A new paper in Human Brain Mapping reports on: Functional magnetic resonance imaging movers and shakers: Does subject-movement cause sampling bias?
Head movement is a well known problem that can badly impact the quality of neuroimaging data, introducing spurious signals and obscuring real ones. It's an issue for all brain scanning research but according to Wylie and colleagues, authors of this paper, it's especially serious for studies comparing disease patients to healthy controls. The authors got 34 people with multiple sclerosis (MS) to perform some simple cognitive tasks during fMRI scanning. They found that the harder the task was, the more the patients moved around during the scan; and in those with more severe MS, the correlation between difficulty and motion was even stronger. In healthy people, harder tasks only caused slightly more motion. And the more people moved, the less brain activity was recorded, probably because movement degraded the data quality.
fMRI data associated with severe head movement is frequently discarded. In discarding these data, it is often assumed that head-movement is a source of random error, and that data can be discarded from subjects with severe movement without biasing the sample. We tested this assumption by examining whether head movement was related to task difficulty and cognitive status among persons with multiple sclerosis (MS).
[In the MS patients] there was a linear increase in movement as task difficulty increased that was larger among subjects with lower cognitive ability. Analyses of the signal-to-noise ratio (SNR) confirmed that increases in movement degraded data quality. Similar, though far smaller, effects were found in healthy control subjects. Therefore, discarding data with severe movement artifact may bias multiple sclerosis samples such that only those with less-severe cognitive impairment are included in the analyses. However, even if such data are not discarded outright, subjects who move more will contribute less to the group-level results because of degraded SNR. Oh dear. fMRI researchers use two main ways to deal with motion - correction, and rejection. Either you try to take account of movement and analyze the data, or you just throw out the results from people who move a lot. Most people use a combined approach, chucking out the really heavy movers and then using correction on the rest. This paper however shows that both techniques have problems. Despite motion correction, data from heavy movers has a lower signal to noise ratio, so if you include them, they will "dilute" your sample. However, if you chuck them, that'll also introduce bias, because heavy movement is not random - people with more severe MS move more, so by excluding heavy movers, you'll be excluding severe cases. Informally, every neuroimaging researcher knows that some people move more than others. Learning to spot likely "movers" and avoid wasting money on scanning them is a fine art. In my experience just about every "patient" population move, on average, more than healthy controls, and children and the elderly move more than young adults. I'm not sure there's an ideal solution but perhaps the best approach is to run all analyses (at least) twice, once including everyone, regardless of movement, and then again, with strict movement exclusion criteria. Results consistent across both analyses are probably solid.
Wylie GR, Genova H, Deluca J, Chiaravalloti N, and Sumowski JF (2012). Functional magnetic resonance imaging movers and shakers: Does subject-movement cause sampling bias? Human brain mapping PMID: 22847906