[FieldTrip] Source reconstruction and NAI

[Marc Recasens]

Thank you Jan-Mathijs,
I actually did not think about that...
However, I did this just to remove the central blobs, independently of 
the effect it may cause in the different conditions.

My aim is to use montecarlo non-parametric statistics afterwards to 
localize the sources. Don't you think there's gonna be a huge difference 
between nai-normalized and non-normalized datasets?


Thanks again!



El 07/04/2011 14:23, jan-mathijs schoffelen escribió:
> Dear Marc,
>
> It seems from your question that you are interested in single trial 
> reconstructed time series. If your purpose is to do a statistical 
> comparison across a set of conditions I would not do a noise 
> normalization at all. You mention that you used a spatial filter 
> common to the three conditions. As a consequence, the estimate of the 
> noise will be independent of the condition, so any normalization you 
> would apply just leads to a scaling of the data, and will not change 
> the outcome of your statistical test.
> Yet, I understand that it sometimes makes sense to do a normalization 
> in order to be able to make a sensible visualization of the data (i.e. 
> removing the big central blob). Also, in this case I would compute a 
> descriptive statistic across the conditions, rather than normalizing 
> with an estimate of the projected noise (which in FieldTrip is rather 
> rudimentary because it assumes the noise to be spatially white). You 
> could for example compute  an F-value from a one-factor, three level 
> anova, or do a pairwise comparison of conditions using a t-test.
> Another alternative (which does not affect your statistical test 
> either), is to work with norm-normalized leadfields. This will take 
> away the blob in the centre of the volume, and facilitates visualization.
>
> Best wishes,
>
> Ja-Mathijs
>
>
> On Apr 5, 2011, at 6:54 PM, Marc Recasens wrote:
>
>> Dear all,
>>
>> As far as I know it is possible to reconstruct the time-course of the 
>> sources (obtained using sourceanalysis) by projecting/multiplying the 
>> filter-weights on the data.
>> In my case, the specific code I am using is:
>>
>> for i=1:nvoxels(inside)
>>
>> for tr=1:ntrials
>>
>>             
>> ts(tr,:)=(source.avg.ori{source.inside(i)}(1,:)*source.avg.filter{source.inside(i)}*data2{condit}.trial{tr});
>>
>> end
>>
>>         datvx(i,:,:)= single_trial_time_course
>>
>> end
>>
>> I used a common filter with 3 different conditions, thus I think this 
>> is the only way I have to reconstruct the activity for one of the 
>> conditions.
>> My Question is about how to apply the NAI normalization here. Can I 
>> just divide the output of the source reconstruction by the projected 
>> noise of the filter? I am tempted to do something like that
>>
>> datvx(i,:,:)= single_trial_time_course ./ 
>> repmat(noise(inside),[size(ts,1), size(ts,2)]
>>
>> That is, I divide the power by the noise estimate (for each voxel) in 
>> every trial and time-point. Could anyone tell me whether this is a 
>> correct way to procedure? Any other workaround?
>>
>>
>> Thanks in advance!
>>
>> -- 
>> Marc Recasens
>> Tel.: +34 639 24 15 98
>>
>> _______________________________________________
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>> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
> Dr. J.M. (Jan-Mathijs) Schoffelen
> Donders Institute for Brain, Cognition and Behaviour,
> Centre for Cognitive Neuroimaging,
> Radboud University Nijmegen, The Netherlands
> J.Schoffelen at donders.ru.nl <mailto:J.Schoffelen at donders.ru.nl>
> Telephone: 0031-24-3614793
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip


-- 
Marc Recasens
Tel.: +34 639 24 15 98

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