[Mne_analysis] How to set atlas

Vivek Sharma vivek.sharma1510 at gmail.com
Mon Feb 11 04:50:19 EST 2019
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I could have 68 labels in variable name label with this line of code:
label = mne.read_labels_from_annot('sub-CC721377_T1w', hemi='both',
parc='aparc', subjects_dir=subjects_dir, regexp=None)
and (68, time ) matrix using this line:
x = stc.extract_label_time_course(label, src, mode='mean_flip',
allow_empty=False, verbose=None)
gives the following output:
Extracting time courses for 68 labels (mode: mean_flip)

as suggested by Eric Lanson I'm using following line of code to generate
stc file with the above output files:
stc1 = mne.labels_to_stc(label, x, tmin=0, tstep=1, subject=None,
verbose=None)

The output I get in stc1 is:
<SourceEstimate  |  296592 vertices, subject : sub-CC721377_T1w, tmin : 0.0
(ms), tmax : 179000.0 (ms), tstep : 1000.0 (ms), data shape : (296592, 180)>
where as I was expecting the number of vertices to be 68.

On Mon, Feb 11, 2019 at 1:48 PM Alexandre Gramfort <
alexandre.gramfort at inria.fr> wrote:

>         External Email - Use Caution
>
> see:
>
>
> http://martinos.org/mne/stable/advanced_setup.html#using-the-development-version-of-mne-latest-master
>
> HTH
> Alex
>
> On Mon, Feb 11, 2019 at 8:16 AM Vivek Sharma <vivek.sharma1510 at gmail.com>
> wrote:
>
>>         External Email - Use Caution
>>
>> Thanks.
>> How can I work with mne-0.18.dev0. I tried downloading but it downloads
>> 0.17.0.
>>
>> On Fri, Feb 8, 2019 at 9:45 PM Eric Larson <larson.eric.d at gmail.com>
>> wrote:
>>
>>>         External Email - Use Caution
>>>
>>> This is probably what you want (only available in `master` currently,
>>> hasn't been released yet):
>>>
>>> http://mne-tools.github.io/dev/generated/mne.labels_to_stc.html
>>>
>>> It takes a set of labels and matching set of time series, and constructs
>>> a stc from them.
>>>
>>> Eric
>>>
>>>
>>> On Fri, Feb 8, 2019 at 6:37 AM Vivek Sharma <vivek.sharma1510 at gmail.com>
>>> wrote:
>>>
>>>>         External Email - Use Caution
>>>>
>>>> Thanks.
>>>> I could generate the 68 time series with this --
>>>> mne.SourceEstimate.extract_label_time_course().
>>>> In a variable x I have 68 time series....
>>>> x = stc.extract_label_time_course(label, src, mode='mean_flip',
>>>> allow_empty=False, verbose=None)
>>>> but I'm unable to plot this as I use to plot stc.
>>>> I use the following line to plot stc file:
>>>> "*brain = mne.viz.plot_source_estimates(stc,
>>>> subject='sub-CC721377_T1w', surface='inflated', hemi='both',
>>>> colormap='auto', time_label='auto', smoothing_steps=10, transparent=True,
>>>> alpha=1.0, time_viewer=True, subjects_dir=subjects_dir, figure=None,
>>>> views='lat', colorbar=True, clim='auto', cortex='high_contrast', size=800,
>>>> background='black', foreground='white', initial_time=peak_time,
>>>> time_unit='s', backend='auto', spacing='oct6', title='eLORETA1',
>>>> verbose=None)*"
>>>>
>>>> How can I plot this extracted time series?
>>>>
>>>> On Fri, Feb 8, 2019 at 1:25 AM Dan McCloy <dan.mccloy at gmail.com> wrote:
>>>>
>>>>>         External Email - Use Caution
>>>>>
>>>>> > Is there a way I can get all the 68 label in a single variable and
>>>>> run this line: stc = stc.in_label(label) and further reduce the vertices to
>>>>> a single label.
>>>>>
>>>>> I'm still not 100% clear what you want to do.  I'm stuck on "reduce
>>>>> the vertices to a single label" --- if you mean "restrict the all the
>>>>> vertices on the cortical surface to only the vertices defined by that
>>>>> label", well, that's exactly what mne.SourceEstimate.in_label() does.  If
>>>>> you need to do it for all 68 labels, you can do it in a for loop.  But that
>>>>> will not reduce to just one data point (or time course) per label... it
>>>>> will still have separate data for each vertex within each label.
>>>>>
>>>>> If you want to start with a SourceEstimate and end up with 68 data
>>>>> points (or 68 time series) --- one for each of the 68 labels --- look
>>>>> closer at mne.SourceEstimate.extract_label_time_course().
