Recent progress and outstanding issues in motion correction in resting state fMRI

By Jonathan D. Power, Bradley L. Schlaggar, Steven E. Petersen. NeuroImage, 2014

949 citations recorded by the source corpus.

The purpose of this review is to communicate and synthesize recent findings related to motion artifact in resting state fMRI. In 2011, three groups reported that small head movements produced spurious but structured noise in brain scans, causing distance-dependent changes in signal correlations. This finding has prompted both methods development and the re-examination of prior findings with more stringent motion correction. Since 2011, over a dozen papers have been published specifically on motion artifact in resting state fMRI. We will attempt to distill these papers to their most essential content. We will point out some aspects of motion artifact that are easily or often overlooked. Throughout the review, we will highlight gaps in current knowledge and avenues for future research.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/77fe2c83f6b916e76f34af30bf406e1b509a68aa

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Citations in the corpus

Methods for cleaning the BOLD fMRI signal (2016, positive): “…to illustrate the quality of fMRI data that facilitate the evaluation of denoising approaches (Power, 2016) and enable to identify the presence of remaining confounds that may affect the interpretation of the results (Burgess et al., 2016; Jo et al., 2013; Power et al., 2015; Power et al., 2016).”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, background): “Keywords: data processing; quality control; resting-state fMRI; standardization; statistical analysis”
Building a Science of Individual Differences from fMRI. (2016, background): “the removal of independent components related to motion [91], or the complete removal of motion-affected frames known as scrubbing [86] (see [92] for a comprehensive review).”
Awake reactivation of emotional memory traces through hippocampal-neocortical interactions (2016, neutral): “To mitigate this concern, we reran this analysis with additional motion artifact corrections, namely global signal regression and spike regression (Power et al., 2015).”
Using connectome-based predictive modeling to predict individual behavior from brain connectivity (2017, neutral): “…preprocessing methods to minimize these confounds (for discussion of various ways to estimate and correct for head motion, see reviews 28,29 ), researchers need to ensure that motion is not correlated with the behavioral data and that connectome-based models cannot predict motion 5 .”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, neutral): “(Satterthwaite et al., 2012; Van Dijk et al., 2012; Power et al., 2014, 2015).”
A practical guide to linking brain-wide gene expression and neuroimaging data (2018, methodology): “The impact of data processing choices on the results of neuroimaging analyses is well documented, with strategies for the correction of motion-related and global signal ﬂ uctuations in functional MRI being a prime example (Power et al., 2015, 2017; Ciric et al., 2017; Parkes et al., 2018).”
Advancing functional connectivity research from association to causation (2019, methodology): “While linear regression was unable to fully address motion effects 85 , other approaches such as removing high-movement time points from time series have been effective in making FC estimates more similar between high-and low-motion subjects 86 .”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “These techniques include scrubbing (Power et al., 2012, 2014a, 2015), spike regression (Satterthwaite et al., 2013), and despiking (Jo et al., 2013; Patel et al., 2014).”
Effective learning is accompanied by high-dimensional and efficient representations of neural activity (2017, background): “gest that removing local white-matter signal and other non-neural sources are potential reasonable alternatives to global signal regression [36, 37].”
An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI (2017, methodology): “One solution, proposed by Power and colleagues (Power et al., 2015; 2014; 2012; 2013), is called “scrubbing”, and involves”
Inscapes: a movie paradigm to improve compliance in functional magnetic resonance imaging (2015, background): “65 66 67 68rwal). l., Inscapes: A movie paradig 6/j.neuroimage.2015.07.069 A major limitation of this approach is that even small head movements can produce systematic artifacts in FC measures (Power et al., 2012, 2015; Van Dijk et al., 2012; Satterthwaite et al., 2013).”
Data Quality Influences Observed Links Between Functional Connectivity and Behavior (2017, background): “A comparative analysis of many of these approaches is given in Power et al. (2015) .”
The Global Signal in fMRI: Nuisance or Information? (2017, positive): “…the frequency contents of the data and the regressors used in preprocessing could also give rise to motion-related global signal changes, but this mismatch has been largely addressed by changes to the preprocessing approaches used in the more recent studies discussed here (Power et al., 2015).”