[Mne_analysis] How to set atlas

Vivek Sharma vivek.sharma1510 at gmail.com
Mon Feb 11 02:15:03 EST 2019
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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
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> _______________________________________________
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>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> --
>>>>>>>>>>>>>> Vivek Sharma
>>>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>>>> Mne_analysis mailing list
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>>>>>>>>>>>>>
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>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>>> Vivek Sharma
>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>> Mne_analysis mailing list
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>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> --
>>>>>>>>>> Vivek Sharma
>>>>>>>>>> _______________________________________________
>>>>>>>>>> Mne_analysis mailing list
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>>>>>>>>>
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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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>>>>
>>>>
>>>> --
>>>> Vivek Sharma
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>>
>>
>> --
>> Vivek Sharma
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-- 
Vivek Sharma
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