Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth

It has recently been reported (Van Dijk et al., 2011) that in-scanner head motion can have a substantial impact on MRI measurements of resting-state functional connectivity. This finding may be of particular relevance for studies of neurodevelopment in youth, confounding analyses to the extent that motion and subject age are related. Furthermore, while Van Dijk et al. demonstrated the effect of motion on seed-based connectivity analyses, it is not known how motion impacts other common measures of connectivity. Here we expand on the findings of Van Dijk et al. by examining the effect of motion on multiple types of resting-state connectivity analyses in a large sample of children and adolescents (n=456). Following replication of the effect of motion on seed-based analyses, we examine the influence of motion on graphical measures of network modularity, dual-regression of independent component analysis, as well as the amplitude and fractional amplitude of low frequency fluctuation. In the entire sample, subject age was highly related to motion. Using a subsample where age and motion were unrelated, we demonstrate that motion has marked effects on connectivity in every analysis examined. While subject age was associated with increased within-network connectivity even when motion was accounted for, controlling for motion substantially attenuated the strength of this relationship. The results demonstrate the pervasive influence of motion on multiple types functional connectivity analysis, and underline the importance of accounting for motion in studies of neurodevelopment.

[1]  Benjamin J. Shannon,et al.  Coherent spontaneous activity identifies a hippocampal-parietal memory network. , 2006, Journal of neurophysiology.

[2]  Alan C. Evans,et al.  Growing Together and Growing Apart: Regional and Sex Differences in the Lifespan Developmental Trajectories of Functional Homotopy , 2010, The Journal of Neuroscience.

[3]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[4]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

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

[6]  Chaozhe Zhu,et al.  Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI , 2007, NeuroImage.

[7]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[8]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[9]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[10]  David C. Van Essen,et al.  Application of Information Technology: An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex , 2001, J. Am. Medical Informatics Assoc..

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

[12]  Justin L. Vincent,et al.  Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. , 2008, Journal of neurophysiology.

[13]  Y. Zang,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI , 2007, Brain and Development.

[14]  Norihiro Sadato,et al.  Removing the effects of task-related motion using independent-component analysis , 2005, NeuroImage.

[15]  S. Petersen,et al.  The “Task B problem” and other considerations in developmental functional neuroimaging , 2010, Human brain mapping.

[16]  Chaozhe Zhu,et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF , 2008, Journal of Neuroscience Methods.

[17]  Tülay Adali,et al.  Comparison of multi‐subject ICA methods for analysis of fMRI data , 2010, Human brain mapping.

[18]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

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

[20]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[21]  Xi-Nian Zuo,et al.  Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.

[22]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[23]  S. Gabrieli.  Toward discovery science of human brain function , 2010 .

[24]  Vince D. Calhoun,et al.  A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data , 2009, NeuroImage.

[25]  Arthur W. Toga,et al.  Automatic independent component labeling for artifact removal in fMRI , 2008, NeuroImage.

[26]  Justin L. Vincent,et al.  Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[28]  R. Gur,et al.  A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: Standardization and initial construct validation , 2010, Journal of Neuroscience Methods.

[29]  R. Gur,et al.  Association of enhanced limbic response to threat with decreased cortical facial recognition memory response in schizophrenia. , 2010, The American journal of psychiatry.

[30]  Jonathan D. Power,et al.  Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..

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

[32]  Rui Liao,et al.  Isolation and minimization of head motion‐induced signal variations in fMRI data using independent component analysis , 2006, Magnetic resonance in medicine.

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

[34]  J. Ragland,et al.  Alterations of fronto-temporal connectivity during word encoding in schizophrenia , 2007, Psychiatry Research: Neuroimaging.

[35]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

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

[37]  Cornelis J. Stam,et al.  Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain , 2008, NeuroImage.

[38]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[39]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[40]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[41]  P. Matthews,et al.  Distinct patterns of brain activity in young carriers of the APOE e4 allele , 2009, NeuroImage.

[42]  Bharat B. Biswal,et al.  The oscillating brain: Complex and reliable , 2010, NeuroImage.

[43]  E. Bullmore,et al.  Functional Connectivity and Brain Networks in Schizophrenia , 2010, The Journal of Neuroscience.

[44]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[45]  E. Bullmore,et al.  Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia , 2008, The Journal of Neuroscience.

[46]  O. Sporns,et al.  Network centrality in the human functional connectome. , 2012, Cerebral cortex.

[47]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[48]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[49]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[50]  S. Petersen,et al.  The maturing architecture of the brain's default network , 2008, Proceedings of the National Academy of Sciences.

[51]  V D Calhoun,et al.  Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.

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

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

[54]  Olaf Sporns,et al.  Weight-conserving characterization of complex functional brain networks , 2011, NeuroImage.