Hi,
Just to add one thing to this - there is also a tool called
fsl_motion_outliers which will look for timepoints in the
image where there seems to be excessive motion or
artifact (based on outliers in the sum of squared changes
after the motion correction). It will then create a confound
matrix which will remove the effect of these volumes/timepoints
from the analysis in the way that Mark described (one EV for
each timepoint that needs to be removed, with zeros in the
EV except for a one at just that point in time). You can then
import this confound matrix using the "Add additional confound EVs"
in the Stats tab of FEAT.
This would be the recommended way of dealing with quick, sharp
motions, even when they are quite large. However, make sure
that if you do have these that the remaining volumes are well
aligned after the motion correction, as sometimes large motions
like this can have a knock-on effect in the motion correction, as it
is done serially.
Hope this helps.
All the best,
Mark
On 13 Nov 2009, at 11:49, Mark Woolrich wrote:
> 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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