task fMRI motion censoring (scrubbing) #1: categorizing

Motion ... whether caused by head movement, other movement, breathing, or something else, it is one of the banes of fMRI. Motion artifacts are a huge issue for resting state fMRI, but not only - it causes big problems in task fMRI as well.The best things to do, of course, is to minimize movement during acquisition, by consistent head positioning, bracing with pads (or other systems). But no system is perfect (or able to eliminate breathing and heart beats), so we need to consider motion in the analyses. Here (as usual, though it's certainly not perfect) I'll use the motion traces (by which I mean the x, y, z, roll, pitch, yaw values produced during realignment and often used as nuisance regressors) as a proxy for motion.

Before deciding on any sort of censoring scheme for a study, it's good to look at the motion from all of the people, to get an idea of general movement categories. This post will show some runs I've decided are representative; exemplars of different sorts of movement. For background, these are individual runs from a cognitive task fMRI study, mostly with an MB4 acquisition scheme (details here).

All of these plots have vertical grey lines at one-minute intervals; the runs are around 12 minutes long. The horizontal green lines show the timing of the three task blocks present in each run; tasks were presented at random times and of varying durations during these blocks. The top pane has the translation (mm) and rotation (degrees) from the Movement_Regressors.txt file produced during (HCP-style) preprocessing. The second pane has the enorm and FD versions of the same motion traces, in mm.

I'll start with really nice traces, then work through to some that are not so nice, illustrating our qualitative categorization. I think it's useful to "calibrate your eyes" in this way to have a baseline understanding of some of the data characteristics before starting serious analyses or data manipulations.

Best possible: freakishly smooth: not even 0.5 mm translation over the entire 12 minute run; the little jiggles are probably related to breathing, and are also incredibly regular.

 

Not perfect, but very good; isolated spiky movement. This trace has very little drifting, almost entirely regular oscillations. This is the sort of movement that seems exactly suited to motion censoring: quite nice, except for a few short periods. (The frames censored with a threshold of FD > 0.9 are marked by red x.)

 

The next category are traces with prominent oscillations, but otherwise pretty clean (not terribly spiky or drifting), and fairly consistent in magnitude and frequency across the run. We'll be using these types of runs without censoring in our analyses (at least for now).

Finally, are the ones of more questionable quality and utility: numerous spikes, drifting, and/or changes in oscillation magnitude. Frames to be censored at FD > 0.9 are marked, but that's only designed to detect spikes. Slow drifts have generally been considered less problematic for task fMRI than spikes, and we generally have comparatively few drifts in this dataset, regardless.

Spiking and drifting are fairly familiar in motion traces; oscillations, less so. (Though I'm sure this sort of movement existed prior to SMS!) It is certainly possible that the oscillation changes (e.g., third image in last set, second in previous pair) reflect changes in respiration rate (perhaps at least somewhat due to entraining to the task timing), which could affect BOLD in all sorts of problematic ways, and for extended periods. We're actively looking into ways to quantify these sorts of effects and minimize (or at least understand) their impacts, but I don't think there are any simple answers. We have respiration and pulse recordings for most runs, but haven't yet been working with those in detail.