[FieldTrip] Source reconstruction and NAI

Fri Apr 8 21:09:01 CEST 2011

Dear Marc
If you do normalize every condition it may be better to normalize all
conditions to one baseline. this way differences between conditions will not
result from differences

On 8 April 2011 20:49, Marc Recasens <recasensmarc at gmail.com> wrote:

>  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?
>
yes, they are not comarable. before normlization the scale is very small
(say 10^ -13) and after it is a ratio, say 1.8.

>
>
> 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
>
>  _______________________________________________
> fieldtrip mailing list
> 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
> Telephone: 0031-24-3614793
>
>
> _______________________________________________
> fieldtrip mailing listfieldtrip at donders.ru.nlhttp://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> --
> Marc Recasens
> Tel.: +34 639 24 15 98
>
>
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