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Thank you Jan-Mathijs,<br>
I actually did not think about that...<br>
However, I did this just to remove the central blobs, independently
of the effect it may cause in the different conditions. <br>
<br>
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?<br>
<br>
<br>
Thanks again!<br>
<br>
<br>
<br>
El 07/04/2011 14:23, jan-mathijs schoffelen escribió:
<blockquote
cite="mid:AA5DEE76-200E-45D0-BECF-71AE60A9C079@donders.ru.nl"
type="cite">
<div>Dear Marc,</div>
<div><br>
</div>
<div>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. </div>
<div>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.</div>
<div>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.</div>
<div><br>
</div>
<div>Best wishes,</div>
<div><br>
</div>
<div>Ja-Mathijs</div>
<div><br>
</div>
<br>
<div>
<div>On Apr 5, 2011, at 6:54 PM, Marc Recasens wrote:</div>
<br class="Apple-interchange-newline">
<blockquote type="cite"><font class="Apple-style-span"
face="arial, helvetica, sans-serif">Dear all,</font>
<div><font class="Apple-style-span" face="arial, helvetica,
sans-serif"><br>
</font></div>
<div><font class="Apple-style-span" face="arial, helvetica,
sans-serif">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.</font></div>
<div><span class="Apple-style-span" style="white-space: pre;"><font
class="Apple-style-span" face="arial, helvetica,
sans-serif">In my case, the specific code I am using is:</font></span></div>
<div><span class="Apple-style-span" style="white-space: pre;">
<p class="p1"> <font class="Apple-style-span" size="2"
face="arial, helvetica, sans-serif"> <span class="s1">for</span>
i=1:nvoxels(inside)</font></p>
<p class="p1"><font class="Apple-style-span" size="2"
face="arial, helvetica, sans-serif"> <span
class="s1">for</span> tr=1:ntrials</font></p>
<p class="p1"><font class="Apple-style-span" size="2"
face="arial, helvetica, sans-serif">
ts(tr,:)=(source.avg.ori{source.inside(i)}(1,:)*source.avg.filter{source.inside(i)}*data2{condit}.trial{tr});</font></p>
<p class="p1"><font class="Apple-style-span" size="2"
face="arial, helvetica, sans-serif"> <span
class="s1">end</span></font></p>
<p class="p1"><font class="Apple-style-span" size="2"
face="arial, helvetica, sans-serif">
datvx(i,:,:)= single_trial_time_course</font></p>
<p class="p1"><span class="Apple-style-span"
style="font-family: arial,helvetica,sans-serif;">end</span></p>
<font class="Apple-style-span" face="arial, helvetica,
sans-serif">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.</font></span></div>
<div><font class="Apple-style-span" face="arial, helvetica,
sans-serif"><span class="Apple-style-span"
style="white-space: pre;">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</span></font></div>
<div><font class="Apple-style-span" face="arial, helvetica,
sans-serif"><span class="Apple-style-span"
style="white-space: pre;"><br>
</span></font></div>
<div><font class="Apple-style-span" face="arial, helvetica,
sans-serif"><span class="Apple-style-span"
style="white-space: pre;"></span></font><span
class="Apple-style-span" style="font-family:
arial,helvetica,sans-serif; white-space: pre;">datvx(i,:,:)=
single_trial_time_course ./
repmat(noise(inside),[size(ts,1), size(ts,2)]</span></div>
<meta charset="utf-8">
<div><span class="Apple-style-span" style="white-space: pre;"><font
class="Apple-style-span" face="arial, helvetica,
sans-serif"><br>
</font></span></div>
<div><span class="Apple-style-span" style="white-space: pre;"><font
class="Apple-style-span" face="arial, helvetica,
sans-serif">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?</font></span></div>
<div><span class="Apple-style-span" style="white-space: pre;"><font
class="Apple-style-span" face="arial, helvetica,
sans-serif"><br>
</font></span></div>
<div><span class="Apple-style-span" style="white-space: pre;"><font
class="Apple-style-span" face="arial, helvetica,
sans-serif"><br>
</font></span></div>
<div><span class="Apple-style-span" style="white-space: pre;"><font
class="Apple-style-span" face="arial, helvetica,
sans-serif">Thanks in advance!</font></span></div>
<div><br>
</div>
<div>-- </div>
<div> Marc Recasens<br>
Tel.: +34 639 24 15 98<br>
<br>
</div>
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<br>
<div apple-content-edited="true"> <span class="Apple-style-span"
style="border-collapse: separate; color: rgb(0, 0, 0);
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<div style="word-wrap: break-word;">
<div>Dr. J.M. (Jan-Mathijs) Schoffelen </div>
<div>Donders Institute for Brain, Cognition and
Behaviour, <br>
Centre for Cognitive Neuroimaging,<br>
Radboud University Nijmegen, The Netherlands</div>
<div><a moz-do-not-send="true"
href="mailto:J.Schoffelen@donders.ru.nl">J.Schoffelen@donders.ru.nl</a></div>
<div>Telephone: 0031-24-3614793</div>
</div>
</span></div>
</span> </div>
<br>
<pre wrap="">
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</blockquote>
<br>
<br>
<pre class="moz-signature" cols="72">--
Marc Recasens
Tel.: +34 639 24 15 98</pre>
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