<div dir="ltr">Following up on my attempt to use bootstat on an ersp array outside of pop_newtimef: I think I have now gotten the former implementation working (shuffling over samples on the across-channels mean difference grand average ersp)! Hopefully this will be useful if anyone else tries to do what I'm doing in the future.<div><br></div><div>I was including a baseline vector in the bootstat input since my ersps were baselined along those samples and I assumed that that should be accounted for in bootstat. However, when I take the basevect out (meaning, if I understand correctly, that bootstat demeans across the whole ersp, which I guess is appropriate since the ersp is already baselined?) and compare ersp plots of the "raw" esrp and the masked ersp, I get what look like sensible time-frequency clusters. Maybe it would be better to not baseline my ersps, but then include the baseline in bootstat?</div><div><br></div><div>Does what I've done make methodological sense? Conceptually, am I correct in assuming that this is similar to the fieldtrip cluster permutation test? I see a paper cited in the bootstrap function that I intend to read, although some direct confirmation/explanation would certainly be appreciated!</div><div><br></div><div>Thanks,</div><div>Max</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Oct 23, 2017 at 5:05 PM, Max Cantor <span dir="ltr"><<a href="mailto:Max.Cantor@colorado.edu" target="_blank">Max.Cantor@colorado.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Ok, last followup for today! I have tried two things- the above, and another method which I'll discuss below. In both cases, comparing tftopo plots both with the signifs argument and without it turned out identically, which makes me skeptical of whether what I'm doing is working at all.<div><br></div><div>The second method I'm trying is:</div><div><br></div><div><div><i> idx = 1;</i></div><div><i> for k = freqs</i></div><div><i> [rsignif(idx,:) rbot{idx}] = bootstat({a(idx,:) b(idx,:)}, 'arg1 - arg2', ...</i></div><span class=""><div><i> 'basevect', [27 28 29 30 31 32 33 34 35 36 37], 'alpha', 0.01, 'dimaccu', 2);</i></div></span><div><i> idx = idx+1;</i></div><div><i> end</i></div></div><div><i><br></i></div><div>Where a and b are freqs x samples matrices for the two conditions, averaged across my channels of interest. So it loops through each frequency, shuffles samples, and if I understand correctly, should make a comparison across permutations based on the difference between the two conditions. I feel like either method should be fine for the purpose of masking the ersps and I'm not even sure if the rsignifs for these two methods would even be all that different, but the fact that it doesn't seem to be working suggests I'm doing something wrong or misunderstanding something.</div><div><br></div><div>Any advice appreciated!</div><div><br></div><div>Best,</div><div>Max</div></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Oct 23, 2017 at 4:13 PM, Max Cantor <span dir="ltr"><<a href="mailto:Max.Cantor@colorado.edu" target="_blank">Max.Cantor@colorado.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">To follow up, I believe I have gotten the code to "work", although I would appreciate confirmation on whether what I'm doing is conceptually sound, or if I am misunderstanding the code or purpose of the statistic.<div><br></div><div>Below is my code:</div><div><div><br></div><div><i>[rsignif rbot] = bootstat(permute(g_ersp, [2,1,3]), 'mean(arg1,3);', 'basevect', [27 28 29 30 31 32 33 34 35 36 37], 'alpha', 0.01, 'dimaccu', 2);</i></div></div><div><i><br></i></div><div>Where g_ersp is a grand average of ersp of 200 samples (of data from ~ -1000 to 2000ms) x 48 frequencies (log-spaced) x 7 channels. This grand average is the difference between two conditions, averaged across all subjects, where each subjects ersp is an average across all trials. I permute the matrix so that it is freqs x samples x chans, which is similar to what bootstat is doing on a subject-by-subject basis in my pop_newtimef code, except there it's only one condition, not a difference between conditions, and the third dimension is trials instead of channels. </div><div><br></div><div>The mean(arg1,3) formula came directly from debugging in bootstat from pop_newtimef. While in pop_newtimef this would be averaging across trials, if I'm interested in finding which time-frequency clusters are significant across all channels (or from the average across all channels), does this still make sense?</div><div><br></div><div>The baseline vector comes from the baseline I used on these ersp data, alpha is consistent with what I did on the single-subject/single-channel level, and I don't even know what dimaccu is but this was also taken from debug.</div><div><br></div><div>rsignif ends up being my freq x 2 matrix which I can input in tftopo, so I run this:</div><div><br></div><div><i>tftopo(mean(g_ersp,3), times, freqs, 'logfreq', 'native', 'signifs', rsignif);<br></i></div><div><i><br></i></div><div>So I'm plotting the grand-average difference ersp averaged across all channels, using the appropriate times and freqs axis, the ersp were natively log-scaled, and my mask is rsignif from above.</div><div><br></div><div>To reiterate, is this mask appropriate for these data? Is this showing me the time-frequency clusters averaged across all channels which are significant (relative to my alpha)?</div><div><br></div><div>Thanks,</div><div>Max</div><div><br></div><div><br></div></div><div class="m_752440775256677368HOEnZb"><div class="m_752440775256677368h5"><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Oct 19, 2017 at 5:33 PM, Max Cantor <span dir="ltr"><<a href="mailto:Max.Cantor@colorado.edu" target="_blank">Max.Cantor@colorado.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi,<div><br></div><div>I've produced grand-averaged TFRs across all of my subjects, at select channels, and taken the difference between two conditions, by looping through pop_newtimef for each subject and each channel of interest across both conditions separately, outputting these into separate 4-D arrays, averaging across subjects, and then taking the difference between the two arrays, such that I end up with an ersp difference array of freq x timepoint x chan.</div><div><br></div><div>This array is compatible with the tftopo function and I would like to apply a bootstrapped statistical mask to this array through the tftopo function. Testing it with an erspboot mask outputted by pop_newtimef was functional, however this erspboot mask was obviously specific to that particular subject and channel and not appropriate for my grand averaged array. </div><div><br></div><div>Is there a conceptually and computationally simple way to run this array through the bootstat function (which I believe is what's producing the statistical masking) outside of pop_newtimef and produce a freqs x 2 erspboot mask that would be compatible with tftopo, or would it be easier for me to just reformat my array into a fieldtrip structure and use their functions for nonparametric cluster-based permutation testing, which to the best of my knowledge is the mathematical implementation eeglab uses anyway?</div><div><br></div><div>Any advice appreciated.</div><div><br></div><div>Thanks,</div><div>Max </div><span class="m_752440775256677368m_9132651566516053414HOEnZb"><font color="#888888"><div><div><br></div>-- <br><div class="m_752440775256677368m_9132651566516053414m_831851007620262564gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div style="font-size:small"><div>Max Cantor<br></div>Graduate Student</div><div style="font-size:small">Cognitive Neuroscience of Language Lab</div><span style="font-size:small">University of Colorado Boulder</span><br></div></div>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="m_752440775256677368m_9132651566516053414gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div style="font-size:small"><div>Max Cantor<br></div>Graduate Student</div><div style="font-size:small">Cognitive Neuroscience of Language Lab</div><span style="font-size:small">University of Colorado Boulder</span><br></div></div>
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</div></div></blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="m_752440775256677368gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div style="font-size:small"><div>Max Cantor<br></div>Graduate Student</div><div style="font-size:small">Cognitive Neuroscience of Language Lab</div><span style="font-size:small">University of Colorado Boulder</span><br></div></div>
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</div></div></blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div style="font-size:small"><div>Max Cantor<br></div>Graduate Student</div><div style="font-size:small">Cognitive Neuroscience of Language Lab</div><span style="font-size:small">University of Colorado Boulder</span><br></div></div>
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