Thanks a lot<br><br><br><br><div class="gmail_quote">On Wed, Jun 29, 2011 at 5:35 PM, Michael Wibral <span dir="ltr"><<a href="mailto:michael.wibral@web.de">michael.wibral@web.de</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div bgcolor="#FFFFFF" style="font-size:12px;background-color:#FFFFFF;font-family:Verdana, Arial, sans-serif">Hi matteo,<br><br>sorry for my sloppy description,<br><br>of course you would only shuffle trials on one channel, not both.<br>
<br>Michael<br><br><br><br><blockquote style="border-left:2px solid blue;margin-left:5px;padding-left:5px;padding-top:5px"><div class="im"><hr><b>Von:</b> "Matteo Demuru" <<a href="mailto:suforraxi@gmail.com" target="_blank">suforraxi@gmail.com</a>><br>
</div><b>Gesendet:</b> Jun 29, 2011 10:01:15 AM<br><b>An:</b> "Email discussion list for the FieldTrip project" <<a href="mailto:fieldtrip@donders.ru.nl" target="_blank">fieldtrip@donders.ru.nl</a>><br><b>Betreff:</b> Re: [FieldTrip] WPLI statistic, permutation like test??<div>
<div></div><div class="h5"><br><br>Hi Micheal,<br><br>It seems to me that if you shuffle the trials and then compute the WPLI, the result is the same as if you do not shuffle.<br><br>For example I have tried compute the WPLI on my trials and then switch trial{1} with trial{2} and the obtained WPLI is the same.<br>
<br>Matteo <br><br><div class="gmail_quote">On Tue, Jun 28, 2011 at 8:34 PM, Michael Wibral <span><<a href="mailto:michael.wibral@web.de" target="_blank">michael.wibral@web.de</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 style="font-size:12px;background-color:rgb(255, 255, 255);font-family:Verdana,Arial,sans-serif">Hi Matteo,<br><br>I am not an expert on the WPLI measures, but to me it seems that in doing<div><br><br>"2) Randomly permute the ch2 time series"<br>
</div>you're destroying a lot of ch2's properties (ie.g. it's spectrum will get a lot of high ferquencies this way) and this will typically lead to false positives. This is why permutation tests for connectivity measures typically shuffle trials (i.e. permute data in a very controlled way, keeping the intrinsic structure of the data).<br>
<br>Michael<br><br><br><blockquote style="border-left:2px solid blue;margin-left:5px;padding-left:5px;padding-top:5px"><hr><b>Von:</b> "Matteo Demuru" <<a href="mailto:suforraxi@gmail.com" target="_blank">suforraxi@gmail.com</a>><br>
<b>Gesendet:</b> Jun 28, 2011 2:46:28 PM<br><b>An:</b> <a href="mailto:fieldtrip@donders.ru.nl" target="_blank">fieldtrip@donders.ru.nl</a><br><b>Betreff:</b> [FieldTrip] WPLI statistic, permutation like test??<div><div> </div>
<div><br><br>Hi,<br><br>I have a couple of questions about using the WPLI index to assess the phase on my MEG data.<br><br>The experiment consists of recordings during a mental calculation task: I have 30 sec in which each subject performed continuously an arithmetic operation.<br>
<div> </div><div>It seems to me that WPLI index required more than one trial in order to be computed. Am I right? (Is this necessary in order to reduce volume conduction problems?)</div><div>I could divide my 30 sec in 5 sec-trials to create my trials, but I was wandering if this could be a misuse of the WPLI, i.e. WPLI is not appropriate for my experiment. </div>
<div> </div><div><div><div>I am also interested in assessing the significance of WPLI index, I would like to gauge the significance per se of my WPLI values. </div><div><div>The idea is to calculate the WPLI distribution under the null hypothesis (not phase coupling) for each pair of channels in this way:</div>
</div><div> </div><div>Example to assess the significance of WPLI value for ch1 vs ch2</div><div> </div><div>1) Calculate the WPLI for ch1 and ch2, this would be the observed WPLI (WPLI_<font size="1">observed</font>) </div>
<div> </div><div>2) Randomly permute the ch2 time series</div><div> </div><div>3) Calculate the WPLI for ch1 and ch2 (WPLI<font size="1">_i</font>)</div><div> </div><div>4) Repeat step 2 and 3 (for instance 100 times) in order to create the WPLI<font size="1">_i </font>distribution</div>
<div> </div><div>5) Calculate the proportion ( # (WPLI<font size="1">_i </font>> WPLI_<font size="1">observed</font>) / # (WPLI<font size="1">_i </font> ) ) of WPLI<font size="1">_i </font>which are greater than the WPLI_<font size="1">observed</font>, if this proportion is < 0.05 I could say that the WPLI_<font size="1">observed </font>represents a significant degree of phase, otherwise not.</div>
<div> </div><div>Does it make sense or is it not the right approach?</div><div> </div><div>Let suppose this is a correct approach, I have two other questions:</div><div> </div><div>First, usually when I compute the WPLI value between two channels I obtain a number of WPLI values according to the cross-spectrum times (one WPLI for each sliding window), in the steps above I am assuming to compute the average WPLI<font size="1">_observed</font> and the average WPLI<font size="1">_i</font> for each step. Does this raise any problems?</div>
<div> </div><div>Second, is it a problem using the same random permutations employed to obtain ch1-ch2 (WPLI<font size="1">_i</font> distribution) to calculate also the ch1-ch3 (WPLI<font size="1">_i</font> distribution). This is just an implementatiion question. I would like to know if I could shuffle the time series of other channels in one step (i.e. for ch1 something like data.trial{other_than_ch1,perm}), and finally extract just the column relative to ch1 from WPLI matrix.</div>
<div> </div><div>thanks</div><div> </div><div>Matteo</div><div> </div><div> </div><div> </div><div> </div><div><font size="1"> </font><font size="1"> </font></div><div> </div><div> </div></div></div></div></div></blockquote>
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