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.

Understanding fMRI artifacts: CONTENTS

An organizational post I'd been meaning to get to for a while. There are some posts to come in this series, in parentheses below. I'll update this page with links as these posts get published.

Understanding fMRI artifacts

An introduction to the post series, defining what we mean by "good" data, and general discussion on viewing and interpreting EPI artifacts in a time series.

Good data

Understanding fMRI artifacts: "Good" axial data

Includes cine loops through time series EPI and statistical images to evaluate the data.

Understanding fMRI artifacts: "Good" coronal and sagittal data

Includes cine loops through time series EPI and statistical images to evaluate the data. (The notes include a description of the slice-dependent gradient switching limits that can prohibit certain slice orientations.)

Common persistent EPI artifacts

Common persistent EPI artifacts: Aliasing, or wraparound

Aliasing effects in the frequency and phase encoding dimensions.


Common persistent EPI artifacts: Gibbs artifact, or ringing

The origin of the ringing problem and demonstrations in phantom and brain data.

Common persistent EPI artifacts: Abnormally high N/2 ghosts (1/2)

Intense stray (static) magnetic field gradients may affect cognition

Have you ever wondered whether it's appropriate to put a research subject into a dark, confined tube that makes an awful din, whereupon the subject may learn that his brain has some abnormality, and still expect the subject's brain to operate in a state representative of his normal cognition (and not that of a stressed out basket-case)? And what about the bioeffects of the high magnetic field itself, or of the rapidly switched gradients and their induced electric currents in body tissue? To date there has been scant evidence that the action of studying human cognition via an MRI scanner actually modifies that brain function in a manner that might be considered a significant issue for interpretation of fMRI results.

Putting aside the cognitive effects of a loud background noise and claustrophobia, the question remains whether the static and time-varying magnetic fields are modifying brain function in a substantial fashion. There are some well-known side effects of high magnetic fields: vertigo (see Note 1), and a metallic taste are the two phenomena tied directly to presence of, or movement through, a high magnetic field. (See Note 2.) But these effects tend to be mild and/or transitory, as a subject acclimatizes to the magnetic field, and can usually be rendered negligible by taking care not to make rapid head movements in or around the magnet.

A colleague forwarded to me yesterday a paper from a Dutch group (van Nierop et al., "Effects of magnetic stray fields from a 7 tesla MRI scanner on neurocognition: a double-blind randomized crossover study." Occup. Environ. Med. 2012 Epub) that investigates the effects of head movements in the intense stray field region of a 7 T magnet. So, first of all, some good news: if you're doing fMRI at 1.5 or 3 T and you're not in the habit of asking your subjects to thrash their heads around wildly at the mouth of the magnet or once inside the magnet bore, then so far as is known today you're in the clear. The effects reported in this paper pertain specifically to head movement in the really intense gradients that comprise the stray magnetic field around the outside of a passively shielded 7 T magnet. (The iron shield is outside the magnet, leaving considerable gradients in the vicinity of the magnet when compared to the actively shielded 1.5 and 3 T magnets most of us have nowadays.)

And with that preamble let's look at the summary of the paper:

OBJECTIVE:  This study characterises neurocognitive domains that are affected by movement-induced time-varying magnetic fields (TVMF) within a static magnetic stray field (SMF) of a 7 Tesla (T) MRI scanner.

METHODS:  Using a double-blind randomised crossover design, 31 healthy volunteers were tested in a sham (0 T), low (0.5 T) and high (1.0 T) SMF exposure condition. Standardised head movements were made before every neurocognitive task to induce TVMF.

RESULTS:  Of the six tested neurocognitive domains, we demonstrated that attention and concentration were negatively affected when exposed to TVMF within an SMF (varying from 5.0% to 21.1% per Tesla exposure, p<0.05), particular in situations were high working memory performance was required. In addition, visuospatial orientation was affected after exposure (46.7% per Tesla exposure, p=0.05).

