Wednesday, September 5, 2012

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.