An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data

By Theodore D. Satterthwaite, Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, Hakon Hakonarson, Ruben C. Gur, Raquel E. Gur and 1 more. NeuroImage, 2012

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Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7c4246814d14c423a8a1fa81ab9544b07a1f7967

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Citations in the corpus

Neuroimaging of the Philadelphia Neurodevelopmental Cohort (2013, neutral): “Optimized processing techniques substantially mitigate the impact of motion (Satterthwaite et al., 2013); nonetheless a more stringent inclusion criteria for imaging data quality may be advisable (see Table 3).”
How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals (2015, background): “Head motion can exert a confounding effect on functional connectivity patterns (Power et al., 2012, 2014; Van Dijk et al., 2012; Satterthwaite et al., 2013).”
Reduction of motion-related artifacts in resting state fMRI using aCompCor (2014, methodology): “, 2012) and that this modulation of motion effects by inter-node distance can be influenced by preprocessing (Power et al., 2014; Satterthwaite et al., 2013).”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “Prior work has demonstrated a relationship between Q and subject motion (Satterthwaite et al., 2012) that is mitigated by participant-level de-noising approaches (Satterthwaite et al., 2013).”
Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI (2018, methodology): “…ventricular and white matter signals, and their ﬁ rst temporal derivatives, quadratic terms, and squares of derivatives (32P) (Ciric et al., 2017; Satterthwaite et al., 2013); and 7) if frame-wise displacement (FD) at any point in time exceeded 0·3 mm, then that time point was scrubbed…”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “Keywords: data processing; quality control; resting-state fMRI; standardization; statistical analysis”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, methodology): “Contents lists available at ScienceDirect NeuroImage journal homepage: www. elsevier. com/locate/ynimg related artifacts from fMRI data is extensive nuisance regression (Friston et al., 1996; Satterthwaite et al., 2013a; Yan et al., 2013).”
Methods to detect, characterize, and remove motion artifact in resting state fMRI (2013, methodology): “7) and the recent reports on its utility for removing motion artifact (Satterthwaite et al., 2013; Yan et al., 2013).”
Multimodal population brain imaging in the UK Biobank prospective epidemiological study (2016, neutral): “While this might not provide statistical certainty for a diagnosis or interventional recommendation, it should allow for single-patient imaging to be used in a similar way to current state-of-the-art patient-tuned genetic testing.”
Integrated strategy for improving functional connectivity mapping using multiecho fMRI (2013, methodology): “Current proposals for solving problems related to motion artifact involve elaborating traditional methods for “denoising” the BOLD fMRI signal time series acquired at one optimized echo time (TE) (5).”
Resting-state FMRI confounds and cleanup (2013, positive): “Regression of up to 36motion-derived parameters alongwithmotion spike regressors to account for movement in individual time points has also been proposed (Satterthwaite et al., 2013).”
Methods for cleaning the BOLD fMRI signal (2016, positive): “Sets of 12, 24 or 36 motion-related regressors, incorporating the original time series, their squared time series, plus one or two temporal shifts, or new motion ‘fingerprint’ regressors have also been employed (Chai et al., 2012; Friston et al., 1996; Lemieux et al., 2007; Power et al., 2012; Power”
Statistical Improvements in Functional Magnetic Resonance Imaging Analyses Produced by Censoring High-Motion Data Points (2013, methodology): “…growing number of reports on incomplete removal of motion-related artifact (using various scanners, pulse sequences, denoising strategies, etc.) [Bright and Murphy, 2013; Satterthwaite et al., 2013; Van Dijk et al., 2012], these findings are likely relevant for many groups and existing data sets.”
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).”
Multi‐echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals (2017, methodology): “To date, the challenges in controlling fMRI artifacts have been met with generic time series signal processing methods such as regression and frequency-restricting bandpass filters (Satterthwaite et al., 2012).”