Methods to detect, characterize, and remove motion artifact in resting state fMRI

Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.

[1]  A. Snyder,et al.  Longitudinal analysis of neural network development in preterm infants. , 2010, Cerebral cortex.

[2]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

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

[4]  M. Fox,et al.  The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.

[5]  Jeffrey S Anderson,et al.  Network anticorrelations, global regression, and phase‐shifted soft tissue correction , 2011, Human brain mapping.

[6]  Alex Martin,et al.  Fractionation of social brain circuits in autism spectrum disorders. , 2012, Brain : a journal of neurology.

[7]  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.

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

[9]  Rajesh Nandy,et al.  Estimation of the intrinsic dimensionality of fMRI data , 2006, NeuroImage.

[10]  M. Schölvinck,et al.  Neural basis of global resting-state fMRI activity , 2010, Proceedings of the National Academy of Sciences.

[11]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

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

[13]  N. Lomb.  Least-squares frequency analysis of unequally spaced data , 1976 .

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

[15]  Damien A. Fair,et al.  Defining functional areas in individual human brains using resting functional connectivity MRI , 2008, NeuroImage.

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

[17]  Wenjun Li,et al.  A method to determine the necessity for global signal regression in resting‐state fMRI studies , 2012, Magnetic resonance in medicine.

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

[19]  D. Seese,et al.  Algorithms for Spectral Analysis of Irregularly Sampled Time Series , 2004 .

[20]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[21]  Rupert Lanzenberger,et al.  Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies , 2009, NeuroImage.

[22]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[23]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

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

[25]  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.

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

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

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

[29]  Society of magnetic resonance in medicine , 1990 .

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

[31]  David M. Groppe,et al.  Neurophysiological Investigation of Spontaneous Correlated and Anticorrelated Fluctuations of the BOLD Signal , 2013, The Journal of Neuroscience.

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

[33]  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.

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

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

[36]  Mark W. Woolrich,et al.  Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.

[37]  Stephen M. Smith,et al.  Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.

[38]  M. Corbetta,et al.  Right Hemisphere Dominance during Spatial Selective Attention and Target Detection Occurs Outside the Dorsal Frontoparietal Network , 2010, The Journal of Neuroscience.