[Mne_analysis] Making Functional labels from spatial temporal clustering
董群喜
dongqunxi at gmail.com
Fri Aug 12 07:24:15 EDT 2016
Dear Marijin,
OK. We have the MEG data related with visual task. By comparing data of
prestimulus and poststimulus, we want to find focal functional labels
related with visual cognitive process.
And then we want to make network analysis between the identified regions of
interest.
Best wishes,
Qunxi Dong
2016-08-12 13:17 GMT+02:00 Marijn van Vliet <w.m.vanvliet at gmail.com>:
> if you threshold the data in the STC, it means you are thresholding based
> on the length of the cluster in time. So if you threshold by a value of
> 190, any vertices that survive are significantly different for at least 190
> consecutive samples.
>
> I'm sorry, but I'm unable to follow your logic. What do you mean by
> *meaningful* ROIs?
>
> Right now, it sounds to me like: if we manipulate the data so and so we
> get the picture we want. Now we want a justification for our manipulation.
> But that is probably not what you meant.
>
> Maybe I can be of more help if you explain a bit more about your data and
> what effect you are trying to visualize.
>
> On Fri, Aug 12, 2016 at 1:34 PM 董群喜 <dongqunxi at gmail.com> wrote:
>
>> Dear Marijin,
>>
>> We prefer to get focal clusters attributed to the stimulus, and if we use
>> 95 percentile as the threshold to shrink the clusters, it can show some
>> meaningful ROIs. But we do not know how to explain the threshold (such as
>> 190).
>>
>> Best wishes,
>> Qunxi Dong
>>
>> 2016-08-12 11:54 GMT+02:00 Marijn van Vliet <w.m.vanvliet at gmail.com>:
>>
>>> 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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> --
> --
> Marijn van Vliet
>
> w.m.vanvliet at gmail.com
> marijn.vanvliet at aalto.fi
>
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