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.

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

D. Sheltraw, B. Inglis

(Submitted on 12 Oct 2012)

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.

Draft checklist for fMRI methods reporting in the literature

It took a little longer to get to than I'd planned, but contained in this post is a first pass at a checklist for acquisition parameters that I think should be included in the methods section of fMRI papers. This draft is an attempt to expand and update the list that was given in the 2008 paper from Poldrack et al. (I have reproduced below the section on acquisition that appeared in that 2008 paper.) Here, I tried to focus on the bulk of fMRI experiments that use 1.5 to 3 T scanners with standard hardware today. I further assumed that you're reporting 2D multislice EPI or spiral scanning. Advanced and custom options, such as multiband EPI, 3D scans and 7 T, will have to be added down the road.

In an attempt to make it didactic I have included explanatory notes. I went verbose instead of shorthand on the assumption that many fMRI papers don't include a lot of experimental detail perhaps because the authors don't possess that level of knowledge. We might as well learn something new whilst satisfying our collective desire for better manuscripts, eh? So, I haven't even tried to determine a shorthand notation yet. As others have already commented, having a checklist is probably more useful in the near term and the idea of a shorthand is a secondary consideration that has most value only if/when a journal is attempting to curtail the length of methods sections. But I'll take a stab at a shorthand notation once the checklist has been refined in light of feedback.

I've sorted the parameters into Essential, Useful and Supplemental categories in terms of value to a reader of a typical fMRI paper. Within each category the parameters are loosely sorted by functional similarity. In the Essential category are parameters whose omission would challenge a reader's ability to comprehend the experiment. Thus, there are several acquisition options - sparse EPI for auditory stimulus delivery is one example - that appear under Essential even though they are rarely used. The assumption is that everyone would report all the Essential parameters, i.e. that a reviewer should be expected to fault a paper that doesn't contain all the Essential parameters (and a journal should be held accountable for not permitting inclusion of all Essential parameters in the published methods section rather than consigning them to supplemental material).

Next-gen platforms for evaluating scientific output

Tal Yarkoni has a paper out in Frontiers in Neuroscience, "Designing next-generation platforms for evaluating scientific output: what scientists can learn from the social web." As someone who has recently taken the plunge into 'pre-publication' submissions, I shall be interested to hear others' opinions on the manifold issues surrounding online publication, peer review, post-publication review, etc.

To be honest I'm a little surprised someone down in the South Bay (that's Silicon Valley to you non-Bay Area locals) hasn't already created a startup company offering us software to do this stuff. Surely there's money to be made. Until then, I for one have moved in toto to faster online models, whether it's this blog for my local user support (which just happens to take precisely the same amount of work whether fifty or fifty million people read it) or arXiv.org for papers. I'm adopting the Nike model: Just do it. But I realize it's a lot more complicated and nuanced than one rebellious Limey who already has a secure job. If we all went off piste there'd be chaos. So, how do we get from Tal's circumspect arguments to a workable platform?

Introduction to MR principles: online resources

I recently came across some extremely informative online resources for learning the basics of (nuclear) magnetic resonance. The first (via Agilent's Spinsights.net blog) is an online simulator that is nicely introduced in a series of four YouTube tutorials (see below). The simulator allows you to demonstrate such concepts as RF excitation, the rotating frame of reference, relaxation and even a 1D gradient for spatial encoding. If you are brand new to MR then you might need some assistance in understanding things for yourself, and I would think this tool (and the supporting tutorials) would be best used by an instructor in a class, but I don't want to dissuade you from taking a stab on your own. Watch the videos first (see below), then check out the simulator. (You can also find technical info and links to the tutorials at www.drcmr.dk/bloch.)

The other resource I found just about blew me away, not so much for the NMR lectures themselves, as good as they are, but because they are part of an extensive biophysics course covering everything from electromagnetic radiation to flow cytometry and sedimentation methods! The lectures are by Yair Meiry, a fellow who is apparently now working as a skydiving instructor in Canada (assuming my Internet sleuthing has improved since yesterday's attempt to divine the Scandinavian country of origin of another YouTube video). Channeling his inner Garrett Lisi, perhaps? I know I'm impressed.

