[Mne_analysis] Elastic Net Inverse Solutions

[Emily Stephen]

Hi Alex,

I was drawn to Elastic Nets because I'm dealing with a dataset that is
unlikely to be strictly sparse spatially, and I expect a large-ish region
to have quite highly correlated activity. My understanding and experience
with L1 regularization is that it chooses a small subset of these
correlated sources, rather than selecting a set of them (I woudl like the
solution to capture all of the correlated active sources). L2
regularization, of course, has the opposite problem, allowing all of the
sources to be nonzero.

In an ideal world I would like an inverse solution that can have a large
contiguous region of highly correlated active sources, and the rest of the
brain as zero or close to zero. I should be able to do this with elastic
nets by tuning the two regularization parameters. Is there a configuration
of mixed norm solvers that can do it?

I'll deal with the time dimension later, although I'm open to the idea of
doing the whole thing in the frequency domain, since I have a long stretch
of stationary data and the interesting effects are all in one frequency
band.

Thanks,
Emily

On Tue, Sep 19, 2017 at 3:35 PM, Alexandre Gramfort <
alexandre.gramfort at inria.fr> wrote:

> Hi Emily
>
>
> Has anyone done an inverse solution using Elastic Nets?
>>
>
> you do have sparse solvers with L1 penalties in MNE. We call them MxNE or
> TF-MxNE and are used with the (tf_)mixed_norm functions.
>
> See:
>
> https://martinos.org/mne/stable/auto_examples/inverse/plot_
> mixed_norm_inverse.html
>
> https://martinos.org/mne/stable/auto_examples/inverse/plot_
> time_frequency_mixed_norm_inverse.html
>
> such solvers take into account the spatio-temporal structure of the M/EEG
> data.
>
> See the referenced papers.
>
> If you want to use with own solver and eventually use the sklearn sparse
> solvers (that will only work with 1 time instant) you can start with this
> example:
>
> http://martinos.org/mne/dev/auto_examples/inverse/plot_custo
> m_inverse_solver.html
>
> Let me know if you have any questions.
>
> I've been working with these for some years now.
>
> HTH
> Alex
>
>
>> Any pointers for setting it up using as much pre-existing, well-tested
>> code as possible? E.g. I see that sklearn has Elastic Net functionality --
>> any best practices for using sklearn linear models with the mne-python data
>> structures?
>>
>> Thanks,
>> Emily
>>
>> _______________________________________________
>> 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.
>>
>>
>
> _______________________________________________
> 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/20170919/12ff9e11/attachment-0001.html