Correction of respiratory artifacts in MRI head motion estimates

Head motion represents one of the greatest technical obstacles for brain MRI. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, this estimation may be corrupted by factitious effects owing to main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and a comparison ‘single-shot’ dataset from Oregon Health & Science University. We show unequivocally that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with degraded quality of functional MRI. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects. Subsequently, we demonstrate that utilizing this filter improves post-processing data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

[1]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[2]  R. L. Barry,et al.  Modeling and suppression of respiration‐related physiological noise in echo‐planar functional magnetic resonance imaging using global and one‐dimensional navigator echo correction , 2005, Magnetic resonance in medicine.

[3]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[4]  Timothy O. Laumann,et al.  Data Quality Influences Observed Links Between Functional Connectivity and Behavior , 2017, Cerebral cortex.

[5]  N. Volkow,et al.  The conception of the ABCD study: From substance use to a broad NIH collaboration , 2017, Developmental Cognitive Neuroscience.

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

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

[8]  Deepti R. Bathula,et al.  Atypical Default Network Connectivity in Youth with Attention-Deficit/Hyperactivity Disorder , 2010, Biological Psychiatry.

[9]  M. Dylan Tisdall,et al.  Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion , 2016, NeuroImage.

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

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

[12]  Steen Moeller,et al.  Evaluation of highly accelerated simultaneous multi-slice EPI for fMRI , 2015, NeuroImage.

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

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

[15]  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.

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

[17]  Kathryn L. Mills,et al.  ADHD and attentional control: Impaired segregation of task positive and task negative brain networks , 2017, Network Neuroscience.

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

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

[20]  Oscar Miranda-Dominguez,et al.  Heritability of the human connectome: A connectotyping study , 2017, Network Neuroscience.

[21]  Thomas Benner,et al.  Real‐time RF pulse adjustment for B0 drift correction , 2006, Magnetic resonance in medicine.

[22]  Steen Moeller,et al.  Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts , 2016, NeuroImage.

[23]  Beth Wilmot,et al.  Working Memory and Vigilance as Multivariate Endophenotypes Related to Common Genetic Risk for Attention-Deficit/Hyperactivity Disorder. , 2018, Journal of the American Academy of Child and Adolescent Psychiatry.

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

[25]  B. Leventhal,et al.  Unraveling the Miswired Connectome: A Developmental Perspective , 2014, Neuron.

[26]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[27]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[28]  Evan M. Gordon,et al.  On the Stability of BOLD fMRI Correlations , 2016, Cerebral cortex.

[29]  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.

[30]  J. Saunders,et al.  k‐space detection and correction of physiological artifacts in fMRI , 1997, Magnetic resonance in medicine.

[31]  Abraham Z. Snyder,et al.  Real-time motion analytics during brain MRI improve data quality and reduce costs , 2017, NeuroImage.

[32]  Alberto Llera,et al.  ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data , 2015, NeuroImage.

[33]  Julien Cohen-Adad,et al.  Improving diffusion MRI using simultaneous multi-slice echo planar imaging , 2012, NeuroImage.

[34]  M. Dylan Tisdall,et al.  Correction of respiratory artifacts in MRI head motion estimates , 2019, NeuroImage.

[35]  Michael C Neale,et al.  The ABCD Study of Neurodevelopment: Identifying Neurocircuit Targets for Prevention and Treatment of Adolescent Substance Abuse , 2017, Current Treatment Options in Psychiatry.

[36]  Kathleen M. Gates,et al.  Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks , 2014, PloS one.

[37]  Timothy O. Laumann,et al.  Sources and implications of whole-brain fMRI signals in humans , 2017, NeuroImage.

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

[39]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[40]  G. Glover,et al.  Correction of physiologically induced global off‐resonance effects in dynamic echo‐planar and spiral functional imaging , 2002, Magnetic resonance in medicine.

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

[42]  E. Fombonne,et al.  Structural and functional connectivity of the human brain in autism spectrum disorders and attention‐deficit/hyperactivity disorder: A rich club‐organization study , 2014, Human brain mapping.

[43]  D. Louis Collins,et al.  Unbiased average age-appropriate atlases for pediatric studies , 2011, NeuroImage.

[44]  Kawin Setsompop,et al.  Interslice leakage artifact reduction technique for simultaneous multislice acquisitions , 2014, Magnetic resonance in medicine.

[45]  Anders M. Dale,et al.  The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.

[46]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[47]  Steen Moeller,et al.  Evaluation of slice accelerations using multiband echo planar imaging at 3T , 2013, NeuroImage.

[48]  Swathi P. Iyer,et al.  Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria. , 2014, JAMA psychiatry.

[49]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[50]  G. Glover,et al.  Respiration‐induced B0 fluctuations and their spatial distribution in the human brain at 7 Tesla , 2002, Magnetic resonance in medicine.

[51]  Timothy O. Laumann,et al.  Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project , 2016, Brain Connect..

[52]  Essa Yacoub,et al.  The rapid development of high speed, resolution and precision in fMRI , 2012, NeuroImage.

[53]  Timothy O. Laumann,et al.  Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation , 2018, Neuron.

[54]  Kathryn L. Mills,et al.  Altered Cortico-Striatal–Thalamic Connectivity in Relation to Spatial Working Memory Capacity in Children with ADHD , 2012, Front. Psychiatry.

[55]  L. Wallis,et al.  Age related reference ranges for respiration rate and heart rate from 4 to 16 years , 2005, Archives of Disease in Childhood.

[56]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[57]  Damien A. Fair,et al.  Behavioral interventions for reducing head motion during MRI scans in children , 2018, NeuroImage.

[58]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[59]  E. Feczko,et al.  Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm , 2017, NeuroImage.

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

[61]  Jonathan D. Power,et al.  Distinctions among real and apparent respiratory motions in human fMRI data , 2019, NeuroImage.

[62]  Samuel D. Carpenter,et al.  Structural and Functional Rich Club Organization of the Brain in Children and Adults , 2014, PloS one.

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

[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]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

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

[67]  Damien A. Fair,et al.  Characterizing heterogeneity in children with and without ADHD based on reward system connectivity , 2015, Developmental Cognitive Neuroscience.

[68]  Felix Breuer,et al.  Simultaneous multislice (SMS) imaging techniques , 2015, Magnetic resonance in medicine.

[69]  Maxim Zaitsev,et al.  Prospective motion correction in functional MRI , 2017, NeuroImage.

[70]  Kawin Setsompop,et al.  Dual‐polarity slice‐GRAPPA for concurrent ghost correction and slice separation in simultaneous multi‐slice EPI , 2018, Magnetic resonance in medicine.

[71]  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.

[72]  Jonathan D. Power.  A simple but useful way to assess fMRI scan qualities , 2017, NeuroImage.

[73]  Christos Davatzikos,et al.  Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.

[74]  Hauke Bartsch,et al.  Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods , 2018, Developmental Cognitive Neuroscience.

[75]  Evan M. Gordon,et al.  Precision Functional Mapping of Individual Human Brains , 2017, Neuron.

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

[77]  Stephan Bender,et al.  Annual research review: Reaction time variability in ADHD and autism spectrum disorders: measurement and mechanisms of a proposed trans-diagnostic phenotype. , 2014, Journal of child psychology and psychiatry, and allied disciplines.

[78]  J. Glover.  Adaptive noise canceling applied to sinusoidal interferences , 1977 .

[79]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.