CONCLUSION:  Neurocognitive functioning is modulated when exposed to movement-induced TVMF within an SMF of a 7 T MRI scanner. Domains that were affected include attention/concentration and visuospatial orientation. Further studies are needed to better understand the mechanisms and possible practical safety and health implications of these acute neurocognitive effects.

Okay, so let's make sure we're clear that although the test magnetic field strengths mentioned are 0.5 and 1.0 T, this refers to two heterogeneous regions of a stray magnetic field on the outside of a 7 T magnet:

i-fMRI: Prospective motion correction for fMRI?

An ideal fMRI scanner might have the ability to update some scan parameters on-the-fly, in order to reduce or eliminate the effects of subject motion. Today, this approach is commonly referred to as "prospective motion correction" because the idea is to adapt the acquisition so that (some of) the effects of motion aren't recorded in the data, in contrast to the routinely employed retrospective motion correction schemes, such as an affine registration algorithm applied during post-processing; that is, in between the acquisition and the stats/modeling, which can lead some people to refer to such steps as "pre-processing" if you have a stats/modeling-centric view of the fMRI pipeline.

On the face of it, ameliorating motion effects by not permitting them to be recorded in the time series data is a wonderful idea. Indeed, as the subtitle to this blog attests, I am a huge fan of fixes applied during the acquisition rather than waiting until afterwards to try to post-process away unwanted effects. But this preference assumes that any method actually works, and works robustly, in everyday use. For sure there will be limitations and compromises, yet the central question is whether the benefits outweigh the costs. In the specific case of prospective motion correction, then, does a scheme (a) eliminate the need to use retrospective motion correction, and (b) does it reduce the effects of motion without bizarre failure modes that can't be predicted or circumvented easily?

A good place to begin evaluating prospective motion correction schemes - indeed, all motion correction schemes - is to first asses their vulnerabilities. It's no good if the act of fixing one part of the acquisition introduces an instability elsewhere. Failure modes should be benign. Below, I list the major hurdles for motion correction schemes to overcome, then I consider how elaborate any solutions might need to be. The goal is to decide whether - or when - prospective motion correction can be considered better than the alternative (default) approach of trying to limit all subject motion, and deal with the consequences in post-processing.


What do we mean by motion correction anyway?

As conducted today, motion correction applied during post-processing generally refers to an affine or sometimes a non-linear registration algorithm that seeks to maintain a constant anatomical content in a stack of slices throughout a time series acquisition. Prospective motion correction generally refers to the same goal: conserving the anatomical content over time. But, as is well known, there are concomitant changes in the imaging signal, and perhaps the noise, when a head moves inside the magnet. Other signal changes that are driven by motion may remain in the time series data after "correction." Indeed, depending on the cost function being used, the performance of the motion correction algorithm to maintain constant anatomy over time may be compromised by these concomitant modulations.

Now, we obviously want to try to maintain the anatomical content of a particular voxel constant through time or we have a big problem for analysis! But as a goal we should use a more restrictive definition for an ideal motion correction method: after correction we seek the elimination of all motion-driven signal (and noise) modulations. The only signal changes remaining should be neurally-driven BOLD changes (if we're using BOLD contrast, which I assume in this post) and "physiologic noise" that isn't strongly coupled to head (skull) motion. (Accounting for physiologic noise is usually treated separately. That's the assumption I'll use in this post, although at a very fine spatial scale it's clear that physiologic noise is another form of motion sensitivity.)


Motion sensitivities in fMRI experiments

A useful first task is to consider all the substantial signal changes in a time series acquisition that can be driven by subject motion. What signal changes are concomitant with changes of anatomical content as the brain moves relative to the imaging volume? How complicated is this motion sensitivity? What aspects of the signal changes will require hardware upgrades to the scanner, and/or pulse sequence modifications in order to negate them? And are these capabilities already designed into a modern scanner or will they require substantial re-design? These are the questions to keep in mind as we review the major motion sensitivities.