Ah, that was it! The files were in Neurological orientation. I changed
the orientation in the header to Radiological and it worked just fine.
As a side question, is it preferable to change the orientation in the
header vs. changing the actual data?
Thanks so much for the help!
Jeremy
Mark Jenkinson wrote:
> Hi,
>
> The command line looks fine.
> We did have a bug with this working for some neurologically-ordered
> data. Can you run "fslorient" on the input data and tell me what the
> result is?
>
> All the best,
> Mark
>
>
> On 23 May 2009, at 20:28, Jeremy Elman wrote:
>
>> 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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