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Sure, you can try to fix it during data processing, but you're usually better off fixing the acquisition!

Saturday, October 27, 2012

Motion problems in fMRI: Receive field contrast effects

Motion has been identified as a pernicious artifact in resting-state connectivity studies in particular. What part might the scanner hardware play in exacerbating the effects of subject motion?

My colleague over at MathematiCal Neuroimaging has been busy doing simulations of the interaction between the image contrast imposed by the receiver coil (the so-called "head coil") and motion of a sample (the head) inside that coil. The effects are striking. Typical amounts of motion create signal amplitude changes that easily rival the BOLD signal changes, and spurious spatial correlations can be introduced in a time series of simulated data.

The issue of receive field contrast was recognized in a recent review article by Larry Wald:
"Highly parallel array coils and accelerated imaging cause some problems as well as the benefits discussed above. The most problematic issue is the increased sensitivity to motion. Part of the problem arises from the use of reference data or coil sensitivity maps taken at the beginning of the scan. Movement then leads to changing levels of residual aliasing in the time-series. A second issue derives from the spatially varying signal levels present in an array coil image. Even after perfect rigid-body alignment (motion correction), the signal time-course in a given brain structure will be modulated by the motion of that structure through the steep sensitivity gradient. Motion correction (prospective or retrospective) brings brain structures into alignment across the time-series but does not alter their intensity changes incurred from moving through the coil profiles of the fixed-position coils. This effect can be partially removed by regression of the residuals of the motion parameters; a step that has been shown to be very successful in removing nuisance variance in ultra-high field array coil data (Hutton et al., 2011). An improved strategy might be to model and remove the expected nuisance intensity changes using the motion parameters and the coil sensitivity map."

In our recent work we take a first step towards understanding the rank importance of the receive field contrast as it may introduce spurious correlations in fMRI data. It's early days, there are more simulations ongoing, and at this point we don't have much of anything to offer by way of solutions. But, as a first step we are able to show that receive field contrast is ignored at our peril. With luck, improved definition of the problem will lead to clever ways to separate instrumental effects from truly biological ones.

Anyway, if you're doing connectivity analysis or otherwise have an interest in resting-state fMRI in general, take a read of MathematiCal Neuroimaging's latest blog post and then peruse the paper submitted to arXiv, abstract below.


A Simulation of the Effects of Receive Field Contrast on Motion-Corrected EPI Time Series

D. Sheltraw, B. Inglis
The receive field of MRI imparts an image contrast which is spatially fixed relative to the receive coil. If motion correction is used to correct subject motion occurring during an EPI time series then the receiver contrast will effectively move relative to the subject and produce temporal modulations in the image amplitude. This effect, which we will call the RFC-MoCo effect, may have consequences in the analysis and interpretation of fMRI results. There are many potential causes of motion-related noise and systematic error in EPI time series and isolating the RFC-MoCo effect would be difficult. Therefore, we have undertaken a simulation of this effect to better understand its severity. The simulations examine this effect for a receive-only single-channel 16-leg birdcage coil and a receive-only 12-channel phased array. In particular we study: (1) The effect size; (2) Its consequences to the temporal correlations between signals arising at different spatial locations (spatial-temporal correlations) as is often calculated in resting state fMRI analyses; and (3) Its impact on the temporal signal-to-noise ratio of an EPI time series. We find that signal changes arising from the RFC-MoCo effect are likely to compete with BOLD (blood-oxygen-level-dependent) signal changes in the presence of significant motion, even under the assumption of perfect motion correction. Consequently, we find that the RFC-MoCo effect may lead to spurious temporal correlations across the image space, and that temporal SNR may be degraded with increasing motion.