Quench!!!

I was persuaded by Tobias Gilk to post a video of the quench of Berkeley's old 4 T magnet, a fairly momentous event that a lot of people have enjoyed watching in private (whether they were absent or witnessed it live). The quench happened back in 2009. We didn't publicize the video at the time because we didn't want a bunch of know-nothings accusing us of wasting resources. (See the FAQ in the video comments if you want to know what happened to the magnet - we turned it into a mock scanner - and why we didn't try to recover the helium.) But there comes a time when the value to others becomes greater than the annoyance of poorly informed trolls venting their spleens on YouTube. So here it is, finally:

In case you missed seeing some of our antics in the couple of days leading up to the quench, here's that video, too:

And finally, while uploading the most recent video I tripped over another quench video from what looks and sounds like some Scandinavians: (I'm not even going to guess between Finland, Denmark, Norway, Sweden, Iceland,...)

Looks like these guys had as much fun as we did! What's really clear in their tests is the oscillation of magnetic objects between the regions of peak gradient at either end of the magnet - a couple of feet out from the faces of the magnet at either end, the magnetic field and cryostat being symmetrical. The speed of movement is sufficiently slow at 1.5 T to see things clearly, versus the crazy violent movement of objects in the 4 T field. They have better music, too.

We're arXiving! (Another post on GRAPPA.)

In another move to accelerate the development of methods for neuroimaging applications, some colleagues and I recently decided to abandon a second attempt to publish a paper in traditional journals and opted for the immediacy of arXiv instead. (Damn, it feels good to be free of reviewers claiming "What problem? I don't see why a solution is even needed?" Whatever.) We've got another paper coming out on arXiv in a few days, too, although in this case we are exploring the possibility of a simultaneous submission to IEEE Trans Med Physics since it allows such tactics, and my colleagues in "real" physics do this all the time. Whether or not the IEEE submission happens the material will be out there in the world, naked, for all to view and poke at. Isn't this how science is supposed to work? I love it!

Anyway, for today, here's the skinny on the arXiv submission from August (which I inadvertently forgot to hawk on this blog even after tweeting it):

http://arxiv.org/abs/1208.0972

(Get a PDF fo' free via the link.)

Simultaneous Reduction of Two Common Autocalibration Errors in GRAPPA EPI Time Series Data

 

D. Sheltraw, B. Inglis, V. Deshpande, M. Trumpis *

(Submitted on 5 Aug 2012)

The GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions) method of parallel MRI makes use of an autocalibration scan (ACS) to determine a set of synthesis coefficients to be used in the image reconstruction. For EPI time series the ACS data is usually acquired once prior to the time series. In this case the interleaved R-shot EPI trajectory, where R is the GRAPPA reduction factor, offers advantages which we justify from a theoretical and experimental perspective. Unfortunately, interleaved R-shot ACS can be corrupted due to perturbations to the signal (such as direct and indirect motion effects) occurring between the shots, and these perturbations may lead to artifacts in GRAPPA-reconstructed images. Consequently we also present a method of acquiring interleaved ACS data in a manner which can reduce the effects of inter-shot signal perturbations. This method makes use of the phase correction data, conveniently a part of many standard EPI sequences, to assess the signal perturbations between the segments of R-shot EPI ACS scans. The phase correction scans serve as navigator echoes, or more accurately a perturbation-sensitive signal, to which a root-mean-square deviation perturbation metric is applied for the determination of the best available complete ACS data set among multiple complete sets of ACS data acquired prior to the EPI time series. This best set (assumed to be that with the smallest valued perturbation metric) is used in the GRAPPA autocalibration algorithm, thereby permitting considerable improvement in both image quality and temporal signal-to-noise ratio of the subsequent EPI time series at the expense of a small increase in overall acquisition time.

* For some strange arXiv technical reason the author list is reordered from that which appears (correctly) on the PDF. C'est la vie.