<div dir="ltr">oops, missing words below in red<br><br><div class="gmail_quote">On 8 April 2011 22:09, Yuval Harpaz <span dir="ltr"><<a href="mailto:yuvharpaz@gmail.com">yuvharpaz@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div dir="ltr">Dear Marc<br>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<br></div></blockquote><div><span style="color: rgb(255, 0, 0);">between the baselines</span> <br></div><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div dir="ltr"><br><div class="gmail_quote"><div class="im">On 8 April 2011 20:49, Marc Recasens <span dir="ltr"><<a href="mailto:recasensmarc@gmail.com" target="_blank">recasensmarc@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div bgcolor="#ffffff" text="#000000">
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></div></blockquote></div><div>yes, they are not comarable. before normlization the scale is very small (say 10^ -13) and after it is a ratio, say 1.8.<br>
</div><div><div></div><div class="h5">
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"><div bgcolor="#ffffff" text="#000000">
<br>
<br>
Thanks again!<br>
<br>
<br>
<br>
El 07/04/2011 14:23, jan-mathijs schoffelen escribió:
<div><div></div><div><blockquote 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>
<blockquote type="cite"><font face="arial, helvetica, sans-serif">Dear all,</font>
<div><font face="arial, helvetica,
sans-serif"><br>
</font></div>
<div><font 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 style="white-space: pre-wrap;"><font face="arial, helvetica,
sans-serif">In my case, the specific code I am using is:</font></span></div>
<div><span style="white-space: pre-wrap;">
<p> <font size="2" face="arial, helvetica, sans-serif"> <span>for</span>
i=1:nvoxels(inside)</font></p>
<p><font size="2" face="arial, helvetica, sans-serif"> <span>for</span> tr=1:ntrials</font></p>
<p><font 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><font size="2" face="arial, helvetica, sans-serif"> <span>end</span></font></p>
<p><font size="2" face="arial, helvetica, sans-serif">
datvx(i,:,:)= single_trial_time_course</font></p>
<p><span style="font-family: arial,helvetica,sans-serif;">end</span></p>
<font 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 face="arial, helvetica,
sans-serif"><span style="white-space: pre-wrap;">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 face="arial, helvetica,
sans-serif"><span style="white-space: pre-wrap;"><br>
</span></font></div>
<div><font face="arial, helvetica,
sans-serif"><span style="white-space: pre-wrap;"></span></font><span style="font-family: arial,helvetica,sans-serif; white-space: pre-wrap;">datvx(i,:,:)=
single_trial_time_course ./
repmat(noise(inside),[size(ts,1), size(ts,2)]</span></div>
<div><span style="white-space: pre-wrap;"><font face="arial, helvetica,
sans-serif"><br>
</font></span></div>
<div><span style="white-space: pre-wrap;"><font 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 style="white-space: pre-wrap;"><font face="arial, helvetica,
sans-serif"><br>
</font></span></div>
<div><span style="white-space: pre-wrap;"><font face="arial, helvetica,
sans-serif"><br>
</font></span></div>
<div><span style="white-space: pre-wrap;"><font 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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</div>
<br>
<div> <span style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; font-size: medium;">
<div style="word-wrap: break-word;"><span style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">
<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 href="mailto:J.Schoffelen@donders.ru.nl" target="_blank">J.Schoffelen@donders.ru.nl</a></div>
<div>Telephone: 0031-24-3614793</div>
</div>
</span></div>
</span> </div>
<br>
<pre><fieldset></fieldset>
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</blockquote>
<br>
<br>
</div></div><font color="#888888"><pre cols="72">--
Marc Recasens
Tel.: +34 639 24 15 98</pre>
</font></div>
<br>_______________________________________________<br>
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<a href="http://mailman.science.ru.nl/mailman/listinfo/fieldtrip" target="_blank">http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a><br></blockquote></div></div></div><br><br clear="all"><br>-- <br><div dir="ltr">
Y.Harpaz<br>
<br>a link to the BIU MEG lab:<br><a href="http://faculty.biu.ac.il/%7Egoldsa/index.html" target="_blank">http://faculty.biu.ac.il/~goldsa/index.html</a><div><div><br></div><div> " Why, Dan," ask the people in Artificial Intelligence, "do you waste your time conferring with those neuroscientists? They wave their hands about information processing and worry about where it happens, and which neurotransmitters are involved, and all those boring facts, but they haven't a clue about the computational requirements of higher cognitive functions." "Why," ask the neuroscientists, "do you waste your time on the fantasies of Artificial Intelligence? They just invent whatever machinery they want, and say unpardonably ignorant things about the brain." The cognitive psychologists, meanwhile, are accused of concocting models with neither biological plausibility nor proven computational powers; the anthropologists wouldn't know a model if they saw one, and the philosophers, as we all know, just take in each other's laundry, warning about confusions they themselves have created, in an arena bereft of both data and empirically testable theories. With so many idiots working on the problem, no wonder consciousness is still a mystery.<i> Philosopher Daniel Dennet, consciousness explained, pp. 225</i></div>
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</div></div>
</blockquote></div><br><br clear="all"><br>-- <br><div dir="ltr">Y.Harpaz<br><br>a link to the BIU MEG lab:<br><a href="http://faculty.biu.ac.il/%7Egoldsa/index.html" target="_blank">http://faculty.biu.ac.il/~goldsa/index.html</a><div>
<div><br></div><div> " Why, Dan," ask the people in Artificial Intelligence, "do you waste your time conferring with those neuroscientists? They wave their hands about information processing and worry about where it happens, and which neurotransmitters are involved, and all those boring facts, but they haven't a clue about the computational requirements of higher cognitive functions." "Why," ask the neuroscientists, "do you waste your time on the fantasies of Artificial Intelligence? They just invent whatever machinery they want, and say unpardonably ignorant things about the brain." The cognitive psychologists, meanwhile, are accused of concocting models with neither biological plausibility nor proven computational powers; the anthropologists wouldn't know a model if they saw one, and the philosophers, as we all know, just take in each other's laundry, warning about confusions they themselves have created, in an arena bereft of both data and empirically testable theories. With so many idiots working on the problem, no wonder consciousness is still a mystery.<i> Philosopher Daniel Dennet, consciousness explained, pp. 225</i></div>
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