[Mne_analysis] Applying the fMRI weighted w file on the inverse operator

[Thinh Nguyen]

Hi Hari,

Thank you for the reply, I guess it makes little sense trying to use both
fMRI prior and dSPM/sLORETA algorithm. I was just concern about the
accuracy of the results using WMNE alone, as it is accepted that
dSPM/sLORETA out perform WMNE. I don't have any reliable way to validate my
results so I was just trying things out. Maybe I don't fully understand the
concept, intuition behind the normalizations done in dSPM/sLORETA, if you
have any good literature/papers in mind that you could point me to, it
would be very much appreciated.

Best,
Thinh Nguyen



On Tue, Jul 1, 2014 at 5:06 PM, Hari Bharadwaj <hari at nmr.mgh.harvard.edu>
wrote:

> Hi Thinh,
>    Could you share any plots/images of your unexpected/inaccurate results?
> Also, could you motivate why you want to do a dSPM (on a whole brain
> basis) given that you have fMRI priors?
>
> As mentioned by Alex, the dSPM/sLORETA normalizations make it such that
> everywhere on the brain, the baseline is of unit variance.. This would
> indeed negate the fMRI prior weighting because the fMRI prior scales down
> some vertices relative to the others (whereas dSPM tends to equalize them
> by making them unity)..
>
> In my mind, given that you have an fMRI prior, if you are still interested
> in obtaining a z-score type metric, rather than currents, then perhaps one
> way to do that would be to mask out the vertices that are not active in
> the fMRI before converting to z-score..
>
> HTH,
> Hari
>
> On Tue, July 1, 2014 5:54 pm, Thinh Nguyen wrote:
> > Thanks for your answer,
> >
> > Is there anyway to use the dSPM or sLORETA algorithm and still be able to
> > reliably apply the fMRI prior weighting constraints?
> > Because it seems to me that, with fMRI constraints, WMNE algorithm
> > performs
> > well with reasonable, as expected results, but after the noise
> > normalization steps (dSPM/sLORETA), the results become unexpected, if not
> > inaccurate.
> > Thank you MNE users
> >
> > Thinh Nguyen
> >
> >
> > On Thu, Jun 26, 2014 at 10:01 AM, Alexandre Gramfort <
> > alexandre.gramfort at telecom-paristech.fr> wrote:
> >
> >> hi Thinh,
> >>
> >> real quick and hand waving:
> >>
> >> dSPM / sLORETA use scaling to downweight locations
> >> where the noise is strong / highly amplified. This amplification
> >> factor is proportional to the fMRI weight. So if you normalize
> >> by division you pretty much cancel the main effect of the fMRI weight.
> >>
> >> HTH
> >> Alex
> >>
> >>
> >>
> >> On Wed, Jun 25, 2014 at 10:42 PM, Thinh Nguyen
> >> <thinhnguyen0405 at gmail.com> wrote:
> >> > Hi MNE users,
> >> >
> >> > I'm currently trying to applying a a prior source constraints via the
> >> fMRI
> >> > weighting file. But according to MNE documentation:
> >> >  "It turns out that the fMRI weighting has a strong influence on the
> >> MNE
> >> but
> >> > the noise-normalized estimates are much less affected by it."
> >> > (http://martinos.org/mne/stable/manual/mne.html#cbbdijhi)
> >> > Can anyone please explain to me why this is such a case, maybe in both
> >> > mathematical and intuitive sense. Thank you very much in advance.
> >> >
> >> > Regards,
> >> > Thinh Nguyen
> >> > Research Assistant
> >> > University of Houston - Biomedical Engineering department
> >> >
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>
> --
> Hari Bharadwaj
> PhD Candidate, Biomedical Engineering,
> Boston University
> 677 Beacon St.,
> Boston, MA 02215
>
> Martinos Center for Biomedical Imaging,
> Massachusetts General Hospital
> 149 Thirteenth Street,
> Charlestown, MA 02129
>
> hari at nmr.mgh.harvard.edu
> Ph: 734-883-5954
>
>
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