>>>>>
>>>>>
>>>>> On Thu, Feb 7, 2019 at 7:01 AM Vivek Sharma <
>>>>> vivek.sharma1510 at gmail.com> wrote:
>>>>>
>>>>>>         External Email - Use Caution
>>>>>>
>>>>>> Thank you so much. Your answer clears the confusion.
>>>>>> If I define the regexp, the output I get consists of single label but
>>>>>> I want 68 labels.
>>>>>> If I do not define regexp, and also not the indexing, I could not run
>>>>>> the code: stc = stc.in_label(label)
>>>>>> Is there a way I can get all the 68 label in a single variable and
>>>>>> run this line: stc = stc.in_label(label) and further reduce the vertices to
>>>>>> a single label.
>>>>>>
>>>>>> This is what exactly I want:  # reduces a label to a single vertex;
>>>>>> if what you really want is just 68 vertices, one for each label.
>>>>>>
>>>>>> On Wed, Feb 6, 2019 at 12:36 AM Dan McCloy <dan.mccloy at gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>>         External Email - Use Caution
>>>>>>>
>>>>>>> Perhaps you are confusing labels and vertices?  The desikan atlas
>>>>>>> contains 68 *labels*, and what the output is telling you is that it loads
>>>>>>> 34 labels for the left hemisphere, and 34 labels for the right hemisphere,
>>>>>>> for a total of 68 labels.  So that is working as expected.  But each
>>>>>>> individual *label* has different numbers of vertices (depending on the size
>>>>>>> of the label).  Again, your output is telling you this: the variable
>>>>>>> `label` is for the banks of the superior temporal sulcus - left hemisphere
>>>>>>> (bankssts-lh), and contains 1265 vertices.
>>>>>>>
>>>>>>> I'm still not 100% clear on what you're trying to do, but one of
>>>>>>> these might be the right direction:
>>>>>>>
>>>>>>> 1.  Use the regexp argument of mne.read_labels_from_annot() to get
>>>>>>> the label(s) you want (instead of indexing with [0]). If you want, you can
>>>>>>> run it multiple times with different regexp arguments, and combine several
>>>>>>> labels with the + operator.  From there you can use
>>>>>>> mne.SourceEstimate.in_label().
>>>>>>> 2.  mne.SourceEstimate.extract_label_time_course()  # takes a label
>>>>>>> or list of labels
>>>>>>> 3.  mne.Label.center_of_mass()  # reduces a label to a single
>>>>>>> vertex; if what you really want is just 68 vertices, one for each label
>>>>>>>
>>>>>>> On Mon, Feb 4, 2019 at 10:12 PM Vivek Sharma <
>>>>>>> vivek.sharma1510 at gmail.com> wrote:
>>>>>>>
>>>>>>>>         External Email - Use Caution
>>>>>>>>
>>>>>>>> please find my comments marked in red.
>>>>>>>>
>>>>>>>> On Mon, Feb 4, 2019 at 11:56 PM Dan McCloy <dan.mccloy at gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>>         External Email - Use Caution
>>>>>>>>>
>>>>>>>>> This line:
>>>>>>>>> *label = mne.read_labels_from_annot('subject', hemi='both',
>>>>>>>>> parc='aparc', subjects_dir=subjects_dir, regexp=None)[0]*
>>>>>>>>>
>>>>>>>>> selects the alphabetically first label from the parcellation*.*
>>>>>>>>> Is that really what you want? "I want all the 68 labels not only
>>>>>>>>> the first one...... when I execute this code:
>>>>>>>>>
>>>>>>>>  *label = mne.read_labels_from_annot('subject', hemi='both',
>>>>>>>> parc='aparc', subjects_dir=subjects_dir, regexp=None)[0]*
>>>>>>>> it gives the following output
>>>>>>>> Reading labels from parcellation...
>>>>>>>>    read 34 labels from
>>>>>>>> /home/vivek/Downloads/freesurfer/subjects/sub-CC721377_T1w/label/lh.aparc.annot
>>>>>>>>    read 34 labels from
>>>>>>>> /home/vivek/Downloads/freesurfer/subjects/sub-CC721377_T1w/label/rh.aparc.annot
>>>>>>>> and the variable label contains: <Label  |  sub-CC721377_T1w,
>>>>>>>> 'bankssts-lh', lh : 1265 vertices>
>>>>>>>> if I remove [0] from end in the code, the output changes to lengthy
>>>>>>>> list of labels. but this output I cannot include using code: *stc1
>>>>>>>> = stc.in_label(label). *It gives following error:
>>>>>>>> Traceback (most recent call last):
>>>>>>>>   File "<stdin>", line 1, in <module>
>>>>>>>>   File
>>>>>>>> "/home/vivek/anaconda3/lib/python3.7/site-packages/mne/source_estimate.py",
>>>>>>>> line 1197, in in_label
>>>>>>>>     if label.subject is not None and self.subject is not None \
>>>>>>>> AttributeError: 'list' object has no attribute 'subject'
>>>>>>>>
>>>>>>>>> " More clearly: mne.read_labels_from_annot returns a list of
>>>>>>>>> labels **sorted by label name (ascending)**.  Perhaps you're getting the
>>>>>>>>> wrong number of vertices because you're selecting the wrong label?
