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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.
Highlights
Recent technological advances in the field of functional magnetic resonance imaging have made it possible to obtain the information about brain activations in real-time
SPM8's spm_dctmt.m (SPM8); corresponds to a high-pass filter based on a set of discrete cosine transform (DCT) functions; the free parameter is the cutoff period that determines the order of the SPM8 Detrend, whose values we evaluated for n 1⁄4 3, 4, 5, 6, and 7
Before comparing performance of the different detrending approaches, we optimized their free parameters for the experimental design used here
Summary
Recent technological advances in the field of functional magnetic resonance imaging (fMRI) have made it possible to obtain the information about brain activations in real-time. Real-time fMRI data analysis benefits from steadily increasing computational power (Moore, 1965), from the optimization of real-time analysis algorithms (Hinds et al, 2011; Koush et al, 2012; Magland et al, 2011), and from the adaptation of sophisticated data analysis techniques for real-time purposes (Esposito et al, 2003; Hollmann et al, 2011; Koush et al, 2013; LaConte et al, 2007; Sitaram et al, 2011; Zilverstand et al, 2014) Despite these constant advances in the field of real-time fMRI, unresolved challenges limit its robustness and its applicability. Despite all these improvements of the last years it is a well-known problem that residual slow head movements and scanner instabilities (e.g., due to gradual changes in temperature of the components and strength of the local magnetic field) can result in low frequency signal drifts that debilitate fMRI data analysis (Fig. 1)
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