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

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...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2019-05, Vol.191, p.421-429
Main Authors: Kopel, R., Sladky, R., Laub, P., Koush, Y., Robineau, F., Hutton, C., Weiskopf, N., Vuilleumier, P., Van De Ville, D., Scharnowski, F.
Format: Article
Language:English
Subjects:
Algorithms
Brain - physiology
Brain mapping
Brain Mapping - methods
Data analysis
Data processing
Detrending
Experiments
Feedback
Functional magnetic resonance imaging
Humans
Image Processing, Computer-Assisted - methods
Incremental GLM
Interfaces
Internet
Magnetic fields
Magnetic Resonance Imaging - methods
Methods
Moving average
Neuroimaging
Noise
Physiology
Quality assurance
Real-time fMRI
Respiration
Signal drifts
Signal processing
Temporal stability
Time series
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Summary: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. •fMRI time series are almost always affected by signal drifts.•Signal drifts can be reduced in real-time using different correction methods.•Robustness of these methods was tested in the presence of different artifacts.•Incremental GLM (iGLM, iGLMwindow) were found optimal in most cases.
ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2019.02.058