[Mne_analysis] Generating labels with mne_python

Sat Aug 24 10:40:13 EDT 2013

Hi Luke,

thanks for sharing. I've simplified a bit the example and opened a pull request:

https://github.com/mne-tools/mne-python/pull/730

You can follow the progress there.

Alex

On Fri, Aug 23, 2013 at 11:19 PM, Luke Bloy <luke.bloy at gmail.com> wrote:
> Hi Everyone,
>
> I put together a quick example of generating labels in case others end up
> with a similar problem.
>
> Enjoy.
> Luke
>
>
> On Fri, Aug 9, 2013 at 2:34 PM, Luke Bloy <luke.bloy at gmail.com> wrote:
>>
>> Thank you both for the help. I got it working reasonably well.
>>
>> I might throw together an example, and send it to the list, if i get some
>> time.
>>
>> Thanks again,
>> Luke
>>
>>
>> On Wed, Aug 7, 2013 at 4:58 PM, Eric Larson <larson.eric.d at gmail.com>
>> wrote:
>>>
>>> FYI, the branch for PySurfer to handle mne-python's label objects has
>>> actually already been merged, the linked PR is for a different (much bigger)
>>> can of worms... you should be able to do Brain.add_label(label) once you've
>>> loaded
>>>
>>> If you don't want to deal with the private _find_clusters functions, you
>>> should also be able to use the standard statistical clustering functions,
>>> but just use a dummy stat_fun (instead of e.g., ttest_1samp). If you can't
>>> figure it out, give me a shout and I can probably write a few lines of code
>>> for it.
>>>
>>> Eric
>>>
>>>
>>>
>>> On Wed, Aug 7, 2013 at 12:50 PM, Martin Luessi
>>> <mluessi at nmr.mgh.harvard.edu> wrote:
>>>>
>>>> Hi Luke,
>>>>
>>>> On 08/07/13 15:40, Luke Bloy wrote:
>>>> > Thanks Martin,
>>>> >
>>>> > What's the best way to expose mne.stats._find_clusters to my code. I'd
>>>> > rather not fiddle with ../stats/__init.py if I can avoid it.
>>>>
>>>> use
>>>>
>>>> from mne.stats.cluster_level import _find_clusters
>>>>
>>>> what I wrote below (mne.stats._find_clusters) doesn't work because the
>>>> function is not directly visible from mne.stats. Also, not that the
>>>> leading "_" indicates that this is a "private function", i.e., it is
>>>> only used internally in MNE-Python and the API may change from one
>>>> release to the next without notification. But this code is fairly
>>>> stable, so this shouldn't be a problem.
>>>>
>>>> Also, I forgot to answer your second question. You currently have to
>>>> save the label as a file to show it in PySurfer. There is work in the
>>>> pipeline to make it possible to pass labels directly, see:
>>>>
>>>> https://github.com/nipy/PySurfer/pull/53
>>>>
>>>> Best,
>>>>
>>>> Martin
>>>>
>>>>
>>>> > Luke
>>>> >
>>>> > -----Original Message-----
>>>> > From: Martin Luessi [mailto:mluessi at nmr.mgh.harvard.edu]
>>>> > Sent: Wednesday, August 07, 2013 2:24 PM
>>>> > To: Luke Bloy
>>>> > Cc: mne_analysis at nmr.mgh.harvard.edu
>>>> > Subject: Re: [Mne_analysis] Generating labels with mne_python
>>>> >
>>>> > Hi Luke,
>>>> >
>>>> > MNE-Python currently doesn't have a function that does exactly what
>>>> > you
>>>> > want, but it should be possible to put something together to get the
>>>> > desired
>>>> > result. I would do something like this:
>>>> >
>>>> > 1) Use mne.spatial_src_connectivity to get a connectivity matrix that
>>>> > describes the neighbor relations in source space. The "src" parameter
>>>> > is the
>>>> > source space, e.g., "inv['src']", where inv is your inverse operator.
>>>> >
>>>> > 2) Use mne.stats._find_clusters and pass in "stc.data[:, idx]" where
>>>> > idx
>>>> > selects a single time point. This function will return a list of
>>>> > clusters
>>>> > (connected components)
>>>> >
>>>> > 3) For each cluster, generate a SourceEstimate using the original
>>>> > source
>>>> > space (so stc_cluster.vertices is the same as in stc). Set the data of
>>>> > stc_cluster to all-zero except inside the cluster (use e.g. 1.0). Pass
>>>> > stc_cluster to mne.stc_to_label to generate a Label from it. Save the
>>>> > labels, add it to a list, etc.
>>>> >
>>>> > I hope this helps,
>>>> >
>>>> > Martin
>>>> >
>>>> > On 08/07/13 13:57, Luke Bloy wrote:
>>>> >> Hi all,
>>>> >>
>>>> >> I am interested in generating a label from an stc. What I would like
>>>> >> is to be able to supply a seed and a threshold, and to get as output
>>>> >> a
>>>> >> label consisting of the source points  connected to the seed and
>>>> >> above
>>>> >> threshold. Basically I'd like to be able to compute connected
>>>> >> components on stcs. Has this been implemented? If not could someone
>>>> >> maybe help me out by pointing to how I could get neighborhood
>>>> >> information from the src space? Alternatively, can someone think of a
>>>> >> way to combine thresholding stc.data with mne.label.grow_label to do
>>>> >> what
>>>> > I want?
>>>> >>
>>>> >> A second easier question. Is there an simple way to show labels on a
>>>> >> pysurfer plot. At the moment I'm generating the label, then saving it
>>>> >> to disk and using 'brain.add_label' to load the label from disk. Is
>>>> >> there a more straight forward way that I'm missing.
>>>> >>
>>>> >> Thanks
>>>> >>
>>>> >> Luke
>>>> >>
>>>> >>
>>>> >>
>>>> >> _______________________________________________
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>>>> >> Mne_analysis at nmr.mgh.harvard.edu
>>>> >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>>> >>
>>>> >
>>>> >
>>>> > --
>>>> > Martin Luessi, Ph.D.
>>>> >
>>>> > Research Fellow
>>>> >
>>>> > Department of Radiology
>>>> > Athinoula A. Martinos Center for Biomedical Imaging Massachusetts
>>>> > General
>>>> > Hospital Harvard Medical School
>>>> > 149 13th Street
>>>> > Charlestown, MA 02129
>>>> >
>>>> > Fax: +1 617 726-7422
>>>> >
>>>> >
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>>>> >
>>>>
>>>>
>>>> --
>>>> Martin Luessi, Ph.D.
>>>>
>>>> Research Fellow
>>>>
>>>> Department of Radiology
>>>> Athinoula A. Martinos Center for Biomedical Imaging
>>>> Massachusetts General Hospital
>>>> Harvard Medical School
>>>> 149 13th Street
>>>> Charlestown, MA 02129
>>>>
>>>> Fax: +1 617 726-7422
>>>> _______________________________________________
>>>> Mne_analysis mailing list
>>>> Mne_analysis at nmr.mgh.harvard.edu
>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>>
>>>
>>
>
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