Active head motion reduction in magnetic resonance imaging using tactile feedback

Head motion is a common problem in clinical as well as empirical (functional) Magnetic Resonance Imaging applications, as it can lead to severe artefacts that reduce image quality. The scanned individuals themselves, however, are often not aware of their head motion. The current study explored whether providing subjects with this information using tactile feedback would reduce their head motion and consequently improve image quality. In a single session that included six runs, 24 participants performed three different cognitive tasks: (1) passive viewing, (2) mental imagery, and (3) speeded responses. These tasks occurred in two different conditions: (a) with a strip of medical tape applied from one side of the MR head-coil, via the participant’s forehead, to the other side, and (b) without the medical tape being applied. Results revealed that application of medical tape to the forehead of subjects to provide tactile feedback significantly reduced both translational as well as rotational head motion. While this effect did not differ between the three cognitive tasks, there was a negative quadratic relationship between head motion with and without feedback. That is, the more head motion a subject produced without feedback, the stronger the motion reduction given the feedback. In conclusion, the here tested method provides a simple and cost-efficient way to reduce subjects’ head motion, and might be especially beneficial when extensive head motion is expected a priori.

[1]  Bettina Sorger,et al.  Real-time fMRI-based self-regulation of brain activation across different visual feedback presentations , 2017 .

[2]  Oliver Speck,et al.  False fMRI activation after motion correction , 2017, Human brain mapping.

[3]  Sven Haller,et al.  Real-time fMRI neurofeedback: Progress and challenges , 2013, NeuroImage.

[4]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[5]  Richard D. Morey,et al.  Confidence Intervals from Normalized Data: A correction to Cousineau (2005) , 2008 .

[6]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

[7]  Oliver Lindemann,et al.  Expyriment: A Python library for cognitive and neuroscientific experiments , 2014, Behavior research methods.

[8]  Adolf Pfefferbaum,et al.  Design and efficacy of a head-coil bite bar for reducing movement-related artifacts during functional MRI scanning , 1997 .

[9]  Sebastian Bassi,et al.  Python Language Reference , 2009 .

[10]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[11]  Michael Lührs,et al.  Active head motion reduction in magnetic resonance imaging using tactile feedback , 2019, Human brain mapping.

[12]  M C Gilardi,et al.  Head holder for PET, CT, and MR studies. , 1991, Journal of computer assisted tomography.

[13]  Richard C. Reynolds,et al.  An improved model of motion-related signal changes in fMRI , 2017, NeuroImage.

[14]  Wolfgang Grodd,et al.  Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) , 2004, IEEE Transactions on Biomedical Engineering.

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

[16]  Nikolaus Weiskopf,et al.  Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking , 2015, NeuroImage.

[17]  Bettina Sorger,et al.  Human Cortical Object Recognition from a Visual Motion Flowfield , 2003, The Journal of Neuroscience.

[18]  C Windischberger,et al.  Quantification of fMRI artifact reduction by a novel plaster cast head holder , 2000, Human brain mapping.

[19]  Yves Rosseel,et al.  On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data , 2013, PloS one.

[20]  B. Schlaggar,et al.  Considerations for MRI study design and implementation in pediatric and clinical populations , 2015, Developmental Cognitive Neuroscience.

[21]  Oliver Speck,et al.  Highest Resolution In Vivo Human Brain MRI Using Prospective Motion Correction , 2015, PloS one.

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

[23]  Henrik Singmann,et al.  afex – Analysis of Factorial EXperiments , 2015 .

[24]  S. Hallerb,et al.  Real-time fMRI neurofeedback : progress and challenges , 2017 .

[25]  Michael Erb,et al.  Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data , 2003, NeuroImage.

[26]  Yihong Yang,et al.  Head motion suppression using real-time feedback of motion information and its effects on task performance in fMRI , 2005, NeuroImage.

[27]  M. Zaitsev,et al.  Motion artifacts in MRI: A complex problem with many partial solutions , 2015, Journal of magnetic resonance imaging : JMRI.

[28]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[29]  Rainer Goebel,et al.  BrainVoyager — Past, present, future , 2012, NeuroImage.

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

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

[32]  Oliver Speck,et al.  Prospective motion correction in brain imaging: A review , 2013, Magnetic resonance in medicine.

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

[34]  Mark J. Lowe,et al.  SimPACE: Generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction , 2014, NeuroImage.