Thanks for the feedback. I was using avwmerge -t, I'm not sure why I wrote
-z. I will try letting melodic run for a few hours on the concatenated
data and see if it eventually finishes or times out with an error. Also, I
will try the transform you mention.
Cheers,
Pete
> Hi
>
> if you want to concatenate the 4t dimension with avwmerge (or ++)
> you'll need to use the -t option. The final results should always be
> 4D, not 5D. If melodic does not finish when using option1 what
> exactly is the error message? Wrt both options note that when
> transforming all images (or IC maps) to a common space you don't need
> to upsample to 2mm resolution. If you have all the transformation
> matrices computed then you can transform a 4D data set into standard
> space while keeping it at e.g. 4x4x4mm resolution using flirt
>
> flirt -in 4D -ref ${FSLDIR}/etc/standard/avg152 -out 4Dstd -
> appyisoxfm 4 transformation.mat
>
> hope this helps
> Christian
>
>
>
>
> On 30 May 2007, at 21:56, Peter Fried wrote:
>
>> Hi,
>>
>> I am having some frustrations while trying to conduct a meta-
>> analysis of
>> resting-state FMRI data. Hopefully someone can help.
>>
>> So far, I have processed the raw data using pre-stats in FEAT and run
>> MELODIC on each subject in their native space.
>>
>> Following the history of posts on this issue, I have tried both of the
>> following using a test case of 4 subjects:
>>
>> 1) Transformed each subject's filtered_func_data (feat output) file to
>> standard space; concatenated the data together across time using
>> avwmerge++ -z; run melodic on the new 5D file.
>>
>> Problem: melodic finishes in < 5 sec w/ no results.
>>
>> 2) Transformed each subject's melodic_IC file (melodic output - 30
>> components) to standard space; smoothed the data using a 5 mm Gaussian
>> kernel (avwmaths++ -fmean -kernel gauss 5); concatenated the data
>> together
>> across time (avwmerge++ -z); run melodic on the new 5D file.
>>
>> Problem: melodic hangs in the command window while eating up most
>> available CPU speed and RAM (after 30 minutes of 0 progress I
>> killed the
>> process).
>>
>> It certainly seems possible that method (2) may eventually work,
>> but if it
>> takes all available CPU and most RAM over an extended period of
>> time for
>> only 4 subjects, I'd hate to see what happens when I run it on 60+.
>>
>> I'm not sure what's going on with method (1) since I have no problem
>> running melodic on an individual subject's filtered_func_data.
>>
>> Any suggestions on what I'm doing wrong or alternative methods to
>> attempt
>> would be much appreciated.
>>
>> Cheers,
>>
>> Peter Fried
>> Center for Magnetic Resonance Research
>> University of Minnesota
>
> ____
> Christian F. Beckmann
> University Research Lecturer
> Oxford University Centre for Functional MRI of the Brain (FMRIB)
> John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
> [log in to unmask] http://www.fmrib.ox.ac.uk/~beckmann
> tel: +44 1865 222551 fax: +44 1865 222717
>
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