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FSL  July 2008

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Subject:

Re: fsl_regfilt

From:

"Christian F. Beckmann" <[log in to unmask]>

Reply-To:

FSL - FMRIB's Software Library <[log in to unmask]>

Date:

Mon, 7 Jul 2008 23:31:50 +0100

Content-Type:

text/plain

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text/plain (101 lines)

Hi,

yes, the mean should remain unchanged - it's being removed first  
before regression and then re-intorduced later - will check your data  
once back from travels.
cheers
Christian

On 1 Jul 2008, at 12:41, wolf zinke wrote:

> Hi Christian,
>
> Thanks for your explanations.
>
> So, If I got it right, it is better to use a complete modell for  
> filtering with fsl_regfilt to avoid an overfitting of the noise. On  
> the other side FSL offers the unconfound tool, which filters the  
> entire (noise) design matrix, and thus would give different results.
> Dave Flitney allready had a look into my data and pointed out, that  
> indeed there was a fluctuation of the voxel timeseries, however,  
> this was below 1% of the mean signal and therefore not visible by eye.
> The strange thing is, that using fsl_regfilt somehow increases the  
> mean signal intensity, but keeps or reduced the amplitude of the  
> fluctuations. Doing the same filtering with unconfound, using only  
> the noise part of the design matrix, gives reasonable results. So it  
> seems that somehow fsl_regfilt does not like my complete design  
> matrix and does something nasty to my data :-) .
> Maybe Dave Flitney still has got the submitted files, otherwise I  
> can again send them.
>
> Thanks for all the support,
> wolf
>
>
> Christian F. Beckmann wrote:
>> Hi
>>
>>> Hello all,
>>>
>>> I've got two (and a half) questions about fsl_regfilt.
>>>
>>> 1) When regressing out part of a design matrix, does it matter  
>>> whether this design matrix consists only of all possible noise  
>>> time courses, or whether it also includes the events of interest?  
>>> To be more specific, I want to filter motion related and created a  
>>> noise only design matrix that I used for filtering. However, in my  
>>> study, motion is correlated with the events of interest. When  
>>> including the noise regressors in the design for a feat analysis  
>>> the influence of the noise gets nicely reduced, giving more  
>>> pronounced activation patches. However, I am concerned that my  
>>> noise only model would reduce effects, when always  full model  
>>> will be fitted with fsl_regfilt. So, does fsl_regfilt fit only the  
>>> selected regressors, or will the full modell be fitted befor the  
>>> selected regressors are filtered out?
>>
>> No, what happens is this:
>>
>> -you specify a full design matrix (X, say)and using -f) the set of  
>> regressors you would want to be removed. This effectively  
>> partitions the matrix X into a signal and a noise part. fsl_regfilt  
>> then projects the data onto all of X to generate parameter estimate  
>> maps S. Using the information provided with the -f option these  
>> maps are then also sub-divided into noise maps and signal maps. The  
>> filtered data is
>>
>> data - X_{noise} * S_{noise}
>>
>> i.e. the new data is obtained by regressing the full model and then  
>> removing the noise terms.
>>
>>
>>> 2) In the FAQ melodic is suggested for filtering noise related  
>>> ICs, but the melodic webpage links to a page, where fsl_regfilt is  
>>> used. Are this two options of doing exactly the same thing, or is  
>>> there a difference? If they are different, what is the recommended  
>>> method?
>>>
>>
>> fsl_regfilt exists because melodic ended up being loaded with too  
>> much different functionality - the filtering will at some point be  
>> entirely removed from melodic and fsl_regfilt will be the only tool  
>> provided for this purpose.
>>
>>
>>>
>>> .5) After filtering the data, There is no motion visible when  
>>> using the movie loop with fslview. I am pretty impressed by this  
>>> effect, since I have data dominated by artefacts. Is fsl_regfilt  
>>> really that effective or is this just a bug in fslview causing  
>>> only one volume being displayed?
>>>
>>
>> not sure what's going on - maybe it is that good ;) If you upload  
>> the data we can have a look.
>> cheers
>> christian
>>
>>>
>>> Thanks for your help,
>>> wolf

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