[Mne_analysis] Making Functional labels from spatial temporal clustering

Fri Aug 12 05:54:44 EDT 2016

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.

On Fri, Aug 12, 2016 at 10:14 AM 董群喜 <dongqunxi at gmail.com> wrote:

> 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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>>
>>
>>
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-- 
--
Marijn van Vliet

w.m.vanvliet at gmail.com
marijn.vanvliet at aalto.fi
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