Hi Mark,
This is very helpful, as I had been wondering the same thing. However,
when I ran this tool it seemed to find outlier volumes, but did not
write an output file. Any ideas what I am doing wrong?
Here is an example of my command line:
fsl_motion_outliers /mtl/LDR/functional/LDR001-1/bold/004/f.nii 0
/mtl/LDR/functional/LDR001-1/bold/004/MotionOutliers.txt
Thanks for your help,
Jeremy
Mark Jenkinson wrote:
> Hi,
>
> There is a tool designed precisely for this.
>
> It is called fsl_motion_outliers and will check your motion
> corrected data looking for points in time where there is an
> unusual amount of residual intensity change (after motion
> correction). Any outliers with respect to this are then
> identified and a confound matrix created that you can
> use in FEAT to effectively remove any changes associated
> with these timepoints. Note that this is different from deleting
> volumes as (i) it does not require adjusting the other model
> EVs, and importantly, (ii) it correctly accounts for any changes
> in signal and autocorrelation on either side of the "lost"
> timepoint(s) as well as adjusting the degrees of freedom
> correctly.
>
> To use it you just run fsl_motion_outliers on the original
> (unfiltered and not motion corrected) data for each
> subject/session individually. In each case it will create a
> confound matrix which you add into the analysis for this
> subject using the "Add additional confound EV(s)" button
> on the "Stats" tab in FEAT. And that's it!
>
> Hope this sorts your problem out.
> All the best,
> Mark
>
>
>
>
> On 21 May 2009, at 10:44, Klara Mareckova wrote:
>
>> Hello,
>>
>> do you happen to know if there is a relatively easy way in FSL how to
>> idicate which slices and particular time series should be cut off from
>> the analysis?
>>
>> I've analyzed the data for 50 subjects but found that they were moving
>> a lot and therefore even if I would set quite lenient criteria and
>> exclude everybody who moved more than 2 mm, I would end up with only
>> 29 subjects. This is way too much and that is why I was thinking about
>> cutting off the slices with the biggest movement (fslsplit &fslmerge).
>> However, if I do this a problem with the time series comes out. Is
>> there an easy way how to take care about this or do I have to go to
>> each particular subject's design, exclude the particular time series
>> and rerun the whole analysis?
>>
>> Do you also happen to have some guidelines about the exclusion
>> criteria for motion correction? In some articles about adult
>> participants I've seen exclusion criteria 1mm or 1degree but this
>> seems to be too strict for my subjects.
>>
>> Many thanks for your help.
>>
>> Klara
>>
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