<div dir="ltr">Well, if the cluster permutation test returns clusters that span the entire brain, then that's the way it is. The signals are different pre- and post-stimulus all across the brain.</div><br><div class="gmail_quote"><div dir="ltr">On Fri, Aug 12, 2016 at 10:14 AM 董群喜 <<a href="mailto:dongqunxi@gmail.com">dongqunxi@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div dir="ltr" style="font-size:14px">Dear Marijin,<div><br></div><div>Thanks for your response. </div><div>I need to introduce how I use 2sample spatial clustering on our data:</div><div>I make groups of prestimulus data and poststimulus data, and then the comparisons are made</div><div>between the two groups data. I want to identify clusters significant to the stimulus.</div><div>The p_value for f_threshold is 0.001, p_value for comparisons corrected is 0.001.</div><div>I get two significant clusters only, that is one cluster per hemisphere.</div><div>When I ploted as you said, the two clusters nearly cover the whole Brain. For your convenience,</div><div>I provide one STC file for your testing, and the plot of the clusters. </div></div><div class="gmail_extra" style="font-size:14px"><br clear="all"><div><div data-smartmail="gmail_signature"><div dir="ltr">Best wishes,<br>Qunxi Dong</div></div></div></div></div><div class="gmail_extra"><br clear="all"><div><div data-smartmail="gmail_signature"><div dir="ltr">Best wishes,<br>Qunxi Dong<br></div></div></div></div><div class="gmail_extra">
<br><div class="gmail_quote">2016-08-12 8:09 GMT+02:00 董群喜 <span dir="ltr"><<a href="mailto:dongqunxi@gmail.com" target="_blank">dongqunxi@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Dear Marijin,<div><br></div><div>Thanks for your response. </div><div>I need to introduce how I use 2sample spatial clustering on our data:</div><div>I make groups of prestimulus data and poststimulus data, and then the comparisons are made</div><div>between the two groups data. I want to identify clusters significant to the stimulus.</div><div>The p_value for f_threshold is 0.001, p_value for comparisons corrected is 0.001.</div><div>I get two significant clusters only, that is one cluster per hemisphere.</div><div>When I ploted as you said, the two clusters nearly cover the whole Brain. For your convenience,</div><div>I provide one STC file for your testing, and the plot of the clusters. </div></div><div class="gmail_extra"><br clear="all"><div><div data-smartmail="gmail_signature"><div dir="ltr">Best wishes,<br>Qunxi Dong<br></div></div></div><div><div>
<br><div class="gmail_quote">2016-08-11 20:05 GMT+02:00 Marijn van Vliet <span dir="ltr"><<a href="mailto:w.m.vanvliet@gmail.com" target="_blank">w.m.vanvliet@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Dear Qunxi,<br>
<br>
the output of `summarize_clusters_stc’ is a bit poorly documented (I’ve opened a pull request for it to be fixed in future versions of MNE).<br>
<br>
The output is as follows:<br>
<br>
out : instance of SourceEstimate<br>
A summary of the clusters. The first time point in this SourceEstimate<br>
object is the summation of all the clusters. Subsequent time points<br>
contain each individual cluster. The magniture of the activity<br>
corresponds to the length the cluster spans in time (in samples).<br>
<br>
So it is perfectly reasonable to create labels from the clusters. However, you do not need to take the mean across the time points or anything like that. Also, thresholding does not do what you want. Instead, this should work:<br>
<br>
stc = summarize_clusters_stc(clu, p_thre, tstep=tstep,<br>
tmin=tmin, vertices=fsave_vertices,<br>
subject='fsaverage')<br>
lh_labels, rh_labels = mne.stc_to_label(stc, src=src, smooth=True,<br>
subjects_dir=subjects_dir, connected=True)<br>
<br>
The labels look bigger than the clusters as visualised with stc.plot(…), because the plotting functions applies its own thesholding. Try to visualise it without any thresholding by doing this:<br>
<br>
b = stc.plot(hemi=‘both’, subject=subject, subject_dir=subject_dir)<br>
b.scale_data_colormap(0, stc.data.mean(), stc.data.max(), True)<br>
<br>
Let me know if you have further questions.<br>
<br>
Marijn.<br>
<br>
--<br>
Marijn van Vliet<br>
<a href="mailto:w.m.vanvliet@gmail.com" target="_blank">w.m.vanvliet@gmail.com</a><br>
<div><div><br>
<br>
<br>
<br>
<br>
> On 11 Aug 2016, at 19:09, 董群喜 <<a href="mailto:dongqunxi@gmail.com" target="_blank">dongqunxi@gmail.com</a>> wrote:<br>
><br>
> Dear all,<br>
><br>
> For you easier understanding my problem, I made a gist and paste the critical codes in the following link:<br>
> <a href="https://gist.github.com/dongqunxi/daca753366c592927ff789c03aa6ed0b" rel="noreferrer" target="_blank">https://gist.github.com/dongqunxi/daca753366c592927ff789c03aa6ed0b</a><br>
> Thanks, looking forward to your response.<br>
><br>
> Best wishes,<br>
> Qunxi Dong<br>
><br>
> 2016-08-11 17:52 GMT+02:00 董群喜 <<a href="mailto:dongqunxi@gmail.com" target="_blank">dongqunxi@gmail.com</a>>:<br>
> Dear All,<br>
><br>
> Recently, I am trying to make functional labels from a group of subjects.<br>
> I first refer the 2sample clustering scripts. I get some significant clusters and derive the source estimates.<br>
> if I apply 'stc_to_label' to make functional labels directly, the size of the functional labels is too large.<br>
> I also try to use percentile of 95 to restrict the size, but I can not explain what the actual meaning<br>
> of this threshold.<br>
> Can someone give me some tips?<br>
> Thanks a lot!<br>
><br>
> Best wishes,<br>
> Qunxi Dong<br>
><br>
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</blockquote></div><div dir="ltr">-- <br></div><div data-smartmail="gmail_signature"><div dir="ltr"><div>--</div>Marijn van Vliet<div><br><div><a href="mailto:w.m.vanvliet@gmail.com">w.m.vanvliet@gmail.com</a></div><div><a href="mailto:marijn.vanvliet@aalto.fi">marijn.vanvliet@aalto.fi</a></div></div></div></div>