Motion correction and the use of motion covariates in multiple‐subject fMRI analysis

By Tom Johnstone, Kathleen S. Ores Walsh, L. Greischar, Andrew L. Alexander, Andrew S. Fox, Richard J. Davidson, T. Oakes. Human Brain Mapping, 2006

346 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/552e70066a823c780629ba0cf6df93f8f1d40746

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Citations in the corpus

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion (2011, neutral): “It is well established that head motion is undesirable in fMRI studies (Friston et al., 1996; Hutton et al., 2002; Jiang et al., 1995; Johnstone et al., 2006; Oakes et al., 2005; Wu et al., 1997).”
Don't fear ‘fear conditioning’: Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear (2017, neutral): “obtained from realignment) to the first-level model may reduce sensitivity (while enhancing specificity) (Johnstone et al., 2006), alternative approaches such as independent component analysis might be considered (Kochiyama et al.”
Failure to Regulate: Counterproductive Recruitment of Top-Down Prefrontal-Subcortical Circuitry in Major Depression (2007, neutral): “Individual subject data were slice-time corrected, motion corrected, and analyzed in AFNI (Cox, 1996) using a GLM with a separate regressor for each trial type, six motion estimate covariates (Johnstone et al., 2006), and a second-order polynomial used to model the baseline and slow signal drift.”
WSPM: Wavelet-based statistical parametric mapping (2007, methodology): “Each condition of interest (i.e., auditory stimulation and silent blocks) is modeled by a boxcar waveform convolved with a canonical hemodynamic response function (with no dispersion or temporal derivatives) and subjected to a multiple regression analysis with six covariates of no interest represent”
Sleep spindles predict neural and behavioral changes in motor sequence consolidation (2013, methodology): “Movement parameters derived from realignment of the functional volumes were not included as this is not recommended when using a block design and manual responses [Johnstone et al., 2006].”
Function in the Human Connectome: Task-fMRI and Individual Differences in Behavior (2013, methodology): “Our description of the processing stream for the tfMRI data presented in the current paper did not include any additional denoising steps, such as the inclusion of regressors indexing the degree of movement on each frame (Johnstone et al., 2006), physiological noise modeling (Brooks et al.”
Statistical Improvements in Functional Magnetic Resonance Imaging Analyses Produced by Censoring High-Motion Data Points (2013, background): “…in functional magnetic resonance imaging (fMRI) studies [Barch et al., 1999; Birn et al., 1998, 1999, 2004; Friston et al., 1996; Gopinath et al., 2009; Hutton et al., 2002; Jiang et al., 1995; Johnstone et al., 2006; Lemieux et al., 2007; Oakes et al., 2005; Wu et al., 1997; Yetkin et al., 1996].”
Brain plasticity related to the consolidation of motor sequence learning and motor adaptation (2010, methodology): “Movement parameters derived from realignment of the functional volumes were not included as it is not recommended when using a block design and manual responses (36).”
An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data (2012, methodology): “There is substantial variation regarding how motion is modeled during confound regression (Auer, 2008; Johnstone et al., 2006): some studies include only the six motion parameters themselves, while others include the temporal derivatives, or even the quadratic of both the raw parameters and…”
A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series (2014, background): “Any regression strategy risks removal of BOLD-related signal variance that occurs synchronously with subject movement (Johnstone et al., 2006), but there is a risk, when combining despiking with regression in low-motion cohorts (in which additional consideration for motion may not have been…”
Strong Evidence for Pattern Separation in Human Dentate Gyrus (2016, neutral): “Furthermore, six motion correction parameters were added as regressors of no interest to minimize false-positive activations due to task-correlated motion (Johnstone et al., 2006).”
Effects of motivation on reward and attentional networks: an fMRI study (2012, methodology): “The six-movement estimates from the realignment procedure were entered as covariates of no interest (Johnstone et al. 2006).”