[Mne_analysis] Localizing spontaneous activity: Best method / pipeline

Alexandre Gramfort alexandre.gramfort at telecom-paristech.fr
Thu Nov 3 09:40:46 EDT 2016
Search archives:

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.apply_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#
> Dynamic_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.apply_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
>
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> The information in this e-mail is intended only for the person to whom it
> is
> addressed. If you believe this e-mail was sent to you in error and the
> e-mail
> contains patient information, please contact the Partners Compliance
> HelpLine at
> http://www.partners.org/complianceline . If the e-mail was sent to you in
> error
> but does not contain patient information, please contact the sender and
> properly
> dispose of the e-mail.
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20161103/4a068a74/attachment.html 


More information about the Mne_analysis mailing list