[Mne_analysis] extracting ROI sources using mne_compute_raw_inverse

Hari Bharadwaj hari at nmr.mgh.harvard.edu
Tue Oct 18 18:24:50 EDT 2011
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Hi Matt,
   This is just a hunch.. So in viewing using mne_analyze, for the options
you have selected you are averaging the absolute value of the signal
across vertices.. On the other hand when using mne_compute_raw_inverse,
you seem to be doing a signed averaging (Which is the correct thing to
do given you want to do frequency analysis). What happens when you
average the signed signal across vertices is that since the orientation
of the sources is not the same and the MNE spreads, some vertices have
positive polarity deflections and some have negative polarity
deflection and they cancel.. The avoid this cancellation there is a
--align_z option  in mne_compute_raw_inverse that you could use which
is described in the manual.

Hope it helps,


Regards,
Hari


On Tue, October 18, 2011 6:02 pm, Matt Panichello wrote:
> Hi everyone,
>
> I am trying extract the sources from a series of functional-anatomical
> rois
> for frequency analysis, but am having some trouble getting good quality
> data. I'm using mne_compute_raw_inverse to extract the data before loading
> it into matlab.
>
> Subjects completed an object recognition task during recording. To check
> the
> quality of the extracted data, I've been averaging the timecourses in an
> early visual ROI across all vertices and visualizing the evoked response.
> For some of the subjects (e.g., S003-a, attached), the evoked response
> looks
> normal. The evoked response for many subjects, however, looks like
> senseless
> noise (S007-a) or shows an unexpected negative deflection (S010-a).
>
> I don't think these problems are solely due to the labels I've drawn, or
> to
> the intrinsic quality of the raw data. This is because when I average the
> raw data for each subject using mne_process_raw, and then view the average
> of the vertices inside the same ROIs using mne_analyze, the evoked
> responses
> all look as expected, often very different from the averages produced from
> my mne_compute_raw_inverse pipeline (see S007-b and S010-b).
>
> Does anyone have an idea what the problem might be? Are there any special
> considerations to take in account when using mne_compute_raw_inverse to
> extract sources from ROIs? Why might the label averages using the two
> different methods described look so different?
>
> Thanks in advance for any help,
>
> Matt
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-- 
Hari Bharadwaj



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