[Mne_analysis] Making Functional labels from spatial temporal clustering
董群喜
dongqunxi at gmail.com
Fri Aug 12 03:13:26 EDT 2016
Dear Marijin,
Thanks for your response.
I need to introduce how I use 2sample spatial clustering on our data:
I make groups of prestimulus data and poststimulus data, and then the
comparisons are made
between the two groups data. I want to identify clusters significant to the
stimulus.
The p_value for f_threshold is 0.001, p_value for comparisons corrected is
0.001.
I get two significant clusters only, that is one cluster per hemisphere.
When I ploted as you said, the two clusters nearly cover the whole Brain.
For your convenience,
I provide one STC file for your testing, and the plot of the clusters.
Best wishes,
Qunxi Dong
Best wishes,
Qunxi Dong
2016-08-12 8:09 GMT+02:00 董群喜 <dongqunxi at gmail.com>:
> Dear Marijin,
>
> Thanks for your response.
> I need to introduce how I use 2sample spatial clustering on our data:
> I make groups of prestimulus data and poststimulus data, and then the
> comparisons are made
> between the two groups data. I want to identify clusters significant to
> the stimulus.
> The p_value for f_threshold is 0.001, p_value for comparisons corrected is
> 0.001.
> I get two significant clusters only, that is one cluster per hemisphere.
> When I ploted as you said, the two clusters nearly cover the whole Brain.
> For your convenience,
> I provide one STC file for your testing, and the plot of the clusters.
>
> Best wishes,
> Qunxi Dong
>
> 2016-08-11 20:05 GMT+02:00 Marijn van Vliet <w.m.vanvliet at gmail.com>:
>
>> Dear Qunxi,
>>
>> 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).
>>
>> The output is as follows:
>>
>> out : instance of SourceEstimate
>> A summary of the clusters. The first time point in this
>> SourceEstimate
>> object is the summation of all the clusters. Subsequent time
>> points
>> contain each individual cluster. The magniture of the activity
>> corresponds to the length the cluster spans in time (in samples).
>>
>> 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:
>>
>> stc = summarize_clusters_stc(clu, p_thre, tstep=tstep,
>> tmin=tmin,
>> vertices=fsave_vertices,
>> subject='fsaverage')
>> lh_labels, rh_labels = mne.stc_to_label(stc, src=src, smooth=True,
>> subjects_dir=subjects_dir,
>> connected=True)
>>
>> 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:
>>
>> b = stc.plot(hemi=‘both’, subject=subject, subject_dir=subject_dir)
>> b.scale_data_colormap(0, stc.data.mean(), stc.data.max(), True)
>>
>> Let me know if you have further questions.
>>
>> Marijn.
>>
>> --
>> Marijn van Vliet
>> w.m.vanvliet at gmail.com
>>
>>
>>
>>
>>
>> > On 11 Aug 2016, at 19:09, 董群喜 <dongqunxi at gmail.com> wrote:
>> >
>> > Dear all,
>> >
>> > For you easier understanding my problem, I made a gist and paste the
>> critical codes in the following link:
>> > https://gist.github.com/dongqunxi/daca753366c592927ff789c03aa6ed0b
>> > Thanks, looking forward to your response.
>> >
>> > Best wishes,
>> > Qunxi Dong
>> >
>> > 2016-08-11 17:52 GMT+02:00 董群喜 <dongqunxi at gmail.com>:
>> > Dear All,
>> >
>> > Recently, I am trying to make functional labels from a group of
>> subjects.
>> > I first refer the 2sample clustering scripts. I get some significant
>> clusters and derive the source estimates.
>> > if I apply 'stc_to_label' to make functional labels directly, the size
>> of the functional labels is too large.
>> > I also try to use percentile of 95 to restrict the size, but I can not
>> explain what the actual meaning
>> > of this threshold.
>> > Can someone give me some tips?
>> > Thanks a lot!
>> >
>> > Best wishes,
>> > Qunxi Dong
>> >
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