[Mne_analysis] customizing the source space

Wed Apr 4 02:48:21 EDT 2012

hi jeff,

as Dan suggested, it's not a great idea to solve the inverse problem
with a few hundreds of sources. You're likely to under fit your data
and have to mislocated activation peaks. The MNE way is to compute
the estimates on the full source space and then extract the time series
from a label as given automatically by FreeSurfer.

Examples in Python are:

http://martinos.org/mne/auto_examples/inverse/plot_label_source_activations.html

http://martinos.org/mne/auto_examples/inverse/plot_compute_mne_inverse_raw_in_label.html

http://martinos.org/mne/auto_examples/inverse/plot_compute_mne_inverse_epochs_in_label.html

the last example also shows you have to compute a "label mean" i.e.
how to average the time courses in a label to obtain one time course
per label.

hope this helps

Alex

On Wed, Apr 4, 2012 at 1:33 AM, dgw <dgwakeman at gmail.com> wrote:
> As I tried to explain earlier. The easy way to do what you want is
> create labels for each area you care about then just extract the data
> from each of the labels (as described in the MNE manual) using
> 10,242/hemi source space. For more details read the MNE manual sections
> on labels.
>
> For the option you spoke about earlier where you would calculate out a
> dipole for each of the labels the solution is computationally equivalent
> to the solution described above i.e. the answer you would get from doing
> the above is identical to that you would get from 124 equivalent dipoles
> (assuming the 124 dipoles are composed from all of the points in the
> source space). The only way you would "improve" source localization (I
> do not think calling this an improvement is accurate in most situations.
> You are making very strong assumptions (which you may be able to
> justify)), would be if you did not use the entire source space i.e.
> there were unlabeled areas.
>
> D
>
> Jeff Eriksen wrote:
>> D et al.,
>>
>> Let me give some more details, and some real numbers, to make it more
>> clear what I am after. Let us assume I had an icosahedral cortical surface
>> model with 10,240 vertices (dipoles). Then assume I have a set of ROIs on
>> the inflated spherical cortical surface. I with to mask the icosahedral
>> surface with the ROI mask, and replace each set of dipoles in each ROI
>> with a single dipole representing that ROI. So if I have a set of ROIs
>> (Brodmann or otherwise), say 62 in each hemisphere, I would end up with a
>> total of 124 dipoles to use as my source space, instead of 10,240. In
>> other works, there would only be 124 unknowns (dipole magnitudes) to
>> estimate. The position and orientation of each of the 124 dipoles would be
>> some sort of average position and orientation derived from the set in each
>> ROI.
>>
>> I realize this example represents a severe simplification of the cortical
>> surface, but it could provide some useful information, trading resolution
>> for a more robust inverse. And we are also going to use a variety of less
>> severe simplifications; this one is mainly for easy discussion here. A
>> variant of this idea is to keep all the original 10,240 dipoles, but force
>> them to vary as a group within each ROI. Again, there would be only 124
>> unknowns, not 10,240, as all the dipoles in each ROI would be linked to
>> the same dipole magnitude. In this case, it would be necessary to
>> calculate the forward solution for each of the original 10,240 dipoles.
>>
>> Perhaps I do not fully understand the MNE process, but what I am after
>> does not seem possible by just averaging forward solutions.
>>
>> Thanks,
>> -Jeff
>>
>>
>> On 4/3/12 3:43 PM, "dgw" <dgwakeman at gmail.com> wrote:
>>
>>> Well, the later is trivial to do just specify which Brodmann area each
>>> vertex corresponds to and create labels for them (FreeSurfer 5.1.0 does
>>> create estimated labels of some Brodmann areas by default).
>>>
>>> To do the Former you could do all the work of the later and then average
>>> /sum (depending on your assumptions) the forward solutions of each
>>> label. You would then need to write your data out to a forward solution
>>> (I don't know of any available code for this) (or just use this info to
>>> calculate everything with your own code).
>>>
>>> D
>>>
>>> Jeff Eriksen wrote:
>>>> I worked a bit learning MNE last Oct-Nov, then had to do other things
>>>> for a while. Now I am diving back into MNE, trying to relearn what I
>>>> did
>>>> last fall. One new area I am curious about is how to make a customized
>>>> source space. Reading the V2.9 Dec 2007 manual sections 3.5 and 5.4 it
>>>> seems one can use the "traditional" or "icosahedral" methods. The
>>>> latter
>>>> is clear to me, but the "traditional" method is not  what do you mean
>>>> by traditional method here?
>>>>
>>>> What I really want is to be able to pre-specify functional areas and
>>>> use
>>>> them as the basic elements of the source space. We are considering
>>>> functional networks defined by fMRI experiments, but for the sake of
>>>> argument we can here consider Brodmann areas. Can you imagine or
>>>> suggest
>>>> a way that I could use an individual subject's Brodmann areas as the
>>>> source space? At one extreme would be assigning a single dipole to each
>>>> Brodman area in one hemisphere. At the other would be to model each
>>>> Brodmann area as a set of adjacent dipoles uniformly covering it, but
>>>> linked as a unit.
>>>>
>>>> Thanks,
>>>> -Jeff
>>>>
>>>>
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