfGeneva Neuroscience Center, Geneva, Switzerland; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
P VuilleumierLaboratory for Behavioral Neurology and Imaging of Cognition, Department of Neuroscience, University Medical Center, Geneva, Switzerland; Geneva Neuroscience Center, Geneva, Switzerland.
D Van De VilleDepartment of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
F ScharnowskiDepartment of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Psychiatric, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057, Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057, Zürich, Switzerland.

Funding

Wellcome Trust
6 · The paper itself

Abstract

As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLM

Indexed as

AlgorithmsArtifactsBrainBrain MappingHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingDetrendingIncremental GLMMoving averageReal-time fMRISignal driftsTemporal stability

Identifiers

PMID30818024
PMCPMC6503944

What OpenQuestion holds

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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.