[Mne_analysis] Elastic Net Inverse Solutions

[Emily Stephen]

These papers look really interesting. Thanks!

On Tue, Sep 19, 2017 at 4:41 PM, Liu, Feng <feng.liu at mavs.uta.edu> wrote:

> Hi Emily,
>
>
> I think for the spatial extents estimation, you can choose to use an
> L1 norm of total variation (TV) term defined using cortex spatial
> information.
>
> Please check the papers from Dr. Lei Ding "Reconstructing cortical
> current density by exploring sparseness in the transform domain" or you
> can check "s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain
> Tomography" which has a higher order of smoothness definition.
>
>
> Best regards,
>
> Feng Liu
> ------------------------------
> *From:* mne_analysis-bounces at nmr.mgh.harvard.edu <
> mne_analysis-bounces at nmr.mgh.harvard.edu> on behalf of Emily Stephen <
> emilyps14 at gmail.com>
> *Sent:* Tuesday, September 19, 2017 3:29:36 PM
> *To:* Discussion and support forum for the users of MNE Software
> *Subject:* Re: [Mne_analysis] Elastic Net Inverse Solutions
>
> OK, one more question:
>
> My ultimate goal *is* to estimate the spatial extent of the sources, and
> I'm thinking very carefully about ways to approach the problem. Elastic
> nets by themselves won't do it, but I'm hoping that I can make progress by
> (1) choosing the tuning parameters in a data-driven way and (2) rigorously
> describing the uncertainty in the resulting estimates.
>
> Are you aware of other/better ways of doing spatial extent estimation?
>
> Thanks,
> Emily
>
>
>
> On Tue, Sep 19, 2017 at 4:21 PM, Emily Stephen <emilyps14 at gmail.com>
> wrote:
>
>> Thanks, Alex! I'll keep thinking carefully about my options.
>>
>> Emily
>>
>> On Tue, Sep 19, 2017 at 4:11 PM, Alexandre Gramfort <
>> alexandre.gramfort at inria.fr> wrote:
>>
>>> hi,
>>>
>>> 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.
>>>>
>>>
>>> this is correct for ENet vs L1 but my reaction is that it then boils
>>> down to the problem of interpretation of an activation foci. ENet will not
>>> give you the extent of the course and it's just a "proxy" towards a region
>>> of confidence / uncertainty around the localized focal dipolar foci the L1
>>> solver will give you.
>>>
>>>
>>>> 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.
>>>>
>>>
>>> careful not to interpret this as source extension.
>>>
>>>
>>>> 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?
>>>>
>>>
>>> we never implemented E-Net with MxNE in MNE sorry.
>>>
>>> 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.
>>>>
>>>
>>> if it's stationary you can use MxNE after filtering the data in the band
>>> of interest.
>>>
>>> HTH
>>> Alex
>>>
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>>
>
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