LANGUAGE PRODUCTION
4. SUMMARY
1.2. Motion Effects in Functional MRI
Of all the sources of physiological noise in a functional imaging experiment, motion of the head in all its forms is the most significant. As the techniques for its amelioration after the image data are collected are incomplete in their effectiveness, prevention or lim-itation of excessive motion during the phase of data acquisition is the best way to achieve the highest degree of specificity and sensitivity in functional brain imaging. The methods available to limit or prevent subject head motion in imaging experiments are myriad,
including: (1) insertion of foam cushions between the subject and the head coil, (2) restraint with a custom thermoplastic mask, (3) on-line motion feedback provided to the subject (Yang, Ross, Zhang, Stein, & Yang, 2005), (4) prior subject compliance train-ing (Slifer, Cataldo, Cataldo, Llorente, & Gerson, 1993), and (5) relatively rigid coupltrain-ing of the subject to the head coil with a bite-bar fashioned from dental acrylic. These approaches are differentially effective and range widely in practicality and comfort.
Although all work reasonably well in reducing rigid-body motion of the head, none reduce the susceptibility artifacts arising from articulator motion or the parenchymal brain motion arising from cardiovascular and respiratory effects. As the overall accuracy of fMRI is principally limited by inter-scan head motion artifacts present in statistical maps of task-related brain activity, and the existing post-processing algorithms for image realignment have not been completely successful when applied to time series of echo-planar images, it is obvious that better methods to prevent subject motion of all kinds are needed.
Subject motion relevant to imaging studies involving language and communication can be categorized into three types: (1) rigid-body motion of the head, (2) parenchymal motion of the brain, and (3) motion of the articulators, particularly the jaw.
Rigid-body motion refers to the translational and rotational changes the head can make, even when the subject is comfortably supine on the scanner table. Relatively small degrees of translational or rotational motion of the head result in misregistration of sequentially collected brain volumes, resulting in signal intensity changes related to spa-tially varying partial volume effects. Speech responses are particularly likely to cause head motion, resulting from the mechanical coupling of the jaw and skull such that relatively small jaw movements can result in large translational movements of the skull.
Since even small amounts of interscan motion in a time series can result in large artifacts in statistical maps derived from that series, it is best to take precautions to minimize head motion. Although the motion is global, the effects of the motion are regionally specific, being most prominent in regions of variable tissue contrast. Examples include boundaries between gray matter and cerebrospinal fluid. This may result in an easily appreciated
“rim” artifact around the edge of the brain in statistical maps generated from time series with excessive interscan motion. If peak-to-peak rigid-body motion exceeds 5–10% of the image voxel width, statistical maps are likely to exhibit obvious motion artifacts.
Therefore, assuming constant head motion, statistical map artifact will increase with increasing spatial resolution and decreased voxel width.
In spite of strenuous efforts on the part of the investigators to prevent subject mo-tion, tasks requiring speech responses are inevitably associated with some head move-ment. This problem is likely to be especially severe in studies requiring articulation.
Determination of the magnitude of these unwanted movements provides information useful in deciding whether or not to employ techniques to compensate for the motion.
To this end, head motion can be estimated by computing the motion of the center-of-intensity of each image volume. By comparing this location across time points, it is possible to estimate rigid-body motion of the head during the scanning interval. If the
motion detection process reveals head motion greater than 0.10 pixel width in any di-mension, then head motion correction is performed using reregistration algorithms (Woods, Grafton, Holmes, Cherry, & Mazziotta, 1998a; Woods, Grafton, Watson, Sicotte, & Mazziotta, 1998b). These algorithms allow rigid-body transformations guided by a least-squares misregistration minimization technique. Having determined that sufficient head motion is present to warrant correction, it is possible to employ these automated realignment procedures to determine the coordinate transformation that will bring the members of the time series back into register (see Figure 1). This is usually the most time consuming and computationally intensive part of the entire analysis procedure. Having determined the appropriate coordinate transformation to reregister the volumes, a resampling algorithm is employed to generate the realigned image volume. For this procedure there is a trade-off between time and accuracy, with the most accurate resampling procedures (sinc interpolation) being significantly slower than the less accurate procedures (nearest neighbor interpolation). Although relatively effective, these techniques correct only for rigid-body motion.
Even in the absence of rigid-body head motion, regionally varying parenchymal motion of the brain can produce significant artifacts in statistical parametric maps. This
Figure 1. Time plots of rigid-body head motion during a reading task comparing silent and aloud reading. Tasks, such as reading aloud, that associated with jaw and tongue movements invariably are associated with more inter-scan head motion than their silent counterparts.
parenchymal motion results from an interaction between the viscoelastic properties of the brain tissue and local pressure changes induced by arterial and venous pressure modula-tions of cardiac and respiratory origin (Poncelet, Wedeen, Weisskoff, & Cohen, 1992).
These effects result in relatively large MR signal changes at frequencies above the sampling rates customarily employed in fMRI studies (0.25–0.5 Hz) and therefore appear as aliased noise in lower frequency components of the fMRI time series. This aliased noise has most prominent effects in the regions of the derivative statistical maps around the ventricles, where the viscoelastic parenchymal motion is most prominent. Attempts have been made to reduce these effects by designing digital filters that attenuate signal at the appropriate frequencies (Biswal, DeYoe, & Hyde, 1996; Hu, Le, Parrish, & Erhard, 1995; Le & Hu, 1996).
Motion of the articulators, particularly those that involve prominent jaw motion can result in image artifacts that are extremely difficult to remove during post-processing.
The frequent observation that image artifacts related to motion of the articulatory appa-ratus may result in prominent false-positive signals led many investigators to conclude, perhaps prematurely, that fMRI was not a suitable technique for studying the neural mechanisms of speech, as experimental designs requiring verbal responses are necessa-rily associated with jaw movements that can induce noticeable susceptibility artifacts in medial and inferior temporal cortical regions. As these areas are involved in language, it is obvious that jaw motion can generate potentially serious image artifacts in regions that would be expected to show task-related signal change. Other artifacts resulting from the susceptibility changes that are induced by jaw movements include signal loss and image warping in nearby brain regions. These will vary with the position of the articulators responsible for the production of a given word. Spoken responses are also accompanied by tongue movement and swallowing, which provide an additional source of local sus-ceptibility artifact as the shape of the oral cavity is continuously transformed (Birn, Bandettini, Cox, & Shaker, 1999). All of these effects are somewhat paradoxical in that they can occur well out of the acquisition field of view, resulting from the large changes in magnetic susceptibility related to modulation of the shape and volume of the oral cav-ity during jaw movement.
To summarize, there are a number of sources of motion in functional neuroimaging experiments, each contributing in a specific way to an overall degradation in the efficacy of signal detection and estimation. Although prevention is to be preferred by a wide mar-gin, mitigation of motion effects can sometimes be an effective strategy.