[Mne_analysis] Localizing spontaneous activity: Best method / pipeline

[Alexandre Gramfort]

here we go:

https://github.com/mne-tools/mne-python/pull/3732

ALex


On Thu, Nov 3, 2016 at 6:14 PM, Leonardo Barbosa <lsbarbosa at gmail.com>
wrote:

> Hi Alex,
>
> That is exactly what I was looking for! I grepped a little and found some
> nice examples using it.
>
> http://martinos.org/mne/stable/auto_examples/inverse/
> plot_compute_mne_inverse_epochs_in_label.html
>
> Btw, can I make a suggestion? Change the name in the main menu from
> "Gallery" to "Examples"!
> I was mostly looking at Tutorials, imagining that Gallery was something
> like figures and print-screens :)
>
> Thank you!
> Leonardo
>
>
>
> 2016-11-03 8:40 GMT-05:00 Alexandre Gramfort <
> alexandre.gramfort at telecom-paristech.fr>:
>
>> hi Leonardo,
>>
>> I will not comment on what's best but you can use all linear inverse
>> solvers
>> (beamformers, MNE/dSPM/sLORETA) on epochs or even raw data.
>>
>> Have a look at
>>
>> http://martinos.org/mne/stable/generated/mne.minimum_norm.ap
>> ply_inverse_epochs.html#mne.minimum_norm.apply_inverse_epochs
>> http://martinos.org/mne/stable/generated/mne.beamformer.dics
>> _epochs.html#mne.beamformer.dics_epochs
>> http://martinos.org/mne/stable/generated/mne.beamformer.lcmv
>> _epochs.html#mne.beamformer.lcmv_epochs
>>
>> HTH
>> Alex
>>
>>
>> On Wed, Nov 2, 2016 at 11:48 PM, Leonardo Barbosa <lsbarbosa at gmail.com>
>> wrote:
>>
>>> Dear MNE users!
>>>
>>> I've been trying to localize spontaneous activity for a week now, and
>>> couldn't find much about this topic in the MNE list archives:
>>>
>>> https://mail.nmr.mgh.harvard.edu/mailman/swish/mne_analysis/
>>> swish.cgi?query=spontaneous
>>>
>>> I think my main question for the moment would be: which method /
>>> pipeline would be the most appropriate?
>>>
>>> Looking into the literature, it seems like DICS is most used, probably
>>> because normally you are interested in some form of oscillatory component,
>>> and DICS uses the cross-spectral density matrix
>>>
>>> http://martinos.org/mne/stable/auto_examples/inverse/plot_tf_dics.html
>>> http://www.scholarpedia.org/article/Source_localization#Dyna
>>> mic_imaging_of_coherent_sources_.28DICS.29
>>>
>>> Interestingly, tf_dics seems to be the only source localization /
>>> inverse calculation that accept Epochs as parameter (although it calculates
>>> the csd for each epoch, averages afterwards and return one source
>>> localization for the average)
>>> All the other methods expect Evoked data
>>>
>>> http://martinos.org/mne/stable/generated/mne.minimum_norm.ap
>>> ply_inverse.html
>>> http://martinos.org/mne/stable/generated/mne.inverse_sparse.
>>> tf_mixed_norm.html
>>>
>>> etc
>>>
>>> So a more specific question would be:* How can I source localize each
>>> epoch using for instance mne.inver_sparse, perform a PSD/TF tranformation
>>> in*
>>> *source space for each epoch, and then average the results? **Is there
>>> some example in this sense?*
>>>
>>> There is an interesting post by Matti Hamalainen about that
>>>
>>> https://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/2009
>>> -December/000336.html
>>>
>>> And taking into consideration the other posts and all the technical
>>> aspects, I would prefer to apply the TF in source space and avoid
>>> introducing temporal correlations in the signal if possible.
>>>
>>> Thank you in advance for any help!
>>>
>>> Leonardo
>>>
>>> ------------------------
>>>
>>> Leonardo S. Barbosa, PhD
>>> Postdoctoral Research Scientist
>>> University of Wisconsin, Madison
>>> Center for Sleep and Consciousness Studies
>>>
>>>
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>>
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