Association between in-scanner head motion with cerebral white matter microstructure: a multiband diffusion-weighted MRI study

Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) has emerged as the most popular neuroimaging technique used to depict the biological microstructural properties of human brain white matter. However, like other MRI techniques, traditional DW-MRI data remains subject to head motion artifacts during scanning. For example, previous studies have indicated that, with traditional DW-MRI data, head motion artifacts significantly affect the evaluation of diffusion metrics. Actually, DW-MRI data scanned with higher sampling rate are important for accurately evaluating diffusion metrics because it allows for full-brain coverage through the acquisition of multiple slices simultaneously and more gradient directions. Here, we employed a publicly available multiband DW-MRI dataset to investigate the association between motion and diffusion metrics with the standard pipeline, tract-based spatial statistics (TBSS). The diffusion metrics used in this study included not only the commonly used metrics (i.e., FA and MD) in DW-MRI studies, but also newly proposed inter-voxel metric, local diffusion homogeneity (LDH). We found that the motion effects in FA and MD seems to be mitigated to some extent, but the effect on MD still exists. Furthermore, the effect in LDH is much more pronounced. These results indicate that researchers shall be cautious when conducting data analysis and interpretation. Finally, the motion-diffusion association is discussed.

[1]  Do P. M. Tromp,et al.  Diffusion Tensor Imaging in Autism Spectrum Disorder: A Review , 2012, Autism research : official journal of the International Society for Autism Research.

[2]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

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

[4]  Walter H Backes,et al.  Assessing and minimizing the effects of noise and motion in clinical DTI at 3 T , 2009, Human brain mapping.

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

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

[7]  Heidi Johansen-Berg,et al.  Behavioural relevance of variation in white matter microstructure. , 2010, Current opinion in neurology.

[8]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[9]  A. Mayer,et al.  Head injury or head motion? Assessment and quantification of motion artifacts in diffusion tensor imaging studies , 2012, Human brain mapping.

[10]  G. Gong Local Diffusion Homogeneity (LDH): An Inter-Voxel Diffusion MRI Metric for Assessing Inter-Subject White Matter Variability , 2013, PloS one.

[11]  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.

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

[13]  J. López-Ibor,et al.  Microstructural white matter damage at orbitofrontal areas in borderline personality disorder. , 2012, Journal of affective disorders.

[14]  Jochen Ditterich,et al.  Splash: A Software Tool for Stereotactic Planning of Recording Chamber Placement and Electrode Trajectories , 2011, Front. Neuroinform..

[15]  Patricia Gruner,et al.  Abnormal cingulum bundle development in autism: A probabilistic tractography study , 2014, Psychiatry Research: Neuroimaging.

[16]  S. Schmidt,et al.  The Effects of Hand Preference on Attention , 2013 .

[17]  Jerry L. Prince,et al.  Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T , 2007, NeuroImage.

[18]  M. Maiuri,et al.  Targeting the Intracellular Environment in Cystic Fibrosis: Restoring Autophagy as a Novel Strategy to Circumvent the CFTR Defect , 2013, Front. Pharmacol..

[19]  R. Kikinis,et al.  Interactive Diffusion Tensor Tractography Visualization for Neurosurgical Planning , 2011, Neurosurgery.

[20]  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.

[21]  P. Grant,et al.  Dopaminergic foundations of schizotypy as measured by the German version of the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE)—a suitable endophenotype of schizophrenia , 2013, Front. Hum. Neurosci..

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

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

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

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

[26]  D. Le Bihan,et al.  Diffusion tensor imaging: Concepts and applications , 2001, Journal of magnetic resonance imaging : JMRI.

[27]  Thomas E. Nichols,et al.  Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics , 2007, Nature Protocols.

[28]  Measuring Regional Diffusivity Dependency via Mutual Information , 2014, ISBI 2014.

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

[30]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

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

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

[33]  Denis Le Bihan,et al.  Looking into the functional architecture of the brain with diffusion MRI , 2003, Nature Reviews Neuroscience.

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

[35]  B. J. Casey,et al.  Differential patterns of striatal activation in young children with and without ADHD , 2003, Biological Psychiatry.

[36]  John DeLuca,et al.  Functional magnetic resonance imaging movers and shakers: Does subject‐movement cause sampling bias? , 2014, Human brain mapping.

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

[38]  Satrajit S. Ghosh,et al.  Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python , 2011, Front. Neuroinform..

[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]  S. A. Wijtenburg,et al.  Acute nicotine administration effects on fractional anisotropy of cerebral white matter and associated attention performance , 2013, Front. Pharmacol..

[41]  Xiang-zhen Kong Head motion in children with ADHD during resting-state brain imaging , 2014 .

[42]  Alan C. Evans,et al.  Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. , 2009, Cerebral cortex.

[43]  J. Hennig,et al.  Three‐dimensional MR‐encephalography: Fast volumetric brain imaging using rosette trajectories , 2011, Magnetic resonance in medicine.