open-discussion > Motion Correction in Task-Based fMRI (frame censoring vs. motion regression)
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Sep 6, 2023  02:09 PM | Nadja
Motion Correction in Task-Based fMRI (frame censoring vs. motion regression)


I have a question about motion correction in task-related fMRI (block design).

I’ve read that "scrubbing" or "frame censoring" (i.e. removing volumes where the framewise displacement (FD) exceeds a certain threshold) performs better than adding the 6 rigid body realignment parameters (RP6) as nuisance regressors (e.g. Jones et al, 2022; Siegel et al., 2014).

The study by Jones et al. (2022) states for example: “As a rule, frame censoring performed better than RP6 and RP24 motion correction”

In addition, I have read that in block designs, where mostly motion is correlated with task condition, it may be better to opt for motion censoring rather than motion regression.

If I now decide to use frame censoring as motion correction, does this mean that I omit the RP6 as regressors? And if so, what about the participants who have no outliers based on the FD threshold? Is there no need to perform motion correction on these subjects (apart from the normal pre processing steps realignment etc.)? 

I’ve also seen some studies that combine FD frame censoring and RP6. Unfortunately, I can't find any guidance on whether I should use both motion correction approaches or only frame censoring (just include scan-nulling-regressors). 

Does anyone have experience with this? Or literature I can refer to?

Many thanks!


Jones MS, Zhu Z, Bajracharya A, Luor A, Peelle JE. A Multi-Dataset Evaluation of Frame Censoring for Motion Correction in Task-Based fMRI. Apert Neuro. 2022;2:1-25. doi: 10.52294/apertureneuro.2022.2.nxor2026.

Siegel JS, Power JD, Dubis JW, Vogel AC, Church JA, Schlaggar BL, Petersen SE. Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points. Hum Brain Mapp. 2014 May;35(5):1981-96. doi: 10.1002/hbm.22307 . Epub 2013 Jul 17.