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

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2014-07, Vol.95 (100), p.287-304
• Main Authors: Patel, Ameera X., Kundu, Prantik, Rubinov, Mikail, Jones, P. Simon, Vértes, Petra E., Ersche, Karen D., Suckling, John, Bullmore, Edward T.
• Format: Article
• Language: English
• Subjects: Acquisitions & mergers; Adult; Age; Biological and medical sciences; Brain Mapping - methods; Child; Connectivity; Despike; Female; fMRI; Fundamental and applied biological sciences. Psychology; Head Movements; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Methods; Motion; NMR; Non-stationary; Nuclear magnetic resonance; Resting-state; Software; Spike; Studies; Vertebrates: nervous system and sense organs; Wavelet
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.03.012

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. [Display omitted] •Motion artifacts in fMRI data may manifest in complex temporal and spatial patterns.•These artifacts are hard to model, and are often poorly denoised by popular methods.•Wavelet-based despiking is a novel, data driven, locally adaptive denoising method.•Wavelet Despiking can remove linear, non-linear, high and low frequency artifacts.•Wavelet Despiking attenuates motion-induced bias in functional connectivity.