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.