>>>>>>>>>
>>>>>>>>> On Mon, Feb 4, 2019 at 1:09 AM Vivek Sharma <
>>>>>>>>> vivek.sharma1510 at gmail.com> wrote:
>>>>>>>>>
>>>>>>>>>>         External Email - Use Caution
>>>>>>>>>>
>>>>>>>>>> I'm still not able to reduce the number of vertices to 68.
>>>>>>>>>> let me again explain my problem with more detail:
>>>>>>>>>> I'm using the following command to generate source estimates...
>>>>>>>>>>
>>>>>>>>>> *stc = mne.minimum_norm.apply_inverse_raw(raw, inverse_operator,
>>>>>>>>>> lambda2, method='eLORETA', label=None, start=60, stop=240, nave=1,
>>>>>>>>>> time_func=None, pick_ori=None, buffer_size=None, prepared=False,
>>>>>>>>>> method_params=None, verbose=None)*
>>>>>>>>>>
>>>>>>>>>> The output of above command contains 8175 vertices and I want to
>>>>>>>>>> reduce the number of vertices to 68 which is according to Desikan atlas.
>>>>>>>>>> To reduce the vertices I use the following code:
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> *label = mne.read_labels_from_annot('subject', hemi='both',
>>>>>>>>>> parc='aparc', subjects_dir=subjects_dir, regexp=None)[0]*
>>>>>>>>>> *stc1 = stc.in_label(label)*
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Now the stc1 contain 35 vertices but I want 68 (Desikan)
>>>>>>>>>>
>>>>>>>>>> On Wed, Jan 23, 2019 at 9:40 PM Diptyajit Das <
>>>>>>>>>> bmedasdiptyajit at gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>>>         External Email - Use Caution
>>>>>>>>>>>
>>>>>>>>>>> .in_label(label) takes a single argument i.e., single label.
>>>>>>>>>>> Just combine the both labels and continue. For details, see this:
>>>>>>>>>>>
>>>>>>>>>>> https://martinos.org/mne/stable/generated/mne.SourceEstimate.html?highlight=in_label#mne.SourceEstimate.in_label
>>>>>>>>>>>
>>>>>>>>>>> On Wed, Jan 23, 2019 at 4:59 PM Vivek Sharma <
>>>>>>>>>>> vivek.sharma1510 at gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>>         External Email - Use Caution
>>>>>>>>>>>>
>>>>>>>>>>>> When I run this command, label =
>>>>>>>>>>>> mne.read_labels_from_annot(subject, hemi=hemi, parc='aparc',
>>>>>>>>>>>> subjects_dir=subjects_dir, regexp=regexp)[0], with [0] at the end I could
>>>>>>>>>>>> run the next command stc = stc.in_label(label), successfully but it reduces
>>>>>>>>>>>> the number of vertices to 35, whereas when I do not use '[0]' at the end of
>>>>>>>>>>>> command, I could not run the next command, it gives the following error:
>>>>>>>>>>>> >>> label = mne.read_labels_from_annot('sub-CC721377_T1w',
>>>>>>>>>>>> hemi='both', parc='aparc', subjects_dir=subjects_dir, regexp=None)
>>>>>>>>>>>> Reading labels from parcellation...
