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

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.

[1]  Yufeng Zang,et al.  Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes , 2013, NeuroImage.

[2]  Daniel P. Kennedy,et al.  Largely typical patterns of resting-state functional connectivity in high-functioning adults with autism. , 2014, Cerebral cortex.

[3]  Christos Davatzikos,et al.  Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth , 2013, NeuroImage.

[4]  Jonathan D. Power,et al.  Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high‐motion data points , 2014, Human brain mapping.

[5]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[6]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[7]  Sarah W. Feldstein Ewing,et al.  A quality control method for detecting and suppressing uncorrected residual motion in fMRI studies. , 2013, Magnetic resonance imaging.

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[10]  L. Lemieux,et al.  Modelling large motion events in fMRI studies of patients with epilepsy. , 2007, Magnetic resonance imaging.

[11]  Hang Joon Jo,et al.  Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression , 2012, Brain Connect..

[12]  Daniel P. Kennedy,et al.  The intrinsic functional organization of the brain is altered in autism , 2008, NeuroImage.

[13]  Oliver Speck,et al.  The impact of physiological noise correction on fMRI at 7 T , 2011, NeuroImage.

[14]  Swathi P. Iyer,et al.  Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data , 2012, Front. Syst. Neurosci..

[15]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[16]  Joshua Carp,et al.  Optimizing the order of operations for movement scrubbing: Comment on Power et al. , 2013, NeuroImage.

[17]  Beatriz Luna,et al.  The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity , 2013, NeuroImage.

[18]  Koene R. A. Van Dijk,et al.  Frequency-Dependent Relationship Between Resting-State Functional Magnetic Resonance Imaging Signal Power and Head Motion Is Localized Within Distributed Association Networks , 2013, Brain Connect..

[19]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[20]  Wen-Ming Luh,et al.  Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI , 2012, NeuroImage.

[21]  S. Petersen,et al.  Development of distinct control networks through segregation and integration , 2007, Proceedings of the National Academy of Sciences.

[22]  Assumpta Caixàs,et al.  Does motion-related brain functional connectivity reflect both artifacts and genuine neural activity? , 2014, NeuroImage.

[23]  Justin L. Vincent,et al.  Disruption of Large-Scale Brain Systems in Advanced Aging , 2007, Neuron.

[24]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

[25]  Yong He,et al.  Addressing head motion dependencies for small-world topologies in functional connectomics , 2013, Front. Hum. Neurosci..

[26]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[27]  Hang Joon Jo,et al.  Correcting Brain-Wide Correlation Differences in Resting-State FMRI , 2013, Brain Connect..

[28]  Kevin Murphy,et al.  Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data , 2013, NeuroImage.

[29]  R. Cameron Craddock,et al.  A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics , 2013, NeuroImage.

[30]  Dustin Scheinost,et al.  The impact of image smoothness on intrinsic functional connectivity and head motion confounds , 2014, NeuroImage.

[31]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[32]  Noah D. Brenowitz,et al.  Integrated strategy for improving functional connectivity mapping using multiecho fMRI , 2013, Proceedings of the National Academy of Sciences.

[33]  Hang Joon Jo,et al.  Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI , 2013, J. Appl. Math..

[34]  Douglas C. Noll,et al.  Overt Verbal Responding during fMRI Scanning: Empirical Investigations of Problems and Potential Solutions , 1999, NeuroImage.

[35]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[36]  Karl J. Friston,et al.  Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.

[37]  Abraham Z. Snyder,et al.  Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp , 2013, NeuroImage.

[38]  Jeff H. Duyn,et al.  Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal , 2007, NeuroImage.

[39]  Mark A. Elliott,et al.  Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth , 2012, NeuroImage.

[40]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[41]  Archana Venkataraman,et al.  Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. , 2010, Journal of neurophysiology.

[42]  D. Hu,et al.  Neurobiological basis of head motion in brain imaging , 2014, Proceedings of the National Academy of Sciences.

[43]  J. Hajnal,et al.  Artifacts due to stimulus correlated motion in functional imaging of the brain , 1994, Magnetic resonance in medicine.

[44]  Nancy Kanwisher,et al.  Spurious group differences due to head motion in a diffusion MRI study , 2013, NeuroImage.

[45]  Marko Wilke,et al.  An alternative approach towards assessing and accounting for individual motion in fMRI timeseries , 2012, NeuroImage.

[46]  Jonathan Winawer,et al.  GLMdenoise: a fast, automated technique for denoising task-based fMRI data , 2013, Front. Neurosci..

[47]  M. Fukunaga,et al.  Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. , 2009, Magnetic resonance imaging.

[48]  Lars T. Westlye,et al.  Network-specific effects of age and in-scanner subject motion: A resting-state fMRI study of 238 healthy adults , 2012, NeuroImage.

[49]  Mary Beth Nebel,et al.  Reduction of motion-related artifacts in resting state fMRI using aCompCor , 2014, NeuroImage.

[50]  Hang Joon Jo,et al.  Mapping sources of correlation in resting state FMRI, with artifact detection and removal , 2010, NeuroImage.

[51]  John Suckling,et al.  A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series , 2014, NeuroImage.

[52]  E. Courchesne,et al.  Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long-distance disconnection , 2005, Current Opinion in Neurobiology.

[53]  E. Bullmore,et al.  Methods for diagnosis and treatment of stimulus‐correlated motion in generic brain activation studies using fMRI , 1999, Human brain mapping.

[54]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[55]  Mark J. Lowe,et al.  SimPACE: Generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction , 2014, NeuroImage.

[56]  Nancy Kanwisher,et al.  Differences in the right inferior longitudinal fasciculus but no general disruption of white matter tracts in children with autism spectrum disorder , 2014, Proceedings of the National Academy of Sciences.