False fMRI activation after motion correction

Abstract

Motion correction of echo-planar imaging (EPI) data used in functional MRI (fMRI) is an essential pre-processing step performed prior to statistical analysis. At ultra-high resolution fMRI, current requirements regarding translational and rotational motion may no longer be acceptable. This prompts the need for a systematic investigation of the effects of motion correction procedures with in-vivo fMRI data. Here we systematically evaluated the effect of retrospective motion correction with freely available fMRI analysis software packages (FSL, AFNI and SPM) on activation maps using fMRI data acquired with prospective motion detection, to identify and quantify confounding effects of retrospective motion correction, and to evaluate its dependence on spatial resolution and motion correction algorithms. Brain activation maps were obtained for two different resolutions, an ultra-high, i.e. 0.653 mm3, and a more widely used 2.03 mm3 isotropic resolutions at 7T. The EPI data were acquired using simultaneous non-image-based optical moiré phase tracking (MPT) of physical motion. The results showed that image-based motion detection, performed by SPM8 software package, may be erroneous in high-field fMRI data with partial brain coverage and can introduce spurious motion leading to false-positive and false-negative activation. Further analyses demonstrated that limited acquisition field of view has the dominant influence on the effect.

Measurement of microvascular cerebral blood volume changes over the cardiac cycle with ferumoxytol‐enhanced T2* MRI

“…In the present study, motion corrections were performed for 2D‐EPI, but not for 3D spiral data. Motion correction of the time series is important . If possible, for 2D‐EPI, selecting regions of interest where motion is deliberately low might further help reduced motion confounders.…”

Active head motion reduction in magnetic resonance imaging using tactile feedback

“…Being one of the most frequent sources of artefacts, bulkhead motion negatively affects the quality of the recorded images (for a review, see Zaitsev, Maclaren, & Herbst, 2015). However, computational algorithms for motion correction are known to leave residual motion-related artefacts in the data (Beall & Lowe, 2014;Friston et al, 1996;Maclaren, Herbst, Speck, & Zaitsev, 2013;Power et al, 2014) and can even induce false fMRI activations (Yakupov, Lei, Hoffmann, & Speck, 2017). However, computational algorithms for motion correction are known to leave residual motion-related artefacts in the data (Beall & Lowe, 2014;Friston et al, 1996;Maclaren, Herbst, Speck, & Zaitsev, 2013;Power et al, 2014) and can even induce false fMRI activations (Yakupov, Lei, Hoffmann, & Speck, 2017).…”

“…For functional MRI (fMRI) recordings, the issue is usually addressed by retrospectively correcting the data with information from either the functional images themselves (Friston et al, 1995;Friston, Williams, Howard, Frackowiak, & Turner, 1996) or real-time motion tracking with a camera (Stucht et al, 2015;Todd, Josephs, Callaghan, Lutti, & Weiskopf, 2011). However, computational algorithms for motion correction are known to leave residual motion-related artefacts in the data (Beall & Lowe, 2014;Friston et al, 1996;Maclaren, Herbst, Speck, & Zaitsev, 2013;Power et al, 2014) and can even induce false fMRI activations (Yakupov, Lei, Hoffmann, & Speck, 2017). Therefore, other solutions aim to address the issue at the source and try to prevent head motion from occurring by immobilising the subject, for instance, by fixating the subject's head with a plaster cast head holder (Edward et al, 2000) or a bite bar (Bettinardi et al, 1991;Menon, Lim, Anderson, Johnson, & Pfefferbaum, 1997).…”

Active head motion reduction in Magnetic Resonance Imaging using tactile feedback

“…For functional MRI (fMRI) recordings, the issue is usually addressed by retrospectively correcting the data with information from either the functional images themselves (Friston, Ashburner, Frith, Poline, Heather & Frackowiak, 1995;Friston, Williams, Howard, Frackowiak, & Turner 1996) or real-time motion tracking with a camera (Todd, Josephs, Callaghan, Lutti & Weiskopf, 2011;Stucht, Danishad, Schulze, Godenschweger, Zaitsev & Speck, 2015). However, computational algorithms for motion correction are known to leave residual motionrelated artefacts in the data (Friston et al, 1996;Maclaren, Herbst, Speck & Zaitsev, 2013;Power, Mitra, Laumann, Snyder, Schlaggar & Petersen, 2014;Beall & Lowe, 2014) and can even induce false fMRI activations (Yakupov, Lei, Hoffmann & Speck, 2017). Therefore, other solutions aim to address the issue at the source and try to prevent head motion from occurring by immobilising the subject, for instance by fixating the subject's head with a plaster cast head holder (Edward et al, 2000) or a bite bar (Bettinardi et al, 1991;Menon, Lim, Anderson, Johnson & Pfefferbaum, 1997).…”