[Mne_analysis] Making Functional labels from spatial temporal clustering
Marijn van Vliet
w.m.vanvliet at gmail.com
Fri Aug 12 07:17:43 EDT 2016
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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>> --
>> --
>> 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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