>>>>>>>>>>>>    read 34 labels from
>>>>>>>>>>>> /home/vivek/Downloads/freesurfer/subjects/sub-CC721377_T1w/label/lh.aparc.annot
>>>>>>>>>>>>    read 34 labels from
>>>>>>>>>>>> /home/vivek/Downloads/freesurfer/subjects/sub-CC721377_T1w/label/rh.aparc.annot
>>>>>>>>>>>> >>> stc_label.in_label(label)
>>>>>>>>>>>> Traceback (most recent call last):
>>>>>>>>>>>>   File "<stdin>", line 1, in <module>
>>>>>>>>>>>>   File
>>>>>>>>>>>> "/home/vivek/anaconda3/lib/python3.7/site-packages/mne/source_estimate.py",
>>>>>>>>>>>> line 1197, in in_label
>>>>>>>>>>>>     if label.subject is not None and self.subject is not None \
>>>>>>>>>>>> AttributeError: 'list' object has no attribute 'subject'
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, Jan 23, 2019 at 6:52 PM Diptyajit Das <
>>>>>>>>>>>> bmedasdiptyajit at gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>>         External Email - Use Caution
>>>>>>>>>>>>>
>>>>>>>>>>>>> Follow this:
>>>>>>>>>>>>> https://github.com/mne-tools/mne-python/issues/5850
>>>>>>>>>>>>>
>>>>>>>>>>>>> best,
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Tue, Jan 22, 2019 at 11:18 AM Vivek Sharma <
>>>>>>>>>>>>> vivek.sharma1510 at gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>>         External Email - Use Caution
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>> Thanks for the code.
>>>>>>>>>>>>>> I tried with this method but it reduces the number of
>>>>>>>>>>>>>> vertices to 35, I want it to be 68 according to Desikan atlas.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Thu, Jan 17, 2019 at 5:12 PM Diptyajit Das <
>>>>>>>>>>>>>> bmedasdiptyajit at gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>         External Email - Use Caution
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> You do cortical parcellation by using some atlas. I think
>>>>>>>>>>>>>>> what you meant is to restrict the dipoles activity to some particular brain
>>>>>>>>>>>>>>> regions. For that,  you need to pass the 'label'  during source estimation
>>>>>>>>>>>>>>> or you can do something like this after the source estimate:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> code:
>>>>>>>>>>>>>>> regexp = 'bankssts'     # name the brain region that you are
>>>>>>>>>>>>>>> interested in
>>>>>>>>>>>>>>> hemi = 'both'  # taking both hemisphere
>>>>>>>>>>>>>>> label = mne.read_labels_from_annot(subject, hemi=hemi,
>>>>>>>>>>>>>>> parc='aparc', subjects_dir=subjects_dir, regexp=regexp)[0]    # read the
>>>>>>>>>>>>>>> label of the particular region based on Desikan atlas (i.e., defined by
>>>>>>>>>>>>>>> 'aparc')
>>>>>>>>>>>>>>> stc = stc.in_label(label)  # restrict the dipoles to that
>>>>>>>>>>>>>>> particular label
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> best,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Dip
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Thu, Jan 17, 2019 at 12:05 PM Vivek Sharma <
>>>>>>>>>>>>>>> vivek.sharma1510 at gmail.com> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>         External Email - Use Caution
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Okay.
>>>>>>>>>>>>>>>> The source estimate file I'm getting consists of 8175
>>>>>>>>>>>>>>>> vertices (SourceEstimate  |  8175 vertices) , I wanted to reduce the number
>>>>>>>>>>>>>>>> of vertices to the ROIs, in my case I wanted to use Desikan atlas.
>>>>>>>>>>>>>>>> How can I reduce the number of vertices, specific to
>>>>>>>>>>>>>>>> certain atlases?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Thu, Jan 17, 2019 at 2:10 PM Alexandre Gramfort <
>>>>>>>>>>>>>>>> alexandre.gramfort at inria.fr> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>         External Email - Use Caution
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> make_watershed_bem uses an atlas to get a good skull
>>>>>>>>>>>>>>>>> segmentation
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> it's not an atlas of the cortical surface as you suggest
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> HTH
>>>>>>>>>>>>>>>>> A
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>>>>>>> Mne_analysis mailing list
>>>>>>>>>>>>>>>>> Mne_analysis at nmr.mgh.harvard.edu
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> --
>>>>>>>>>>>>>>>> Vivek Sharma
>>>>>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>>>>>> Mne_analysis mailing list
>>>>>>>>>>>>>>>> Mne_analysis at nmr.mgh.harvard.edu
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>>>>> Mne_analysis mailing list
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>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> --
>>>>>>>>>>>>>> Vivek Sharma
>>>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>>>> 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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>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>>> Vivek Sharma
>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>> 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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>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> --
>>>>>>>>>> Vivek Sharma
>>>>>>>>>> _______________________________________________
>>>>>>>>>> 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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>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> Vivek Sharma
>>>>>>>> _______________________________________________
>>>>>>>> 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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>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Vivek Sharma
>>>>>> _______________________________________________
>>>>>> Mne_analysis mailing list
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>>>>>
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>>>>
>>>>
>>>> --
>>>> Vivek Sharma
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>>
>>
>>
>> --
>> Vivek Sharma
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-- 
Vivek Sharma
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