A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

By Ameera X. Patel, Prantik Kundu, Mikail Rubinov, Peter B. Jones, Petra E. Vértes, Karen D. Ersche, John Suckling, E. Bullmore. NeuroImage, 2014

374 citations recorded by the source corpus.

The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously bias estimates of functional connectivity. Here we propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modeling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard pre-processing pipelines. With this method, we demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts and can consequently remove a range of high and low frequency artifacts from fMRI time series, that may be linearly or non-linearly related to physical movements. Our methods are demonstrated by the analysis of three cohorts of resting-state fMRI data, including two high-motion datasets: a previously published dataset on children (N = 22) and a new dataset on adults with stimulant drug dependence (N = 40). We conclude that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements. The Wavelet Despiking software described in this article is freely available for download at www.brainwavelet.org.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/79bea5c3d46763463a39b43f5fc7250d759d586b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, background): “Consequently, there has been significant research on rs-fMRI denoising (Chai et al., 2012; Hallquist et al., 2013; Jo et al., 2013; Patel et al., 2014; Bright et al., 2017), but there is still no consensus approach for rs-fMRI preprocessing (Murphy et al., 2013; Geerligs et al., 2017; Murphy and…”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, background): “stimuli that help ensure survival (Peciña and Berridge, 2005).”
Recent progress and outstanding issues in motion correction in resting state fMRI (2014, positive): “Recently, Patel and colleagues proposed a denoising method that decomposes voxel signals into wavelet space, identifies trains of “bad” high-amplitude wavelets that likely reflect motion artifact, and reconstitutes denoised voxel signals from the “good” wavelets (Patel et al., 2014).”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “…evaluation included many of the most commonly used techniques, other approaches which require substantial training or parameter selection (i.e., ICA-FIX (SalimiKhorshidi et al., 2014, Griffanti et al., 2014), wavelet de-spiking (Patel et al., 2014)) may be valuable and merit further consideration.”
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample (2017, methodology): “…i) to assess functional connectivity using independent components analysis (ICA), ii) remove residual motion artifacts usingwavelet de-spiking (Patel et al., 2014) and regression of WM/CSF signals and higher-order expansions of the movement parameters (Geerligs et al, in press), iii)…”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, methodology): “…each other (e.g. 24RP-regression and SOCK) and did not include all currently available strategies for motion artifact removal, e.g. wavelet-despiking (Patel et al., 2014), SLOMOCO (Beall and Lowe, 2014), RDI (Spisák et al., 2014) or alternative ICA-based strategies (Thomas et al., 2002; Kochiyama…”