Yes, I'm doing this via Feat. What about the stimulus? Will it be
removed too?
Il 2017-03-16 12:54 Matthew Webster ha scritto:
> Hello Marzia,
> If you are doing this via FEAT, then res4d + mean_func ( using
> fslmaths ) will effectively give you the cleaned data, since fslmaths
> will add mean_func onto each timepoint in the res4d image.
>
> Kind Regards
> Matthew
>
>> On 16 Mar 2017, at 11:35, Marzia Restuccia
>> <[log in to unmask]> wrote:
>>
>> Thank you Anderson. I don't succeed to understand how can I get a 4D
>> "clean" dataset (I need this for a spectral analysis).
>>
>> I am running a GLM including 5 regressors for a task-based fMRI study.
>> My model is Y = x_stim*B_stim + x_c1*B_c1 + x_c2*B_c2 + x_r1*B_r1 +
>> x_r2*B_r2 + e, where:
>> x_stim : stimulus time course convolved with HRF
>> x_c1 and x_c2: cardiac regressors
>> x_r1 and x_r2: respiratory regressors
>> I would like now to get the "clean" data, and what I mean is the data
>> from which I removed the contribution of the cardiac and respiratory
>> regressors. In order to do this I applied the following formula:
>> Yclean = Y - ( x_c1*.B_c1 + x_c2*.B_c2 + x_r1*.B_r1 + x_r2*.B_r2 )
>>
>> Anyway, from the comparison between the clean data and the model
>> fitting I'm convinced that there's something wrong. Could you please
>> tell me how would you do to reach this purpose?
>>
>> Thank you and sorry for bothering you!
|