Hi Estephan,
> Hello, I have some block-design functional runs (3mm isotropic
> voxels) with
> varied peaks of motion. Most of them have a single-TR peak of 4-6mm,
> while a
> few had peaks of 12-20mm. As I understand one have two options to
> try to
> correct head motion in FSL: a) feeding the motion parameters from the
> prestats in the GLM model of FEAT; and b) using melodic to find the
> extraneous components that are not task-related.
>
> I am using as a rule-of-thumb that motions beyond the voxel
> dimension (3mm)
> or that are time-correlated with the stimulus are to be rejected from
> further analysis. As I am trying to salvage some of those runs that
> do not
> follow this rule, some questions came to mind:
>
> 1) How can one tell if feeding the motion parameters into GLM
> succeeded in
> removing the motion-related blobs? Are looking the blobs in the FEAT
> output
> or in fslview the only options - which are fairly subjective?
Putting in motion parameters into the GLM is a good idea but there is
no easy way to predict or interpret the effect it has. There can be
two intermingled effects it has on the results, either:
1) z-stats go down: due to your motion regressors correctly stopping
your effects of interest from erroneously modelling motion that is
stimulus correlated.
2) z-stats go up: due to explaining noise variance in the data due to
motion that is not stimulus correlated.
>
> 2) Should I use both methods in conjunction (motion parameters in
> GLM+melodic) to obtain a more robust motion correction?
No reason why not.
>
> 3) What are the limits of using these methods; would they be able to
> compensate a 12 or even a 20mm motion peak?
Hard to give a general rule of thumb. Motion greater than 6mm is
getting pretty extreme to handle. Also depends on the type of motion.
Short sharp motion within a TR is more problematic than slow drifting
motion.
>
> 4) Motions that are time-locked with stimulus presentation can be
> safely
> removed using the above methods?
Yes - using the motion parameters as confounds will (as mentioned
above) stop effects of interest from erroneously modelling motion that
is stimulus correlated. Of course one is implicitly relying on the
motion correction having worked well, so if you a group with large
motion you;ll want to keep a qualitative eye on that as well.
>
> 5) If only one epoch (stimulus+rest) is affected by the peak motion,
> removing the TRs composing the affected epoch using flsmaths would
> be an
> acceptable approach?
>
Yes. Although it is easier to leave the data intact and to use a dummy
custom EV with all 0s except for 1s in those TRs you want to "remove".
Make sure that for the dummy EV you switch off convolution , temporal
filtering and temporal derivative.
> If anybody has other methods to correct head motion or ways to tell
> if the
> correction was successful, I would love to hear about it.
>
> Thanks.
>
> Estephan Moana, DDS
> Graduate Student
> Oral Biology PhD Program - Neurobiology track
> School of Dentistry, UNC- Chapel Hill
>
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