<html><head></head><body bgcolor='#FFFFFF' style='font-size:12px;background-color:#FFFFFF;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<br/><br/>"2) Randomly permute the ch2 time series"<br/><br/>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" <suforraxi@gmail.com><br/><b>Gesendet:</b> Jun 28, 2011 2:46:28 PM<br/><b>An:</b> fieldtrip@donders.ru.nl<br/><b>Betreff:</b> [FieldTrip] WPLI statistic, permutation like test??<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 class="Apple-style-span" 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 class="Apple-style-span" 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 class="Apple-style-span" size="1">_i </font>distribution</div><div> </div><div>5) Calculate the proportion ( # (WPLI<font class="Apple-style-span" size="1">_i </font>> WPLI_<font class="Apple-style-span" size="1">observed</font>) / # (WPLI<font class="Apple-style-span" size="1">_i </font> ) ) of WPLI<font class="Apple-style-span" size="1">_i </font>which are greater than the WPLI_<font class="Apple-style-span" size="1">observed</font>, if this proportion is < 0.05 I could say that the WPLI_<font class="Apple-style-span" 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 class="Apple-style-span" size="1">_observed</font> and the average WPLI<font class="Apple-style-span" 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 class="Apple-style-span" size="1">_i</font> distribution) to calculate also the ch1-ch3 (WPLI<font class="Apple-style-span" 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 class="Apple-style-span" size="1"> </font><font class="Apple-style-span" size="1"> </font></div><div> </div><div> </div></div></div></blockquote></body></html>