[Mne_analysis] extracting ROI sources using mne_compute_raw_inverse

[Hari Bharadwaj]

Hi Matt,
    Glad that was helpful.. However you'll still need to align the
polarities or do frequency analysis at each vertex (power spectrum for
instance) and then pool across vertices since the absolute value
operation is highly non-linear and the abs valued signal is not
suitable for frequency analyses.

Regards,
Hari


On Fri, October 21, 2011 10:39 am, Matt Panichello wrote:
> Hi Hari,
>
> Your hunch about the source orientation was correct. I took a look at the
> individual traces at each vertex that were used in calculating the evoked
> average for the label; even with the --align_z option the polarity wasn't
> consistent across all of the vertices. I baseline corrected the traces to
> zero and took their absolute value before averaging, and now the evoked
> response looks fine. Thanks for pointing out the problem to me; this had
> been a big headache.
>
> Thanks,
>
> Matt
>
>
> On Wed, Oct 19, 2011 at 12:48 PM, Hari Bharadwaj
> <hari at nmr.mgh.harvard.edu>wrote:
>
>> Hi Matt,
>>   I'm not sure if the implementation of mne_compute_raw_inverse ignores
>> the --align_z flag if (1) --picknormalcomp is used and/or if (2) Your
>> source space contains sources of fixed orientation... So unfortunately,
>> I don't have a better suggestion than to try leaving out the
>> picknormalcomp option or if you are using a fixed orientation solution
>> try loose..
>>
>> I can share python code (that's not tested much) that works with a fixed
>> orientation inverse operator if you are interested in playing with it..
>>
>> Regards,
>> Hari
>>
>>
>>
>> On Wed, October 19, 2011 12:09 pm, Matt Panichello wrote:
>> > Hi Hari,
>> >
>> > Thanks for your response. I had included the --align_z flag with
>> > mne_compute_raw inverse, so unfortunately I don't think this is the
>> issue.
>> > Do any other possibilities come to mind?
>> >
>> > This is a shot in the dark on my part, but the script I inherited for
>> this
>> > analysis also included the --picknormalcomp flag with
>> > mne_compute_raw_inverse. Could this be causing trouble for any reason?
>> >
>> > Thanks again,
>> >
>> > Matt
>> >
>> >
>> > On Tue, Oct 18, 2011 at 6:24 PM, Hari Bharadwaj
>> > <hari at nmr.mgh.harvard.edu>wrote:
>> >
>> >> 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
>> >> > _______________________________________________
>> >> > Mne_analysis mailing list
>> >> > Mne_analysis at nmr.mgh.harvard.edu
>> >> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>> >>
>> >>
>> >> --
>> >> Hari Bharadwaj
>> >>
>> >>
>> >>
>> >>
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>> >
>> >
>> > --
>> > Matthew Panichello
>> > Research Coordinator, Bar Group
>> > Massachusetts General Hospital
>> > Phone: 617-726-9034
>> >
>>
>>
>> --
>> Hari Bharadwaj
>>
>>
>>
>
>
> --
> Matthew Panichello
> Research Coordinator, Bar Group
> Massachusetts General Hospital
> Phone: 617-726-9034
>


-- 
Hari Bharadwaj