[Mne_analysis] Localizing spontaneous activity: Best method / pipeline

[Leonardo Barbosa]

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
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