[Mne_analysis] Making Functional labels from spatial temporal clustering

Marijn van Vliet w.m.vanvliet at gmail.com
Fri Aug 12 10:10:59 EDT 2016
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Dear Qunxi,

if I understand correctly, then the permutation cluster tests in MNE may
not be suitable for what you want to do.

Consider this figure:
http://imgur.com/a/E8ais

This is the time course for a single dipole on the brain (simulated data).
If I understand your goal correctly, you would like to include this vertex
as part of a ROI, because it has increased activity after the stimulus has
been shown.

A cluster test as implemented in MNE would determine whether any
post-stimulus samples are higher than pre-stimulus samples *in a pairwise
fashion*. Thus, whether pre-stimulus sample 1 is higher than post-stimulus
sample 1, pre-stimulus sample 2 is higher than post-stimulus sample 2, etc.

What you most likely want instead is estimate some confidence interval for
the pre-stimulus values in general (red dashed line in the figure) and then
determine, given the post-interval data, whether to include the vertex yes
or no.

It is not surprising that the cluster test marked the entire brain as ROI,
because it is very likely for the time course of a vertex to be higher than
the pre-stimulus at some point, even if the stimulus didn't activate the
vertex at all (and the pre-stimulus and post-stimulus data were drawn from
the same distribution).

At this point, a thresholding operation that only passes vertices for which
the activation surpasses the pre-stimulus activity for a minimum amount of
time makes sense. However, you would need to be careful to set it to a
sensible value.

I think you'll need to implement the procedure to mark the vertices to
include yourself. Then, you can use the stc_to_label function to cut it up
in spatially connected ROIs.

At any rate, I think the result of "showing a stimulus activates the entire
brain" actually makes sense. Showing a stimulus would do that, although not
all parts in the equal amounts.

That's all the help I can give you. Good luck with your study. May your
p-values be significant! :-)

Kind regards,
Marijn.

On Fri, Aug 12, 2016 at 2:24 PM 董群喜 <dongqunxi at gmail.com> wrote:

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

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