In case you don't follow practiCal fMRI (you should!), his last two posts describe a series of tests exploring whether or not the multiband (AKA simultaneous multi-slice) sequence is especially liable to respiration artifacts: start here, then this one. Read his posts for the details; I think a takeaway for us non-physicists is that the startlingly-strong respiration signal I (and others) have been seeing in multiband sequence timecourses and motion regressors is not from the multiband itself, but rather that respiration and other motion-type signals are a much bigger deal when voxels are small (e.g., 2 mm isotropic).
This week I dove into the literature on motion regression, artifact correction, etc. Hopefully I'll do some research blogging about a few papers, but here I'll muse about one specific question: how many motion regressors should we use (as regressors of no interest) for our task GLMs? 6? 12? 24? This is one of those questions I hadn't realized was a question until running into people using more than 6 motion regressors (the 6 (x,y,z,roll,pitch,yaw) come from the realignment during preprocessing; transformations of these values are used to make the additional regressors).
Using more than 6 motion regressors seems more common in the resting state and functional connectivity literature than for task fMRI (Power et al. 2015, and Bright & Murphy 2015 , for example). I found a few (only a few) task papers mentioning more than 6 motion regressors, such as Johnstone et al. 2006, who mention testing "several alternative covariates of no interest derived from the estimated motion parameters", but they "lent no additional insight or sensitivity", and Lund et al. 2005, who concluded that including 24 regressors was better than none.
Out of curiosity, we ran a person through an afni TENT GLM (FIR model) using 6 (left) and 24 (right) motion regressors. This is a simple control analysis: all trials from two runs (one in blue, the other orange), averaging coefficients within my favorite left motor Gordon parcel 45 (there were button pushes in the trials). It's hard to tell the difference between the model with 6 and 24 regressors: both are similar and reasonable; at least in this test, the extra regressors didn't have much of an effect.
My thinking is that sticking with the usual practice of 6 regressors of no interest is sensible for task fMRI: adding regressors of no interest uses more degrees of freedom in the model, risks compounding the influence of task-linked motion, and hasn't been shown superior. But any other opinions or experiences?