Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth

Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group levels. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased within-module connectivity occurring with development. Finally, we show that subject's age can still be accurately estimated from the multivariate pattern of functional connectivity even while controlling for motion. Taken together, these results indicate that while motion artifact has a marked and heterogeneous impact on estimates of connectivity change during adolescence, functional connectivity remains a valuable phenotype for the study of neurodevelopment.

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

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

[3]  Bharat B. Biswal,et al.  Competition between functional brain networks mediates behavioral variability , 2008, NeuroImage.

[4]  Fred Tam,et al.  Retrospective coregistration of functional magnetic resonance imaging data using external monitoring , 2005, Magnetic resonance in medicine.

[5]  Kaustubh Supekar,et al.  Development of Large-Scale Functional Brain Networks in Children , 2009, NeuroImage.

[6]  Raquel E Gur,et al.  Age group and sex differences in performance on a computerized neurocognitive battery in children age 8-21. , 2012, Neuropsychology.

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

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

[9]  Gaël Varoquaux,et al.  Mayavi: 3D Visualization of Scientific Data , 2010, Computing in Science & Engineering.

[10]  Adriana Galvan,et al.  The adolescent brain. , 2008, Developmental review : DR.

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

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

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

[14]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[15]  Krzysztof J. Gorgolewski,et al.  Making Data Sharing Count: A Publication-Based Solution , 2012, Front. Neurosci..

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

[17]  R. Buckner,et al.  Functional-Anatomic Fractionation of the Brain's Default Network , 2010, Neuron.

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

[19]  Patrick Royston,et al.  Multivariable Modeling with Cubic Regression Splines: A Principled Approach , 2007 .

[20]  Vijay K. Venkatraman,et al.  Neuroanatomical Assessment of Biological Maturity , 2012, Current Biology.

[21]  Karl J. Friston,et al.  Detecting Activations in PET and fMRI: Levels of Inference and Power , 1996, 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]  Joshua Carp,et al.  Optimizing the order of operations for movement scrubbing: Comment on Power et al. , 2013, NeuroImage.

[24]  Margaret D. King,et al.  The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry , 2012, Front. Neurosci..

[25]  Oliver Speck,et al.  Measurement and Correction of Microscopic Head Motion during Magnetic Resonance Imaging of the Brain , 2012, PloS one.

[26]  Angela R. Laird,et al.  Investigating function and connectivity of morphometric findings — Exemplified on cerebellar atrophy in spinocerebellar ataxia 17 (SCA17) , 2012, NeuroImage.

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

[28]  Jonathan D. Power,et al.  A Parcellation Scheme for Human Left Lateral Parietal Cortex , 2010, Neuron.

[29]  Jeffrey S. Anderson,et al.  Connectivity Gradients Between the Default Mode and Attention Control Networks , 2011, Brain Connect..

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

[31]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

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

[33]  Bharat B. Biswal,et al.  Making data sharing work: The FCP/INDI experience , 2013, NeuroImage.

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

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

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

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

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

[39]  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.

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

[41]  P. Fox,et al.  Introspective Minds: Using ALE Meta-Analyses to Study Commonalities in the Neural Correlates of Emotional Processing, Social & Unconstrained Cognition , 2012, PloS one.

[42]  M. Milham.  Open Neuroscience Solutions for the Connectome-wide Association Era , 2012, Neuron.

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

[44]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[45]  R. G. Carpenter,et al.  Matching when covariables are normally distributed , 1977 .

[46]  D. Hu,et al.  Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI , 2012, PloS one.

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

[48]  Christian Schwarzbauer,et al.  A dual echo approach to motion correction for functional connectivity studies , 2012, NeuroImage.

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

[50]  L. Somerville,et al.  Developmental neurobiology of cognitive control and motivational systems , 2010, Current Opinion in Neurobiology.

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

[52]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[53]  Thomas R Insel,et al.  Translating scientific opportunity into public health impact: a strategic plan for research on mental illness. , 2009, Archives of general psychiatry.

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

[55]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[56]  Roman Filipovych,et al.  Sparse Dictionary Learning of Resting State fMRI Networks , 2012, 2012 Second International Workshop on Pattern Recognition in NeuroImaging.

[57]  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.

[58]  Angela R. Laird,et al.  Across-study and within-subject functional connectivity of a right temporo-parietal junction subregion involved in stimulus–context integration , 2012, NeuroImage.

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

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

[61]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[62]  Jonathan D. Power,et al.  The Development of Human Functional Brain Networks , 2010, Neuron.

[63]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[64]  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..

[65]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[66]  T. Paus,et al.  Why do many psychiatric disorders emerge during adolescence? , 2008, Nature Reviews Neuroscience.

[67]  B. Luna.  Developmental changes in cognitive control through adolescence. , 2009, Advances in child development and behavior.