[Mne_analysis] Elastic Net Inverse Solutions

[Emily Stephen]

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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