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

Kopel, R., Sladky, R., Laub, P., Koush, Y., Robineau, F., Hutton, C., et al. (2019). No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI. NeuroImage, 191, 421-429. doi:10.1016/j.neuroimage.2019.02.058.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-2482-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-8ACC-C
Genre: Journal Article

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 Creators:
Kopel, R.1, 2, Author
Sladky, R.3, 4, Author
Laub, P.2, Author
Koush, Y.1, 2, 5, Author
Robineau, F.6, 7, Author
Hutton, C.8, Author
Weiskopf, Nikolaus7, 9, Author              
Vuilleumier, P.6, 7, Author
Van De ville, D.1, 2, Author
Scharnowski, F.1, 2, 3, 10, 11, Author
Affiliations:
1Department of Radiology and Medical Informatics, University of Geneva, Switzerland, ou_persistent22              
2Institute for Bioengineering, Swiss Federal Institute of Technology in Lausanne, Switzerland, ou_persistent22              
3Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital Zurich, Switzerland, ou_persistent22              
4Social, Cognitive and Affective Neuroscience Unit, Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University Vienna, Austria, ou_persistent22              
5Department of Radiology and Medical Imaging, Yale School of Medicine, New Haven, CT, USA, ou_persistent22              
6Department of Neuroscience, University of Geneva, Switzerland, ou_persistent22              
7Geneva Neuroscience Center, Switzerland, ou_persistent22              
8Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              
9Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
10Neuroscience Center Zurich, University of Zurich, Switzerland, ou_persistent22              
11Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Switzerland, ou_persistent22              

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Free keywords: Real-time fMRI; Detrending; Temporal stability; Signal drifts; Incremental GLM; Moving average
 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 (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.

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Language(s): eng - English
 Dates: 2019-02-192018-11-082019-02-222019-02-252019-05-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2019.02.058
PMID: 30818024
Other: Epub ahead of print
 Degree: -

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Project name : -
Grant ID : PZ00P3_131932
Funding program : -
Funding organization : Swiss National Science Foundation
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Grant ID : 100014_178841
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Funding organization : Swiss National Science Foundation
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Grant ID : BSSG10_155915
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Funding organization : Swiss National Science Foundation
Project name : -
Grant ID : 32003B_166566
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Funding organization : Swiss National Science Foundation
Project name : -
Grant ID : PMCDP2_162223
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Funding organization : Swiss National Science Foundation
Project name : -
Grant ID : PMCDP2_145442
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Funding organization : Swiss National Science Foundation
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Grant ID : STWF-17-012
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Funding organization : Foundation for Research in Science and the Humanities at the University of Zurich
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Funding program : -
Funding organization : Baugarten Stiftung
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Funding organization : European Union (EU)
Project name : -
Grant ID : -
Funding program : -
Funding organization : Wellcome Trust (WT)

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 191 Sequence Number: - Start / End Page: 421 - 429 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166